Improved estimation of non-photosynthetic vegetation cover using a novel multispectral slope difference index with soil information, Sentinel-1 data, and machine learning

作者：Xinmeng Chen, Yanling Ding, Xingming Zheng, Chi Xu, Zhuo Wu, Qiaoyun Xie · 发表于：Ecological Informatics · 年份：2024 · DOI：10.1016/j.ecoinf.2024.102930 · 被引用次数：12 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Soil Geostatistics and Mapping

Non-photosynthetic vegetation (NPV) plays a crucial role in vegetation–soil ecosystems by influencing the dynamic uptake of carbon, water, and nutrients. The accurate estimation of the fractional cover of NPV (F NPV ) is vital for managing natural resources, modeling carbon dynamics, and monitoring vegetation systems. NPV indices (NPVIs) are widely utilized for estimating F NPV over large areas. However, distinguishing NPV from bare soil (BS) remains challenging owing to subtle differences in their multispectral reflectance and the interference of the soil background. To address this, we propose a novel NPV spectral slope difference index (NSSDI) based on the distinct spectral curve shapes of NPV and BS. The NSSDI incorporates three spectral slopes: two between the visible and near-infrared bands and one between the shortwave infrared 1 (SWIR1) and SWIR2 bands from Sentinel-2 (S2) and Landsat-8 (L8). Additionally, we investigate the integration of soil information and radar data to improve the F NPV estimation using three machine learning algorithms. Validation against in situ measurements reveals that the NSSDI from S2 is more sensitive to F NPV than the recently published NPV–soil separation index (NSSI), whereas the NSSDI from L8 outperforms traditional NPVIs. Incorporating soil properties such as soil organic matter and soil moisture improves the F NPV estimation accuracy compared with using S2 or L8 NPVIs alone. The combination of Sentinel-1 (S1) synthetic aperture radar...