数字农科院2.0

Ratio Drought Index (RDI): A soil moisture index based on new NIR-red triangle space

文献类型： 外文期刊

作者： Dong, Zhe;Wang, Ling;Gao, Maofang;Zhu, Xicun;Feng, Wenbin;Li, Nan

作者机构：

关键词： NIR-red;feature space;soil moisture;Ratio Drought Index

期刊名称： INTERNATIONAL JOURNAL OF REMOTE SENSING

ISSN： 0143-1161

年卷期： 2023 年

页码：

收录情况： SCIE（2023版） ; ; EI（2023版）

摘要： Accurate monitoring of soil moisture and the development of timely interventions are important to reduce the social and economic losses caused by drought. Compared to short-wave infrared (SWIR) and thermal infrared (TIR), near-infrared (NIR) and visible bands are widely used in almost all optical satellites. Drought monitoring using NIR and visible bands is therefore more relevant for optical satellites. Among the visible bands, the red band is often used in combination with the NIR band for drought monitoring due to its sensitivity to vegetation. However, current drought indexes based on the NIR and the red band applied to areas of high vegetation suffer from insufficient accuracy or tedious calculations. In this study, the ratio drought index (RDI) was developed after constructing a new feature space by examining the spectral properties of soil and vegetation at different water levels in the NIR and red bands. The accuracy of soil moisture inversion under two types of bare soil and vegetation was evaluated using in situ data from Tai'an City, Shandong Province. The perpendicular drought index (PDI) and modified perpendicular drought index (MPDI) were also used to compare for the RDI. The results showed that the RDI correlation coefficients (R-2) of 0.653 and 0.641 were better than the MPDI of 0.616 and 0.594 and the PDI of 0.602 and 0.546 for soil moisture measurements from vegetation and bare soil cover. The RDI attenuates the effect of vegetation on soil moisture inversion, as its root mean square error (RMSE) in vegetated areas is lower than that of the PDI and MPDI. The RDI calculation can be used as a theoretical guide for large-scale soil moisture estimation because it is fast, accurate and does not require additional quantitative remote sensing inversion factors.

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