European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions

Earth system science data, Journal Year: 2024, Volume and Issue: 16(5), P. 2367 - 2383

Published: May 16, 2024

Abstract. Soil bulk density (BD) serves as a fundamental indicator of soil health and quality, exerting significant influence on critical factors such plant growth, nutrient availability, water retention. Due to its limited availability in databases, the application pedotransfer functions (PTFs) has emerged potent tool for predicting BD using other easily measurable properties, while impact these PTFs' performance organic carbon (SOC) stock calculation been rarely explored. In this study, we proposed an innovative local modeling approach fine earth (BDfine) across Europe recently released BDfine data from LUCAS (Land Use Coverage Area Frame Survey Soil) 2018 (0–20 cm) relevant predictors. Our involved combination neighbor sample search, forward recursive feature selection (FRFS), random forest (RF) models (local-RFFRFS). The results showed that local-RFFRFS had good (R2 0.58, root mean square error (RMSE) 0.19 g cm−3, relative (RE) 16.27 %), surpassing earlier-published PTFs 0.40–0.45, RMSE 0.22 RE 19.11 %–21.18 %) global RF with without FRFS 0.56–0.57, 16.47 %–16.74 %). Interestingly, found best PTF = 0.84, 1.39 kg m−2, 17.57 performed close 0.85, 1.32 15.01 SOC predictions. However, still better (ΔR2 > 0.2) samples low stocks (< 3 m−2). Therefore, suggest is promising method prediction, would be more efficient when subsequently utilized calculating stock. Finally, produced two topsoil datasets (18 945 15 389 samples) at 0–20 cm local-RFFRFS, respectively. This dataset archived Zenodo platform https://doi.org/10.5281/zenodo.10211884 (S. Chen et al., 2023). outcomes study present meaningful advancement enhancing predictive accuracy BDfine, resultant enable precise hydrological biological modeling.

Language: Английский

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A China dataset of soil properties for land surface modelling (version 2, CSDLv2)

Earth system science data, Journal Year: 2025, Volume and Issue: 17(2), P. 517 - 543

Published: Feb. 7, 2025

Abstract. Accurate and high-resolution spatial soil information is crucial for efficient sustainable land use, management, conservation. Since the establishment of digital mapping (DSM) GlobalSoilMap working group, significant advances have been made in terms availability quality globally. However, accurately predicting variation over large complex areas with limited samples remains a challenge, especially China, which has diverse landscapes. To address this we utilised 11 209 representative multi-source legacy profiles (including Second National Soil Survey World Information Service, First regional databases) soil-forming environment characterisation. Using advanced ensemble machine learning high-performance parallel-computing strategy, developed comprehensive maps 23 physical chemical properties at six standard depth layers from 0 to 2 m China 90 resolution (China dataset surface modelling version 2, CSDLv2). Data-splitting independent-sample validation strategies were employed evaluate accuracy predicted maps' quality. The results showed that significantly more accurate detailed compared traditional type linkage methods (i.e. CSDLv1, first dataset), SoilGrids 2.0, HWSD 2.0 products, effectively representing across China. prediction all intervals ranged good moderate, median model efficiency coefficients most ranging 0.29 0.70 during data-splitting 0.25 0.84 validation. wide range between 5 % lower 95 upper limits may indicate substantial room improvement current predictions. relative importance environmental covariates predictions varied property depth, indicating complexity interactions among multiple factors formation processes. As used study mainly originate conducted 1970s 1980s, they could provide new perspectives on changes, together existing based 2010s. findings make important contributions project can also be Earth system better represent role hydrological biogeochemical cycles This freely available https://www.scidb.cn/s/ZZJzAz (last access: 17 November 2024​​​​​​​) or https://doi.org/10.11888/Terre.tpdc.301235 (Shi Shangguan, 2024).

Language: Английский

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On the effectiveness of multi-scale landscape metrics in soil organic carbon mapping

Geoderma, Journal Year: 2024, Volume and Issue: 449, P. 117026 - 117026

Published: Sept. 1, 2024

Language: Английский

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Ensemble modelling-based pedotransfer functions for predicting soil bulk density in China

Geoderma, Journal Year: 2024, Volume and Issue: 448, P. 116969 - 116969

Published: July 15, 2024

Understanding and managing soil organic carbon stocks (SOCS) are integral to ensuring environmental sustainability the health of terrestrial ecosystems. The information bulk density (BD) is important in accurately determining SOCS while it often missing database. Using 3,504 profiles (14,170 samples) that represented diverse regions across China, we investigated effectiveness various pedotransfer functions (PTFs), including traditional PTFs, machine learning (ML), ensemble model (EM), predicting BD. results showed refitting parameter(s) PTFs was essential for BD prediction (coefficient determination (R2) 0.299–0.432, root mean squared error (RMSE) 0.156–0.162 g cm−3, Lin's concordance coefficient (LCCC) 0.428–0.605). Compared ML can greatly improve performance with R2 0.425–0.616, RMSE 0.129–0.158 cm−3 LCCC 0.622–0.765. Our also EM further by ensembling four models (R2 = 0.630, 0.126 0.775). model, filled (1207 3,112 our database built SOC stock (4,275 17,282 samples). This study be a good reference gap-filling depending on data availability, thus contribute deeper understanding C related climate change mitigation, ecological balance preservation promotion.

