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Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems

文献类型：期刊论文

作者Zhang, Liangxia4; Zhou, Decheng4; Fan, Jiangwen1; Guo, Qun1; Chen, Shiping2; Wang, Ranghui4; Li, Yuzhe1
刊名REMOTE SENSING
出版日期2019
卷号11期号:11
关键词gross primary productivity light use efficiency model satellite remote sensing MODIS EVI eddy covariance grassland ecosystem temperate steppe alpine meadow
DOI10.3390/rs11111333
文献子类Article
英文摘要Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (R-2 0.84), than in the temperate steppe (R-2 0.68). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts.
学科主题Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
出版地BASEL
电子版国际标准刊号2072-4292
WOS关键词GROSS PRIMARY PRODUCTION ; LIGHT USE EFFICIENCY ; NET PRIMARY PRODUCTIVITY ; LEYMUS-CHINENSIS STEPPE ; COMPARING GLOBAL-MODELS ; ALPINE SWAMP MEADOW ; TERRESTRIAL ECOSYSTEMS ; NORTHERN CHINA ; INTERANNUAL VARIABILITY ; TIBETAN PLATEAU
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000472648000076
出版者MDPI
资助机构National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [31870413] ; National Key R&D Program of China [2017YFA0604804] ; Chinese Academy of Sciences Technology Service Plan [KFJ_STS-ZDTP-013-02] ; Qinglan Project of Jiangsu Province of China
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/19854]  
专题植被与环境变化国家重点实验室
作者单位1.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing 210044, Jiangsu, Peoples R China
2.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Chinese Acad Sci, State Key Lab Vegetat & Environm Change, Inst Bot, 20 Nanxincun, Beijing 100093, Peoples R China
4.Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Agr Meteorol, Nanjing 210044, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Liangxia,Zhou, Decheng,Fan, Jiangwen,et al. Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems[J]. REMOTE SENSING,2019,11(11).
APA Zhang, Liangxia.,Zhou, Decheng.,Fan, Jiangwen.,Guo, Qun.,Chen, Shiping.,...&Li, Yuzhe.(2019).Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems.REMOTE SENSING,11(11).
MLA Zhang, Liangxia,et al."Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems".REMOTE SENSING 11.11(2019).

入库方式： OAI收割

来源：植物研究所

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