Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

Remote Sensing

Jianlin Zhao¹  Longhua Yang²  Weiqiang Liu³  Sai Hu⁴  Long Li⁵  Ting Zhang⁵  Liang Cheng⁵  Lina Yuan⁵  Mingxin Wen⁵  Longqian Chen⁵ 

[1] Department of Geology Engineering and Geomatics, Chang’an University, Yanta Road 120, Xi’an 710054, China;Department of Research and Development, Shanghai Gongjing Environmental Protection Co., Ltd., Yuanjiang Road 525, Shanghai 201100, China;School of Environment and Spatial Informatics, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China;School of Humanities and Law, Jiangsu Ocean University, Cangwu Road 59, Lianyungang 222005, China;School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China;

关键词: MODIS;    relative soil moisture;    Chinese Loess Plateau;    ATI;    TVDI;   

DOI  :  10.3390/rs13040589

来源: DOAJ

【 摘 要 】

Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI₀ was used for computing TVDI, and both NDVI_ATI and NDVI_TVDI for dividing the entire CLP) were firstly identified with the best validation results () of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum of 0.82 ± 0.007 for Criterion 2 and of 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 10⁴ km² and 667.44 × 10⁴ km² with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management.

【 授权许可】

Unknown