数字农科院2.0

Sensitivity of Different Soil-Resistant Vegetation Indices to Soil Moisture and Soil Type

文献类型： 外文期刊

作者： Li, Zeteng;Chen, Jin;Chen, Xuehong;Guo, Yongli;Shen, Miaogen;Xu, Dawei;Wang, Cong

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期刊名称： JOURNAL OF REMOTE SENSING

ISSN：

年卷期： 2026 年 6 卷

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收录情况： EI（2025版） ; ; 北大核心（2023版） ; ; CSCD（2025-2026年度） ; ; 科技核心（2024版） ; ; 农林核心（2024版）ESCI（2025版）

摘要： Vegetation indices (VIs) are widely used as proxies for vegetation greenness and vigor. However, soil background effects-especially those caused by variations in soil moisture and type-create complex spectral mixtures of vegetation and soil, substantially impacting the accuracy and reliability of these indices. This study assessed the sensitivity of normalized difference vegetation index (NDVI) and 31 soil-resistant indices to soil effects, focusing on the independent effects of soil moisture and soil type, and overall soil effect. The soil-resistant indices are categorized into 6 groups according to the design mechanisms: soil-line adjusted, photosynthesis oriented, shape separation, shortwave infrared (SWIR) adjusted, RedEdge adjusted, and green triangular indices. Using both 3-dimensional (3D) radiative transfer model (RTM) simulations and ground-based experiments, the analysis shows that (a) in sparse canopy conditions where soil background effects are most pronounced, 22 (simulation experiments) and 26 (ground-based experiments) of the 31 soil-resistant indices exhibited lower overall sensitivity compared to NDVI, with soil-line adjusted and SWIR adjusted indices outperforming the other VIs. (b) Fifteen indices demonstrated superior resistance to both soil type and soil moisture compared to NDVI, and 8 exhibited enhanced resistance specifically to soil type, primarily belonging to the SWIR adjusted group, while 3 indices, all RedEdge adjusted, exhibited improved resistance to soil moisture. (c) Sensitivity to soil type predominantly determines overall soil effects. These findings provide important insights into understanding how soil-resistant indices can reduce soil-related uncertainties, and provide a sound basis for selecting appropriate VIs in remote sensing applications related to agricultural and ecological studies.

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