文献类型：期刊文献

英文题名：NPP Estimation of High Heterogeneous Region based on Spatiotemporal Fusion

作者：Li, Wenmei[1] Wu, Jiaqi[1,2,3] Zhao, Lei[2,3]

第一作者：Li, Wenmei

机构：[1] School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, NanJing, 210023, China; [2] Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China; [3] The Key Laboratory of Forestry Remote Sensing and Information System, Nfga, Beijing, 100091, China

年份：2022

卷号：2022-July

起止页码：2841-2844

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

收录：EI(收录号：20224313008272)

语种：英文

摘要：Net Primary Productivity (NPP) is an important part of the carbon cycle of terrestrial ecosystems. The technical advantages and huge potential of remote sensing technology in NPP estimation make it a hot spot in the research field. The vigorous development of many remote sensing fusion algorithms provides fine-resolution remote sensing data support for high-precision NPP dynamic monitoring. In recent years, the expansion of urban areas and climate change have had a great impact on the NPP of vegetation. In accordance with the requirements of large-scale and high-temporal-spatial resolution productivity assessment of urban area, we chose the northern Jiangsu area as the research area and uses three remote sensing data spatiotemporal fusion methods, STARFM, ESTARFM, and STDFA to blend Landsat and MODIS data. Three methods are compared in aspects of NDVI reconstruction ability in large-scale, highly heterogeneous regional application scenarios, the accuracy of NPP estimation through CASA model, and the ability of fine spatial description. The results of the study show that STDFA gets the highest correlation coefficient between its NDVI reconstruction results and MODIS products, which is 0.82. Correlation coefficient of STARFM and ESTARFM are 0.77 and 0.75, respectively. The STARFM is significantly lower in the NPP estimation results among the three methods, meanwhile STDFA performs best in our test site. ? 2022 IEEE.