The 2020 fire season includes the single largest fire in California’s history, the August Complex Fire. Ash and soot contained in wildfire smoke have a low albedo and can absorb incoming UV radiation. As a result, one would hypothesize that at a local level the surface UV irradiance dosage would change in areas leeward of large fires. To test this hypothesis, the direct and diffused UV irradiance recorded east of the August Complex Fire at the UVMRP station in Davis, CA were compared between 2020 and 2016. Direct and diffused UV irradiance levels at local noon of an entire year were compared between these two years, trying to identify how wildfire impacts surface UV. Using satellite imagery to determine when smoke was present in the skies over Davis, CA, this study investigated how UV irradiance changes during those time periods.
U.S. Landsat Analysis Ready Data (ARD) recently included the Land Surface Temperature (LST) product, which contains widespread and irregularly-shaped missing pixels due to cloud contamination or incomplete satellite coverage. Many analyses rely on complete LST images therefore techniques that accurately fill data gaps are needed. Here, the development of a partial-convolution based model with the U-Net like architecture to reconstruct the missing pixels in the ARD LST images is discussed. The original partial convolution layer is modified to consider both the convolution kernel weights and the number of valid pixels in the calculation of the mask correction ratio. In addition, the new partial merge layer is developed to merge feature maps according to their masks. Pixel reconstruction using this model was conducted using Landsat 8 ARD LST images in Colorado between 2014 and 2018. Complete LST patches (64x64) for two identical scenes acquired at different dates (up to 48 days apart) were randomly paired with ARD cloud masks to generate the model inputs. The model was trained for 10 epochs and the validation results show that the average RMSE values for a restored LST image in the unmasked, masked, and whole region are 0.29K, 1.00K, and 0.62K, respectively. In general, the model is capable of capturing the high-level semantics from the inputs and bridging the difference in acquisition dates for gap filling. The transition between the masked and unmasked regions (including the edge area of the image) in restored images is smooth and reflects realistic features (e.g., LST gradients). For large masked areas, the reference provides semantics at both low and high levels.
The changes of land use affect the sustainable development of society through its influences on the interactive balance among the population, resource, environment and ecological development. In the process of Chinese urbanization, increasingly serious contradictions between human and land have been caused by the dramatic increase in the demand of land resources. This paper used a case study on Guian New District, which is a national-level new district in China. The research focused on the change of land use in the new-style urbanizing process of Guian New District. The sustainable development in this district was analyzed by applying the technology of Remote Sensing and Geography Information System to collect the spatial data of land use in 2010 and 2018 of Guian New District, utilizing the theory of ecosystem service value to obtain quantitative description of the ecological outcome of land use, and comparing the variance in land use and ecological benefit before and after the establishment of Guian New District. The study has shown that the land use of Guian New District almost meets the basic requirements of sustainable development. Furthermore, in order to achieve sustainable development in this district, suggestions were provided on how to improve the structure and location of land use, as well as taking account of the impacts on the long-term ecological benefit.
Surface ozone can trigger many health problems for human (e.g. coughing, bronchitis, emphysema, and asthma), especially for children and the elderly. It also has harmful effects on plants (e.g. chlorosis, necrosis, and yield reduction). The United State (U.S.) Environmental Protection Agency (EPA) has been monitoring surface ozone concentrations across the U.S. since 1980s. However, their stations are sparsely distributed and mainly in urban areas. Evaluation of surface ozone effects at any given locations in the U.S. requires spatial interpolation of ozone observations. In this study, we implemented two traditional spatial interpolation methods (i.e. triangulation-based linear interpolation and geostatistics-based method). One limitation of these two methods is their reliance on single-scene observations in constructing the spatial relationship, which is prone to influence of noisy observations and has large uncertainty. Deep learning, on the other hand, is capable of simulating common patterns (including complex spatial patterns) from a large amount of training samples. Therefore, we also implemented three deep learning algorithms for the spatial interpolation problem: mixture model network (MoNet), Convolutional Neural Network for Graphs (ChebNet), and Recurrent Neural Network (RNN). The training and validation data of this study are the 2016 EPA hourly surface ozone observations within ±3-degree box centered at the Billings, Oklahoma station (USDA UV-B Monitoring and Research Program). The results showed that among the five methods, RNN and MoNet outperformed the two traditional spatial interpolation methods and RNN has the lowest validation error (mean absolute error: 2.82 ppb; standard deviation: 2.76 ppb). Finally, we used the integrated gradients method to analyze the attribution of RNN inputs on the surface ozone prediction. The results showed that surface ozone observation is the most important input feature followed by distance and absolute locations (i.e. elevations, longitudes, and latitudes).
