Integration of a process-based model into the digital soil mapping improves the space-time soil organic carbon modelling in intensively human-impacted area

Xie, Enze; Zhang, Xiu; Lu, Fengying; Peng, Yuxuan; Jian, Chen; Zhao, Yuanchun

doi:10.1016/j.geoderma.2021.115599

Cited by 20 publications

(7 citation statements)

References 65 publications

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“…Comparing with the microbial model and manipulative experiment method, it could better reflect inner mechanism and the correlation between initial conditions and projected soil carbon from long time series and large scale. Additionally, we take advantage of a reconstructed data set [64][65] based on global SOC measurements, which skates over the issue of uneven spatial distribution and heterogeneities from direct observed stations. The long-time series of SOC stocks were estimated by using digital soil mapping model and the RothC-simulated method, and incorporated climate, landform, soil and land use types.…”

Section: Results Comparison and Uncertaintiesmentioning

confidence: 99%

Increasing global land surface soil organic carbon under climate change

Liu,

Chen,

Zhang

et al. 2023

Preprint

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Soil organic carbon (SOC), especially land surface SOC is sensitive to climate change as a sink or source of atmospheric carbon dioxide, which is an important component of the global carbon cycle and could play a key role in global carbon neutrality. However, it is still an open question about how global surface SOC change with climate warming. Here we show global SOC has a significant upward trend during 1981-2019. Temperature and precipitation are the dominant climate drivers at global scale, but vegetation cover is a crucial local factor. By integrating with climate scenarios of CMIP6 models, we project the future increase in global surface SOC with warming temperature, especially in middle and low latitudes. SOC change shows a lag feedback and limited balance role depending on the regional vegetation changes. Under 1.5oC global warming level, land carbon sink from SOC will maximally increase by 13.0 PgC from SSP2-4.5, but it only accounts for 19% of carbon emission capacity from current 1.1 to 1.5 oC global warming level. It is far from the Paris Agreement plan of four out of one thousand soil carbon stock of 40cm below the surface increase per year in the next 20 years (2.72 PgC yr-1). Moreover, this contribution is full of uncertainties and confined to support large-scale carbon dioxide removal from the atmosphere. Over-reliance on natural carbon sinks is a risky strategy. These findings highlight the urgency to carry out mitigation and removal strategies of reducing greenhouse gas emissions for the global carbon equilibrium and the Paris Agreement target.

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Section: Results Comparison and Uncertaintiesmentioning

confidence: 99%

Increasing global land surface soil organic carbon under climate change

Liu,

Chen,

Zhang

et al. 2023

Preprint

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“…A long time series SOC dataset [42] was selected in this study to capture the dynamics of organic carbon in topsoil (0-30 cm), a depth defined by the Tier 1 method in IPCC Guidelines [31]. It was created by combining the process-based RothC model and a geographically weighted regression kriging method, achieving a root-mean-squared accuracy of 27.76 tC/ha.…”

Section: Global Soc Datasetmentioning

confidence: 99%

“…Given that the soil organic carbon (SOC) pool can be easily affected by various factors (e.g., temperature, precipitation, and land cover change) and tends to vary significantly over time [39], dynamic SOC information is crucial for accurately estimating the carbon emissions between different periods. Instead of the widely used static maps [40,41], Xie et al [42] created a time series SOC dataset covering 1981-2018, providing strong support for addressing this need.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets

Liu,

Zhang,

et al. 2024

Remote Sensing

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Previous studies on global carbon emissions from forest loss have been marked by great discrepancies due to uncertainties regarding the lost area and the densities of different carbon pools. In this study, we employed a new global 30 m land cover dynamic dataset (GLC_FCS30D) to improve the assessment of forest loss areas; then, we combined multi-sourced carbon stock products to enhance the information on carbon density. Afterwards, we estimated the global carbon emissions from forest loss over the period of 1985–2020 based on the method recommended by the Intergovernmental Panel on Climate Change Guidelines (IPCC). The results indicate that global forest loss continued to accelerate over the past 35 years, totaling about 582.17 Mha and leading to total committed carbon emissions of 35.22 ± 9.38 PgC. Tropical zones dominated global carbon emissions (~2/3) due to their higher carbon density and greater forest loss. Furthermore, global emissions more than doubled in the period of 2015–2020 (1.77 ± 0.44 PgC/yr) compared to those in 1985–2000 (0.69 ± 0.21 PgC/yr). Notably, the forest loss at high altitudes (i.e., above 1000 m) more than tripled in mountainous regions, resulting in more pronounced carbon emissions in these areas. Therefore, the accelerating trend of global carbon emissions from forest loss indicates that great challenges still remain for achieving the COP 26 Declaration to halt forest loss by 2030.

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“…Soil carbon dynamics have recently gotten increasing scientific interest (Li et al 2016, Mishra et al 2019, Xie et al 2021. However, most of the current studies only focus on static soil carbon mapping.…”

Section: Agricultural Informationmentioning

confidence: 99%

“…century, Rothamsted Carbon model (RothC), and DeNitrification and DeComposition, may be a potential solution to model soil carbon in space and time. For example, Xie et al (2021) proposed the idea of incorporating the process-based RothC model into the geographically weighted regression kriging to better the space-time modeling of SOC contents in the heavily humanimpacted area and the attempt resulted in improved model performance. Process-based models can benefit from simulating point results, while ML models show opportunities to extrapolate relationships by using a large number of covariates, so a method that combines the advantages of the two can be designed for optimal direction.…”

Section: Incorporating Pedological Knowledge Into Statistical/ml Modelsmentioning

confidence: 99%

A review on digital mapping of soil carbon in cropland: progress, challenge, and prospect

Huang¹,

Yang²,

Zhang³

et al. 2022

Environ. Res. Lett.

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Cropland soil carbon not only serves food security but also contributes to the stability of the terrestrial ecosystem carbon pool due to the strong interconnection with atmospheric carbon dioxide. Therefore, the better monitoring of soil carbon in cropland is helpful for carbon sequestration and sustainable soil management. However, severe anthropogenic disturbance in cropland mainly in gentle terrain creates uncertainty in obtaining accurate soil information with limited sample data. Within the past twenty years, digital soil mapping has been recognized as a promising technology in mapping soil carbon. Herein, to advance existing knowledge and highlight new directions, the article reviews the research on mapping soil carbon in cropland from 2005 to 2021. There is a significant shift from linear statistical models to machine learning models because nonlinear models may be more efficient in explaining the complex soil-environment relationship. Climate covariates and parent material play an important role in soil carbon on the regional scale, while on a local scale, the variability of soil carbon often depends on topography, agricultural management, and soil properties. Recently, several kinds of agricultural covariates have been explored in mapping soil carbon based on survey or remote sensing technique, while, obtaining agricultural covariates with high resolution remains a challenge. Based on the review, we concluded several challenges in three categories: sampling, agricultural covariates, and representation of soil processes in models. We thus propose a conceptual framework with four future strategies: representative sampling strategies, establishing standardized monitoring and sharing system to acquire more efficient crop management information, exploring time-series sensing data, as well as integrating pedological knowledge into predictive models. It is intended that this review will support prospective researchers by providing knowledge clusters and gaps concerning the digital mapping of soil carbon in cropland.

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Integration of a process-based model into the digital soil mapping improves the space-time soil organic carbon modelling in intensively human-impacted area

Cited by 20 publications

References 65 publications

Increasing global land surface soil organic carbon under climate change

Increasing global land surface soil organic carbon under climate change

Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets

A review on digital mapping of soil carbon in cropland: progress, challenge, and prospect

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