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      A Spatial Downscaling Method for Remote Sensing Soil Moisture Based on Random Forest Considering Soil Moisture Memory and Mass Conservation

      , , , , , , , ,
      Remote Sensing
      MDPI AG

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          Abstract

          Remote sensing soil moisture (SM) has been widely used in various earth science studies and applications, but their low resolution limits their usage and downscaling of them is needed. In this study, we proposed a spatial downscaling method for SM based on random forest considering soil moisture memory and mass conservation to improve downscaling performance. The lagged SM was added as a predictor to represent soil moisture memory, in addition to the regular predictors in previous downscaling studies. The Soil Moisture Active Passive (SMAP) SM data of the Pearl River Basin were used to test our downscaling method. The results show that the downscaling model obtained good performance on the test set (R2 = 0.848, ubRMSE = 0.034 m3/m3 and Bias = 0.008 m3/m3). The spatial and temporal performance of the RF downscaling model can be improved by adding lagged SM variables. Downscaled data obtained can retain the information of the original SMAP SM data well and show more spatial details, and mass conservation correction is considered to be useful to eliminate systematic bias of the downscaling model. Downscaled SM achieved acceptable performance in in situ validation, though it was inevitably limited by the performance of the original SMAP data. The proposed downscaling method can serve as a powerful tool for the development of high-resolution SM information.

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          Most cited references63

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          Investigating soil moisture–climate interactions in a changing climate: A review

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            Multisensor historical climatology of satellite-derived global land surface moisture

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              The Soil Moisture Active Passive (SMAP) Mission

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                Author and article information

                Contributors
                Journal
                Remote Sensing
                Remote Sensing
                MDPI AG
                2072-4292
                August 2022
                August 09 2022
                : 14
                : 16
                : 3858
                Article
                10.3390/rs14163858
                64c86d4d-e02d-4626-b71b-f38b3acec655
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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