Sea surface temperature (SST) is an important factor that affects the changes of marine fishery resources. In this paper, the characteristics of distribution and variation in sea surface temperature was retrieved in northwestern Pacific Ocean by MODIS from 2008 to 2017. The results showed that the distribution of SST in northwestern Pacific Ocean was found to be characterized by regional and seasonal changes. Annually, periodical changes in SST was found unconspicuously, and spatially, the SST high value area showed a trend of moving from high-latitude to low-latitude. In August each year, there seemed to be a temperature boundary at 40°N, and the boundary will move south in September. Finally, we analyzed the SST distribution of the two main fishing periods of Cololabis saira in August and September each year, and preliminarily explained the cause of the "fish shortage" of saury recently years in Japan. The long-term variations in SST were discussed macroscopically in this paper, and this could give a new insight into fishing industry research in the Northwest Pacific.
Previous remote sensing studies of intelligent feature extraction led to the successful image fusion, merging, and cloudy pixel reconstruction destined for the spatiotemporal change detection. Based on fused satellite images with better spatial and temporal resolution, this study explores a thorough comparative analysis in terms of feature extraction capability of deep learning, regular learning, fast learning, and ensemble learning relative to some traditional feature extraction algorithms (2-band and linear regression models). In specific, this study aims to evaluate the systematic influences of fast and deep learning models with potential to create a new ensemble learning tool for better feature extraction based on fused remote sensing images. In ensemble learning step, the whole ground-truth dataset is fed into the selected ensemble learning algorithm (i.e., a classifier fusion algorithm) with the aid of singular value decomposition to create an integrative tool. Practical implementation was assessed by a case study of water quality monitoring over dry and wet seasons in Lake Nicaragua, Central America. Both deep and fast learning algorithms outperform the regular learning algorithm with a single layer forward network and ensemble learning may take advantage of merits of deep, fast, and regular learning algorithms. Final water quality assessment was generated based on the integrative algorithm of the two with bio-optical models for eutrophication assessment in Lake Nicaragua. Although deep learning has better results in validation and the ensemble learning model aggregates different types of strength from all models based on all limited ground-truth samples.
The accuracy of the temperature and humidity profiles is important for the atmospheric duct estimation, which is a special atmosphere layer for the radio-wave propagation. In order to use the dataset of satellite to monitor the atmospheric duct, we compare the temperature and humidity profiles between the radiosonde observation data (RAOB) and the NOAA-Unique CrIS/ATMS Product System (NUCAPS), and analyze the result of the atmospheric duct. Results show that the retrieved temperature and humidity profiles have higher accuracy under various weather conditions. However, when the RAOB data can calculate the atmospheric duct, the inversion profiles are difficult to monitor the same situation. The temperature inversion and humidity’s sharp decrease with height are the main synoptic conditions for the formation of atmospheric waveguides. Currently, the temperature and humidity profile of satellite inversion still lack capturing of turning point information. In order to effectively improve the application of satellite inversion data in atmospheric duct estimation, it is necessary to strengthen the profile’s vertical resolution and humidity inversion accuracy.
The reliability of the measurement of ultraviolet radiation has always been a hot spot of research. The observation of ultraviolet radiation is not only affected by the solar elevation angle, aerosol thickness, ozone, dioxide, there is also a great connection with the systematic error of the measuring instrument. In fact, in the ultraviolet radiation observation, due to the lack of routine maintenance and periodic calibration, the radiation meter will obviously decline after a period of time, and the longer the use time, the more obvious the attenuation. Therefore, in order to obtained the consistent time series of the stable observational values, some reasonable methods must be adopted to correct the measured values. The data source of this research was part of the UV-MFRSR type ultraviolet radiometer observations from 2003 to 2010. These data were obtained by these daily time series calibration method. In theory, these time series points represent the response time of the instrument, and they should be stable for several months or even years. However, the performance of the in-situ calibration method was influenced by the aerosol / ozone loading mode in practice. The purpose of this study was to get a smooth observation curve by eliminating some observational anomalies. In addition, the actual data in the observation process, some date data is missing, so the reasonable prediction model is used to estimate the value of these data. In this paper, the ARIMA and GARCH models were used to predict the missing data and compared between the predicted value and the true value, it is found that the fitting degree of the predicted value and the true value based on the AR-GARCH model is higher.
