Mapping and monitoring cropland areas and distributions from remote sensing data could provide early warning information of threats to the global and regional food security. Winter wheat is traditionally the most cultivated food crop in China, and the Huang-Huai-Hai (HHH) plain is an important winter wheat production base. Due to a long latitudinal distance, the same winter wheat growth stage delays from the north to the south of the plain. Influenced by the monsoon climate, isolines of the winter wheat phenology are oriented in a northeast–southwest direction, which is similar to that of the temperature distribution. In this paper, Moderate Resolution Imaging Spectroradiometer-enhanced vegetation index (EVI) was applied to estimate the winter wheat planting information on the HHH plain, using a model built according to seasonal change of the winter wheat EVI. The result shows that the average accuracy of the estimation was 75.4% with a standard deviation of 26.1%, when the impacts from the phenology delay and the monsoon climate were not considered. When winter wheat phenology delay was considered with and without the influences from the monsoon climate, the accuracy was 93.2% with a standard deviation of 6.1% and 84.7% with a standard deviation of 11.0%, respectively. The accuracy increased evidently. Therefore, both the phenology delay and monsoon climate impacts should be taken into consideration when estimating the winter wheat planting information in a large monsoon climate region.
Land surface emissivity (LSE) is a key parameter for characterizing the land surface, and is vital for a wide variety of surface-atmosphere studies. This paper retrieved LSEs of land surfaces over the city of Madrid, Spain from airborne hyperspectral scanner (AHS) thermal infrared data using temperature emissivity separation (TES) method. Six different kinds of urban surfaces: asphalt, bare soil, granite, pavement, shrub and grass pavement, were selected to evaluate the performance of the TES method in urban areas. The results demonstrate that the TES method can be successfully applied to retrieve LSEs in urban area. The six urban surfaces have similar curve shape of emissivity spectra, with the lowest emissivity in band 73, and highest in band 78; the LSE for bare soil varies significantly with spectra, approximately from 0.90 in band 72 to 0.98 in band 78, whereas the LSE for grass has the smallest spectral variation, approximately from 0.965 in band 72 to 0.974 in band 78, and the shrub presents higher LSE than other surfaces in bands 72, 73, 75-77, but a little lower in bands 78 and 79. Furthermore, it is worth noting that band 73 is suitable for discriminating different urban surfaces because large LSE differences exist in this channel for different urban surfaces.
We evaluate the land surface temperature (LST) generated from the spinning enhanced visible and infrared imager (SEVIRI) onboard the MSG-2 satellite, which was retrieved using the split-window method where the land surface emissivity (LSE) was estimated from the day/night temperature-independent spectral indices-based method. The SEVIRI-derived LST was compared with the MODIS-derived LST extracted from the MOD11B1 V5 product during 7 clear-sky days. The results show that (1) discrepancies exist between the two LST products, with a maximum average difference of 4.9 K; (2) these differences are considered to be time-dependent, since higher discrepancies are observed during the daytime; (3) these differences are land-cover dependent, e.g., bare areas generally present larger differences than vegetated areas; and (4) these differences are inversely proportional to view zenith angle differences. Finally, the main sources of LST differences are investigated and identified in terms of LSE, instrumental noise equivalent temperature difference (NEΔT), and misregistration of the two LST products. The LST differences arising from NEΔT and misregistration are within 0.4 K. Therefore, these discrepancies may mainly result from errors in LSE, which are caused primarily by the atmospheric correction error for the SEVIRI-derived LST.
The goal of this paper is to introduce how to make use of the artificial neural network technique
to develop a new method which can fast recognize atmospheric profiles' characters from hyperspectral
infrared thermal remote sensing. This technique would accelerate the calculation speed of hyperspectral
infrared atmospheric radiative transfer model (RTM). As the launch of hyperspectral infrared sensors such
as Infrared Atmospheric Sounding Interferometer (IASI), it becomes possible for people to take advantage
of the hyperspectral data which contains abundance of precise spectral information, to add constraint
conditions for the researches of some physical models. But in practice, normal hyperspectral infrared
atmospheric RTM are relatively complex and time costing. The calculation speed of these models is not fast
enough to make these models to respond to the variety of atmospheric radiative, or the bright temperature
timely. Therefore, the practical and effective physical models and research methods, such as the practical
surface temperate inversion model, couldn't be founded relay on these transfer models. In order to solve
this problem, institutions and researchers around the world have tried some methods to develop the fast
calculation of atmospheric RTM. But these methods still have problems on speed, accuracy and the
applicability for certain sensors.
Hyperspectral remote sensing can provide tens, even hundreds of spectral bands imagery, which helps us detect the diagnostical spectral characteristics of detected objects. However, there is relatively high correlation between different bands and much redundancy in hyperspectral data sets. Therefore, one of the most important procedures before application is to select optimal bands for extracting information from hyperspectral data effectively. In this paper, we first introduce the characteristics of EO-1/Hyperion, and apply several important pre-processing procedures to Hyperion L1R data, such as radiometric calibration, destriping, smile correction etc. Then we apply spectrum reconstruction approach to feature selection, which uses several basis functions and corresponding spectral intervals to describe the spectrum extracted from Hyperion hyperspectral data sets in Subei region, China. The feature selection method based on spectrum reconstruction is incrementally adding bands to the initial bands, followed by adjustment of band widths and locations. At last, we aggregate several Hyperion bands into a new simulated band in each interval and apply Maximum Likelihood Classification (MLC) method to it. The overall accuracy of classification is 92% compared with in situ measurement, which supports the validity of this feature selection method.
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