@Article{XuYaYaXuHuGoLi:2022:DoSMSo,
               author = "Xu, Mengyuan and Yao, Ning and Yang, Haoxuan and Xu, Jia and Hu, 
                         Annan and Gon{\c{c}}alves, Lu{\'{\i}}s Gustavo Gon{\c{c}}alves 
                         de and Liu, Gang",
          affiliation = "{China Agricultural University} and {Northwest Agriculture and 
                         Forestry University} and {Tongji University} and {China 
                         Agricultural University} and {University College London} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {China 
                         Agricultural University}",
                title = "Downscaling SMAP soil moisture using a wide \& deep learning 
                         method over the Continental United States",
              journal = "Journal of Hydrology",
                 year = "2022",
               volume = "609",
                pages = "e127784",
                month = "June",
             keywords = "SMAP, Soil moisture downscaling, The Continental United States, 
                         Wide \& Deep learning method.",
             abstract = "Soil moisture (SM) plays a critical role in drought monitoring, 
                         agricultural management, flood forecasting, and other practical 
                         applications. However, the relatively coarse spatial resolutions 
                         of SM products derived from passive microwave satellite retrievals 
                         (approximately 2555 km) greatly hamper their local-scale 
                         applications. In this research, we proposed an SM downscaling 
                         framework based on the Wide \& Deep Learning (WDL) method to 
                         improve the spatial resolution of the level-3 daily composite of 
                         Soil Moisture Active Passive (SMAP) radiometer SM product 
                         (L3_SM_P). In this method, horizontally and vertically polarized 
                         Brightness Temperature (TBh, and TBv, respectively), surface 
                         reflectance and Land Surface Temperature (LST), topographic 
                         attributes, soil properties, climate types, and landcover types 
                         collected in the Continental United States (CONUS) during the 
                         annual unfrozen season (April 1st to November 1st) from 2015 to 
                         2017 were used as auxiliary datasets to downscale the spatial 
                         resolution of the SMAP SM (L3_SM_P) product from its original 36 
                         km to 1 km. Precipitation and in-situ SM measurements obtained 
                         from 211 sites distributed across the International Soil Moisture 
                         Network (ISMN) over the CONUS were utilized to validate the 
                         downscaled SM. The results demonstrated that the correlation (R) 
                         between the downscaled and the in-situ SM ranged from 0.325 to 
                         0.997; the average R value was 0.715. The unbiased Root Mean 
                         Square Error (ubRMSE) values ranged from 0.010 to 0.141 m3/m3, 
                         with an average ubRMSE of 0.041 m3/m3, which meets the accuracy of 
                         SMAP SM requirement of ubRMSE approximately 0.04 m 3/m3. The 
                         downscaled SM also showed good temporal consistency with the 
                         in-situ SM and exhibited a high response to the precipitation 
                         data. The downscaled SM not only maintained high spatial 
                         consistency with the original SMAP SM but also provides more 
                         detailed spatial SM variations.",
                  doi = "10.1016/j.jhydrol.2022.127784",
                  url = "http://dx.doi.org/10.1016/j.jhydrol.2022.127784",
                 issn = "0022-1694",
             language = "en",
           targetfile = "xu_2022.pdf",
        urlaccessdate = "12 maio 2024"
}