This guest editorial introduces the Special Section on Instrument Calibration and Product Validation of GOES-R.

We compare the performance of three different filters that identify solar artifacts in the Geostationary Lightning Mapper (GLM) data. These filters differ in their complexity and intended use. We use the most appropriate filters to document the severity of GLM solar contamination and to compute the frequency of extreme Americas lightning. Solar artifacts occur at an average rate of one every ∼8  min. Due to their continuous emissions across a portion of the GLM CCD array, these events disproportionately contribute to the GLM event and group rates. Up to 40% of all events recorded between 10:00 and 12:00 local time during 2018 were solar artifacts. The frequency-domain solar filter also reveals a carrier wave at 50 to 60 Hz in some lightning flashes and solar episodes that appears to be anthropogenic in origin. Removing the relatively frequent solar artifact cases reveals rare cases of extreme lightning flashes. Exceptional flashes on the order of 500+ km in length and 10+ s in duration only occur a few times per year and are produced by storms that are predisposed for exceptional lightning rather than as single random occurrences.

Image navigation and registration (INR) processing for the Geostationary Lightning Mapper (GLM) assigns geographic coordinates to lightning events using orbit and attitude telemetry and a geometric calibration that matches coastline features from the GLM background scene against a digital map. Required performance is expressed as an optical angle at the aperture of the instrument of 112  μrad (3σ), equivalent to 4 km at the subsatellite point. This is only one-half the linear dimension of the ground footprint of a detector element at the center of the field of view and is challenging to both attain and validate. Our validation approach uses imagery from the Advanced Baseline Imager (ABI) Band 3 (B03), the ABI channel closest to the GLM spectrally, with 1-km pixel resolution at the subsatellite point, as an INR reference. The finer spatial resolution and the high-accuracy INR (<28  μrad, 3σ) of ABI make it well suited for this application. Since both instruments are on the same platform, no parallax correction is required. We measure positions of feature templates extracted from the GLM background scene relative to the ABI reference image to assign spatial coordinates to the center-points of each template. Almost any feature, except clear-sky ocean scenes, can be used for matching, which allows for spatially dense measurement of INR errors in the forward navigation calculation of a GLM pixel. Cloud motion is compensated during the time between the acquisition of the ABI reference image and the GLM background scene, which further improves validation accuracy. We show that GLM on both GOES-16 and -17 spacecraft satisfy their INR requirements. The spatial and temporal density of validation measurements permits examination of smaller systematic errors, including thermally driven alignment offsets, a discontinuity between focal plane hemispheres arising from a readout artifact, the optical distortion of the GLM lens, and a calibration of the telescope focal length.

Image navigation and registration (INR) for the GOES-R series advanced baseline imager (ABI) is performed in ground processing of the raw image data. A Kalman filter processes ABI star measurements to compute time-varying optical misalignment parameters that allow real-time alignment of image samples to the GOES-R Fixed Grid. For the system to meet INR requirements, the filter measurements noise covariance (R) and process noise covariance (Q) matrices must closely approximate behavior of the true system. Prelaunch estimates of the Q and R parameters did not match actual GOES-16 performance. Several attempts to manually determine improved Q&R parameters were not completely successful. Instead, the Kalman filter was calibrated (tuned) using maximum likelihood (ML) estimation of the parameters: computed utilizing partial derivatives of the Kalman equations, Gauss–Newton iteration, and nonlinear optimization logic. The updated parameters significantly improved performance and are currently operational for GOES-16 and 17. ML estimation of Kalman filter parameters has been used successfully for other systems but has not widely been documented. Greater use is encouraged. We also include a review of ABI INR theory and implementation.

An image navigation (NAV) and registration (INR) performance assessment tool set (IPATS) was developed to assess the US Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance. IPATS produces five INR metrics for level 1B ABI images: navigation, channel-to-channel registration, frame-to-frame registration, swath-to-swath registration, and within-frame registration. IPATS also produces one INR metric for GLM: navigation of background images. The high-precision INR metrics produced by IPATS are critical to INR performance evaluation and long-term monitoring. IPATS INR metrics also provide feedback to INR engineers for tuning the navigation algorithms and parameters to further refine INR performance. IPATS utilizes a modular algorithm design to allow the user-selectable data processing sequence and configuration parameters. We first describe the algorithmic design and the implementation of IPATS. Next, it describes the investigation of the optimization of the configuration parameters to reduce measurement errors. Finally, sample INR performance is presented, including GOES-16 and GOES-17 ABI NAV performance from postlaunch test to November 2019 and the comparison of example 24-h INR performance against the mission performance requirements. The INR assessment results show that both GOES-R ABIs are in compliance with the mission INR requirements.

The Geostationary Lightning Mapper (GLM) is a new geostationary lightning detection and location instrument, developed for the current generation of Geostationary Operational Environmental Satellite-R Series (GOES-R, S, T, U) satellites. Detection efficiency (DE) and false alarm rate (FAR) are evaluations of what an instrument detects and what it misses, compared with other well-characterized measurements. These are critical parameters to be assessed for the GLM. We now have enough data to produce preliminary DE and FAR assessments of the prelaunch predicted DE and FAR performance specifications for GLM. However, one of the challenges of doing this assessment is that all available ground truth data sets have much smaller coverage, and in some places, lower DE than GLM. In an attempt to overcome these limitations, we have created a “virtual” lightning network by clustering flashes from several different ground-based networks, using a technique similar to the clustering of GLM pixels. All ground truth sources that are within 330 ms and 16.5 km of any other ground truth source are added to the current cluster. Treating this as a single, “virtual” network, we calculated the relative DE and FAR of GLM. We found that the GLM easily meets the DE spec of better than 70% averaged across the whole field-of-view, over 24 h. It meets the FAR spec in most of CONUS, and we suspect it will improve with updated processing software.

NOAA has both geostationary (GEO) and polar orbiting (LEO) satellites that are procured through different vendors. For long-term environmental studies, it is essential to establish consistent radiometric calibration among satellite instruments irrespective of the satellite platforms. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites has been providing critical weather and climate-related data. With the launch of NOAA-20 in November 2017, the global coverage of VIIRS has doubled. With more than 7 years in space, S-NPP VIIRS has been rigorously calibrated and validated. We aim to quantify the radiometric consistency between the two VIIRS instruments through double differencing using GOES-16 Advanced Baseline Imager (ABI) as a reference instrument. We also provide insight into the temporal radiometric consistency between VIIRS and ABI. Since no direct simultaneous nadir overpasses (SNOs) exist between SNPP and NOAA-20 for intercomparison, GEO-LEO intercalibration is performed using SNOs between ABI and VIIRS instruments. We show that the NOAA-20 VIIRS measured top-of-atmosphere (TOA) reflectance is consistently lower than that of the S-NPP, mostly on the order of 2% to 3%, consistent with the past studies. GOES-16 ABI-observed reflectance is higher than both VIIRS instruments. SNOs over the all-sky tropical ocean are analyzed to quantify the bias for both NOAA-20 and S-NPP VIIRS relative to ABI. The impact on bias due to spectral differences is accounted for using spectral band adjustment factors estimated using hyperspectral measurements from SCIAMACHY. Uncertainties exist mainly due to calibration uncertainties in VIIRS and ABI, the time difference between the VIIRS and ABI observations, differences in cloud contamination, spectral response function differences, and lack of in-situ hyperspectral data to account for the spectral bias.

The resolution of an imaging system can be quantified using the modulation transfer function (MTF) derived from an image with an edge feature. For Earth-viewing satellites, imaging an appropriate terrestrial edge feature is difficult when optical blur from the atmosphere contaminates the MTF. With a sharp edge and no atmosphere, our Moon has provided researchers with an alternative by deriving the line spread function from the lunar edge. The Moon’s surface, however, has features, e.g., craters and seas, that depart from clean step-like behavior, making curve fitting to the data difficult and diminishing the effectiveness of the method. We demonstrate an improvement in which a reflectance (albedo) map of the Moon, created from a mosaic of the U.S. Department of Defense/NASA Clementine images, is used to flatten the surface features before the edge spread function is measured. The objective is to improve the reliability of MTF measurements for on-orbit calibration of the National Oceanic and Atmospheric Administration/NASA Geostationary Operational Environmental Satellite (GOES) weather satellites and similar missions. The technique is applied to the reflective bands of the Advanced Baseline Imager on GOES-16 and GOES-17 weather monitoring satellites.

The large focal plane arrays used in the new generation of the Geostationary Operational Environmental Satellites (GOES) advanced baseline imager (ABI) introduce new calibration challenges compared with the heritage GOES imagers. The increased number of detectors allows for increased spatial, spectral, and temporal performance, but as a trade-off, it has an increased risk of image striping. We detail the development of a new postlaunch relative calibration capability for ABI reflective solar bands that utilizes ABI special scans to generate a set of relative gains that can be applied to improve image quality and reduce image striping. Results demonstrate that the method reduces image striping in the ABI solar reflective bands over varying scene content and time, both diurnally and over an extended period. This methodology ensures a calibration strategy that is consistent with heritage approaches yet adapts to the new postlaunch validation challenges presented by the new class of operational imagers in the GOES-R series. The developed approach is ready for operational use, as needed, and can be easily implemented into operations to support the operational production of geostationary imagery of the Earth.

The advanced baseline imager (ABI) instrument onboard Geostationary Operational Environmental Satellite (GOES)-16 is the first of National Oceanic and Atmospheric Administration (NOAA’s) new-generation geostationary earth orbiting (GEO) imagers that provides high-quality calibrated and geolocated Earth observations in six reflective solar bands (RSBs). The spectral similarity between the Visible Infrared Imaging Radiometer Suite (VIIRS) and ABI RSB offers an opportunity for deriving VIIRS-quality cloud retrievals from the ABI radiances. NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project utilizes GEO imager (including ABI) radiances to retrieve clouds and derive broadband fluxes that are used to account for the regional diurnal flux variation between the CERES measurements and to convert the CERES observed radiances into fluxes. In order to derive a seamless cloud and flux datasets for CERES, it is important that the GEO, MODIS, and VIIRS imagers are all placed on the same radiometric scale. We describe an absolute radiometric intercomparison between the NOAA-20 VIIRS and GOES-16 ABI RSB using ray-matched radiance/reflectance pairs over all-sky tropical ocean scenes as well as a deep convective cloud invariant target calibration algorithm. Results indicate that the ABI and VIIRS RSB calibration are within 5%, except for the 0.47-μm band, for which the radiometric inconsistency is found to be ∼7  %  . The GOES-16 radiometric scaling factors referenced to NOAA-20 VIIRS were computed from the two independent calibration methods to agree within 1% for ABI bands 1 to 4, and within 3% for bands 5 and 6. Results from this study were used to propose a future CERES GEO intercalibration algorithm referenced to NOAA-20 VIIRS, given the eventual demise of the Terra and Aqua satellites.

