文献类型：会议论文

英文题名：NPP ESTIMATION USING TIME-SERIES GF-1 DATA IN SPARSE VEGETATION AREA -A CASE STUDY IN ZHENGLANQI OF INNNER MONGLOLIA, CHINA

作者：Sun, Bin[1] Li, Zengyuan[1] Gao, Zhihai[1] Gao, Wentao[1] Zhang, Yuanyuan[1] Ding, Xiangyuan[1] Li, Changlong[1]

第一作者：孙斌

通信作者：Gao, ZH[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, 1 Dongxiaofu, Beijing 100091, Peoples R China

会议论文集：38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

会议日期：JUL 22-27, 2018

会议地点：Valencia, SPAIN

语种：英文

外文关键词：GF data; NPP; CASA model; Parameter optimization

年份：2018

摘要：Net primary productivity (NPP) is an important ecological indicator to evaluate ecosystem, and it is useful for land degradation assessment and monitoring. However, owing to dryland's particularity, retrieving vegetation properties from satellite remote sensing presents some significant challenges in sparse vegetation area. In this study, based on the wildly used time-series GF-1 data, the NPP estimation in sparse vegetation area was analyzed. Results showed that GF-1 data have high spatial and high temporal resolution characteristics, it is useful to distinguish land cover types in semi-arid areas based on NDVI time series data, the accuracy was 83.37% and Kappa coefficient was 0.79. Some key parameters of grassland were simulated and optimized based on CASA model. Compared with the measured data, the result was R-2 with 0.71, and results indicated that NPP estimation by GF-1 data based on the new parameters in semi-arid area is feasible.