Remotely estimate the cropland fractional vegetation cover using linear spectral mixture analysis and improved band operations
文献类型： 外文期刊
作者： Yao, Yihan 1 ; Shen, Jianing 1 ; Yue, Jibo 1 ; Liu, Yang 2 ; Feng, Haikuan 2 ; Shu, Meiyan 1 ; Fu, Yuanyuan 1 ; Qiao, Hongbo 1 ; Sun, Tong 1 ; Zheng, Guang 1 ;
作者机构： 1.Henan Agr Univ, Coll Informat & Management Sci, 63 Agr Rd, Zhengzhou 450002, Henan, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing, Peoples R China
3.China Agr Univ, Minist Educ, Key Lab Smart Agr Syst, Beijing, Peoples R China
关键词： Fractional vegetation cover; remote sensing; linear spectral mixture analysis
期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：2.6； 五年影响因子：2.9 ）
ISSN： 0143-1161
年卷期： 2025 年 46 卷 9 期
页码：
收录情况： SCI
摘要： 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 (R-2 = 0.75, RMSE = 0.097, MAE = 0.068, MAPE = 43.8%). (2) Improved band computation VIs may be more suitable than traditional spectral VIs for pixel dichotomy models in estimating crop FVC. The proposed FVC estimation method, based on improved band computation VIs and LSMA, offers a novel approach for agricultural ecological monitoring, facilitating improved dynamic monitoring of crop coverage. However, the potential of this new method for sustainable agricultural management warrants further exploration and research.
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