Pearl Millet Crop Biophysical Parameter Retrieval From Space Borne Polarimetric SAR Data Using Machine Learning

Abstract

Abstract The potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated. Various polarimetric parameters along with coherent and incoherent decomposition techniques were assessed for its sensitivity toward crop parameters like Wet and Dry Biomass, Crop Height, Leaf Area Index and Vegetation Water Content. A set of 39 polarimetric observables extracted from the Quad‐Pol data were used for regression analysis. In this study two Machine Learning techniques Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were assessed for the prediction of Wet Biomass (gm −2 ) and Height (cm). The most significant (6 out of 39) variables were applied for prediction. The results revealed that RFR algorithm performed better than MLR. The coefficient of determination ( R 2 ) and root‐mean‐square‐error of estimating wet biomass and height were 0.646, 655.65 (gm −2 ) and 0.71, 14.5 (cm) respectively in RFR and 0.566, 683.86 (gm −2 ) and 0.65, 16.14 (cm) respectively in MLR. Thus this study explored the effective application of quad‐pol data for assessing sensitivity and accurate retrieval of parameters using optimum PolSAR observables.

Plant phenotyping relevance

PolSARセンサーデータと機械学習により、作物のバイオマスおよび草丈を推定し、回帰手法の性能を比較・評価しているため、表現型取得手法が中心である。

Code and data availability

The paper's Data Availability Statement deposits two paper-specific public assets: the ground-truth field campaign dataset (GT Points, Zenodo 10.5281/zenodo.10403380) and the authors' RFR/MLR prediction code (Zenodo 10.5281/zenodo.10403352). Generic ESA software (PolSARpro, SNAP) is excluded as a general library.