The advanced baseline imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-R Series is a great improvement compared to the legacy GOES imager. For example, there are more spectral bands at improved spatial resolution and more frequent imagery. The vast majority of the images generated by the ABIs are free of visual defects, well calibrated, and produced in a timely fashion. Yet, there are rare times when visual artifacts, or anomalies, occur. Our study highlights and explains a number of these artifacts, some of which are traditional imagery defects for imagers such as striping and stray light, and colorfully named artifacts such as “caterpillar tracks” and “shark fins.” In addition, multiple resources are presented for more information about image quality and near-real-time image monitoring.

This guest editorial introduces the Special Section on Representation Learning and Big Data Analytics for Remote Sensing.

River segmentation is an important aspect of remote sensing image analysis. In the actual remote sensing images of rivers, the background is mostly complex and heterogeneous, the traditional detection methods cannot identify the small tributaries and the edge information is rough. To solve the aforementioned problems, we propose a separable attention network based on different size fusion. In this method, residual neural network is used as the backbone network to obtain the information features of rivers, and the deep feature information is fused with the shallow feature information through attention modules of different scales. The shallow feature and large-scale attention module are used to locate the main position of the river, while the deep feature and small-scale attention module are responsible for the fine segmentation of the river edge, so as to accurately extract the river from the background. The experimental results show that, compared with previous methods, the accuracy and detection speed of proposed method are significantly improved. The mean intersection over union (MIoU) on the river segmentation data set reaches 97.07%. Meanwhile, this model has excellent generalization performance, and the MIoU of the public data set CamVid reaches 69.4%.

Due to the problem that current hyperspectral image tensor classification methods cannot make full use of the multiple spectral–spatial features of hyperspectral images (HSIs), a HSI spectral–spatial classification method based on multikernel support tensor machine is proposed. In the tensor structural space, using the 3-D Gabor wavelet and morphological attribute filters to obtain multiple texture features and multiple attribute structural features, respectively, our study combines multiple spectral–spatial features to classify the HSIs in the multikernel learning framework. By forming the multiple kernel function with the linear weighted combination of the single kernel functions, the proposed method calculates the weights using the iterative method to effectively utilize the multicomplementary features of hyperspectral imagery to perform collaborative classification. The experiments are performed on the widely used Pavia University and Salinas hyperspectral data sets. The classification results indicate that the proposed method can effectively integrate complementary spatial–spectral features, which have higher classification accuracy and better spatial continuity classification mapping when applied to the classification images.

Nearest regularized subspace (NRS) has been recently proposed for hyperspectral image (HSI) classification. The NRS outperforms both collaborative representation classification and sparse representation-based techniques because the NRS makes use of the distance-weighted Tikhonov regularization to ensure appropriate representation from similar samples within-class. However, typical NRS only considers Euclidean distance, which may be suboptimal to resolve the problem of sensitivity in the absolute magnitude of a spectrum. An NRS-Manhattan distance (MD) strategy is proposed for HSI classification. The proposed distance metric controls over magnitude change and emphasizes the shape of the spectrum. Furthermore, the MD metric uses the entire information of the spectral bands in full dimensionality of the HSI pixels, which makes NRS-MD a more efficient pixelwise classifier. Validations are done with several hyperspectral data, i.e., Indian Pines, Botswana, Salinas, and Houston. Results demonstrate that the proposed NRS-MD is superior to other state-of-the-art methods.

There is a growing concern over surface water dynamics due to an increased understanding of water availability and management with current climate trends. Remote sensing has now become an effective means of water extraction due to the availability of an enormous amount of data with diverse spatial, spectral, and temporal resolutions. However, water extraction from optical remote sensing data is associated with several major difficulties, such as the applicability of the extraction method over large areas and complex environments; shadow contamination from clouds, buildings, and mountains; and disclosure of shadowed water and exclusion of floating and submerged plants. To address these difficulties, a learning vector quantization (LVQ) neural network-based method was proposed and implemented to extract water using Landsat 8 imageries. This method is capable of separating water from clouds, build-up areas, shadows, and shadowed water by the ideal input of bands 1 to 7 and normalized difference vegetation index. This model learns water across Sri Lanka. Eight OLI scenes were tested, and the performance was compared with five widely used machine learning algorithms: support vector machine, K-nearest neighbor, discriminant analysis, combination of modified normalized difference water index and modified fuzzy clustering method, and K-means clustering methods. This method performed the best, achieving overall accuracies and the kappa coefficients between 97.8% and 99.7% and between 0.96 and 0.99, respectively. Results have demonstrated robustness, consistency, and preciseness in various dark surfaces, noisiest water environments, and highly water scarce scenes. LVQ revealed a good generalizing ability to detect all types of water with less amount of training samples. This method can be easily adaptable for other sensors and global water to support water resource studies.

Local binary pattern (LBP) is a simple yet efficient image feature description algorithm that has been widely used in applications related to image classification. To more effectively make use of the local features of a hyperspectral image (HSI), a tensor-based spatial–spectral local binary pattern (TSSLBP) that can fully utilize the local spatial-spectral feature of an HSI is proposed. We specifically make efforts in three aspects. First, we construct a third-order tensor model for HSI to combine the local spatial and spectral features. Second, a dimension reduction method based on tensor decomposition is proposed to reduce redundant information of the HSI tensor model. Third, a joint spatial-spectral LBP coding algorithm is implemented based on the HSI tensor model to calculate the local spatial–spectral feature vector of HSI. To verify the effectiveness of the proposed TSSLBP, we apply it to the task of HSI classification and compare it with six existing algorithms. Experimental results on the three popular HSI datasets show that TSSLBP can fully utilize rich information in the local spatial–spectral domain and can significantly improve the classification accuracy of HSI, especially in the case of scarce training samples.

Stacked denoising autoencoder (SDAE) model has a strong feature learning ability and has shown great success in the classification of remote sensing images. However, built-up area (BUA) information is easily interfered with by broken rocks, bare land, and other features with similar spectral features. SDAEs are vulnerable to broken and similar features in the image. We propose a multiscale SDAE model to overcome these problems, which can extract BUA features in different scales and recognize the type of land object from multiple scales. The model effectively improves the recognition rate of BUA. The experimental results show that our algorithm can resist the disturbance information, and the classification accuracies are better than support vector machine, backpropagation, random forests, and SDAE. Then we investigate an application in Wuhan (China) metropolitan area analysis with the classification results of our algorithm. The range of the metropolitan area is 1.5-h isochronous circle calculated by Tencent map big data and is divided into three layers: core metropolitan area, subcore metropolitan area, and daily metropolitan. Finally, from the comprehensive statistical data and traffic data, we know that the Wuhan metropolitan area has a “target-shaped” distribution structure radiating outward from the core metropolitan area. It includes five metropolitan development corridors: Wuhan–Huanggang, Wuhan–Xiaogan–Suizhou, Wuhan–Ezhou–Huangshi, Wuhan–Xiantao–Tianmen, and Wuhan–Xianan–Chibi. The corridor is of great significance to the development of metropolitan areas.

Remote sensing data are often adversely affected by noises due to atmospheric water absorption, transmission errors, sensor sensitivity, and saturation. Therefore, the performance of the classical principal component analysis (PCA) is negatively affected when dealing with such noisy data. To overcome this problem, a robust deflated PCA via multiple instance factorings (RDPCA-MIF) for dimension reduction in remote sensing images is proposed. In contrast to the traditional PCA, a penalty is imposed on the instance space through instance factoring technique to suppress the effect of noise in pursuing projections. To achieve this, we use two strategies, total distance, and cosine similarity metrics. Both strategies discriminate between noise and authentic data points by iteratively learning the relationship between instances and projections. Moreover, a deflation strategy that enables multiple relationships learning between instances and projections is applied to thoroughly evaluate the importance of instances in relation to all projections. Extensive experimental evaluations of the proposed RDPCA-MIF on Salinas-A dataset and shallow and deep features from the NWPU-RESISC45 remote sensing dataset prove its superiority over six comparative methods in dimension reduction and classification tasks.

Cloud/snow recognition technology has application significance in meteorological detection, aviation control, remote sensing disaster prevention, and mitigation. At present, the method of labeling cloud and snow area manually is time- and labor-consuming. Shallow learning methods not only have limited ability to extract cloud/snow semantic features but also have wake learning and expressive ability. These problems lead to the low accuracy of cloud/snow recognition in shallow learning methods. Deep learning methods extract cloud and snow semantic features layer-by-layer, and this feature extraction improves the accuracy of cloud/snow recognition. However, due to the complexity of cloud/snow texture features and the high similarity of cloud/snow spectral features, it is difficult to obtain satisfactory results for the classification of cloud and snow in plateau areas, by the existing deep learning models. In order to solve the above problems, a multiscale fusion attention network is proposed to recognize cloud and snow areas in plateau remote sensing images. In the proposed model, the main network is DenseNet25, which enhances the propagation and reuse of cloud/snow features in the network. A multiscale fusion is proposed to extract more complex cloud/snow texture and spectral features from spatial dimension. The high weight attention mechanism is introduced to obtain dynamic features based on input, and it is able to improve the discriminating ability of cloud and snow features. The experimental results demonstrate that the proposed model can extract and utilize cloud/snow feature information better than existing models and improve the accuracy and generalization of cloud/snow recognition.

Satellite remote sensing has wide applications in many fields. However, the quality of the observed images captured from the satellite sensors exhibits significant variances and most images are low resolution. Therefore, they adversely affect the system performance in a variety of real-world applications such as object recognition and analysis. In order to enhance the resolution of remote sensing images, we propose a super-resolution neural network called progressive residual depth neural network (PRDNN). The progressive residual structure used by PRDNN can gradually discover the feature information of satellite images at different levels and different receptive fields, thus providing more detailed features for reconstructing super-resolution satellite images. The experimental results of the DOTA satellite image database demonstrate that the proposed method is superior to the most advanced super-resolution algorithm in recent years.

