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      https://www.ias.ac.in/article/fulltext/jess/127/02/0018

    • Keywords

       

      Soil moisture; SAR; RISAT-1; TDR; semi-empirical model.

    • Abstract

       

      We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (σoRH), differences of circular vertical and horizontal σooRV− σo RH) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (RMSheight). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., σo. Near surface SM measurements were related to σoRH, σoRV−σoRH derived using 5.35 GHz (C-band) image of RISAT-1 and RMSheight. The roughness component derived in terms of RMSheight showed a good positive correlation with σoRH−σoRH (R2 = 0.65). By considering all the major influencing factors (σoRH, σoRV− σoRH, and RMSheight), an SEM was developed where SM (volumetric) predicted values depend on σoRH, σoRV− σoRH, and RMSheight. This SEM showed R2 of 0.87 and adjusted R2 of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement (SMObserved) showed root mean square error (RMSE) = 0.06, relative- RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash–Sutcliffe efficiency (NSE) = 0.91 (≈1), index of agreement (d) = 1, coefficient of determination (R2) = 0.87, mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences (S2d) = 0.004. The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on σo. By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.

    • Author Affiliations

       

      Kishan Singh Rawat1 2 Vinay Kumar Sehgal1 Sanatan Pradhan1 Shibendu S Ray3

      1. Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, India.
      2. Center for Remote Sensing and Geo-Informatics, Satyabama University, Chennai 600 119, India.
      3. Mahalanobis National Crop Forecast Centre (MNCFC), Pusa Campus, New Delhi 110 012, India.
    • Dates

       
  • Journal of Earth System Science | News

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