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Non-Ignorable Differences in NIRv-Based Estimations of Gross Primary Productivity Considering Land Cover Change and Discrepancies in Multisource Products

文献类型：期刊论文

作者Jin, Jiaxin1,2; Hou, Weiye1; Wang, Longhao1,3; Wang, Songhan4,5; Wang, Ying6; Zhu, Qiuan1; Fang, Xiuqin1; Ren, Liliang1
刊名REMOTE SENSING
出版日期2023-10-01
卷号15期号:19页码:17
关键词NIRv GPP land cover ESA CCI LC maps Yellow River basin
DOI10.3390/rs15194693
通讯作者Wang, Longhao(wanglonghao0857@igsnrr.ac.cn)
英文摘要The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001-2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm-2yr-1 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP.
WOS关键词PLANT FUNCTIONAL TYPES ; CLIMATE-CHANGE ; GREEN PROJECT ; MODIS ; CHINA ; GRAIN ; EVAPOTRANSPIRATION ; PHOTOSYNTHESIS ; MODELS ; IMPACT
资助项目The authors would like to thank the following institutions or projects for generously providing the data used in this study: the United States Geological Survey (USGS), the European Space Agency (ESA), Google Earth Engine (GEE), FLUXNET, ChinaFLUX, and the ; United States Geological Survey (USGS) ; European Space Agency (ESA) ; Google Earth Engine (GEE), FLUXNET ; North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:001081997100001
资助机构The authors would like to thank the following institutions or projects for generously providing the data used in this study: the United States Geological Survey (USGS), the European Space Agency (ESA), Google Earth Engine (GEE), FLUXNET, ChinaFLUX, and the ; United States Geological Survey (USGS) ; European Space Agency (ESA) ; Google Earth Engine (GEE), FLUXNET ; North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/198744]  
专题中国科学院地理科学与资源研究所
通讯作者Wang, Longhao
作者单位1.Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210024, Peoples R China
2.Hohai Univ, Key Lab Water Big Data Technol, Minist Water Resources, Nanjing 210024, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
4.Nanjing Agr Univ, Jiangsu Collaborat Innovat Ctr Modern Crop Prod, Key Lab Urban Land Resources Monitoring & Simulat, Coll Agr,Minist Nat Resources, Nanjing 210095, Peoples R China
5.Nanjing Agr Univ, Coll Agr, Key Lab Crop Physiol & Ecol Southern China, Nanjing 210095, Peoples R China
6.Nanjing Xiaozhuang Univ, Tourism & Social Adm Coll, Nanjing 211171, Peoples R China
推荐引用方式
GB/T 7714		 Jin, Jiaxin,Hou, Weiye,Wang, Longhao,et al. Non-Ignorable Differences in NIRv-Based Estimations of Gross Primary Productivity Considering Land Cover Change and Discrepancies in Multisource Products[J]. REMOTE SENSING,2023,15(19):17.
APA Jin, Jiaxin.,Hou, Weiye.,Wang, Longhao.,Wang, Songhan.,Wang, Ying.,...&Ren, Liliang.(2023).Non-Ignorable Differences in NIRv-Based Estimations of Gross Primary Productivity Considering Land Cover Change and Discrepancies in Multisource Products.REMOTE SENSING,15(19),17.
MLA Jin, Jiaxin,et al."Non-Ignorable Differences in NIRv-Based Estimations of Gross Primary Productivity Considering Land Cover Change and Discrepancies in Multisource Products".REMOTE SENSING 15.19(2023):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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