ABSTRACT
The sensible heat flux component calculation in the Surface Energy Balance Algorithm for Land (SEBAL) completely relies upon the anchor pixel locations. The fragmented agriculture regions with diverse crops make the selection process more challenging with less probability to correlate with ground realities. The conventional methods for anchor pixel selection have limitations in capturing the soil moisture variations beneath the canopy as they utilize only the thermal remote sensing images and ancillary datasets like land-cover and crop-type maps. This study attempts to reduce the gap between the ground realities and simulated results by adding soil moisture as a supplementary parameter. The prospects of the semi-empirical Water Cloud Model (WCM) to estimate the soil moisture content was tested in a fragmented agricultural region for different time periods. The concurrent satellite data obtained from Sentinel 1A and Landsat 8 were utilized to supply the inputs for inversion modeling based on the Levenberg-Marquardt approach. The present research introduced the “virtual Normalized Difference Vegetation Index” concept to refine the WCM and yielded reliable soil moisture output to supplement the anchor pixel selection process. The robustness of the approach was justified by considering the available energy at anchor pixel locations. The research outcomes revealed that the anchor pixel selection “with” and “without” soil moisture criterion significantly impacts actual evapotranspiration estimation. The research also explores the scope of the synergetic use of optical and Synthetic Aperture Radar (SAR) inputs in SEBAL.
Birla Institute of Technology and Science
