Normalizing the Local Incidence Angle in Sentinel-1 Imagery to Improve Leaf Area Index, Vegetation Height, and Crop Coefficient Estimations

Public domain synthetic-aperture radar (SAR) imagery, particularly from Sentinel-1, has widened the scope of day and night vegetation monitoring, even when cloud cover limits optical Earth observation. Yet, it is challenging to combine SAR images acquired at different incidence angles ascending descending orbits because backscatter dependence on angle. This study demonstrates two transformations that facilitate collective use Sentinel-1 regardless acquisition geometry, for agricultural monitoring several crops in Israel (wheat, processing tomatoes, cotton). First, backscattering coefficient (?0) was multiplied by local angle (?) every pixel. transformation improved empirical prediction crop (Kc), leaf area index (LAI), height all three crops. The second method, which based brightness (?0), proved useful estimating Kc, LAI, tomatoes cotton. Following suggested transformations, R2 increased 0.0172 0.668, RMSE 5 52%. Additionally, models were found be superior dual-polarization (RVI). Consequently, using imagery viewing geometries became more effective.