Incorporation of first-order backscattered power in Water Cloud Model for improving the Leaf Area Index and Soil Moisture retrieval using dual-polarized Sentinel-1 SAR data

Abstract

The novel modification in the WCM (mWCM) is proposed in this study to simulate total backscattering contribution and to improve Leaf Area Index (LAI) and Soil Moisture (SM) retrieval using Sentinel-1 Single Look Complex (SLC) datasets at VV and VH polarizations for the wheat crop. The intended modification was achieved through two steps; (1) Proposing the scaling constants of vegetation (fveg), soil (fsoil) and vegetation-soil interaction (finter) within the traditional WCM. The scaling constants are dimensionless quantity and were derived utilizing the degree of polarization (which were computed using the Hermitian covariance matrix); (2) Incorporation of the first order scattering component derived from the novel Vegetation-Soil Scattering Model (VSSM) to the total backscattering within the traditional WCM. The aim of including the vegetation-soil interaction in land surface models is to predict the combined effect of vegetation and soil on the total backscattering. The model parameters (i.e., A, B, C, and E) were calibrated using a non-linear least square regression algorithm. The accuracy of the retrieved and measured LAI and SM is evaluated using the different statistical indicators, e.g., coefficient of determination (R²), Root Mean Square Error (RMSE), and Nash Sutcliffe Efficiency (NSE). The retrieval from mWCM produced better accuracy with lower error than traditional WCM. The forward simulation results of mWCM revealed a notably higher accuracy for the total simulated radar backscattered coefficient at the VH polarization (σ0VH). The VH results showed a high R² = 0.86, a high NSE = 0.85, and a low RMSE = 0.51 dB, outperforming the simulated σ0VV with R² = 0.84, NSE = 0.84, RMSE = 0.66 dB. Consequently, the inversion of the mWCM yielded significantly improved accuracy in retrieving LAI at the VH polarization. The VH retrieval results exhibited a R² = 0.80, NSE = 0.78, and RMSE = 0.44 m²m⁻², while the VV polarization achieved an R² = 0.78, NSE = 0.77, and RMSE = 0.53 m²m⁻² for LAI estimation. In SM retrieval, higher accuracy was observed at VV polarization with R² value of 0.77, NSE value = 0.79, and RMSE = 0.048 m³m⁻³ than the VH polarization with R² = 0.75, NSE = 0.76, and RMSE = 0.050 m³m⁻³.

Plant phenotyping relevance

Sentinel-1 SARを用いてLAIという植物形質を推定する新規改良モデルを開発し、従来法と精度比較・検証しているため、植物フェノタイピング手法が中心である。

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