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 and from ascending and descending orbits because of the backscatter dependence on the incidence angle. This study demonstrates two transformations that facilitate collective use of Sentinel-1 imagery, regardless of the acquisition geometry, for agricultural monitoring of several crops in Israel (wheat, processing tomatoes, and cotton). First, the radar backscattering coefficient (sigma(0)) was multiplied by the local incidence angle (theta) of every pixel. This transformation improved the empirical prediction of the crop coefficient (K-c), leaf area index (LAI), and crop height in all three crops. The second method, which is based on the radar brightness coefficient (beta(0)), proved useful for estimating K-c, LAI, and crop height in processing tomatoes and cotton. Following the suggested transformations, R-2 increased by 0.0172 to 0.668, and RMSE improved by 5 to 52%. Additionally, the models based on the suggested transformations were found to be superior to the models based on the dual-polarization radar vegetation index (RVI). Consequently, vegetation monitoring using SAR imagery acquired at different viewing geometries became more effective.

Automated summary

This study demonstrates two transformations that facilitate collective use of Sentinel-1 imagery for agricultural monitoring of several crops in Israel. These transformations improve the prediction accuracy of crop coefficient, leaf area index, and crop height, making vegetation monitoring using SAR imagery more effective.