The USDA UV-B Monitoring and Research Program (UVMRP) comprises of 36 climatological sites along with 4 long-duration research sites, in 27 states, one Canadian province, and the south island of New Zealand. Each station is equipped with an Ultraviolet multi-filter rotating shadowband radiometer (UV-MFRSR) which can provide response-weighted irradiances at 7 wavelengths (300, 305.5, 311.4, 317.6, 325.4, and 368 nm) with a nominal full width at half maximun of 2 nm. These UV irradiance data from the long term monitoring station at Mauna Loa, Hawaii, are used as input to a retrieval algorithm in order to derive high time frequency total ozone columns. The sensitivity of the algorithm to the different wavelength inputs is tested and the uncertainty of the retrievals is assessed based on error propagation methods. For the validation of the method, collocated hourly ozone data from the Dobson Network of the Global Monitoring Division (GMD) of the Earth System Radiation Laboratory (ESRL) under the jurisdiction of the US National Oceanic & Atmospheric Administration (NOAA) for the period 2010-2015 were used.
The USDA UV-B Monitoring and Research Program (UVMRP) is an ongoing effort aiming to establish a valuable,
longstanding database of ground-based ultraviolet (UV) solar radiation measurements over the US. Furthermore, the
program aims to achieve a better understanding of UV variations through time, and develop a UV climatology for
the Northern American section. By providing high quality radiometric measurements of UV solar radiation,
UVMRP is also focusing on advancing science for agricultural, forest, and range systems in order to mitigate climate
impacts. Within these foci, the goal of the present study is to investigate, analyze, and validate the accuracy of the
measurements of the UV multi-filter rotating shadowband radiometer (UV-MFRSR) and Yankee (YES) UVB-1
sensor at the high altitude, pristine site at Mauna Loa, Hawaii. The response-weighted irradiances at 7 UV channels
of the UV-MFRSR along with the erythemal dose rates from the UVB-1 radiometer are discussed, and evaluated for
the period 2006-2015. Uncertainties during the calibration procedures are also analyzed, while collocated groundbased
measurements from a Brewer spectrophotometer along with model simulations are used as a baseline for the
validation of the data. Besides this quantitative research, the limitations and merits of the existing UVMRP methods
are considered and further improvements are introduced.
As global warming problem is becoming serious in recent decades, the global sea level is continuously rising. This will
cause damages to the coastal deltas with the characteristics of low-lying land, dense population, and developed economy.
Continuously reclamation costal intertidal and wetland areas are making Shanghai, the mega city of Yangtze River Delta,
more vulnerable to sea level rise. In this paper, we investigate the land subsidence temporal evolution of patterns and
processes on a stretch of muddy coast located between the Yangtze River Estuary and Hangzou Bay with differential
synthetic aperture radar interferometry (DInSAR) analyses. By exploiting a set of 31 SAR images acquired by the
ENVISAT/ASAR from February 2007 to May 2010 and a set of 48 SAR images acquired by the COSMO-SkyMed
(CSK) sensors from December 2013 to March 2016, coherent point targets as long as land subsidence velocity maps and
time series are identified by using the Small Baseline Subset (SBAS) algorithm. With the DInSAR constrained land
subsidence model, we predict the land subsidence trend and the expected cumulative subsidence in 2020, 2025 and 2030.
Meanwhile, we used altimetrydata and densely distributed in the coastal region are identified (EEMD) algorithm to
obtain the average sea level rise rate in the East China Sea. With the land subsidence predictions, sea level rise
predictions, and high-precision digital elevation model (DEM), we analyze the combined risk of land subsidence and sea
level rise on the coastal areas of Shanghai. The potential inundated areas are mapped under different scenarios.
In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.
