Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method
نویسندگان
چکیده
Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. factors functions are two basic components of downscaling where the former particularly important in era big data. Based machine learning method, this study evaluated Land Surface Temperature (LST), surface Evaporative Efficiency (LEE), geographical from Moderate Resolution Imaging Spectroradiometer (MODIS) products SMAP (Soil Moisture Active Passive) products. This spans 2015 end 2018 locates central United States. Original in-situ at sparse networks core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance LST factors; (2) adding can significantly improve downscaling; (3) integrating LST, LEE, got best performance; (4) using Z-score normalization or hyperbolic-tangent methods did not change above conclusions, neither support vector regression nor feed forward neural network methods. demonstrates possibility alternative when there no available due cloud contamination. It also provides experimental evidence process.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2021
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs13010133