A hybrid machine learning approach for calibration and regionalization of LSM soil and vegetation parameters

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This work explores the combined use of machine learning and traditional model calibration methods to develop a high resolution (1 km) soil and vegetation parameter dataset for North America and Central America. First, we use a traditional calibration method (i.e., genetic algorithm (GA)) to calibrate soil and vegetation parameters to Soil Moisture Active Passive (SMAP) soil moisture at 25 km spatial resolution over the CONUS, Asia, and Europe. Next, we use a series of machine learning algorithms, including Neural Network, Random Forest, and XGBoost, t...