Cloud screening is an essential procedure for in-situ calibration and atmospheric properties retrieval on (UV-)MultiFilter Rotating Shadowband Radiometer [(UV-)MFRSR]. Previous study has explored a cloud screening algorithm for direct-beam (UV-)MFRSR voltage measurements based on the stability assumption on a long time period (typically a half day or a whole day). To design such an algorithm requires in-depth understanding of radiative transfer and delicate data manipulation. Recent rapid developments on deep neural network and computation hardware have opened a window for modeling complicated End-to-End systems with a standardized strategy. In this study, a multi-layer dynamic bidirectional recurrent neural network is built for determining the cloudiness on each time point with a 17-year training dataset and tested with another 1-year dataset. The dataset is the daily 3-minute cosine corrected voltages, airmasses, and the corresponding cloud/clear-sky labels at two stations of the USDA UV-B Monitoring and Research Program. The results show that the optimized neural network model (3-layer, 250 hidden units, and 80 epochs of training) has an overall test accuracy of 97.87% (97.56% for the Oklahoma site and 98.16% for the Hawaii site). Generally, the neural network model grasps the key concept of the original model to use data in the entire day rather than short nearby measurements to perform cloud screening. A scrutiny of the logits layer suggests that the neural network model automatically learns a way to calculate a quantity similar to total optical depth and finds an appropriate threshold for cloud screening.
The Aleutian Low is one of the principal causal factors of the weather and climate systems of the Northern
Hemisphere.Based on reanalysis datasets provided by the National Centers for Environmental Prediction (NCEP) from
1970 to 2005, the climatological features of Aleutian low in winter were characterized. It is shown from the study results
that in the late 1970s, the winter Aleutian low’s intensity changed from weak to strong. Then, the relationship between
Aleutian low and sea surface heat flux in the North Pacific was analyzed by singular value decomposition (SVD) and
correlation analysis. Aleutian low’s intensity was positively correlated with the sea surface heat flux in the central North
Pacific, and negatively correlated with the sea surface heat flux in the west coast of North America.
Shanghai Pudong International airport is one of the three major international airports in China. The airport is located at the Yangtze estuary which is a sensitive belt of sea and land interaction region. The majority of the buildings and facilities in the airport are built on ocean-reclaimed lands and silt tidal flat. Residual ground settlement could probably occur after the completion of the airport construction. The current status of the ground settlement of the airport and whether it is within a safe range are necessary to be investigated. In order to continuously monitor the ground settlement of the airport, two Synthetic Aperture Radar (SAR) time series, acquired by X-band TerraSAR-X (TSX) and TanDEM-X (TDX) sensors from December 2009 to December 2010 and from April 2013 to July 2015, were used for analyzing with SBAS technique. We firstly obtained ground deformation measurement of each SAR subset. Both of the measurements show that obvious ground subsidence phenomenon occurred at the airport, especially in the second runway, the second terminal, the sixth cargo plane and the eighth apron. The maximum vertical ground deformation rates of both SAR subset measurements were greater than -30 mm/year, while the cumulative ground deformations reached up to -30 mm and -35 mm respectively. After generation of SBAS-retrieved ground deformation for each SAR subset, we performed a joint analysis to combine time series of each common coherent point by applying a geotechnical model. The results show that three centralized areas of ground deformation existed in the airport, mainly distributed in the sixth cargo plane, the fifth apron and the fourth apron, The maximum vertical cumulative ground subsidence was more than -70 mm. In addition, by analyzing the combined time series of four selected points, we found that the ground deformation rates of the points located at the second runway, the third runway, and the second terminal, were progressively smaller as time goes by. It indicates that the stabilities of the foundation around these points were gradually enhanced.
Marine environment protection is an important support for sustainable development of marine ranching. Based on the geographic information system(GIS) and remote sensing(RS), this study developed a 3S system, which integrate Sea surface temperature, chlorophyll concentration, turbidity of sea water and other factors into system. And these factors are important components of marine environment. The system provided data service including loading, browsing, information inquiry, cartography, and also supported the analysis of remote sensing image. In the implementation of the system, the key points of the related technologies have been paid much attention. The practical application shows that it can provide assistance for the environmental protection of marine ranching.
