Distribution and Attribution of Gross Primary Productivity Increase Over the Mongolian Plateau, 2001-2018

Chen, Xiaona; Xin, Tao; Yang, Yibo

doi:10.1109/access.2022.3155722

Cited by 6 publications

(4 citation statements)

References 54 publications

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“…Therefore, regional climate characteristics are also an important category of impact factor in GPP prediction. This is also consistent with the conclusions of previous studies [76,77]. Based on the contribution ratio data obtained in this study and the experience of existing studies, it can be considered that EVI, NDVI, P, LULC, Tmax, PET, and ET are the key impact factors for GPP prediction.…”

Section: Discussionsupporting

confidence: 92%

Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland

Wang

Shao

et al. 2023

Remote Sensing

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Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon flux, and its accurate simulation and future prediction play an important role in understanding the ecosystem carbon cycle. Machine learning has potential in large-scale GPP prediction, but its application accuracy and impact factors still need further research. This paper takes the Mongolian Plateau as the research area. Six machine learning methods (multilayer perception, random forest, Adaboost, gradient boosting decision tree, XGBoost, LightGBM) were trained using remote sensing data (MODIS GPP) and 14 impact factor data and carried out the prediction of grassland GPP. Then, using flux observation data (positions of flux stations) and remote sensing data (positions of non-flux stations) as reference data, detailed accuracy evaluation and comprehensive trade-offs are carried out on the results, and key factors affecting prediction performance are further explored. The results show that: (1) The prediction results of the six methods are highly consistent with the change tendency of the reference data, demonstrating the applicability of machine learning in GPP prediction. (2) LightGBM has the best overall performance, with small absolute error (mean absolute error less than 1.3), low degree of deviation (root mean square error less than 3.2), strong model reliability (relative percentage difference more than 5.9), and a high degree of fit with reference data (regression determination coefficient more than 0.97), and the prediction results are closest to the reference data (mean bias is only −0.034). (3) Enhanced vegetation index, normalized difference vegetation index, precipitation, land use/land cover, maximum air temperature, potential evapotranspiration, and evapotranspiration are significantly higher than other factors as determining factors, and the total contribution ratio to the prediction accuracy exceeds 95%. They are the main factors influencing GPP prediction. This study can provide a reference for the application of machine learning in GPP prediction and also support the research of large-scale GPP prediction.

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Section: Discussionsupporting

confidence: 92%

Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland

Wang

Shao

et al. 2023

Remote Sensing

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“…The GPP and LAI in the foot of the Hangai Mountains, southeast of the Altai Mountains, and other areas were mainly driven by temperature. The areas between Frontiers in Environmental Science frontiersin.org (Bai et al, 2022;Chen et al, 2022). The increase of CO 2 concentration and average precipitation in the Mongolian Plateau from 2000 to 2018 improved soil water supply capacity, enhanced photosynthesis efficiency, promoted vegetation growth, and increased GPP and LAI (Liu et al, 2022).…”

Section: The Climate Factors Driving Gpp and Lai Changes Were Dividedmentioning

confidence: 99%

“…There are multiple indices that can be used to reflect the vegetation changes (Piao et al, 2020). Among these indices, gross primary productivity (GPP) and leaf area index (LAI) can provide useful information to understand changes in terrestrial ecosystems and global carbon budgets in the context of climate change (Bai et al, 2022;Chen et al, 2022). GPP is defined as the carbon absorbed by terrestrial ecosystems through plant photosynthesis.…”

Section: Introductionmentioning

confidence: 99%

Increasing precipitation promoted vegetation growth in the Mongolian Plateau during 2001–2018

et al. 2023

Front. Environ. Sci.

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Taking the Mongolian Plateau as the research area, this paper studied the vegetation growth from 2001 to 2018. We quantified the vegetation growth changes based on changes in gross primary productivity (GPP) and leaf area index (LAI) and their relationships to climate variables using correlation analysis, partial correlation analysis and multiple correlation analysis. The results showed that from 2001 to 2018 both GPP and LAI showed an increasing trend, with great heterogeneities among different areas and land cover types. The largest increase of GPP and LAI occurred in the northeast plateau with the land cover types of forest and cropland. The main driving factor of vegetation growth was precipitation, while temperature was significantly negatively correlated with vegetation growth. The CO2 concentration had a significant impact on the GPP in farmland, and the increase of solar radiation had a significant impact on tundra. Our study highlights the importance of precipitation in regulating vegetation growth in the Mongolian Plateau, challenging the prevailing views that the temperature dominates the vegetation growth in the northern ecosystems.

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“…The effects of precipitation on greening or browning may be modulated by anthropogenic factors, for example afforestation and land use changes [5,[29][30][31]. It was reported that the greening in China or the increased carbon sequestration in Asian terrestrial ecosystems after 1999 were primarily attributed to the implementation of extensive ecological restoration programs [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

The Effects of Precipitation Event Characteristics and Afforestation on the Greening in Arid Grasslands, China

Guo,

et al. 2023

Remote Sensing

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Global greening and its relationship with climate change remain the hot topics in recent years, and are of critical importance for understanding the interactions between the terrestrial ecosystem carbon cycle and the climate system. China, especially north China, has contributed a lot to global greening during the past few decades. As a water-limited ecosystem, human activities, not precipitation amount, were thought as the main contributor to the greening of north China. Considering the importance of precipitation event characteristics (PEC) in the altered precipitation regimes, we integrated long-term normalized difference vegetation index (NDVI) and meteorological datasets to reveal the role of precipitation regimes, especially PECs, on vegetation growth across temperate grasslands in north China. Accompanied with a significantly decreased growing season precipitation (GSP), NDVI increased significantly in the largest area of the temperate grasslands during 1982–2015, i.e., greening. We found that 28.44% of the area was explained by PECs, including more heavy or extreme precipitation events, alleviated extreme drought, and fewer light events, while only 0.92% of the area was associated with GSP. NDVI did not always increase over the 30 years and there was a decrease during 1996–2005. Taking afforestation projects in desertified lands into account, we found that precipitation, mainly PECs, explained more the increase and decline of NDVI during 1982–1995 and 1996–2005, respectively, while an equivalent explanatory power of precipitation and afforestation projects to the increase in NDVI after 2005. Our study indicates a possible higher productivity under future precipitation regime scenario (e.g., fewer but larger precipitation events) or intensive afforestation activity, implying more carbon sequestration or livestock production of temperate steppe in the future.

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Distribution and Attribution of Gross Primary Productivity Increase Over the Mongolian Plateau, 2001-2018

Cited by 6 publications

References 54 publications

Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland

Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland

Increasing precipitation promoted vegetation growth in the Mongolian Plateau during 2001–2018

The Effects of Precipitation Event Characteristics and Afforestation on the Greening in Arid Grasslands, China

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