文献类型：期刊文献

英文题名：Estimation of grassland use intensity based on the generated high-resolution daily land surface reflectance dataset with GF-6 WFV and HLS

作者：Cui, Hanwen[1,2] Li, Xiaosong[3] Sun, Bin[1,2] Yang, Guangbin[4] Yang, Ziyu[3] Chen, Chaochao[3] Zhao, Licheng[3]

第一作者：Cui, Hanwen

通信作者：Sun, B[1];Sun, B[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing, Peoples R China;[2]NFGA, Key Lab Forestry Remote Sensing & Informat Syst, Beijing, Peoples R China;[3]Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China;[4]Guizhou Normal Univ, Sch Geog & Environm Sci, Guiyang, Peoples R China

年份：2025

外文期刊名：GEO-SPATIAL INFORMATION SCIENCE

收录：;EI(收录号：20253118917932);Scopus(收录号：2-s2.0-105012198926);WOS:【SCI-EXPANDED(收录号:WOS:001540168400001)】；

基金： This work was supported by the National Key R&D Program of China [Grant number 2024YFF1308104], the General Program of the National Natural Science Foundation of China under Grant [Grant number 42271407] and the Civil Space Pre-research Project of the 14th Five-Year Plan under Grant [Grant number D040104].

语种：英文

外文关键词：Daily scale; grassland monitoring; mowed and grazing grasslands; time series of NDVI

摘要：Recently, grassland ecosystems' productivity and ecological service capacity have declined due to human activities and natural changes, and ecological and environmental problems such as grassland degradation and land sanding have become hot issues of global concern. Therefore, timely and accurate information on grassland use based on remote sensing monitoring is of irreplaceable importance in coordinating the relationship between development needs and natural carrying capacity and protecting the ecological environment. Based on a single remote sensing data source, there are limitations in obtaining time-continuous data, which affects the accurate monitoring of grassland distribution, use type, and use intensity. The study proposed a method to integrate a daily land surface reflectance dataset based on Harmonized Landsat-Sentinel (HLS) data and GF-6 WFV data and constructed a grassland use intensity index based on time series data to estimate the use intensity. The results show that the daily land reflectance dataset integrated from HLS and GF-6 WFV data passed the consistency test, with a Correlation Coefficient (R) in all bands greater than 0.85 and a Root Mean Square Error (RMSE) in all bands less than 0.09, verifying the reliability of the fused data. This dataset can be used to solve quality problems, such as missing data, and to improve the observation frequency of the time series data. The identification of grassland use type based on the daily scale Normalized Difference Vegetation Index (NDVI) dataset achieved an overall accuracy with a Kappa coefficient of 0.80. Meanwhile, the grassland use intensity index constructed at the same time can better reflect the differences between different grazed intensities and provide a better estimation of the use intensity of grasslands.