Language: Английский

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Comparison of Machine Learning and Geostatistical Methods on Mapping Soil Organic Carbon Density in Regional Croplands and Visualizing Its Location‐Specific Dominators via Interpretable Model

Land Degradation and Development, Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

ABSTRACT High‐precision soil organic carbon density (SOCD) map is significant for understanding ecosystem cycles and estimating storage. However, the current mapping methods are difficult to balance accuracy interpretability, which brings great challenges of SOCD. In present research, a total 6223 samples were collected, along with data pertaining 30 environmental covariates, from agricultural land located in Poyang Lake Plain Jiangxi Province, southern China. Furthermore, ordinary kriging (OK), geographically weighted regression (GWR), random forest (RF), empirical Bayesian (EBK), three hybrid models (RF‐OK, RF‐EBK, RF‐GWR), constructed. These used SOCD (soil density) study region high resolution m. After that, shapley additive explanations (SHAP) quantify global contribution spatially identify dominant factors that influence variation. The outcomes suggested compared single geostatistics model model, RF method emerged as most effective predictive showcasing superior performance (coefficient determination ( R 2 ) = 0.44, root mean squared error (RMSE) 0.61 kg m −2 , Lin's concordance coefficient (LCCC) 0.58). Using SHAP, we found properties contributed prediction (81.67%). At pixel level, nitrogen dominated 50.33% farmland, followed by parent material (8.11%), available silicon (8.00%), annual precipitation (5.71%), remaining variables accounted less than 5.50%. summary, our offered valuable enlightenment toward achieving between interpretability digital mapping, deepened spatial variation farmland

Language: Английский

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Legacy soil organic carbon stocks in central Spain from whole soil profiles and standardized depths: Influence of land cover and parent material

CATENA, Journal Year: 2025, Volume and Issue: 254, P. 108972 - 108972

Published: April 4, 2025

Language: Английский

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Preliminary Results in Innovative Solutions for Soil Carbon Estimation: Integrating Remote Sensing, Machine Learning, and Proximal Sensing Spectroscopy

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(23), P. 5571 - 5571

Published: Nov. 30, 2023

This paper explores the application and advantages of remote sensing, machine learning, mid-infrared spectroscopy (MIR) as a popular proximal sensing tool in estimation soil organic carbon (SOC). It underscores practical implications benefits integrated approach combining for SOC prediction across range applications, including comprehensive health mapping credit assessment. These advanced technologies offer promising pathway, reducing costs resource utilization while improving precision estimation. We conducted comparative analysis between MIR-predicted values laboratory-measured using 36 samples. The results demonstrate strong fit (R² = 0.83), underscoring potential this approach. While acknowledging that our is based on limited sample size, these initial findings promise serve foundation future research. will be providing updates when we obtain more data. Furthermore, commercialising Australia, with aim helping farmers harness markets. Based study’s findings, coupled insights from existing literature, suggest adopting measurement could significantly benefit local economies, enhance farmers’ ability to monitor changes health, promote sustainable agricultural practices. outcomes align global climate change mitigation efforts. approach, supported by other research, offers template regions worldwide seeking similar solutions.

Language: Английский

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The effects of land use change on soil organic carbon stock in China: A meta-analysis with the empirical modeling approach

Geoderma Regional, Journal Year: 2024, Volume and Issue: 36, P. e00774 - e00774

Published: Feb. 2, 2024

Language: Английский

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Enhancing the quality and reputation of Soil & Environmental Health journal: 2024 updates

Soil & Environmental Health, Journal Year: 2024, Volume and Issue: 2(1), P. 100059 - 100059

Published: Jan. 13, 2024

• Measures to improve the quality of SEH manuscripts; review quality; promote articles; recognize editorial team

Language: Английский

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Offset ratios and temporary contract designs for climate integrity in carbon farming

Carbon Management, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 12, 2024

In this study, we examine how to enhance the climate integrity of carbon credits from farming practices. The key requirements for include permanence, additionality, and measurement verification. Farmers are typically willing make contracts a finite time only in voluntary markets or with government receive sell as offsets. This contradicts requirement permanence sequestered soils. To solve problem facilitate greater participation by farmers sequestration, show temporary can be made address issue using offset ratios. notion ratio refers share one emission unit that replaces. Thus, transforms sequestration permanent emissions reductions. We propose use discounting method calculate ratio. varies contract length, employed discount rate, assumptions about evolution soil stock. apply approach cultivating catch crops on north‒south gradient Finland, Denmark, France. works well every selected country. Carbon profitable provided revenue under exceeds baseline. Profitability is highly dependent crop cost, annual increase carbon, rate. ratios assess some existing crediting programs find yields lower almost all cases yielding number than launched these programs.

Language: Английский

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