Spatial patterns of China's carbon sinks estimated from the fusion of remote sensing and field-observed net primary productivity and heterotrophic respiration

Zeng, Jiali; Zhou, Tao; Wang, Qianfeng; Xu, Yixin; Qiu, Lin; Zhang, Yajie; Wu, Xiaocui; Zhang, Jingzhou; Liu, Xia

doi:10.1016/j.ecoinf.2023.102152

Cited by 15 publications

(4 citation statements)

References 74 publications

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“…Machine learning can explore the nonlinear relationships between multi-source data, provide more accurate simulation results, and have stronger inclusiveness towards the data itself. It also has the characteristics of easy operation, economy, and applicability to multiple scales [ 76 ]. Therefore, compared to traditional carbon sink estimation methods, it is more suitable for carbon sink estimation in the Qinghai Tibet Plateau.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, although machine learning can explore nonlinear relationships between data, it has high requirements for the data itself, and abnormal data may have a significant impact on the results. Therefore, in the future, more and more representative samples are needed to further improve the reliability of the model [ 76 ]. Scaling the site data to the regional level places higher demands on the homogeneity of the samples, especially in the complex terrain of the Qinghai Tibet Plateau, where the uneven distribution of spatial sample points may lead to uncertainty in carbon si60nk estimation results in certain regions [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

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The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial

Zeng,

Zhou,

et al. 2023

Carbon Balance Manage

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Background The Qinghai-Tibet Plateau is the “sensitive area” of climate change, and also the “driver” and “amplifier” of global change. The response and feedback of its carbon dynamics to climate change will significantly affect the content of greenhouse gases in the atmosphere. However, due to the unique geographical environment characteristics of the Qinghai-Tibet Plateau, there is still much controversy about its carbon source and sink estimation results. This study designed a new algorithm based on machine learning to improve the accuracy of carbon source and sink estimation by integrating multiple scale carbon input (net primary productivity, NPP) and output (soil heterotrophic respiration, Rh) information from remote sensing and ground observations. Then, we compared spatial patterns of NPP and Rh derived from the fusion of multiple scale data with other widely used products and tried to quantify the differences and uncertainties of carbon sink simulation at a regional scale. Results Our results indicate that although global warming has potentially increased the Rh of the Qinghai-Tibet Plateau, it will also increase its NPP, and its current performance is a net carbon sink area (carbon sink amount is 22.3 Tg C/year). Comparative analysis with other data products shows that CASA, GLOPEM, and MODIS products based on remote sensing underestimate the carbon input of the Qinghai-Tibet Plateau (30–70%), which is the main reason for the severe underestimation of the carbon sink level of the Qinghai-Tibet Plateau (even considered as a carbon source). Conclusions The estimation of the carbon sink in the Qinghai-Tibet Plateau is of great significance for ensuring its ecological barrier function. It can deepen the community’s understanding of the response to climate change in sensitive areas of the plateau. This study can provide an essential basis for assessing the uncertainty of carbon sources and sinks in the Qinghai-Tibet Plateau, and also provide a scientific reference for helping China achieve “carbon neutrality” by 2060.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial

Zeng,

Zhou,

et al. 2023

Carbon Balance Manage

Self Cite

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“…Climate change is a primary factor causing spatial-temporal disparities in NEP, especially in a country like China, which encompasses diverse geographic environments and spans multiple climatic zones [87]. Due to geographical, climatic, and ecological differences between northern and southern China, there are significant disparities in ecosystems' responses to climatic factors [88,89].…”

Section: Interannual Relationship Between Nep and Meteorological Changesmentioning

confidence: 99%

Characteristic Analysis of Carbon Sink Capacity Changes in Xinjiang’s Terrestrial Ecosystem Based on EEMD

