Endmember spectral variability and index determination for retrieving fractional vegetation cover in the Loess Plateau

作者：Liang He, Du Lyu, Xiaoping Zhang, Baoyuan Liu, Rui Li, Xihua Yang, José A. Gómez · 发表于：International Soil and Water Conservation Research · 年份：2025 · DOI：10.1016/j.iswcr.2025.08.004 · 被引用次数：1 · 研究领域：Remote Sensing in Agriculture、Soil erosion and sediment transport、Remote Sensing and Land Use

Accurate remote sensing retrieval of fractional vegetation cover (FVC) of photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) is essential for assessing regional soil erosion. However, current linear spectral unmixing methods often ignore variability in endmember spectral indices, causing errors in FVC estimation. Using field-measured hyperspectral data and the derived indices of NDVI and the Cellulose Absorption Index (CAI), we analyzed the spectral properties of the endmembers across red, near-infrared, and shortwave infrared bands, examining their variability among different vegetation types and seasons. Furthermore, we identified optimal spectral indices and their combinations for retrieving PV, NPV, and bare soil (BS) fractions using MODIS imagery. Results showed that while endmember NDVI varied significantly with vegetation type (e.g., mean forest PV NDVI of 0.85 vs. 0.64 for grass) and season, the CAI demonstrated no significant variability under the same conditions. A three-component linear spectral unmixing model was developed and evaluated using MODIS-derived indices: NDVI, Enhanced VI (EVI), Kernel-NDVI (kNDVI), and two alternatives for CAI—Shortwave Infrared Ratio (SWIR32) and Dead Fuel Index (DFI). The kNDVI-SWIR32 and NDVI-SWIR32 combinations exhibited the highest predictive accuracy. Determination coefficients for FPV, FNPV, and FBS were 0.92, 0.74, and 0.70, respectively, with Nash-Sutcliffe efficiency coefficients of 0.90, 0.74, and 0.70, a...