Comparative Analysis of Fractional Vegetation Cover Estimation Based on Multi-sensor Data in a Semi-arid Sandy Area

作     者：LIU Qiuyu ZHANG Tinglong LI Yizhe LI Ying BU Chongfeng ZHANG Qingfeng 

作者机构：College of Natural Resources and Environment Northwest A&F University Institute of Soil and Water Conservation Chinese Academy of Sciences and Ministry of Water Resources 

出 版 物：《Chinese Geographical Science》 (中国地理科学（英文版）)

年 卷 期：2019年第29卷第1期

页      面：166-180页

核心收录：

学科分类：07[理学] 

基　　金：National Natural Science Foundation of China(No.41301451,41541008) Fundamental Research Funds for the Central Universities(No.2452018144) 

主　　题：fractional vegetation cover (FVC) Sentinel-2A (S2) unmanned aerial vehicle (UAV)image pixel dichotomy model regression model 

摘      要：The estimation of fractional vegetation cover(FVC) is important for identifying and monitoring desertification, especially in arid and semiarid regions. By using regression and pixel dichotomy models, we present the comparison of Sentinel-2A(S2) multispectral instrument(MSI) and Landsat 8(L8) operational land imager(OLI) data regarding the retrieval of FVC in a semi-arid sandy area(Mu Us Sandland, China, in August 2016). A combination of unmanned aerial vehicle(UAV) high-spatial-resolution images and field plots were used to produce verified data. Based on a normalized difference vegetation index(NDVI) regression model, the results showed that, compared with that of L8, the coefficient of determination(R2) of S2 increased by 26.0%, and the root mean square error(RMSE) and the sum of absolute error(SAE) decreased by 3.0% and 11.4%, respectively. For the ratio vegetation index(RVI) regression model, compared with that of L8, the R2 of S2 increased by 26.0%, and the RMSE and SAE decreased by 8.0% and 20.0%, respectively. When the pixel dichotomy model was used, compared with that of L8, the RMSE of S2 decreased by 21.3%, and the SAE decreased by 26.9%. Overall, S2 performed better than L8 in terms of FVC inversion. Additionally, in this paper, we develop a verified scheme based on UAV data in combination with the object-based classification method. This scheme is feasible and sufficiently robust for building relationships between field data and inversion results from satellite data. Further, the synergy of multi-source sensors(especially UAVs and satellites) is a potential effective way to estimate and evaluate regional ecological environmental parameters(FVC).