A large amount of remote sensing data can be easily acquired due to the increase in the advances in sensor’s technologies. The sensors can generate high-dimensional data in a lower time producing problems related to big data such as management and organization. Since the acquired data is characterized by a large dimension and lack of structure, the information analysis becomes harder. Therefore, an organization stage should structure the data reducing the dimension while maintaining the main properties to enable further analysis. The feature extraction and selection methods can achieve this task. Consequently, we aim to explore various pixel-wise feature extraction and selection algorithms to manage the organization stage of big data for hyperspectral images. Our work covers the comparison between feature vectors computed using the discrete Fourier transform, discrete cosine transform (DCT), and stationary wavelet transform. Moreover, spectral angle mapper, Jeffries–Matusita distance, spectral information divergence, and linear discriminant analysis (LDA) were implemented as feature selectors. Feature extraction and selection methods were combined and evaluated in terms of algorithm complexity, reduction efficiency, and classification accuracy with the aid of a support vector machine and a maximum likelihood classifier. The analysis shows that some linear transformations can perform better in natural landscapes and others in urban images. Furthermore, the study found that the combination of DCT and LDA, which achieves high classification rates with an efficient dimension reduction, can be suitable for the organization stage of a big data remote sensing application of hyperspectral images.

Deep learning techniques have been leveraged in numerous applications and across different data modalities over the past few decades, more recently in the domain of remotely sensed imagery. Given the complexity and depth of convolutional neural network (CNN) architectures, it is difficult to fully evaluate performance, optimize the hyperparameters, and provide robust solutions to a specific machine learning problem that can be applied to nontraditional real-world feature extraction and automation tasks. Ursa Space Systems Inc. develops machine learning approaches to build custom solutions and extract answers from synthetic aperture radar satellite data fused with other remote sensing data sets. One application is identifying the orientation of nontexture linear features in imagery, such as an inlet pipe on top of a cylindrical oil storage tank. We propose a two-phase approach for determining this orientation: first an optimized CNN is used in a nontraditional way to probabilistically determine a coarse location and orientation of the inlet pipe, followed by a maximum likelihood voting scheme to automatically extract the orientation of the angular feature within 7.5 deg. We use a known hyperparameter optimization technique to determine the best deep learning CNN architecture for our specific problem and under user-defined optimization and accuracy constraints, by optimizing model hyperparameters (number of layers, size of the input image, and data set preprocessing) using a manual and grid search approach. The use of this systematic approach for hyperparameter optimization yields increased accuracy for our angular feature extraction and orientation finding algorithm from 86% to 94%. Additionally, this proposed algorithm shows how machine learning can be used to improve real-world remote sensing workflows.

Extraction of information about individual trees using remotely sensed data is essential to supporting ecological and commercial applications in forest environments. Data acquired by consumer-grade cameras onboard unmanned aerial vehicles (UAV) offer an affordable option of high-spatial resolution imagery that can be used to extract forest structural information at a tree level. The aim of this work is to investigate the potential and accuracy of UAV time-series data to automatically detect and delineate tree crowns across an entire woodland. The workflow (presented in a step-by-step manner) involves the construction of a canopy height model (CHM) based on digital elevation models derived from the UAV photogrammetric point clouds. A watershed-based approach is modified to automatically detect and delineate the tree crowns, based on the CHM and the brightness information from the UAV orthomosaics. The accuracy of the proposed method was evaluated by comparing its results against manually delineated tree crowns. The results show an overall accuracy of 63%, where conifer species were more accurately delineated (up to 80%), while broadleaf species returned lower accuracies (<50  %  ). Continued research is necessary to improve the confidence of automated individual tree crown detection and delineation, especially over complex forest structures.

Urbanization in India has been rapid over the past few decades, which results in a substantial replacement of the natural surfaces into built-up lands. We present a performance evaluation of Sentinel-2B, Landsat-8 multispectral, and AVIRIS-NG hyperspectral imagery for extraction of road and roof surfaces using proposed spectral index-based and other conventional algorithms. The new road extraction index (NREI) and new building extraction index (NBEI) are developed for extraction of road and roof surfaces, respectively. Moreover, existing spectral angle mapper (SAM), spectral information divergence (SID), matched filter (MF), and support vector machine (SVM) are utilized as angle, information, filtering, and machine learning-based algorithms, respectively, for detection of both the surfaces. The results of our study suggest that the performance of AVIRIS-NG sensor is the best in comparison to aforesaid multispectral sensors, whereas Sentinel-2B performs better in comparison to Landsat-8 for extraction of road and roof surfaces. The comparison of various algorithms suggests that proposed indices, MF, and SVM produce the best results for extraction of road and roof surfaces, while SAM and SID are superior algorithms for extraction of both the surfaces in AVIRIS-NG imagery. Further, NREI and MF performed well for extraction of roads followed by NBEI and SAM for roofs in Landsat-8. Finally, NREI, SAM, and SID are found to be efficient for extraction of roads subsequently NBEI, SAM, and SID for roofs in Sentinel-2B imagery.

Building footprint segmentation from satellite and aerial images is an essential and challenging step for high-resolution building map generation. In urban management applications, such as building monitoring, infrastructure development, smart three-dimensional cities, and building change detection, building footprints are required to generate precise multiscale building maps. An efficient deep learning-based segmentation approach is proposed for multiscale building footprint extraction, and the results are presented for the most important challenges in photogrammetry and remote sensing, including shadows and occluded areas, vegetation covers, complex roofs, dense building areas, oblique images, and the generalization capability in different locations. The proposed method includes new dilated convolutional blocks containing kernels with different sizes to learn spectral–spatial relationships in multiscale satellite and aerial images with a high level of abstraction. The quantitative assessments of multiscale images from different locations with different spatial resolutions and spectral details show that the average F1 score and the average intersection over union for extracted footprints are about 86% and 76%, respectively. Compared with the state-of-the-art approaches, the proposed method has outstanding generalization capability and provides better performance for building footprint segmentation from multisensor single images.

Atmospheric correction can introduce errors in surface spectral reflectance, and hence induces errors in plant water estimation from remote sensing water indices. We intend to develop water indices that are less impacted by atmospheric effects for plant water content estimation based on the 970-nm water absorption feature. A simulation study using the PROSAIL and 6S models showed that uncertainty in atmospheric water vapor (WV) content can induce large variation in existing 970-nm water indices, such as WI, NWI-1, and NWI-3. An attempt was made to incorporate atmospheric WV absorption at 940 nm to correct for the perturbation due to atmospheric WV variability, leading to the development of improved indices, named as ARWI, NARWI-1, and NARWI-3. The performance of these indices was evaluated using the simulated and field spectral reflectance data, as well as Hyperion and GF5 satellite data. Results showed that the new indices were resistant to uncertainty of WV and could be used to deliver improved estimation of canopy water content, with a smaller root-mean-square-error (ARWI: 7.4  mg/cm², NARWI-1: 8.3  mg/cm², and NARWI-3: 8.8  mg/cm²) compared to that obtained using the traditional water indices (WI: 8.9  mg/cm², NWI-1: 9.4  mg/cm², and NWI-3: 16.6  mg/cm²). The water indices developed in this study, although needing further assessment in wide application scenarios, have great potential for monitoring of vegetation water status using satellite hyperspectral data with reflectance measurement around 970 nm.

The present state of the art technologies for flood mapping are typically tested on small geographical regions due to limitation of resources, which hinders the implementation of real-time flood management activities. We proposed a unified framework (GEE4FLOOD) for rapid flood mapping in Google Earth Engine (GEE) cloud platform. With the unexpected spells of extreme rainfall in August 2018, many parts of Kerala state in India experienced a major disastrous flood. Therefore, we tested the GEE4FLOOD processing chain on August 2018 Kerala flood event. GEE4FLOOD utilizes multitemporal Sentinel-1 synthetic aperture radar images available in GEE catalog and an automatic Otsu’s thresholding algorithm for flood mapping. It also utilizes other remote sensing datasets available in GEE catalog for permanent water body mask creation and result validation. The ground truth data collected during the Kerala flood indicates promising accuracy with 82% overall accuracy and 78.5% accuracy for flood class alone. In addition, the entire process from data fetching to flood map generation at a varying geographical extent (district to state level) took ∼2 to 4 min.

We describe an algorithm based on a convolutional neural network that detects cloud formations and snow cover in satellite images using textures. Herein, multispectral satellite images, received from a multizone scanning instrument used for hydrometeorological support and installed on the Russian satellite Electro-L No. 2, are used as input data. The problem of snow and cloud classification in the absence of a spectral channel in the range of 1.4 to 1.8  μm, which is necessary for their accurate separation, is considered. The developed algorithm can produce cloud and snow cover masks for an area limited by the values of the solar zenith angle in the range of 0 deg to 80 deg for daytime. Algorithm accuracy was evaluated using machine learning metrics and comparing its results with ground truth masks segmented manually by an experienced interpreter. In addition, we compared the resulting masks with a similar cloud mask product from the European Organisation for the Exploitation of Meteorological Satellites based on the data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument installed on the Meteosat-8 satellite. According to the results of this comparison, we conclude that the cloud masks produced by the proposed convolutional neural network-based algorithm have a lower probability of false detection than products based on the SEVIRI data. The proposed algorithm is fully automatic, and it works in any season of the year during the daytime.

