Mapping the net ecosystem exchange of CO2 of global terrestrial systems

Liu, Yi; Li, Haixiao; Renyang, Qianqian; Liu, Le; Dong, Jie; Liu, Xiaoning; Qu, Zhigang; Lee, Lien-Chieh; Chen, Lei; Wang, Dongliang; Zhang, Hu

doi:10.1016/j.jag.2022.103176

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…The MODIS instrument (https://modis.gsfc.nasa.gov/, accessed on 10 May 2024), a crucial tool on the Terra and Aqua satellites, is widely utilized for vegetation growth monitoring due to its large observation coverage (approximately 2330 km) and high data quality. Thus, monthly MOD13C2 products were obtained at a resolution of 0.05 • for this study [31,32].…”

Section: Vegetation Datamentioning

confidence: 99%

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Hu,

Zhang,

Feng

et al. 2024

Remote Sensing

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Carbon satellites are an important observation tool for analyzing ground carbon emission. From the perspective of the Earth’s scale, the spatiotemporal sparse characteristics of raw data observed from carbon satellite requires the accurate interpolation of data, and based on only this work, people predict future carbon emission trends and formulate appropriate management and conservation strategies. The existing research work has not fully considered the close correlation between data and seasons, as well as the characteristics accumulated over a long time scale. In this paper, firstly, by employing extreme random forests and auxiliary data, we reconstruct a daily average CO2 dataset at a resolution of 0.25°, and achieve a validated determination coefficient of 0.92. Secondly, introducing technologies such as Time Convolutional Networks (TCN), Channel Attention Mechanism (CAM), and Long Short-Term Memory networks (LSTM), we conduct atmospheric CO2 concentration interpolation and predictions. When conducting predictive analysis for the Yangtze River Delta region, we train the model by using quarterly data from 2016 to 2020; the correlation coefficient in summer is 0.94, and in winter it is 0.91. These experimental data indicate that compared to other algorithms, this algorithm has a significantly better performance.

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Section: Vegetation Datamentioning

confidence: 99%

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Hu,

Zhang,

Feng

et al. 2024

Remote Sensing

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show abstract

“…In contrast, a random forest model or other MLA is oblivious to the mechanistic relations, which allows its training to focus solely on the separation within the training data and not be limited by a priori choices on how vegetation indices or other variables relate to the target flux. Machine learning algorithms have been used for regional Wolanin et al, 2019;Xiao et al, 2008) and global estimates of carbon flux Jung et al, 2020;Lian et al, 2023;Tramontana et al, 2016;Wei et al, 2017) though the evaluation of carbon flux MLAs is limited within peatlands.…”

Section: Introductionmentioning

confidence: 99%

Random Forest Modelling and Monitoring of Surface-Atmosphere Carbon Dioxide Flux in the Hudson Bay Lowlands

Beaver

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Peatlands are a critical component of the global carbon cycle despite covering only 3% of the earth's surface. Within Canada the largest contiguous peatland is the Hudson Bay Lowlands (HBL), storing an estimated 33 Gt of carbon as peat as a result of a small but persistent difference between gross primary productivity (GPP) and ecosystem respiration (ER) over millennia. The vastness and remote nature of the HBL makes insitu monitoring of its carbon cycle difficult. To address this, random forest (RF) regression models driven solely by remote sensing imagery (either 500 m MODIS or 30 m Harmonized Landsat Sentinel) are trained to predict GPP based on eddy covariance CO2 flux data from 2012-2019 for five sites spanning a climatic gradient in the HBL.There is little difference between daily GPP simulations for spatial resolutions between 30 m and 500 m (R 2 = 0.65-0.78), but better temporal resolution improves model results at the annual time step. Additional RF models are trained to predict ER and the net difference between ER and GPP (net ecosystem exchange; NEE) using MODIS data. NEE models are weaker (R 2 = ~0.48) but generally do better than published semiempirical methods based on light use efficiency and temperature-respiration relationships, while ER remains accurately predicted (R 2 = 0.71) by the models. These RF models are then applied to three 48 km  48 km regions around the field sites and compared with established global products from other machine learning algorithms, process-based, atmospheric inversion, and remote sensing-based models for the period 2000-2020. These products predict greater GPP compared to the RF models as few include peatland-specific parameterizations. However, all models (RF and global products) present a common latitudinal trend with the greatest GPP in the south and iii decreasing northward. Most of the models agree that the HBL is a net sink of CO2 for this period but there is less agreement on the magnitude and latitudinal gradient. Remote sensing-based RF models of CO2 flux show promise for monitoring changes in the HBL's carbon flux, particularly when there is land use change or other impactful disturbances on a large scale. iv Preface This thesis is presented in an integrated format including three research chapters formatted as manuscripts with the intent to submit to journals. These manuscripts are titled: 1. Random Forest Development and Modelling of Gross Primary Productivity in the Hudson Bay Lowlands 2. Modelling Ecosystem-Atmospheric Carbon Fluxes Using Random Forests in the Hudson Bay Lowlands 3. Regional Estimates of Carbon Flux in the Hudson Bay Lowlands Using Remote Sensing Random Forest Models The research was conducted in the department of Geography and Environmental Studies at Carleton University under the co-supervision of Drs. Elyn Humphreys and Doug King and with guidance from the thesis advisory committee members Drs.

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Integrated evaluation of city-level CO2 emissions and sinks from 2010 to 2019: Spatio-temporal patterns in the Yangtze River Economic Belt of China

Bai,

Wang,

Fang

et al. 2024

Journal of Cleaner Production

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Mapping the net ecosystem exchange of CO2 of global terrestrial systems

Cited by 6 publications

References 34 publications

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Random Forest Modelling and Monitoring of Surface-Atmosphere Carbon Dioxide Flux in the Hudson Bay Lowlands

Integrated evaluation of city-level CO2 emissions and sinks from 2010 to 2019: Spatio-temporal patterns in the Yangtze River Economic Belt of China

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