A typical heavy rainfall event occurred in Shanghai on September 13, 2009 was simulated using the Weather Research and Forecasting Model (WRF) to study the impact of microphysics parameterization on heavy precipitation simulations. Sensitivity experiments were conducted using the cumulus parameterization scheme of Betts-Miller-Janjic (BMJ), but with three different microphysics schemes (Lin et al, WRF Single-Moment 5-class scheme (WSM5) and WRF Single-Moment 6-class scheme (WSM6)) under three-way nested domains with horizontal resolutions of 36km, 12km and 4km. The results showed that all three microphysics schemes are able to capture the general pattern of this heavy rainfall event, but differ in simulating the location, center and intensity of precipitation. Specifically, the Lin scheme overestimated the rainfall intensity and simulated the rainfall location drifting northeastwards. However, the WSM5 scheme better simulated the rainfall location but stronger intensity than the observation, while the WSM6 scheme better produced the rainfall intensity, but with an unrealistic rainfall area.
The aim of this work was to identify the coastal wetland plants between Bayes and BP neural network using hyperspectral data in order to optimize the classification method. For this purpose, we chose two dominant plants (invasive S. alterniflora and native P. australis) in the Yangtze Estuary, the leaf spectral reflectance of P. australis and S. alterniflora were measured by ASD field spectral machine. We tested the Bayes method and BP neural network for the identification of these two species. Results showed that three different bands (i.e., 555 nm,711 nm and 920 nm) could be identified as the sensitive bands for the input parameters for the two methods. Bayes method and BP neural network prediction model both performed well (Bayes prediction for 88.57% accuracy, BP neural network model prediction for about 80% accuracy), but Bayes theorem method could give higher accuracy and stability.
Ten years (2005 - 2014) of surface ultraviolet (UV) observations from TOMS-OMI and UVMRP are combined across the continental U.S. via data fusion technique. The combined UV data inherits advantages from both data sources, satellite and ground observations. This research analyzes the combined data both in time and in space, presenting the preliminary statistical results for this ten-year period.
A tidal flat, the important potential land resource, is the sensitive area of intersection between the sea and the land. With Chinese HJ-1A/B remote sensing images of 2014 as data sources, based on the definition of a tidal flat, using DSAS software and Jenks Natural Breaks classification method synthetically, a more reasonable and accurate method of extracting tidal flat was imposed. In addition, the Bohai Rim was taken as an example to carry out investigation on the current situation of tidal flat. This paper can provide basic date and scientific evidence for rational utilization and sustainable development of tidal flat.
As Ozone Monitoring Instrument (OMI) onboard the Aura satellite has provided global scale ozone measurements on a daily basis since 2004, the long-term stability and consistency of ozone retrievals is thus of critical importance, especially for the ozone recovery assessment. This study aims to evaluate the long-term stability of total ozone derived from the OMI Total Ozone Mapping Spectrometer (OMI-TOMS) algorithm, by comparing with collocated ground-based total ozone measurements recorded from 42Dobson spectrophotometers during the period 2004-2015. It is indicative that the OMI-TOMS total ozone is in good agreement with collocated ground-based measurements, with a R2 of 0.96 and root mean square error (RMSE) of 3.3%. Further investigations show that the OMI-TOMS total ozone is of quality, as no significant latitude dependence is observed. In the past 12 years, the OMI-TOMS total ozone is highly consistent with the ground-based Dobson total ozone, with a variation of mean relative difference less than 1%. In general, the OMI-TOMS total ozone performs well and can be used with confidence.
In this paper, the green tide (Large green algae-Ulva prolifera) in the Yellow Sea in 2015 is monitored which is based on remote sensing and geographic information system technology, using GF-1 WFV data, combined with the virtual baseline floating algae height index (VB-FAH) and manual assisted interpretation method. The results show that GF-1 data with high spatial resolution can accurately monitoring the Yellow Sea Ulva prolifera disaster, the Ulva prolifera was first discovered in the eastern waters of Yancheng in May 12th, afterwards drifted from the south to the north and affected the neighboring waters of Shandong Peninsula. In early July, the Ulva prolifera began to enter into a recession, the coverage area began to decrease, by the end of August 6th, the Ulva prolifera all died.
