文献类型：会议论文

英文题名：ESTIMATION OF GROSS PRIMARY PRODUCTIVITY OF FOUR TYPES OF FOREST IN CHINA

作者：Fan Wenwu[1,2] Tian Xin[1] Ling Feilong[2] Yan Min[1]

第一作者：Fan Wenwu

通信作者：Tian, X[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Fuzhou Univ, Minist Educ, Key Lab Spatial Data Min & Informat Sharing, Gongye Rd 525, Fuzhou 350002, Peoples R China

会议论文集：36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

会议日期：JUL 10-15, 2016

会议地点：Beijing, PEOPLES R CHINA

语种：英文

外文关键词：TG model; MOD_17; GPP; forest

年份：2016

摘要：The quantification of forest Gross Primary Productivity (GPP) has been the focus of many scientific studies (e.g. carbon cycle, climate change, etc.). Current remote sensing-based models (i.e., the MODIS MOD_17 model), rely on the accurate meteorological data, specific vegetation parameter, the applicability and explicability of remote sensing data. In this study, the original MODIS GPP products were validated and showed significant underestimation compared to the eddy covariance measurements of the four forest sites over China. Thus the strategy of simple yet accurate and quantitative simulation of carbon fluxes was improved by using Sims TG model which was termed the Temperature and Greenness (TG) model and included the land surface temperature (LST) product and enhanced vegetation index (EVI) product from MODIS. The results indicated that Sims TG model was poor adaptive to tropical and subtropical evergreen forest in China. The model precision of deciduous forest was high but the GPP of evergreen forest are underestimated in Qianyanzhou and Xishuangbanna station in summer. After the parameter was optimized in Sims TG model, the estimation accuracy of evergreen forest GPP was improved to a certain extent and the model better adapt to dynamic change of forest GPP in China.