文献类型：期刊文献

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, Zhihai

机构：[1] Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, No.1 Dongxiaofu, HaidianDistrict, Beijing, 100091, China

年份：2018

卷号：2018-July

起止页码：3971-3974

外文期刊名：International Geoscience and Remote Sensing Symposium (IGARSS)

收录：EI(收录号：20191606778289)

语种：英文

外文关键词：Ecosystems - Parameter estimation - Time series - Remote sensing - Arid regions

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 R2 with 0.71, and results indicated that NPP estimation by GF-1 data based on the new parameters in semi-arid area is feasible. ? 2018 IEEE.