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A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands
Ge, R (Ge, Rong)1,2; He, HL (He, Honglin)1,3; Ren, XL (Ren, Xiaoli)1; Zhang, L (Zhang, Li)1,3; Li, P (Li, Pan)4; Zeng, N (Zeng, Na)1,2; Yu, GR (Yu, Guirui)1,3; Zhang, LY (Zhang, Liyun)1,2; Yu, SY (Yu, Shi-Yong)5; Zhang, FW (Zhang, Fawei)6; Li, HQ (Li, Hongqin)6; Shi, PL (Shi, Peili)1; Chen, SP (Chen, Shiping)7; Wang, YF (Wang, Yanfen)2; Xin, XP (Xin, Xiaoping)8; Ma, YM (Ma, Yaoming)2,9,10; Ma, MG (Ma, Mingguo)11; Zhang, Y (Zhang, Yu)12; Du, MY (Du, Mingyuan)13
Source PublicationREMOTE SENSING
Volume10Issue:1Pages:文献号: 149

It is important to accurately evaluate ecosystem respiration (RE) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST), enhanced vegetation index (EVI), and land surface water index (LSWI) to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China's grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R-2 = 0.79, RMSE = 0.77 g C m(-2) day(-1)) and temperate grasslands (R-2 = 0.75, RMSE = 0.60 g C m(-2) day(-1)). The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided overestimating RE in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of RE for two typical grasslands and a new alternative up-scaling method for large-scale RE evaluation in grassland ecosystems.

Subject Area地理学
WOS IDWOS:000424092300147
Indexed BySCIE
KeywordSoil-water Content Great-plains Modis Data Co2 Flux Temperature Sensitivity Primary Productivity Surface-temperature Alpine Grasslands Regional Patterns Tallgrass Prairie
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
Cooperation Status国际
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Corresponding AuthorHe, HL (He, Honglin); Ren, XL (Ren, Xiaoli)
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China;
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China;
4.Chinese Acad Sci, Inst Geochem, State Key Lab Environm Geochem, Guiyang 550002, Guizhou, Peoples R China;
5.Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Peoples R China;
6.Chinese Acad Sci, Northwest Plateau Inst Biol, Xining 810001, Peoples R China;
7.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China;
8.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China;
9.Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing 100101, Peoples R China;
10.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China;
11.Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data Ap, Sch Geog Sci, Chongqing 400715, Peoples R China;
12.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Gansu, Peoples R China;
13.Natl Agr & Food Res Org, Inst Agroenvironm Sci, Tsukuba, Ibaraki 3058604, Japan.
Recommended Citation
GB/T 7714
Ge, R ,He, HL ,Ren, XL ,et al. A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands[J]. REMOTE SENSING,2018,10(1):文献号: 149.
APA Ge, R .,He, HL .,Ren, XL .,Zhang, L .,Li, P .,...&Du, MY .(2018).A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands.REMOTE SENSING,10(1),文献号: 149.
MLA Ge, R ,et al."A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands".REMOTE SENSING 10.1(2018):文献号: 149.
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