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A Process-Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services
Niu, Zhongen1,2,3; He, Honglin1,2,4; Peng, Shushi5; Ren, Xiaoli1,2; Zhang, Li1,2; Gu, Fengxue6; Zhu, Gaofeng7; Peng, Changhui8,9; Li, Pan10; Wang, Junbang1; Ge, Rong1,2,3; Zeng, Na11; Zhu, Xiaobo12; Lv, Yan1,2,3; Chang, Qingqing1,2,3; Xu, Qian1,2,3; Zhang, Mengyu1,2,3; Liu, Weihua1,2,3
2021-06-01
Source PublicationJOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
Volume13Issue:6Pages:22
Corresponding AuthorHe, Honglin(hehl@igsnrr.ac.cn) ; Zhang, Li(li.zhang@igsnrr.ac.cn)
AbstractTerrestrial ecosystems provide multiple services interacting in complex ways. However, most ecosystem services (ESs) models (e.g., InVEST and ARIES) ignored the relationships among ESs. Process-based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes. Therefore, based on the Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA) models, we developed a process-based ES model (CEVSA-ES) integrating remotely sensed leaf area index to evaluate four important ESs (i.e., productivity provision, carbon sequestration, water retention, and soil retention) at annual timescale in China. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA-ES model was the consideration of soil erosion processes and its impact on carbon cycling. The new version also improved the carbon-water cycle algorithms. Then, the Sobol and DEMC methods that integrated the CEVSA-ES model with nine flux sites comprising 39 site-years were used to identify and optimize parameters. Finally, the model using the optimized parameters was validated at 26 field sites comprising 135 site-years. Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% interannual variabilities of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. The CEVSA-ES model performed well for productivity provision and carbon sequestration, which explained 96% and 81% of the spatial-temporal variations of the observed annual productivity provision and carbon sequestration, respectively. The model also captured the interannual trends of water retention and soil erosion for most sites or basins.
KeywordCEVSA-ES model ecosystem services remote sensing model-data fusion China
DOI10.1029/2020MS002451
WOS KeywordLAND-SURFACE MODELS ; COARSE WOODY DEBRIS ; EDDY COVARIANCE ; ORGANIC-CARBON ; CLIMATE-CHANGE ; SOIL CARBON ; TERRESTRIAL ECOSYSTEMS ; PRIMARY PRODUCTIVITY ; NITROGEN DEPOSITION ; DATA ASSIMILATION
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFC0500200] ; National Natural Science Foundation of China[42030509] ; National Natural Science Foundation of China[31988102]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China
WOS Research AreaMeteorology & Atmospheric Sciences
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000666401800005
PublisherAMER GEOPHYSICAL UNION
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/163782
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHe, Honglin; Zhang, Li
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China
2.Chinese Acad Sci, Natl Ecosyst Sci Data Ctr, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
5.Peking Univ, Coll Urban & Environm Sci, Sino French Inst Earth Syst Sci, Beijing, Peoples R China
6.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Minist Agr, Key Lab Dryland Agr, Beijing, Peoples R China
7.Lanzhou Univ, Coll Earth & Environm Sci, Minist Educ, Key Lab Western Chinas Environm Syst, Lanzhou, Peoples R China
8.Northwest Agr & Forest Univ, Coll Forestry, Ctr Ecol Forecasting & Global Change, Yangling, Shaanxi, Peoples R China
9.Univ Quebec Montreal, Inst Environm Sci, Dept Biol Sci, Montreal, PQ, Canada
10.Tianjin Univ, Inst Surface Earth Syst Sci, Tianjin, Peoples R China
11.Zhejiang A&F Univ, Sch Environm & Resources, Hangzhou, Peoples R China
12.Chongqing Univ Posts & Telecommun, Chongqing Engn Res Ctr Spatial Big Data Intellige, Chongqing, Peoples R China
Recommended Citation
GB/T 7714
Niu, Zhongen,He, Honglin,Peng, Shushi,et al. A Process-Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services[J]. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS,2021,13(6):22.
APA Niu, Zhongen.,He, Honglin.,Peng, Shushi.,Ren, Xiaoli.,Zhang, Li.,...&Liu, Weihua.(2021).A Process-Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services.JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS,13(6),22.
MLA Niu, Zhongen,et al."A Process-Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services".JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 13.6(2021):22.
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