Remotely estimate the cropland fractional vegetation cover using linear spectral mixture analysis and improved band operations

作者：Yihan Yao, Jianing Shen, Jibo Yue, Yang Liu, Haikuan Feng, Meiyan Shu, Yuanyuan Fu, Hongbo Qiao, Tong Sun, Guang Zheng · 发表于：International Journal of Remote Sensing · 年份：2025 · DOI：10.1080/01431161.2025.2478665 · 被引用次数：4 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Remote Sensing and LiDAR Applications

Fractional vegetation cover (FVC) is a critical indicator of crop health and is essential for monitoring crop vitality and formulating agricultural management strategies. Remote sensing technology facilitates the rapid and precise acquisition and analysis of crop growth conditions using crop canopy reflectance, providing vital support for FVC estimation. However, current mainstream techniques for estimating FVC based on vegetation indices (VIs) often encounter saturation phenomena in areas with moderate to high vegetation coverage. Additionally, traditional VIs exhibit a nonlinear response to FVC, leading to reduced estimation accuracy. This study proposes a novel FVC estimation method based on linear spectral mixture analysis (LSMA) and improved band operations. The effectiveness of this method is validated using over 1,105 sets of canopy spectral measurement data. This method integrates multiple spectral improved computation operations to address the saturation and nonlinear response issues associated with traditional VIs, thereby enhancing the accuracy of crop FVC estimation through remote sensing. The study presents the following conclusions: (1) The FVC estimation method based on improved spectra and LSMA alleviates the saturation and nonlinear response problems of traditional VIs, notably improving the remote sensing accuracy of crop FVC (R2 = 0.75, RMSE = 0.097, MAE = 0.068, MAPE = 43.8%). (2) Improved band computation VIs may be more suitable than traditional spectral...