Organic matter (OM), iron (Fe), and zinc (Zn) in black soil are crucial to ensure high-quality production of agriculture, and hyperspectral technology is an effective approach to achieve a rapid estimation of these soil compositions. Eighty black soil samples were collected in Nehe city, Heilongjiang province, China. With indoor spectral data, the correlation between six spectral reflectance, which includes the original and five other transformed reflectance, and the contents of OM, Fe, and Zn on soil were analyzed. Then with the correlation coefficient significance test (bilateral) calculated at α  =  0.01 level to extract sensitive bands, the kernel principal component analysis (KPCA) algorithm was adopted and combined with random forest (RF) and support vector machine (SVM). The combined models were applied for quantitative inversion of soil OM, Fe, and Zn contents and compared them with the models without KPCA dimension reduction. The results show that the determination coefficients and residual prediction deviation for prediction samples of KPCA-RF model (Rp2=0.805 and RPD  =  2.329) that adopted to estimate soil OM content are higher than those of RF model (Rp2=0.681 and RPD  =  1.820), and the root-mean-square errors for prediction samples of KPCA-RF model (RMSEP  =  0.182) are lower than those of RF model (RMSEP  =  0.232). Meanwhile, the accuracy of the KPCA-RF model for estimating soil Fe and Zn contents is also higher with Rp2=0.731, 0.710, RMSEP  =  0.189, 0.003, and RPD  =  1.980, 1.905, respectively. Similarly, the accuracy of the KPCA-SVM model for estimating soil OM, Fe, and Zn contents is higher with Rp2=0.687, 0.609, and 0.585; RMSEP  =  0.230, 0.228, and 0.004; and RPD  =  1.840, 1.642, and 1.592, separately. Therefore, the machine learning models combined with KPCA are more promising in the quantitative inversion of soil composition contents and can be regarded as an effective approach.

In many areas of the globe, the installation of artificial drains on naturally poorly drained soils is a necessary part of farm management. Identifying the location of artificially drained areas is an important step in achieving environmentally sustainable agricultural production. However, in many regions, data on the presence or the distribution of artificial drainage systems are rare. We outline an approach to identify artificially drained soils using Earth observation (EO) satellite imagery and digital elevation data. The method exploits the contrasting phenology of grass during a peak growth stage to identify artificially drained and undrained soils. Two machine-learning techniques, support vector machine and random forest, were tested. Classification accuracy up to 91% was achieved using photointerpreted accuracy points using higher resolution satellite imagery. Additional investigations would be required to establish whether the drained conditions identified were a result of artificial drainage or from naturally well-drained soils occurring within larger soil units. Herein, the Republic of Ireland is used as a test case. Based on our findings, the area of artificially drained grassland within the study area could be revised upward, with 44% (or ∼345  ,  000  ha) of pasture currently classed as “poorly drained” identified as “artificially drained.” At one location, a change in the modeled drainage condition at field level was demonstrated following drain installation. The presented method demonstrates the ability of EO satellites to quickly and accurately map field drainage status at farm management scales over a wide area. This has the potential to improve management decisions at local scales, but also has implications in terms of national policy development and regulation in areas such as water quality and climate change mitigation.

Ordinary multispectral (MS) image change detection (CD) techniques have not excavated the inherent information of MS images. Deep learning methods are a type of effective method that grabs the changed and unchanged information by deep feature extraction. However, a larger number of labeled samples are difficult to obtain for CD in MS images. An unsupervised MS image CD method is proposed based on a teacher student (TS) model. First, pseudotrain samples are produced by an expectation–maximization algorithm, and then the reliable training samples are selected based on the information of local windows. Second, the pseudotraining samples are used as input of the TS model, which is a self-ensembling model and includes a student model and a teacher model. Then the better teacher model of the TS model is updated by the exponential moving average of the student’s weight at each training step. Finally, the trained teacher model is utilized to generate the CD map. The experiments on four sets of bitemporal MS images demonstrate that the proposed CD method performs well in comprehensive indices compared with existing methods.

Water vapor is an important greenhouse gas. The atmospheric water vapor content is monitored by remote methods, which require accurate spectroscopic information about H₂O absorption line parameters. The problems of accuracy of accounting the selective water vapor absorption in the simulation of atmospheric transfer of solar radiation are considered in this work. The atmospheric spectra are simulated using different versions of spectroscopic databases of H₂O absorption line parameters and compared with the solar spectra measured by a ground-based Fourier transform spectrometer. The total column (TC) water vapor is retrieved from the solar spectra measured. The effect of the differences in absorption line parameters in modern spectroscopic databases on the results of retrieval of the TC water vapor from high-resolution spectra is estimated.

The main objective in our study was to derive an accurate wetland inventory of the Dınàgà Wek’èhodì region, Northwest Territories, while also enhancing our previously established wetland mapping workflow. Our methods used multidate optical and radar satellite imagery and fused these data with ArcticDEM topographic variables. Additionally, few studies to date have assessed the ArcticDEM for wetland mapping; our research helps fill this critical gap in the literature. A machine-learning, object-based approach was employed to classify the fused data stacks and included both mean and standard deviation image-object feature extractions. In this study, 18 random forest models were tested, each including various sensor inputs and feature extractions. The highest accuracy was achieved using a fusion of optical, radar, and ArcticDEM data and included both the mean and standard deviation of image objects (88.17% overall accuracy and kappa 0.858). Vegetated wetlands had producer accuracies ranging from 74% to 86%, whereas open water was 92%. Feature importance rankings indicated that 16 of the top 20 variables were derived from optical data, three from radar, and one from the ArcticDEM. The results of our study will be used to assist governments and other interested parties in advancing conservation initiatives for this significant high-latitude region.

In coastal waters, colored dissolved organic matter (CDOM) is often present in moderate to high concentrations due to input from nearby landmasses and rivers. CDOM absorbs light sharply in the UV-blue region, causing waters to appear yellow or brown. This has a significant effect on optical and biogeochemical processes in coastal waters, as it inhibits the penetration of light within the water. It is thus important to be able to monitor CDOM levels on a regular basis. A model is presented linking Sentinel-2 reflectance data to the CDOM absorption coefficient. Such a model is useful for monitoring the CDOM absorption in coastal waters due to the frequency of Sentinel-2 passes and relatively high-spatial resolution. This model was developed and validated using concurrent field measurements of above-surface remote-sensing reflectance and the in situ CDOM absorption coefficient, from the coastal waters of Singapore. The leave-one-out cross-validation scheme was used to validate the model due to the small number of data points. The model is further tested with Sentinel-2 images of the same locations, not more than one day apart from the in situ measurements.

The dust storm is one of the severe natural disasters that has been recently threatening the Middle East region due to climate changes and human activities. This phenomenon has become a national crisis in some countries in this region in previous years, especially in spring and summer. This research aims to detect and monitor the areas covered by the seasonal and occasional dust storm from (Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. MODIS imagery possesses impressive spectral and temporal characteristics that are essential for such an environmental application of Earth observations. An efficient algorithm, based on the spectral and statistical analysis of both thermal and reflectance bands of MODIS data, was developed through a decision tree method. To this end, an index was proposed to detect the dust over the land using the brightness temperature of thermal bands. The results of the proposed algorithm were assessed utilizing ground-based observation of synoptic stations. The proposed method showed high reliability and performance as well as the automatic capability of dust detection in land and sea areas of the image simultaneously. The evaluation of results showed that the proposed algorithm could detect thin and thick dust storms with an overall accuracy of about 80%. Moreover, the dust monitoring results visually agreed well with the Ozone Monitoring Instrument aerosol index dust products.

The unmanned aerial vehicle (UAV) is an emerging technology applied recently in land cover classification, owing to its ability to acquire very high-resolution spatial data, that has provided an effective means for detailed land cover mapping, especially for a small island area. Selecting suitable UAV-acquired data and exploring the combined use of UAV multitype data are of significance for island mapping. Nine classification models were established through a fusion method of visible, multispectral, and light detection and ranging (LIDAR) data acquired by UAVs. A two-level hierarchical land cover classification (level 1 and level 2) of the Donkey Island in China was performed using geographic object-based image analysis with random forest classifier. We investigated the performance of land cover classification models containing different sets of features (spectral, height, intensity, and shape features extracted from UAV data) and evaluated the importance of various features. The results demonstrate that the overall accuracy (OA) of the models generally increase with decreasing detail and the amount of information entering the classification process. The OA achieved range from 82.08% to 92.54% and 74.12% to 85.08% across the nine models for level 1 and level 2, respectively. The best result was achieved with a model combining all features based on multispectral and LIDAR data. Height and intensity information input significantly affect the quality of classification models positively, with height apparently more significant than LIDAR information. When comparing different features, spectral features prominently assist in discriminating land cover classes. The importance of height and intensity features to classification accuracy varies for the classification models, showing greater importance in models based on visible data.

Impervious surfaces (ISs) are significant indicators of the overall environmental health of watersheds. Monitoring the IS spatial patterns of watersheds is helpful for improving water management and regional pollution assessment. A random forest regression (RFR) approach was developed to estimate the subpixel IS percentage (ISP) from Chinese GF-5 hyperspectral imagery in 30 m pixels in the Nansi Lake Basin. Initially, object-based image analysis and overlay analysis were adopted to generate a very high-resolution ISP reference dataset using GF-1 multispectral imagery. Subsequently, an RFR-based ISP prediction model was established and evaluated using training and validation samples, respectively. The experimental results demonstrated that the proposed method shows a good performance with a root mean square error of 0.17, a mean absolute error of 0.15, and an R² value of 0.89. Meanwhile, both band 10 and band 53 of GF-5 made the highest contributions to ISP modeling. Further comparison with other machine learning algorithms revealed that the RFR-based ISP model outperforms support vector machine regression and partial least squares regression, demonstrating the potential usability of the proposed method in ISP estimation from hyperspectral imagery.

The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) instrument has been successfully operating on orbit since November 28, 2017. The day–night band (DNB) onboard NOAA-20 VIIRS is a panchromatic channel covering wavelengths from 0.5 to 0.9  μm, capable of observing the Earth scene in visible/near-infrared spectral range at a spatial resolution of 750 m. The DNB operates at low-, mid-, or high-radiometric gain stages, and it uses an onboard solar diffuser (SD) panel for low-gain stage calibration. The SD observations also provide a means to compute gain ratios between low-to-mid and mid-to-high-gain stages. With their large dynamic range and high sensitivity, the DNB detectors can make observations during both daytime and nighttime. We provide an assessment of the DNB on-orbit performance and behavior in the first two-year mission period and beyond. The calibration methodology used by the VIIRS Characterization Support Team in support of the NASA Earth science community has been described. The trending of on-board calibrators dark-offsets, SD gains, and gain ratios, and signal-to-noise ratio at minimum radiance have been analyzed, especially during key events such as the nadir and cryocooler doors opening. Furthermore, we performed intercomparison studies between Suomi National Polar-orbiting Partnership and NOAA-20 instruments and evaluated DNB radiometric calibration and characterization, including the SD degradation, detector gains, gain ratios, and straylight correction, as well as the calibration comparison between the NOAA Interface Data Processing Segment look-up-tables and our delivery results.

