气候变化领域集成服务门户(CCPortal): A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

CCPortal

DOI	10.3390/rs10010149
	A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands
	Ge, Rong; He, Honglin; Ren, Xiaoli; Zhang, Li; Li, Pan; Zeng, Na; Yu, Guirui; Zhang, Liyun; Yu, Shi-Yong; Zhang, Fawei; Li, Hongqin; Shi, Peili; Chen, Shiping; Wang, Yanfen; Xin, Xiaoping; Ma, Yaoming; Ma, Mingguo; Zhang, Yu; Du, Mingyuan
通讯作者	He, HL ; Ren, XL (通讯作者)
发表日期	2018
EISSN	2072-4292
卷号	10 期号:1
英文摘要	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.
关键词	SOIL-WATER CONTENT CO2 FLUX TEMPERATURE SENSITIVITY PRIMARY PRODUCTIVITY SURFACE-TEMPERATURE ALPINE GRASSLANDS REGIONAL PATTERNS CARBON STORAGE NORTHERN CHINA CLIMATE-CHANGE
英文关键词	ecosystem respiration; MODIS; moisture effect; ChinaFLUX; alpine grasslands; temperate grasslands
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000424092300147
来源期刊	REMOTE SENSING
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259360
推荐引用方式 GB/T 7714	Ge, Rong,He, Honglin,Ren, Xiaoli,et al. A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands[J]. 中国科学院青藏高原研究所,2018,10(1).
APA	Ge, Rong.,He, Honglin.,Ren, Xiaoli.,Zhang, Li.,Li, Pan.,...&Du, Mingyuan.(2018).A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands.REMOTE SENSING,10(1).
MLA	Ge, Rong,et al."A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands".REMOTE SENSING 10.1(2018).