Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

Shu-Di Fan, Yue-Ming Hu, Lu Wang, Zhen-Hua Liu, Zhou Shi, Wen-Bin Wu, Yu-Chun Pan, Guang-Xing Wang, A-Xing Zhu and Bo Li
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Shu-Di Fan: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Yue-Ming Hu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Lu Wang: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Zhen-Hua Liu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Zhou Shi: Institute of Agricultural Remote Sensing & Information System, Zhejiang University, Hangzhou 310029, China
Wen-Bin Wu: Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Yu-Chun Pan: Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
Guang-Xing Wang: Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
A-Xing Zhu: Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
Bo Li: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China

Sustainability, 2018, vol. 10, issue 10, 1-18

Abstract: To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index ( TVDI ) using data from the Project for On-Board Autonomy ( PROBA-V ). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V , and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

Keywords: soil moisture; temperature vegetation drought index; downscaling; SMAP; PROBA-V (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
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