Multi-Filter Rotating Shadowband Radiometer (MFRSR) and its UV version (UV-MFRSR) are ground-based instruments for measuring solar UV and VIS radiation, deployed together in field at most USDA UV-B Monitoring and Research Program (UVMRP) sites. The performance of the traditional calibration method, Langley Analysis (LA), varies with MFRSR channels and sites, resulting in less confidence in some irradiance products. A two-stage calibration method is developed. We attributed the variation in Langley Analysis performance to the monotonically changing total optical depth (TOD) in the cloud screened points. Constant TOD is an assumption in LA. Since (1) aerosol is the main source of TOD variation at the 368nm channel and (2) UV-MFRSR measures direct normal and diffuse horizontal simultaneously, we used the radiative transfer model (i.e. MODTRAN) to create the look-up table of the ratio of direct normal and diffuse (DDR) with respect to aerosol optical depth (AOD) and solar zenith angle to evaluate the quality of the Langley Offset (VLO) by giving lower weights to VLO generated from points with monotonic AOD variation. With one or two calibrated channels as Reference Channels (RC), the most stable points in RC were selected and LA was applied on those time points to generate VLO at the adjacent un-calibrated channel. The test of this method on the UV-B program site at Homestead, Florida showed that (1) The long-term trend of the original LA VLO is impacted by the monotonic changing in AOD at 368nm channel; and (2) more clustered and abundant VLO at all channels are generated compared with the original Langley method.
Classification, as well as finding representative spectrum, is always one of the major applications of satellite hyperspectral image data. A new method to find the set of representative spectrums was introduced in this study. It was firstly used to classify forest types in Tianmu Mountain National Nature Reserve by using Landsat TM images. This method is generic and can be applied to the data set that is hard to find one representative data for one class.
KEYWORDS: Solar radiation models, Solar radiation, Ecosystems, Calibration, Performance modeling, Photosynthesis, Data modeling, Atmospheric modeling, Process modeling, Carbon dioxide
Solar radiation inputs drive many processes in terrestrial ecosystem models. The processes (e.g. photosynthesis) account for most of the fluxes of carbon and water cycling in the models. It is thus clear that errors in solar radiation inputs cause key model outputs to deviate from observations, parameters to become suboptimal, and model predictions to loose confidence. However, errors in solar radiation inputs are unavoidable for most model predictions since models are often run with observations with spatial or / and temporal gaps. As modeled processes are non-linear and interacting with each other, it is unclear how much confidence most model predictions merits without examining the effects of those errors on the model performance. In this study, we examined the effects using a terrestrial ecosystem model, DayCent. DayCent was parameterized for annual grassland in California with six years of daily eddy covariance data totaling 15,337 data points. Using observed solar radiation values, we introduced bias at four different levels. We then simultaneously calibrated 48 DayCent parameters through inverse modeling using the PEST parameter estimation software. The bias in solar radiation inputs affected the calibration only slightly and preserved model performance. Bias slightly worsened simulations of water flux, but did not affect simulations of CO2 fluxes. This arose from distinct parameter set for each bias level, and the parameter sets were surprisingly unconstrained by the extensive observations. We conclude that ecosystem models perform relatively well even with substantial bias in solar radiation inputs. However, model parameters and predictions warrant skepticism because model parameters can accommodate biases in input data despite extensive observations.
Surface ultraviolet (UV) observations can be obtained from satellite or ground observations. This study uses data fusion to combine the advantages from both sources of observations, aiming at achieving a better estimate of surface UV. In this study, ensemble methods were used to estimate the covariances, which are the most important components in data fusion. The combined UV observations not only have the same coverage as satellite data, but also improve their regional accuracy around the ground observatories.
Spartina alterniflora is one of the most serious invasive species in the coastal saltmarshes of China. An accurate quantitative estimation of its canopy leaf chlorophyll content is of great importance for monitoring plant physiological state and vegetation productivity. Hyperspectral reflectance data representing a range of canopy chlorophyll content were simulated by using the PROSAIL radiative transfer model at a 1nm sampling interval, which was based on prior knowledge of S.alterniflora. A set of indices was tested for estimating canopy chlorophyll content. Subsequently, validation were performed for testing the performance of indices, based on the PROSAIL model using in situ data measured by a Spectroradiometer with spectral range of 350-2500nm in a late autumn in a sub-tropical estuarine marsh. PROSAIL simulations showed that the most readily available indices were not good to be directly used in canopy chlorophyll estimation of S.alterniflora. The modified Chlorophyll Absorption in Reflectance Index MCARI[705,750] was linear related to the canopy chlorophyll content (R2=0.94) , but did not achieve a satisfactory estimation results with a high RMSE (RMSE=0.95 g.m-2). We optimized the index MCARI[705,750] by introducing a scale conversion coefficient to the formula to solve data units inconsistent, which is between the practical application unit and the unit used in the process of establishing the index, and balance scale transformation through radiative transfer models and examing corresponding canopy reflectance index values. We proposed index Optimized modified Chlorophyll Absorption in Reflectance Index OMCARI[705, 750]. The results showed that the index OMCARI[705, 750] had higher precision of prediction of chlorophyll for S.alterniflora (R2=0.94,RMSE=0.41 g.m-2 ).
