Abstract
Polarimetric decomposition extracts scattering features that are indicative of the physical characteristics of the target. In this study, three polarimetric decomposition methods were tested for soil moisture estimation over agricultural fields using machine learning algorithms. Features extracted from model-based Freeman–Durden, Eigenvalue and Eigenvector based H/A/α, and Van Zyl decompositions were used as inputs in random forest and neural network regression algorithms. These algorithms were applied to retrieve soil moisture over soybean, wheat, and corn fields. A time series of polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the Soil Moisture Active Passive Experiment 2012 (SMAPVEX12) field campaign was used for the training and validation of the algorithms. Three feature selection methods were tested to determine the best input features for the machine learning algorithms. The most accurate soil moisture estimates were derived from the random forest regression algorithm for soybeans, with a correlation of determination (R2) of 0.86, root mean square error (RMSE) of 0.041 m3 m−3 and mean absolute error (MAE) of 0.030 m3 m−3. Feature selection also impacted results. Some features like anisotropy, Horizontal transmit and Horizontal receive (HH), and surface roughness parameters (correlation length and RMS-H) had a direct effect on all algorithm performance enhancement as these parameters have a direct impact on the backscattered signal.
Highlights
Water is critical for all ecosystems and the availability of the right amount of water at the right time is crucial in agricultural production [1]
One of the main results in this study shows that in some cases the same accuracy could be obtained using a lower number of features and some features like roughness parameters, HH backscatter intensity, and anisotropy features were most important for soil moisture retrieval over this agricultural landscape
The random forest algorithm provides higher accuracies for soil moisture estimation when compared to the neural network when using identical features
Summary
Water is critical for all ecosystems and the availability of the right amount of water at the right time is crucial in agricultural production [1]. A growing global population and shrinking acreages of arable land places pressure on the agricultural sector to increase per acre productivity. A changing climate is creating uncertainty and necessitates efficient use of water for crop production. Data on soil water reserves can help direct cropping decisions with respect to what and when to seed, and decisions on the management of water for crop production. Soil moisture at the surface is the most dynamic over time, and the amount of water in the top few centimeters can impact seeding decisions, germination, and flood risk
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