A novel vegetation-water resistant soil moisture index for remotely assessing soil surface moisture content under the low-moderate wheat cover

文献类型： 外文期刊

第一作者： Yue, Jibo

作者： Yue, Jibo;Li, Ting;Guo, Wei;Qiao, Hongbo;Li, Suju;Liu, Yang;Liu, Yang;Feng, Haikuan;Yang, Hao;Yang, Guijun;Tian, Jia;Tian, Qingjiu

作者机构：

关键词： SMC; Radiative transfer model; VWC; VRSMI; NSDSI

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：7.7； 五年影响因子：8.4 ）

ISSN： 0168-1699

年卷期： 2024 年 224 卷

页码：

收录情况： SCI

摘要： Soil surface moisture content (SMC) is one of the most critical and fundamental indicators of crop drought because water is vital for vegetation growth. In recent decades, satellite high-spatial-resolution optical multi-spectral remote-sensing sensors have entered a new stage. However, current optical remote sensing SMC indices (SMIs) are sensitive to cropland SMC and vegetation water content (VWC). From dry to saturated, the soil spectra decreased quickly as the SMC increased. For the vegetation spectra, the short-wave infrared (SWIR) bands decreased as the VWC increased. The differences in the responses of near-infrared (NIR) and SWIR bands to water may help distinguish soil moisture from mixed soil-vegetation spectra. This study focuses on (a) analyzing how soil and vegetation reflectance are affected by VWC and SMC, (b) designing a new vegetation-water resistant soil moisture index (VRSMI) based on the differences in the responses of NIR bands and SWIR bands to water, and (c) evaluating the potential of using VRSMI to remotely estimate SMC in low-moderate crop-covered regions. VRSMI uses SWIR1-SWIR2 as the numerator to detect the water absorption features and NIRx(SWIR1)(0.5) as the denominator to weaken the vegetation effect. VRSMI and soil-VRSMI (VRSMIs) were tested using laboratory- and field-based spectra datasets. Our study presents the following conclusions: (1) The differences in the responses of NIR and SWIR bands to water help distinguish the effects of soil moisture on mixed soil-vegetation spectra. (2) VRSMIs can provide high-performance SMC estimates under low-moderate vegetation cover (NDVI < 0.55). (3) VRSMIs can be used for cropland SMC evaluation without considering crop VWC (laboratory-based spectra dataset: RMSE = 0.07-0.11, R-2 = 0.96-0.98; field-based spectra dataset: RMSE = 0.17-0.18, R-2 = 0.63-0.79). Our study indicates that VRSMI and VRSMIs can provide high-performance relative SMC estimates under low-to-moderate vegetation cover.

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