We propose a method for detecting multiple scatterers (targets) in the elevation direction for synthetic aperture radar tomography. The proposed method can resolve closely spaced targets through a two-step procedure. In the first step, coarse detection is performed with a successive cancellation scheme in which possible locations of targets are marked. Then, in the second step, by searching in the reduced search space, which is finely gridded, the accurate location of the targets is found. For estimating the actual number of targets, a model order selection scheme is used in two cases of known and unknown noise variance. Also, by analytical investigation of the probability of detection for the proposed method, the effect of the influential parameters on the detection ability is explicitly demonstrated. Compared to the super-resolution methods based on compressed sensing, the proposed method has a lower computational cost and higher estimation accuracy, especially at low signal-to-noise ratio regime. Simulation results show the superiority of the proposed method in terms of both three-dimensional scatterer reconstruction and detection ability.

LiDAR remote sensing data combined with machine learning (ML) techniques have presented great potential for large-scale modeling of tropical forest attributes. However, the large amount of information that can be derived from an aerial LiDAR survey, summed with the intrinsic heterogeneity of tropical environments (e.g., the Amazon), makes it a challenge to accurately estimate forest biophysical variables. The aim of our work is to investigate the potential and accuracy of different ML techniques and a generalized linear model (GLM) to learn the relationships between LiDAR-derived metrics and forest inventory data for aboveground biomass (AGB) prediction in Amazon forest sites under selective logging regimes. The predictive performance of three ML techniques, namely random forest (RF), support vector machine (SVM), and artificial neural network (ANN), was compared against result from the GLM technique, across 85 sample plots. Interestingly, the GLM retrieved the most accurate estimations of forest AGB (rho Spearman’s coefficient = 0.87), compared with the ML techniques (RF = 0.77, SVM = 0.67, and ANN = 0.50). A number of possible factors affecting such results are listed and discussed in the text, including sample size and number of predictor variables. Continued research is necessary to improve the confidence of AGB estimation, especially over complex forest structures.

Optical trapezoid model (OPTRAM) as a method has been proposed to retrieve soil moisture from remote sensing data. It is based on the assumption that a trapezoidal shape would be derived from plotting of vegetation index (VI) versus shortwave-infrared transformed reflectance (STR) data assuming a linear relationship between VI and STR. A literature review and the present study indicate that the relationship between VI and STR under both dry and wet conditions, especially in the wide range of vegetation cover, is nonlinear. Therefore, we modified the OPTRAM model by introducing nonlinear edges to the VI-STR space and then employed the modified OPTRAM (denoted as MOPTRAM) using Sentinel-2 observations to predict surface (θ_surf) and root zone (θ_rz) soil moistures. Soil moisture predicted by MOPTRAM and OPTRAM methods were compared with ground truth volumetric soil moisture data of a maize field. Accuracy of the predictions from the MOPTRAM increased as compared to that from the OPTRAM for both θ_surf and θ_rz with a wide range of vegetation cover. The root-mean-square error and R² for the θ_surf estimates from MOPTRAM were 0.036  cm³  /  cm³ and 0.748, respectively, with corresponding figures of 0.047 and 0.692 from OPTRAM, implying greater prediction accuracy for the modified model in the studied area.

The accurate estimation of global chlorophyll-a (Chla) concentration from the large remote sensing data in a timely manner is crucial for supporting various applications. Moderate resolution imaging spectroradiometer (MODIS) is one of the most widely used earth observation data sources, which has the characteristics of global coverage, high spectral resolution, and short revisit period. So the estimation of global Chla concentration from MODIS imagery in a fast and accurate manner is significant. Nevertheless, the estimation of Chla concentration from MODIS using traditional machine learning approaches is challenging due to their limited modeling capability to capture the complex relationship between MODIS spatial–spectral observations and the Chla concentration, and also their low computational efficiency to address large MODIS data in a timely manner. We, therefore, explore the potential of deep convolutional neural networks (CNNs) for Chla concentration estimation from MODIS imagery. The Ocean Color Climate Change Initiative (OC-CCI) Chla concentration image is used as ground truth because it is a well-recognized Chla concentration product that is produced by assimilating different satellite data through a complex data processing steps. A total of 12 monthly OC-CCI global Chla concentration maps and the associated MODIS images are used to investigate the CNN approach using a cross-validation approach. The classical machine learning approach, i.e., the supported vector regression (SVR), is used to compare with the proposed CNN approach. Comparing with the SVR, the CNN performs better with the mean log root-mean-square error and R² of being 0.129 and 0.901, respectively, indicating that using the MODIS images alone, the CNN approach can achieve results that is close to the OC-CCI Chla concentration images. These results demonstrate that CNNs may provide Chla concentration images that are reliable, stable and timely, and as such CNN constitutes a useful technique for operational Chla concentration estimation from large MODIS data.

Spectral mixture analysis (SMA) is an effective means of finding a unique spectral signature of constituents called endmembers and approximating their proportion of presence (abundance fractions). In the literature of SMA, the challenging task of endmember extraction from the hyperspectral imagery is approached by different methods. The majority of the endmember extraction algorithms are developed based on the convex geometry of the dataset perhaps due to low computation. But the performance of these convex geometry-based algorithms is degraded in the high-level noise scenario. To make the noise-robust algorithm, we propose an algorithm by introducing K-medoids with convex geometry. The proposed algorithm uses the K-medoids clustering approach in the removal of extra convex points, which leads to improving the endmember extraction efficacy. The proposed algorithm is tested by introducing white Gaussian noise under different signal-to-noise ratio conditions in the synthetic dataset, especially for high-level noise. Our experimental results show that the proposed one improves the endmember extraction efficiency in the high-level noise condition. The proposed algorithm is also tested on the real datasets of Cuprite and Mangalore. The proposed algorithm outperforms others on the real benchmark datasets as well.

Cotton root rot (CRR) is a persistent soil-borne fungal disease that is devastating to cotton crops in certain fields, predominantly in Texas. Research has shown that CRR can be prevented or mitigated by applying fungicide during planting, but fungicide application is expensive. The potentially infected area within a field has been shown to be consistent, so it is possible to apply the fungicide only at locations where CRR exists, thus minimizing the amount of fungicide applied across the field. Previous studies have shown that remote sensing from manned aircraft is an effective means of delineating CRR-infected field areas. In 2015, an unmanned aerial vehicle was used to collect high-resolution remote-sensing images in a field known to be infected with CRR. A method was developed to produce a prescription map (PM) from these data, and in 2017, fungicide was applied based on a PM derived from the 2015 image data. The results showed that the PM reduced the fungicide applied by 88.3%, with a reduction in CRR area of 90% compared to 2015. A simple economic model suggested that it is generally better to treat an entire CRR-infested field rather than leaving it untreated, and application based on a PM becomes preferable as the size of the farm and the yield increase while the CRR-infestation level and the number of fields on the farm decrease.

Remote sensing enables multitemporal information of the Earth’s surface and the dynamic processes that affect the environment. Given the considerable data availability, methods to summarize multitemporal datasets are needed to support the analysis. Our study introduces and compares methods to monitor temporal variations of water bodies based on multitemporal image composition. For this purpose, the presence of water at different dates is mapped applying the normalized difference water index using two encoding methods. The first one is based on the cumulative analysis of water in the pixel along time, and the second one uses the principle of binary encoding. The cumulative analysis helps to visualize more humid and dry areas, while binary encoding indicates the monthly variations of the lake surface, storing information about the dynamics of the phenomenon. The methods are compared using Landsat time series of Lake Poopó obtained between 2013 and 2019. The results showed that binary encoding allows detecting when and where severe droughts affect the water body and its recovery. In addition, it was possible to monitor the severe drought that affected the lake in 2016 and it was also noticed that its surface is still below the level registered before the drought in 2013.

We evaluated the opportunities and performance of a new type of systematic pixel sharp calibration site for airborne hyperspectral mineral mapping in the environment of an operational copper deposit in Mongolia. The calibration site was designed to be used for estimation of sensitivity and quantification of key minerals in individual pixels in specific geological scenarios. The layout of the calibration site was done with two different copper-containing rock samples, a low copper-containing rock material from the mine, tailing material from the mine, and calibration materials with well-defined known spectral features. The scaled coverage of the sample materials was designed to develop statistical approaches to quantify target minerals in airborne surveys on a pixel-based approach. The data collection included the description of the calibration materials with geochemical, x-ray diffraction, and microscopic and electron raster microscopic methods. Using visible and near-infrared airborne sensors and shortwave infrared airborne sensors, data of the calibration site were collected with multiple repeats from six altitudes. After rectification and atmospheric correction of pixels, sharp measurements of absorption features of clay minerals at 1400, 1900, and 2200 nm were performed and statistically analyzed. Correlations between coverage and absorption features especially around 2200 nm are shown, and influences of flight altitude on sensitivity of the detection and the stability of the measurements are investigated. The results of the calibration field are used for the quantitative estimations of clay minerals in an exploration area near the mine site. The results are also shining new light on methodologies for ground truthing in hyperspectral surveys.

Morphological and triangular irregular network (TIN) ground filters require setting up different parameters to achieve high accuracy for different terrains. A proposed morphologically iterative TIN (MIT) ground filter only requires maximum building size in the processing of raw light detection and ranging (LiDAR) data. This approach applies morphological and TIN densification in an iterative way for separating ground points from off-ground ones. A radial nearest neighbor is designed to select the surrounding nearest neighbors for each point, and these neighbors are analyzed to define the parameters of a local translational 3D plane surface. Experimental results using ISPRS benchmark datasets show that MIT achieves an average total error of <4.0  %  , and an average kappa coefficient of >85  %  . Further experimental validation with Hong Kong LiDAR datasets reveals that MIT is effective in detecting dense ground points and robust in various terrain situations.

