Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method

被引:26
作者
Sun, Hao [1 ]
Cui, Yajing [1 ]
机构
[1] China Univ Min & Technol Beijing, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
soil moisture; downscaling; SMAP; MODIS; machine learning; LAND-SURFACE TEMPERATURE; PREDICTION; RESOLUTION; NETWORK; MODEL; INDEX;
D O I
10.3390/rs13010133
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.
引用
收藏
页码:1 / 16
页数:16
相关论文
共 40 条
[31]   Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN [J].
Shwetha, H. R. ;
Kumar, D. Nagesh .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2016, 117 :40-55
[32]   Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application [J].
Srivastava, Prashant K. ;
Han, Dawei ;
Ramirez, Miguel Rico ;
Islam, Tanvir .
WATER RESOURCES MANAGEMENT, 2013, 27 (08) :3127-3144
[33]   DSCALE_mod16: A Model for Disaggregating Microwave Satellite Soil Moisture with Land Surface Evapotranspiration Products and Gridded Meteorological Data [J].
Sun, Hao ;
Zhou, Baichi ;
Zhang, Chuanjun ;
Liu, Hongxing ;
Yang, Bo .
REMOTE SENSING, 2020, 12 (06)
[34]   Microwave and Meteorological Fusion: A method of Spatial Downscaling of Remotely Sensed Soil Moisture [J].
Sun, Hao ;
Cai, Chuangchuang ;
Liu, Hongxing ;
Yang, Bo .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2019, 12 (04) :1107-1119
[35]   Spatial Evaluation of Soil Moisture (SM), Land Surface Temperature (LST), and LST-Derived SM Indexes Dynamics during SMAPVEX12 [J].
Sun, Hao ;
Zhou, Baichi ;
Liu, Hongxing .
SENSORS, 2019, 19 (05)
[36]   A new agricultural drought monitoring index combining MODIS NDWI and day-night land surface temperatures: a case study in China [J].
Sun, Hao ;
Zhao, Xiang ;
Chen, Yunhao ;
Gong, Adu ;
Yang, Jing .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2013, 34 (24) :8986-9001
[37]   Operational readiness of microwave remote sensing of soil moisture for hydrologic applications [J].
Wagner, Wolfgang ;
Bloeschl, Guenter ;
Pampaloni, Paolo ;
Calvet, Jean-Christophe ;
Bizzarri, Bizzarro ;
Wigneron, Jean-Pierre ;
Kerr, Yann .
NORDIC HYDROLOGY, 2007, 38 (01) :1-20
[38]   Deep learning in environmental remote sensing: Achievements and challenges [J].
Yuan, Qiangqiang ;
Shen, Huanfeng ;
Li, Tongwen ;
Li, Zhiwei ;
Li, Shuwen ;
Jiang, Yun ;
Xu, Hongzhang ;
Tan, Weiwei ;
Yang, Qianqian ;
Wang, Jiwen ;
Gao, Jianhao ;
Zhang, Liangpei .
REMOTE SENSING OF ENVIRONMENT, 2020, 241
[39]   Deriving Field Scale Soil Moisture from Satellite Observations and Ground Measurements in a Hilly Agricultural Region [J].
Zappa, Luca ;
Forkel, Matthias ;
Xaver, Angelika ;
Dorigo, Wouter .
REMOTE SENSING, 2019, 11 (22)
[40]  
Zhan X., 2002, Soil Moisture Visible/Infrared Imager/Radiometer Suite Algorithm Theoretical Basis Document.