To provide a reference for canopy parameters inversion, sensitivity analysis of plant canopy parameters based on remote sensing model is a prerequisite for the inversion. Because the local sensitivity analysis do not consider the coupling effect among the parameters, the EFAST (i.e., Extended Fourier Amplitude Sensitivity Test), a global sensitivity analysis, can be used not only for the analysis of each parameter, but also consider the interacted effect among each parameter. Based on PROSAIL model, the paper focused on the parameters’ sensitivity by using simulated data and EFAST method. The results showed that the EFAST considered not only the contribution of single parameter, but also the interactive effects among each parameter, and four parameters, leaf area index (LAI), leaf mesophyll structure (N), the controller factor of the average leaf slope (LIDFa) and soil moisture condition (psoil) had great effect on the canopy reflectance in the whole wavelength from 400 to 2500 nm than other canopy parameters, and the EFAST method enlarged the contribution of some parameters that had little effects.
Accurate regional crop growth monitoring and yield prediction is very critical for the national food security assessment and sustainable development of agriculture, especially for China, which has the largest population in the world. Remote sensing data and crop growth model have been successfully used in the crop production prediction. However, both of them have inherent limitation and uncertainty. The data assimilation method which combines crop growth model and remotely sensed data has been proven to be the most effective method in regional yield estimation. The aim of this paper is to improve the estimation of regional winter wheat yield of crop growth model by using data assimilation schemes with Ensemble Kalman Filter (EnKF) algorithm. WOrld FOod STudies (WOFOST) crop growth model was chosen as the crop growth model which was calibrated and validated by the field measured data. MODIS Leaf Area Index (LAI) values were used as remote sensing observations to adjust the LAI simulated by the WOFOST model based on EnKF. The results illustrate that the EnKF algorithm has significantly improved the regional winter wheat yield estimates over the WOFOST simulation without assimilation in both potential and water-limited modes. Although this study clearly implies that the assimilation of the remotely sensed data into crop growth model with EnKF algorithm has the potential to improve the prediction of regional crop yield and has great potential in agricultural applications, high resolution meteorological data and detailed crop field management are necessary to reach a high accuracy of regional crop yield estimation.
Aerosol optical depth (AOD) data from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were inter-compared and validated against ground-based measurements from Aerosol Robotic Network (AERONET) as well as Moderate Resolution Imaging Spectroradiometer (MODIS) over China during June 2006 to December 2012. We have compared the AOD between CALIOP and AERONET site by site using quality control flags to screen the AOD data. In general, CALIOP AOD is lower than AERONET due to cloud effect detected algorithm and retrieval uncertanty. Better agreement is apparent for these sites: XiangHe, Beijing, Xinglong, and SACOL. Low correlations were observed between CALIPSO and ground-based sunphotometer data in in south or east China. Comparison results show that the overall spatio-temporal distribution of CALIPSO AOD and MODIS AOD are basically consistent. As for the spatial distribution, both of the data show several high-value regions and low-value regions in China. CALIPSO is systematically lower than MODIS over China, especially over high AOD value regions for all seasons. As for the temporal variation, both data show a significant seasonal variation: AOD is largest in spring, then less in summer, and smallest in winter and autumn. Statistical frequency analysis show that CALIPSO AOD and MODIS AOD was separated at the cut-off points around 0.2 and 0.8, the frequency distribution curves were almost the same with AOD between 0.2 and 0.8, while AOD was smaller than 0.4, CALIPSO AOD gathered at the low-value region (0-0.2) and the frequency of MODIS AOD was higher than CALIPSO AOD with AOD greater than 0.8. CALIOP AOD values show good correlation with MODIS AOD for all time scales, particularly for yearly AOD with higher correlation coefficient of 0.691. Seasonal scatterplot comparisons suggest the highest correlation coefficient of 0.749 in autumn, followed by winter of 0.665, summer of 0.566, and spring of 0.442. Evaluation of CALIOP AOD retrievals provides prospect application for CALIPSO data.
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