With the development of remote sensing technology, several spatial sources for measuring precipitation have become available: Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data are of particular interest. The CHIRPS product was used, evaluated, and compared within the Tensift basin located in west-central Morocco, for the period 1990 to 2017, using 11 meteorological stations. Four statistical parameters were used to evaluate CHIRPS performance, namely rain gauge measurements at multiple time scales (monthly, seasonal, and annual). Using the standard precipitation index as a drought indicator, the applicability of this long-term satellite precipitation product for drought monitoring was investigated for the period 1981 to 2019. The results show close agreement with observed precipitation data at different time scales (monthly, seasonal, and annual). At the monthly scale, the correlation coefficients vary between 0.56 and 0.86. In addition, a better correlation with observed data was obtained at the seasonal and annual scales. In the meantime, CHIRPS has obtained a good BIAS score and a lower error rate in the different time scales. Based on high-resolution long-term CHIRPS, it is found that the Tensift basin experienced eight severe droughts during the last four decades with the longest one in 2000 to 2002 for 26 months and the driest one in 2019 with drought affected area up to 100%. Droughts occurred frequently in all parts of the Tensift basin, and the plain of Tensift seems to experience more long-term and extreme drought events.

The Advanced Baseline Imager (ABI) is a passive imaging radiometer on-board National Oceanic and Atmospheric Administration’s (NOAA) Geostationary Operational Environmental Satellites-R (GOES-R) series. Its bands 7 to 16 are categorized as infrared (IR) bands, sampling within a spectral range of 3.9 to 13.3  μm in mid-wave infrared (MWIR) and long-wave infrared (LWIR) regions. ABI provides variable area imagery and radiometric information of Earth’s surface, atmosphere, and cloud cover. All of the IR bands are calibrated on-orbit in reference to an internal blackbody. While the ABI aboard the GOES-16 satellite has been working properly, an anomaly with GOES-17 ABI’s cooling system, specifically its loop heat pipe (LHP) subsystem, prevents heat from being efficiently transferred from the ABI electronics to the radiator to be dissipated into space. As a consequence, the heat accumulates inside the instrument, so the temperatures of its key components for IR calibration, including the focal plane modules (FPMs), scan mirrors, and blackbody, cannot be maintained at their designed operational levels. As an example, the temperatures of MWIR and LWIR FPMs, where IR detectors are located, are currently operated at a baseline temperature of ∼20  K warmer than the design and vary by as many as 27 K diurnally. This causes severe degradation to the data quality of ABI IR Level 1b radiance and subsequent Level 2+ products during the hot period of the day. Significant progress has been made to mitigate the effects of the LHP anomaly to optimize the IR performance of GOES-17 ABI. We summarize the efforts made by NOAA’s GOES-R Calibration Working Group, working collaboratively with other teams, to evaluate and alleviate the negative impacts of warmer and floating FPM temperatures on ABI IR calibration, and assess the IR performance accordingly.

Artificial illumination identification within images is a useful tool for many applications. Performing such identification allows for an estimation of the illumination source spectrum, which in turn can be used for additional applications ranging from spectral detection and exploitation to statistics about nighttime light usage. Illumination identification has been performed in laboratory settings but not from an unmanned aerial vehicle (UAV) platform. Here, we test the feasibility of using a UAV and commercial off-the-shelf multispectral imaging sensor to perform such artificial illumination identification through linear discriminant analysis using nighttime UAV images. The results are very promising, showing source classification accuracies of 83.3%, 92.3%, 100%, and 100% for the incandescent, light-emitting diode, high pressure sodium, and metal halide illumination sources, respectively. We show that the information gained from the source identification can be further used to inform additional analysis, such as spectral identification. The high resolution of UAV imaging techniques combined with the knowledge of the illumination source can lead to better exploitation of such nighttime data for many applications.

Extending the temperature measurement range and sustaining the system performance are important requirements in Rayleigh lidars. While replacing hardware and increasing the power aperture product are ways of achieving these objectives, the data analysis approach is another means for achieving the same. A method to retrieve atmospheric temperatures using the penalized maximum likelihood method after denoising the backscattered signal using the dictionary learning technique is presented and compared with the conventional method. The proposed combination has the advantage of improving the measurement range and reducing the standard error (SE) in temperatures. The penalized maximum likelihood function is solved using the method of successive approximations, and the SE in temperature is calculated using Monte Carlo simulations. Observations from the Rayleigh lidar at the National Atmospheric Research Laboratory, India, are used for testing the approach. When compared with the conventional method, the SE in temperatures improved by 5K at 84 km, and the average height improvement was about 6 km.

Evapotranspiration (ET) is a critical component of the water cycle on a global scale. Changes in leaf area index (LAI) influence ET changes. Satellite products are among the effective methods of estimating ET and LAI at local, regional, and global scales. Therefore, the purpose of our study was to evaluate three global remote sensing products of ET and two products of LAI and investigate their simultaneous changes in Iran over the 2000 to 2015 period. Selected ET products include Global Land Evaporation Amsterdam Model (GLEAM), Global Land Data Assimilation System (GLDAS2.0), and Moderate Resolution Imaging Spectroradiometer ET (MODIS ET) products. LAI products include MODIS LAI and Global Land Surface Satellite (GLASS) LAI products. Validation using the 23 selected evaporation stations showed that among the three ET products MODIS has the best performance. Three ET products consistently show an increasing trend in annual ET over a large part of Iran. The mean annual trend values obtained from GLEAM3.0a, GLDAS, and MODIS ET products are 2.022, 1.359, and 0.429 mm/year, respectively. Among LAI products, GLASS had the highest correlation with the selected ET product (0.78). On average, the trend of intra-annual changes of LAI across Iran did not show a significant increasing or decreasing trend. Moreover, in areas where no significant change was observed in LAI, ET changes were unpredictable and depended on other factors. Overall, the results showed a relatively strong correlation between the selected ET and vegetation satellite products in the study area.

Recently, many convolutional neural networks (CNN) have been successfully employed in bitemporal synthetic aperture radar (SAR) image change detection. However, most of the existing networks are too heavy and occupy a large volume of memory for storage and calculation. Motivated by this, we propose a lightweight neural network to reduce the computational and spatial complexity and facilitate the change detection on an edge device. We replace normal convolutional layers with bottleneck layers that keep the same number of channels between input and output. Next, we employ dilated convolutional kernels with a few non-zero entries that reduce the running time in convolutional operators. Comparing with the conventional convolutional neural network, our lightweight neural network will be more efficient with fewer parameters. We verify our light-weighted neural network on four sets of bitemporal SAR images. The experimental results show that the proposed network can obtain better performance than the conventional CNN and has better model generalization, especially on the challenging datasets with complex scenes.

In conventional inverse synthetic aperture radar (ISAR) imaging of complex moving aircrafts, a micro-Doppler effect is generated because of the high-speed rotating parts such as propellers. The rotational parts introduce a sinusoidal frequency-modulated (SFM) signal in the radar echoes, which can significantly affect the quality of the ISAR imaging. To solve this problem, the Doppler frequency forms for the rigid body and rotating parts of an aircraft are analyzed, and a method based on the discrete sinusoidal frequency-modulated transform (DSFMT) is proposed. The SFM signal can be transformed into the DSFMT domain, and the parameters can be estimated by the global maximum. The rigid body is separated after removing the SFM signals using successive elimination technology. This method is not only applicable to targets in uniform motion but also to those in accelerating motion. The results of simulations and a measured data test are given to verify the effectiveness of the proposed method.

Using satellite data to study small water bodies (SWB) and medium-sized water bodies (MSWB) is extremely useful for understanding their status, how to conserve them as water reservoirs, and their vulnerability to climate variability. The images studied in our work correspond to different-sized lagoons located in areas with high and low topography in a tropical region of Chiapas, Mexico. Our research project delineates SWB and MSWB. For this analysis, we considered water bodies to be uniform regions in a synthetic aperture radar image. The robustness of the method was determined based on an analysis of the morphologies of 23 lagoons. Several methods, including Hermite transform, were analyzed and compared with other image denoising methods used to improve speckle reduction. To obtain additional spatial information for image classification, we analyzed texture using the gray-level co-occurrence matrix. The results indicate that the Hermite filter is the best method for identifying water bodies. The advantage of this filter is the identification of local patterns such as edges and lines. It also preserves and improves aspects related to the homogeneity of water bodies, using the Hermite coefficient selection criteria for local pattern feature selection/extraction. The lake water extent products demonstrate that Sentinel-1 is useful for identifying SWB in this study area. The results show very high detection of water bodies, with adequate detection for water bodies larger than 2 ha, and an area accuracy of 80%.

Following the objectives of the mission, Sentinel-2 (S2) makes a significant contribution to land monitoring, climate change, emergency management and security, and related problems. To increase the spatial resolution, researchers have been challenged to obtain a synthesized panchromatic band with a fine resolution for all S2 bands. The capacity to collect satellite imagery with a short revisit time at different spatial resolutions increases the ability for more accurate data fusion applications. Our work presents an investigation of different synthesized panchromatic bands for producing high-resolution S2 data. We produced a synthesized environment from PlanetScope (PS) data to evaluate the performance of different panchromatic bands for the enhancement of S2 10- and 20-m bands. For this, imagery over three different study areas with different characteristics was chosen: Pakistan, North Macedonia, and Turkey. After the three different synthesized panchromatic bands were produced, we fused the panchromatic bands with the 10- and 20-m S2 bands with three different state-of-the-art pansharpening techniques. Experimental comparison between the three newly produced panchromatic bands indicates that all synthesized bands can enhance the spatial resolution of the original multispectral bands. Since the difference in spatial resolution may be critical for more detailed image classification, for future studies, we recommend investigation of the fused data for different land cover applications.

Spectral variability is one of the most limiting factors in hyperspectral unmixing, so it is important to further study the characteristics of spectral variability to improve the accuracy of unmixing. After conducting simulations under varying irradiation conditions, a linear mixed model combining endmember and band is proposed by introducing a band scaling factor to the endmember scaled spectrum. The total variation constraint is used to smooth the spatial distribution of both endmember and band scaling factors and then alternating iterative optimization is applied to solve the optimization problem. Experiments conducted with both simulated and real hyperspectral data sets indicate that the proposed algorithm is effective in hyperspectral unmixing and is superior to other state-of-the-art algorithms based on spectral variability.

