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A Triangular Perspective on Algorithmic Adaptability: Machine Learning for Agricultural Soil Moisture Retrieval Under Environmental Heterogeneity

https://doi.org/10.2139/ssrn.5348754
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High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scale Spatial Variability Across the United States
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Vegetation modeled as a water cloud
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Ensemble methods: bagging and random forests
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Seasonal biological carryover dominates northern vegetation growth
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Global soil moisture data derived through machine learning trained with in-situ measurements
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Michigan microwave canopy scattering model
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Deep Residual Learning for Image Recognition
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Quasi-Global Assessment of Deep Learning-Based CYGNSS Soil Moisture Retrieval
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XGBoost
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Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales
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Comparative Analysis of Machine-Learning Models for Soil Moisture Estimation Using High-Resolution Remote-Sensing Data
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Applications of Remote Sensing in Precision Agriculture: A Review
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Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine
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Advancements and Perspective in the Quantitative Assessment of Soil Salinity Utilizing Remote Sensing and Machine Learning Algorithms: A Review
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On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
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ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks
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Differentiable modeling for soil moisture retrieval by unifying deep neural networks and water cloud model
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Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality
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A comprehensive study of deep learning for soil moisture prediction
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