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Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations

机译：对土壤水分预测的时空深度学习方法评估土壤水分观察中的空白填充空隙

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Mohamed ElSaadani; Emad Habib; Ahmed M. Abdelhameed; Magdy Bayoumi;
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2. A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations [J] . Guojie Wang, Damien Garcia, Yi Liu, Environmental Modelling & Software . 2012,第期

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3. Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework [J] . Mao Hanzi, Kathuria Dhruva, Duffield Nick, Water resources research . 2019,第8期

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5. Validation of remotely-sensed soil moisture observations for bare soil at 1.4 GHz: A quantitative approach through radiative transfer models to characterize abrupt transitions caused by a ponding event in an agricultural field, modifications to the radiative transfer models, and a mobile ground-based system [D] . Erbas, Cihan 2009

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6. Research on soil moisture prediction model based on deep learning [O] . Yu Cai, Wengang Zheng, Xin Zhang, 2012

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7. Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products [O] . Jian Kang, Rui Jin, Xin Li, 2021

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Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations

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