The heavy-tailed properties of modulation transfer function (MTF) typically introduce noise and an aliasing effect in the MTF compensation (MTFC) effort to improve the spatial quality of optical satellite images. These degradative effects compromise the image’s signal-to-noise ratio (SNR). Consequently, users must evaluate the relative importance of image sharpness versus SNR for their applications to decide whether MTFC processing is appropriate. We propose a high-fidelity MTFC method that executes an optimal trade-off between noise regularization and detail preservation in the image restoration process to address this problem. To this end, we exploit the merit of image prior characteristics in both local smoothness and nonlocal self-similarity properties of an image in a hybrid domain (viz., space spatial and frequency) to design effective regularization terms that reflect these image properties. Furthermore, we establish a simple joint statistical model in the curvelet domain to combine these two properties. To make this regularization-based MTFC method tractable and robust, we employ the multiobjective bilevel optimization approach to solve the severely ill-posed inverse problem of MTFC. We conduct extensive experiments to evaluate the proposed regularization-based MTFC method using synthetically blurred images simulated from the level 2A product of IKONOS and real satellite images with unknown blur. Quantitative measurements of image quality reveal that the proposed method produces competitive restoration results with minimum computational complexity and exhibits a good convergence property. These experimental results show that the proposed method can find a compromise between regularizing noise and preserving image fidelity.

Dimensionality reduction is an important step in processing hyperspectral images (HSIs) to overcome the dimensionality problem. Linear dimensionality reduction methods such as independent component analysis (ICA) and linear discriminant analysis (LDA) are commonly employed to reduce the dimensionality of HSI. These methods fail to capture nonlinear dependency in the HSI data, as data lies in the nonlinear manifold. To handle this, nonlinear transformation techniques based on kernel methods were introduced for dimensionality reduction of HSI. However, the kernel methods involve cubic computational complexity while computing the kernel matrix, thus its potential cannot be explored when the number of pixels (samples) is large. In literature, a fewer number of pixels are randomly selected to partially overcome this issue; however, this suboptimal strategy might neglect important information in the HSI. We propose randomized solutions to the ICA and LDA dimensionality reduction methods using random Fourier features, and we call them randomized ICA and randomized LDA. Our proposed method overcomes the scalability issue and handles the nonlinearities present in the data more efficiently. Experiments conducted with three real-world hyperspectral datasets demonstrate that our proposed randomized methods outperform the conventional kernel ICA and kernel LDA overall and in terms of per-class accuracies, and computational time.

The projection of objects on the earth’s surface caused by the sunlight produces shadows. They are inevitable in high-spatial-resolution satellite remote sensing images and reduce the accuracy of change detection, land cover classification, target recognition, and many more applications. Dark-colored land covers in these satellite images, such as water bodies, road, and soil, appear with similar spectral properties as those of shadows and often result in difficulty in shadow detection, especially in complex urban settings. We propose an object-oriented building shadow extraction method and tested it using six selected study areas from TripleSat-2 satellite imagery with 3.2-m spatial resolution. The method’s main steps include (1) selecting six image features that can highlight the shadow information and then segment the image based on edge; (2) extracting shadow region based on multiple object features; and (3) masking nonbuilding shadow regions by the shadow and dark object separation index, image features including spectral, textural, and geometric features, and contextual information. The average precision, recall, and F1-score of the shadow detection were 85.6%, 88.6%, and 87.0%, respectively, and the ranges were 73.0% to 91.0%, 76.6% to 94.1%, and 74.7% to 91.2%, respectively. Compared with multiscale segmentation, edge-based segmentation is more efficient and helpful to completely and accurately extract shadows.

Graph embedding (GE) frameworks are used for extracting the discriminative features of hyperspectral images (HSIs). However, it is difficult to select a proper neighborhood size for graph construction. To overcome this difficulty, a semisupervised feature extraction (FE) method, called semisupervised collaborative representation graph embedding (SCRGE), is proposed. The proposed algorithm utilizes collaborative representation (CR) to obtain the collaborative coefficients of labeled and unlabeled samples. Then, a semisupervised graph is constructed using the collaborative coefficients of the labeled samples within the same class and the collaborative coefficients of the unlabeled samples, and an interclass graph is constructed using the collaborative coefficients of the labeled samples in different classes. Finally, a projection matrix for FE is obtained by embedding these graphs into a low-dimensional space. SCRGE not only inherits the merits of CR to reveal the collaborative reconstructive properties of data but also enhances intraclass compactness and interclass separability to improve the discriminating power for classification. Experimental results on three real HSIs datasets demonstrate that SCRGE outperforms other state-of-the-art FE methods in terms of classification accuracy.

In order to achieve accurate locations of multiple targets on an airborne optoelectronic platform, we propose a multitarget location system based on the digital elevation model and a multitarget location model based on the target vector. Through the target detection algorithm, the pixel coordinates of each target in the field of view can be obtained and the visual axis vector of each target is constructed. By integrating the measurement data of each sensor in an unmanned aerial vehicle and the photoelectric platform, the geographical location information of each target can be calculated. In the condition of a flight height of 3000 m, the location error for the main target is 16.47 m, and the location error for the secondary target is about 27.36 m. The effect of the improved filtering model is analyzed. After filtering, the main target location error is reduced to 7 m, and the secondary target location error is reduced to 11 m. The proposed method has the advantages of high real-time performance, high location accuracy, and convenient engineering application.

Images from high-resolution inverse synthetic aperture radar (ISAR) can provide more information about the targets. Multiband fusion imaging techniques can achieve higher range resolution without increasing hardware costs. A multiband fusion imaging algorithm based on variational Bayesian inference (VBI) is proposed to improve the range resolution of ISAR images. First, a multiband fusion ISAR imaging model is established based on sparse representation. Second, the scattering coefficients and noise are assumed to be the Laplacian scale mixture distribution and the complex Gaussian distribution, respectively. Finally, the fusion image is directly reconstructed in the complex domain by the VBI based on Laplace approximation method. The effectiveness and robustness of the proposed algorithm are verified by the experimental fusion results of one-dimensional signals and two-dimensional ISAR images.

Our work aims to address the problem of estimating the parameters of constant-amplitude, time-unsynchronized linear frequency-modulated (LFM) signals that have single or multiple components, which is a crucial task in electronic countermeasure techniques. A method for estimating the parameters, center frequency f₀, and chirp rate μ of an LFM signal is proposed; the method is referred to as the Wigner–Ville distribution complex-valued convolutional neural network (WVD-CV-CNN). The method can be regarded as an application of neural networks for extracting parameter features from the signal spectrogram, wherein the CV-CNN is the main body of the network, which takes a complex-valued WVD matrix as the input and outputs several sets of estimated parameters. A performance analysis shows that the estimation accuracy and computational efficiency of the proposed method are significantly improved compared with those of the conventional methods. Further, the proposed method shows strong robustness to changes in modulation parameters. We apply the CV-CNN to other spectrograms and prove compatibility of the WVD and CV-CNN by comparison. We also demonstrate that the estimation accuracy of the proposed method is robust against cross interference on the WVD. Our study shows the advantages of using deep learning systems in signal parameter estimation.

Hyperspectral images (HSIs) contain a significant amount of spectral and spatial information, together with underlying redundancy and noise, causing difficulty in HSI-processing tasks. State-of-the-art deep learning methods have obtained unprecedented performance in HSI classification and analysis. However, these architectures face challenges of declining accuracy and lengthy training times. We propose a framework to mitigate these issues, composed of a densely connected spectral block and preactivation bottleneck residual spatial block to separately learn spectral and spatial features. The spectral extraction block can involve more spectral features with the increase of the network depth, and it solves the problem of lengthy training time in traditional methods, and its densely connected structure achieves higher accuracy. In the spatial extraction block, we use the improved residual structure and introduce batch normalization and a parametric rectified linear unit before convolutional layers to preactivate the network, reducing parameters, and overfitting. In experiments using three classification approaches for comparison, it can be observed that even compared to the state-of-the-art method: spectral–spatial residual network for HSI classification, the proposed model shows improvements in accuracy of 0.49%, 0.19%, and 0.35% on the Indian Pines, University of Pavia, and Kennedy Space Center datasets, respectively. The experimental results reveal that the model obtains better classification results while effectively decreasing the training time.

An increasing number of applications require land cover information from remote sensing images, thereby resulting in an urgent demand for automatic land use and land cover classification. Therefore, effectively improving the accuracy of land cover classification is a main objective in remote sensing image processing. We propose a land cover classification postprocessing framework based on iterative self-adaptive superpixel segmentation (LCPP-ISSS) for remote sensing image data. This framework can further optimize the land cover classification results obtained by neural networks without changing the network structure. First, we propose the iterative self-adaptive superpixel segmentation algorithm for high-resolution remote sensing images to extract the boundary information of different land cover classes. Then, we propose a land cover classification result optimization method based on patch complexity to optimize the classification result by combining the boundary information with the semantic information. In an experiment, we compare the classification accuracy before and after using LCPP-ISSS and with other common methods. The results show that LCPP-ISSS outperforms the dense conditional random field and provides a 4% increase in the mean intersection over union and a 10% increase in overall accuracy.

A sparse representation-based bistatic inverse synthetic aperture radar (ISAR) imaging method can achieve a high-resolution image of a target with sparse aperture data. However, the bistatic ISAR system is more sensitive to noise than the monostatic one because of its nonmirror reflection geometry. To overcome this drawback, we propose the sparse aperture bistatic ISAR imaging method based on joint sparse model. Considering the joint sparse information of bistatic ISAR echo, a joint sparse imaging model is constructed. Then, the dechirped sparse aperture bistatic ISAR echo after translational compensation is transformed into range fast time and azimuth slow time domains by the joint sparse imaging model, and a corresponding azimuth sparse basis is constructed. Then a joint sparse complex approximate message passing algorithm is proposed to joint sparse imaging model. The joint sparse imaging problem is converted to a block sparse imaging problem by vectorization. Using the relationship between the vectorization of the matrix and the Kronecker product, a matrix iteration structure is proposed to solve the joint sparse model efficiently and accurately. The experimental results based on both scattering point model and electromagnetic calculation model data verify the effectiveness of the proposed imaging method.

