Retrieval of Leaf Area Index for Wheat and Oilseed Rape Based on Modified Water Cloud Model and SAR Data

Abstract

The accurate and timely determination of crop leaf area indices (LAIs) assists in making agricultural decisions. The objective of this study was to estimate crop LAIs using C-band RADARSAT-2 synthetic aperture radar (SAR) datasets and a modified water cloud model (MWCM). The WCM was improved through two steps: (1) constructing a vegetation coverage ratio (fv) using normalized difference vegetation indices calculated from Landsat-8 images and introducing it into the traditional WCM, and (2) incorporating field-collected crop height into the vegetation canopy described in the scattering model. The proposed MWCM parameters were calibrated using an iterative optimization algorithm named the Levenberg–Marquardt (LM) algorithm. The model’s performance before and after improvement was systematically calibrated and validated using field data collected from Yigen Farm (Hulunbuir City, Inner Mongolia Autonomous Region, China). The results show that the MWCM performed better than the original WCM in four polarization channels—HH, VV, HV, and VH—for both wheat and rape oilseed LAI inversion. HH polarization showed the best performance using both the MWCM and WCM for wheat, with R2 values of 0.4626 and 0.3327, respectively; meanwhile, for oilseed rape, the R2 values were 0.4912 and 0.3128, respectively. The RMSEs of the wheat inversion results were reduced from 1.5227 m2m−2 to 1.4898 m2m−2, and those for oilseed rape were reduced from 1.0411 m2m−2 to 0.7968 m2m−2. This study proved the feasibility and superiority of the MWCM, which provides new technical support for accurate crop growth monitoring