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

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


Journal:
COMPUTERS AND ELECTRONICS IN AGRICULTURE


Issue Date:
2020


Abstract(summary):

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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