Terrain classification is an important application for polarimetric synthetic radar (PolSAR) image processing. Inspired by the popular deep belief network (DBN), a PolSAR classification method is proposed, which is called multilayer Wishart restricted Boltzmann machine (MWRBM). For PolSAR data, the traditional DBN is limited by binary value distribution, which is not suitable for PolSAR image classification. Therefore, according to the statistical distribution of PolSAR data, a new type of Wishart-restricted Boltzmann machine is proposed. An MWRBM, as one of the deep learning models, is proposed for PolSAR image classification. For improving the classification result, the labeled samples are used to fine-tune the parameters of the proposed deep model. Finally, two real PolSAR datasets are tested to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method is very effective and compare favorably to the state-of-the-art methods.

The surveillance of large areas to ensure local security requires remote sensors with high temporal and spatial resolution. Captive balloons with infrared and visible sensors, like ALTAVE captive balloon system, can perform a long-term day–night surveillance and provide security of large areas by monitoring people and vehicles, but it is an exhaustive task for a human. In order to provide a more efficient and less arduous monitoring, a deep learning model was trained to detect people and vehicles in images from captive balloons infrared and visible sensors. Two databases containing about 700 images each, one for each sensor, were manually built. Two networks were fine-tuned from a pretrained faster region-based convolution neural network (R-CNN). The network reached accuracies of 87.1% for the infrared network and 86.1% for the visible one. Both networks were able to satisfactorily detect multiple objects in an image with a variety of angles, positions, types (for vehicles), scales, and even with some noise and overlap. Thus a faster R-CNN pretrained only in common RGB (red, green, and blue) images can be fine-tuned to work satisfactorily on visible remote sensing (RS) images and even on the infrared RS images.

Drawing a spectrally homogeneous region of interest in a remotely sensed image is a common task for an image analyst when performing, for instance, atmospheric correction or end-member selection. Manually selecting a homogeneous sample of pixels can be tedious and error prone due to the limits of human perception and data visualization. I present a region shrinkage method that automates the extraction of a spectrally homogeneous and spatially contiguous region from a user selected seed pixel. The proposed technique combines divisive clustering, connected component analysis, and image noise estimation to generate a series of candidate regions of increasingly smaller size until they converge to the seed pixel through similarity space. From these candidate regions, an optimal one is identified that is spectrally homogeneous, spatially contiguous, and as large as possible. Experimental results demonstrate that the proposed method achieved detection rates of up to 95%, false alarm rates below 1%, and was robust to the main user input, the seed pixel location.

Hyperspectral imaging provides a useful tool for extracting complex information when visual spectral bands are not enough to solve certain tasks. However, processing hyperspectral images (HSIs) is usually computationally expensive due to the great amount of both spatial and spectral data they incorporate. We present a low-cost convolutional neural network designed for HSI classification. Its architecture consists of two parts: a series of densely connected three-dimensional (3-D) convolutions used as a feature extractor, and a series of two-dimensional (2-D) separable convolutions used as a spatial encoder. We show that this design involves fewer trainable parameters compared to other approaches, yet without detriment to its performance. What is more, we achieve comparable state-of-the-art results testing our architecture on four public remote sensing datasets: Indian Pines, Pavia University, Salinas, and EuroSAT; and a dataset of Kochia leaves [Bassia scoparia] with three different levels of herbicide resistance. The source code and datasets are available online. (Hyper3DNet codebase: https://github.com/GiorgioMorales/hyper3dnet.)

A synthetic aperture radar (SAR) target classification method is proposed by selecting deep features learned by convolutional neural network (CNN) using the nonlinear correlation information entropy (NCIE). The deep feature maps are learned from original SAR images by CNN. Considering the possible indiscrimination and redundancy, all the deep feature maps from different layers are analyzed by NCIE to select out those discriminative ones. The selected feature maps share high inner correlations, which are jointly classified based on joint sparse representation (JSR). Finally, the target label is determined based on the reconstruction errors from JSR. The famous moving and stationary target acquisition and recognition dataset are employed to setup the standard operating condition and several extended operating conditions, including configuration variants, depression angle differences, noise corruption, and partial occlusion, to comprehensively evaluate the performance of the proposed method. The results confirm the superior effectiveness and robustness of the proposed method in comparison with some state-of-the-art SAR target classification methods.

Since its launch in December 1999, Terra Moderate Resolution Imaging Spectroradiometer (MODIS) has successfully operated for more than 20 years, with its observations generating a broad range of science data products that have greatly enabled the remote sensing community and users worldwide in their studies of many key geophysical parameters of the Earth’s systems. MODIS collects data in 36 spectral bands, covering wavelengths from 0.41 to 14.4  μm, which are calibrated by a set of onboard calibrators (OBCs). Also contributing to the sensor’s mission-long on-orbit calibration and characterization are near-monthly scheduled lunar observations and multiple time series of the sensor’s responses over select ground targets at a variety of scan angles. To a large extent, the quality of MODIS data products relies strongly on the dedicated efforts to operate and calibrate the instrument, to derive and update calibration parameters, and to develop and implement new calibration strategies and algorithms in response to on-orbit changes of the sensor’s characteristics and its OBC functions. We provide an overview of the Terra MODIS on-orbit operation and calibration activities over the last 20 years, including changes made to extend and preserve the instrument and OBC functions and their operation strategies. It also illustrates the sensor’s on-orbit performance with results derived from its OBC, lunar observations, and select ground targets and discusses major changes in sensor characteristics and corrections applied to the L1B algorithms as well as calibration lookup table updates. To date, the Terra MODIS instrument and its OBCs continue to operate and function normally. Except for those identified prelaunch, most spectral bands and detectors continue to meet their specified calibration requirements. Also discussed in our paper are lessons learned from Terra MODIS operation and calibration, as well as future efforts to further extend and maintain the quality of its long-term data records.

The fusion of a high-spatial-resolution (HSR) panchromatic band and several multispectral bands with a relative low spatial resolution has become a research focus with the development of HSR remote sensing technology. Previous studies have demonstrated that fused spectra of mixed pixels (MPs) remain mixed, which considerably contributes to spectral distortions observed in fused images produced by most of the current pansharpening methods. Several works have attempted to reduce spectral distortions of fused spectra of MPs to improve the quality of fused products generated by some fusion methods based on component substitution (CS). An image fusion framework for reducing spectral distortions caused by the incorrect fusion of MPs is proposed for both CS and fusion methods based on multiresolution analysis (MRA). Using the proposed framework based on image segmentation, the fused products of two classic MRA-based pansharpening methods were improved by improving the fusion spectra of MPs. The improved fused images were compared with the original fusion products through a fusion experiment using three datasets recorded by WorldView-2, GeoEye-1, and WorldView-3. Experimental results showed that the improved fused products yielded higher Q2ⁿ and quality with no reference values and lower relative average spectral error, dimensionless global relative error of synthesis, and spectral angle mapper values than the corresponding original fusion products. This indicates that the proposed image fusion framework is effective for reducing spectral distortions of fused images generated by the two MRA-based fusion methods.

Evapotranspiration (ET) is an important part of the hydrological cycle. It is essential to evaluating the accuracy of different ET products in practical applications. The operational simplified surface energy balance (SSEBop) ET products are widely used because of their high resolution. The SSEBop products were validated at the site and basin levels using eddy covariance data from ChinaFLUX stations and compared with that of the MOD16 products in China. The results are as follows: (1) at the site level, SSEBop ET and MOD16 ET showed overall comparable accuracies; at the basin level, the annual ET simulation values of the SSEBop and MOD16 products were biased toward high values, and the accuracy of the SSEBop products was better than that of the MOD16 products. (2) The distribution of China’s multiyear ET pattern obtained from the SSEBop products is reasonable. The largest change in annual ET obtained by the SSEBop products is in the Songhua River Basin, whereas the smallest is in the Southwestern Rivers. Therefore, the results demonstrate that the SSEBop products provided reliable ET estimations at multiscales and performed well in the simulation of spatiotemporal variation of ET in China, which is of great significance to regional water resource management and efficient water utilization in China.

Spatial data mining discovers patterns and knowledge in spatial data. The geospatial data analysis plays a decisive role in framing essential policies related to the environment at the national as well as global level. It helps in the prediction of weather, studying the impact of climate change, the effect of global warming, forest fire, deforestation, and other causes of changes in nature. Clustering is a process of assigning similar objects to a group and dissimilar objects to different groups. We proposed a clustering algorithm, spatial clustering using neighborhood (SCN), in which similar neighboring pixels groups together to form a cluster. It is a two-step process: first, identification of clusters of similar neighboring pixels and second, merging of spatially separated similar clusters. Experiments with the implementation of the proposed approach have been carried out on three Landsat 5 thematic mapper images of Delhi, Chilika lake, and Kolkata region. In terms of normalized cluster quality parameters, SCN produced better clusters than other discussed algorithms in the literature.

Evapotranspiration (ET) quantification improves the comprehension of the water, heat, and carbon interactions and the feedback to the climate, which is essential for global change research. We aimed to model ET using artificial neural networks (ANNs) based on Landsat-8 and reanalysis data from the National Centers for Environmental Prediction over the grasslands of the Pampa biome. The output variable was the ET trained by eddy covariance (EC) measurements acquired from a flux tower located in Santa Maria, Brazil. ANN was performed using the backpropagation algorithm with four remote sensing input variables (albedo, normalized difference vegetation index, land surface temperature, and surface net radiation). In addition, four meteorological variables from the Environmental Prediction Climate Forecast System Version 2 hourly product were included in the model (air temperature, atmospheric pressure, relative humid, and wind speed). We analyzed 67 clear-sky scenes between 2014 and 2019. Results produced very robust daily ET estimates. ANN exhibited a correlation of 0.88 relative to in situ EC data, demonstrating a good linear relationship between ET estimated and measured and producing a root-mean-square error (mean absolute error) of 0.75 (0.58) mm/day. The ANN model was also compared with the widely known simplified surface energy balance index (S-SEBI) model. S-SEBI exhibited lower correlation with the ET in situ compared to the ANN model. Furthermore, the ANN model had a superior performance in summer and winter seasons in which S-SEBI was found to outperform the ET in situ. The model developed in our research is an alternative to approaches that need a great number of input variables or in situ data since it is only dependent on freely available data. Therefore, it should support future integrated strategies of water resources allocation over the natural grasslands of the Brazilian Pampa.