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Spatial-Temporal Correlation Considering Environmental Factor Fusion for Estimating Gross Primary Productivity in Tibetan Grasslands

文献类型：期刊论文

作者Yang, Qinmeng1; Nie, Ningming1,4; Wang, Yangang1,4; Wu, Xiaojing2,3; Liu, Weihua2,3,4; Ren, Xiaoli2,3; Wang, Zijian1; Wan, Meng1; Cao, Rongqiang1,4
刊名APPLIED SCIENCES-BASEL
出版日期2023-05-21
卷号13期号:10页码:19
关键词deep learning GeoMAN model gross primary productivity attention mechanism interdisciplinary
DOI10.3390/app13106290
通讯作者Wang, Yangang(wangyg@sccas.cn)
英文摘要Gross primary productivity (GPP) is an important indicator in research on carbon cycling in terrestrial ecosystems. High-accuracy GPP prediction is crucial for ecosystem health and climate change assessments. We developed a site-level GPP prediction method based on the GeoMAN model, which was able to extract spatiotemporal features and fuse external environmental factors to predict GPP on the Tibetan Plateau. We evaluated four models' behavior-Random Forest (RF), Support Vector Machine (SVM), Deep Belief Network (DBN), and GeoMAN-in predicting GPP at nine flux observation sites on the Tibetan Plateau. The GeoMAN model achieved the best results (R-2 = 0.870, RMSE = 0.788 g Cm-2 d(-1), MAE = 0.440 g Cm-2 d(-1)). Distance and vegetation type of the flux sites influenced GPP prediction, with the latter being more significant. The different grassland vegetation types exhibited different sensitivity to environmental factors (Ta, PAR, EVI, NDVI, and LSWI) for GPP prediction. Among them, the site located in the alpine swamp meadow was insensitive to changes in environmental factors; the GPP prediction accuracy of the site located in the alpine meadow steppe decreased significantly with the changes in environmental factors; and the GPP prediction accuracy of the site located in the alpine Kobresia meadow also varied with environmental factor changes, but to a lesser extent than the former. This study provides a good reference that deep learning model is able to achieve good accuracy in GPP simulation when considers spatial, temporal, and environmental factors, and the judgement made by deep learning model conforms to basic knowledge in the relevant field.
WOS关键词LIGHT USE EFFICIENCY ; MODIS
资助项目National Key Research and Development Program of China[2021YFF0703902]
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
出版者MDPI
WOS记录号WOS:000994342100001
资助机构National Key Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/197349]  
专题中国科学院地理科学与资源研究所
通讯作者Wang, Yangang
作者单位1.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Natl Ecosyst Sci Data Ctr, Beijing 100101, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Qinmeng,Nie, Ningming,Wang, Yangang,et al. Spatial-Temporal Correlation Considering Environmental Factor Fusion for Estimating Gross Primary Productivity in Tibetan Grasslands[J]. APPLIED SCIENCES-BASEL,2023,13(10):19.
APA Yang, Qinmeng.,Nie, Ningming.,Wang, Yangang.,Wu, Xiaojing.,Liu, Weihua.,...&Cao, Rongqiang.(2023).Spatial-Temporal Correlation Considering Environmental Factor Fusion for Estimating Gross Primary Productivity in Tibetan Grasslands.APPLIED SCIENCES-BASEL,13(10),19.
MLA Yang, Qinmeng,et al."Spatial-Temporal Correlation Considering Environmental Factor Fusion for Estimating Gross Primary Productivity in Tibetan Grasslands".APPLIED SCIENCES-BASEL 13.10(2023):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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