A Spatial Downscaling Method for Remote Sensing Soil Moisture Based on Random Forest Considering Soil Moisture Memory and Mass Conservation
نویسندگان
چکیده
Remote sensing soil moisture (SM) has been widely used in various earth science studies and applications, but their low resolution limits usage downscaling of them is needed. In this study, we proposed a spatial method for SM based on random forest considering memory mass conservation to improve performance. The lagged was added as predictor represent memory, addition the regular predictors previous studies. Soil Moisture Active Passive (SMAP) data Pearl River Basin were test our method. results show that model obtained good performance set (R2 = 0.848, ubRMSE 0.034 m3/m3 Bias 0.008 m3/m3). temporal RF can be improved by adding variables. Downscaled retain information original SMAP well more details, correction considered useful eliminate systematic bias model. achieved acceptable situ validation, though it inevitably limited data. serve powerful tool development high-resolution information.
منابع مشابه
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14163858