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Now showing items 1 - 2 of 2

  • An improved inversion algorithm for spatio-temporal retrieval of soil moisture through modified water cloud model using C- band Sentinel-1A SAR data

    Yadav, Vijay Pratap   Prasad, Rajendra   Bala, Ruchi   Vishwakarma, A. K.  

    An approach to incorporate vegetation fraction into Modified Water Cloud Model (MWCM) and the evaluation of potential of multi-target random forest regression (MTFER) were done for the retrieval of spatio-temporal variability of soil moisture (SM) in Varanasi district of Uttar Pradesh, India. The Sentinel-1A SAR images were acquired for three different dates (19/12/2016, 05/02/2017 and 25/03/2017) for two types of spatial regions covered with vegetated and sparse vegetated soil field for SM retrieval. The vegetation fraction (f(ve)(g)), computed from Landsat - 8 satellite data, was inserted into the modified water cloud model (MWCM) as a modification factor. Leaf area index (LAI) was used as a vegetation descriptor parameter (V) in the MWCM. Subsequently, a machine learning based MTRFR algorithm with a regularization routine was used for stable and optimum solution for complex problems related to the inversion of the MWCM for the accurate estimation of SM. The coefficient of determination (R-2), root mean square error (RMSE) and nash sutcliffe efficiency (NSE) indicated significantly better results in the region-2 for all the temporal changes occurred than those of region-1. The results showed that incorporation of f(veg) to the MWCM provided high potential to retrieve spatio-temporal SM in the region-2 where soil fields were mostly covered with wheat and barley crops rather than in region-1 having sparse vegetated soil field. The overall accuracy of spatio-temporal retrieval of SM after incorporating vegetation factor to MWCM showed significantly better R-2 =3D 0.82, RMSE =3D 3.18 (%) and NSE =3D 0.85. The inversion results proximate that the MTRFR techniques applied to the MWCM, including vegetation factor, has great a capability for an accurate SM retrieval in the vegetated soil field.
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  • Disaggregation of modis land surface temperature in urban areas using improved thermal sharpening techniques

    Bala, Ruchi   Prasad, Rajendra   Yadav, Vijay Pratap  

    Applications of satellite thermal images are usually impeded by the low spatial resolution, leading to the development of various downscaling techniques. The thermal sharpening model based on the relationship between LST and Normalized Difference Vegetation Index (NDVI) was developed which shows good results in agricultural areas but may not be applicable for urban areas. Therefore, the present study focuses on determining improved downscaling techniques that shows good results in different urban regions. Hence, the performance of six different indices, namely NDVI, Enhanced Vegetation Index (EVI), Normalized Difference Built-up Index (NDBI), Urban Index (UI), Normalized Difference Soil Index (NDSI) and Normalized Difference Water Index (NDWI) were compared for thermal sharpening using Disaggregation of Radiometric Temperature (Distrad) Model over four different cities in India i.e. Bikaner, Hyderabad, Vadodara and Varanasi. LST obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors (930 m) were disaggregated to the spatial resolution of Landsat 8 Thermal Infrared Sensor (TIRS) (100 m) and compared with the Landsat LST. The performance of NDBI was found better as compared to other indices in the four cities having Root Mean Square Error (RMSE) =3D 1.54 K, 1.24 K, 1.10 K and 1.03 K, respectively. Further, NDBI was used for disaggregation using two robust regression techniques i.e. Least Median Square Regression (LMSR) and Bi-square regression which shows better results as compared to that of Distrad model in the four study sites. Bi-square regression method shows RMSE values of 1.30 K, 1.21 K, 0.98 K and 0.97 K, respectively for the four study sites. The LMSR and Bi-square regressions are less sensitive to outliers resulting in increased accuracy of downscaled LST. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
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