Crop height retrieval from polarimetric SAR data using machine learning: A comparative and validation study

Abstract

Accurate and efficient crop height information retrieval is crucial for applications such as farmland management, growth monitoring, yield estimation, and pest monitoring. Polarimetric Synthetic Aperture Radar (PolSAR) is known for its high sensitivity to the shape, structure, and dielectric constant of vegetation, presenting great potential for crop height retrieval. In this study, we compare the performance of three machine learning algorithms, Random Forest Regression (RFR), Bagging Decision Tree (BAGTREE), and Extreme Gradient Boosting (XGBoost), in the retrieval of crop height from PolSAR data. Using a comprehensive approach, we constructed a set of 32 polarimetric features as the initial input for the model. Subsequently, feature selection is employed to generate a subset aimed at reducing redundancy and improving the final estimation accuracy. Multi-temporal C-band PolSAR RADARSAT-2 data collected over three distinct agricultural types (corn, wheat, and soybean) in Canada are chosen for this study. The results indicate that the optimal average Root Mean Square Error (RMSE) for height retrieval in corn, wheat, and soybean throughout their growth cycles is 43.69 cm, 10.78 cm, and 20.92 cm, respectively. Among the three algorithms, RFR consistently demonstrates stable retrieval performance, and the polarimetric decomposition parameters exhibit the highest sensitivity to crop height. This study offers a valuable technical reference for SAR-based crop height retrieval and remote sensing-based crop growth monitoring without interferometry.

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