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Remote Sensing Technology and Application  2022, Vol. 37 Issue (5): 1190-1197    DOI: 10.11873/j.issn.1004-0323.2022.5.1190
    
Remote Sensing Monitoring of Terrestrial Ecosystem Carbon Budget based on Machine Learning and Big Data Platform
Shuai Gao1(),Xuehui Hou2,Yun Wang3,Qian Wang4,Yue Chen1,Rui Xing1,Jing Wang1,5
1.State Key Laboratory of Remote Sensing Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China
2.Institute of Agricultural Information and Economy,Shandong Academy of Agricultural Sciences,Jinan 250100,China
3.The College of Forestry of Beijing Forestry University,Beijing 100083,China
4.School of Geographic and Environmental Sciences,Tianjin Normal University,Tianjin 300387,China
5.School of Earth Sciences and Resources,China University of Geosciences,Beijing 100083,China
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Abstract  

The carbon budget of terrestrial ecosystems is an important indicator of global carbon cycle research and an important parameter of climate change. Based on the terrestrial ecosystem flux observation and remote sensing satellite observation data, machine learning methods are applied for carbon budget estimation. In this study, random forest algorithm is established to automatically learn features from training data and differences in time series dependencies, and carbon related parameters (Gross Primary Production, GPP; Net Ecosystem Production, NEP) could be estimated. Finally, standard indicators are selected to objectively evaluate the model using the validation data set. The result analysis shows that compared with MODIS GPP products, this method has greatly improved the estimation accuracy. Among them, the prediction result of deciduous broad-leaved forest is the best, the decision coefficient R2 is 0.82, and the root mean square error is 1.93 gCm-2 d-1.It is also significantly better than traditional light energy utilization model products in other vegetation types. The NEP machine learning model established based on the same method has also obtained good estimation results. The correlation between the output results of the deciduous broad-leaved forest model prediction model and the NEP obtained by the flux tower is 0.70 and RMSE=1.75 g C m-2 d-1. The difference in accuracy between GPP and NEP models indicates that when machine learning modeling is performed, the selection of independent variables in the training data set still needs to consider theoretical model. In order to quickly estimate the carbon budget of the terrestrial ecosystem, a remote sensing monitoring platform is established. The platform uses the GEE (Google Earth Engine) big data platform as the data storage and computing backend, and Django, HTML, CSS, JavaScript, etc. as the front-end, in order to quick calculation, real-time visualization and other functions. Based on the platform and algorithm, the global (60° N—60° S) GPP results obtained from 2002 to 2016 show that there are obvious spatial differences in the global average GPP, and the significant increase is mainly concentrated in eastern Asia and forested areas in North America. Research shows that remote sensing monitoring of carbon budget parameters based on machine learning and big data platforms can quickly provide regional and global-scale carbon storage and the results are consistent with true ground observations. The obtained estimation results avoid the complicated parameter setting of the physiological process model, and reduce the uncertainty of regional and global large-scale carbon budget monitor.

Key words:  Machine learning      Big data platform      Carbon budget      Random forest      Spatio-temporal expansion     
Received:  17 August 2021      Published:  13 December 2022
ZTFLH:  X16  
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Shuai Gao
Xuehui Hou
Yun Wang
Qian Wang
Yue Chen
Rui Xing
Jing Wang

Cite this article: 

Shuai Gao,Xuehui Hou,Yun Wang,Qian Wang,Yue Chen,Rui Xing,Jing Wang. Remote Sensing Monitoring of Terrestrial Ecosystem Carbon Budget based on Machine Learning and Big Data Platform. Remote Sensing Technology and Application, 2022, 37(5): 1190-1197.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.5.1190     OR     http://www.rsta.ac.cn/EN/Y2022/V37/I5/1190

Fig.1  Workflow of the research
Fig.2  The relationship between iterations and residuals of the model
Fig.3  Basic architecture of carbon budget monitoring platform
Fig.4  Cloud platform Web application overall interface
IGBPGPP_RFRGPP_MODISNEP_RFR
R2

RMSE

(g C m-2 d-1)

R2

RMSE

(g C m-2 d-1)

R2

RMSE

(g C m-2 d-1)

DBF0.812.020.692.690.701.75
GRA0.781.770.62.510.371.44
WSA0.781.120.481.720.411.03
OSH0.710.640.531.170.340.76
CRO0.693.010.414.470.552.37
ENF0.681.920.612.290.351.67
MF0.682.030.612.310.431.64
WET0.612.270.482.690.431.54
EBF0.592.050.442.570.181.90
SAV0.431.870.192.420.241.51
Table 1  The GPP model, MODIS GPP product and NEP model compared with flux tower sites
Fig.5  Spatial distribution of global average GPP from 2002—2016 based on machine learning model
Fig.6  Global GPP from 2002—2016
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