Zhang,

Zheng,

Zhang

et al. 2024

Sustainability

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Net Ecosystem Productivity (NEP) is an important measure to assess the carbon balance and dynamics of ecosystems, providing a direct measure of carbon source–sink dynamics in terrestrial ecosystems and finding widespread applications in carbon cycle research. However, the nonlinear characteristics of NEP in Xinjiang’s terrestrial ecosystems remain unclear. Additionally, the influence of land use patterns, temperature, and precipitation variations on carbon sink capacity remains unclear. Ensemble Empirical Mode Decomposition (EEMD) is used to investigate the nonlinear variation of NEP in Xinjiang. Landscape pattern analysis of Xinjiang’s land use patterns from 1981 to 2019 is conducted using a 30 km moving window, and the interannual relationships between NEP, land use patterns, and meteorological factors are investigated through EEMD detrending analysis and Pearson correlation. The findings indicate that: (1) NEP exhibits interannual variations, primarily concentrated in the foothills of the Tianshan Mountains, with a three-year cycle. (2) Although NEP changes in most regions are not significant, urban clusters on the northern slopes of the Tianshan Mountains show noteworthy trends, with initial decrease followed by an increase, covering around 34.87% of the total area. Areas at risk of NEP decline constitute approximately 7.32% of the total area. (3) Across Xinjiang, we observe a widespread rise in patch fragmentation and complexity, coupled with a decline in patch connectivity and the size of the dominant patch. Additionally, there is a notable increase in both the diversity and evenness of land use types. However, the correlation between land use patterns and NEP is generally found to be insignificant in the majority of areas, with a percentage exceeding 85%. (4) Approximately 62% of regions in Xinjiang have NEP that is positively correlated with temperature, with significance observed in 33% of these areas. Furthermore, almost 95% of regions demonstrate that NEP is positively correlated with precipitation, with significance noted in 83% of these regions. It appears that precipitation exerts a more pronounced influence on NEP fluctuations in Xinjiang when compared to temperature.

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“…Reservoir water level variations during the flood season can be up to tens of meters and spatiotemporal changes are highly dynamic, necessitating high temporal and spatial resolution for effectively monitoring changes in the water surface area [21]. Remote sensing has advantages in accurately obtaining the water surface area and has become the mainstream method in hydrology research [22][23][24]. Landsat-8 and MODIS are proven to have relative spatial and temporal advantages for water body monitoring [25].…”

Section: Introductionmentioning

confidence: 99%

High Spatiotemporal Estimation of Reservoir Evaporation Water Loss by Integrating Remote-Sensing Data and the Generalized Complementary Relationship

Li,

Cheng

et al. 2024

Remote Sensing

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Accurately estimating the reservoir evaporation loss is crucial for water resources management. The existing research on reservoir evaporation loss estimates primarily focuses on large spatiotemporal scales and neglects the rapid dynamic changes to reservoirs’ surface area. For reservoirs essential for frequent flood control and regular water supply, high spatiotemporal evaporation data are crucial. By integrating remote sensing and the evaporation model, this study proposes a new method for the high spatiotemporal estimation of the evaporation losses from reservoirs. The proposed method is applied to the largest artificial freshwater lake in Asia, i.e., Danjiangkou (DJK) Reservoir. The daily reservoir water surface area is extracted at a spatial resolution of 30 m during the period 2014–2018 based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). The daily evaporation rate is estimated at a spatial resolution of 100 m using the generalized complementary relationship (GCR). The results show that the water surface area of the DJK Reservoir exhibits rapid and frequent fluctuations from 2015 to 2018, with a multi-year average area of 731.9 km2 and a maximum and minimum difference of 304 km2. Significant seasonal variations are observed in both the evaporation rate and volume, with a multi-year average evaporation rate of 806 mm and evaporation volume of 595 million m3. The estimated results align well with three other independent estimates, indicating that the GCR is capable of water surface evaporation estimation. Further analysis suggests that the data resolution has a great influence on the evaporative water loss from the reservoir. The estimated mean annual evaporation volume based on the 1000 m resolution water surface area data is 14% lower than that estimated using the 30 m resolution water surface area data. This study not only provides a new method for the high spatiotemporal estimation of reservoir evaporation by integrating remote-sensing data and the GCR method but also highlights that reservoir evaporation water loss should be quantified using the volume rather than the rate and that the estimated loss is noticeably affected by the estimation spatial resolution.

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Spatial patterns of China's carbon sinks estimated from the fusion of remote sensing and field-observed net primary productivity and heterotrophic respiration

Cited by 15 publications

References 74 publications

The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial

The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial

Characteristic Analysis of Carbon Sink Capacity Changes in Xinjiang’s Terrestrial Ecosystem Based on EEMD

High Spatiotemporal Estimation of Reservoir Evaporation Water Loss by Integrating Remote-Sensing Data and the Generalized Complementary Relationship

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