A high-resolution map of soil organic carbon in cropland of Southern China

CATENA, Год журнала: 2024, Номер 237, С. 107813 - 107813

Опубликована: Янв. 12, 2024

Язык: Английский

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Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus

Agriculture, Год журнала: 2022, Номер 12(7), С. 1062 - 1062

Опубликована: Июль 20, 2022

Predicting soil chemical properties such as organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC Ava-P influenced by both natural anthropogenic factors. This study aimed at (1) predicting a piedmont plain Northeast Iran using the Random Forests (RF) Cubist mathematical models hybrid (Regression Kriging), (2) comparing models’ results, (3) identifying key variables that influence spatial dynamics under agricultural practices. machine learning were trained with 201 composite surface samples 24 ancillary data, including climate (C), organism (O), topography- relief (R), parent material (P) features (S) according to SCORPAN digital mapping framework, which can predictively represent formation factors spatially. Clay, one most well-known relationship SOC, was important predictor followed open-access multispectral satellite images-based vegetation indices. had similar set effective variables. Hybrid approaches did not improve model accuracy significantly, but they reduce map uncertainty. In validation set, calculated RF algorithm normalized root mean square (NRMSE) 96.8, while an NRMSE 94.2. These values change when technique for Ava-P; however, changed just 1% SOC. management supply activities be guided maps. Produced maps scientist plays active role used identify concentrations are high need protected, uncertainty sampling required further monitoring.

Язык: Английский

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Fine-resolution mapping of cropland topsoil pH of Southern China and its environmental application

Geoderma, Год журнала: 2024, Номер 442, С. 116798 - 116798

Опубликована: Фев. 1, 2024

Soil pH is one of the critical indicators soil quality. A fine resolution map urgently required to address practical issues agricultural production, environmental protection, and ecosystem functioning, which often fall short meeting demands for local applications. To fill this gap, we used data from an extensive survey 13,424 surface samples (0–0.2 m) across cropland Jiangxi Province in Southern China. Using digital mapping techniques with 46 covariates, produced a 30 m topsoil We integrate different variable selection algorithms machine learning methods. Our results indicate Random Forest covariates selected by recursive feature had best performance r 0.583 RMSE 0.41. The prediction interval coverage probability our was 0.92, indicating low estimated uncertainty. Climate identified as most predicting contribution 37.42 %, followed properties (29.09 %), management (21.86 parent material (6.22 biota (5.39 %) factors. mean 5.21, great pressure acidification region. high values were mainly distributed Northern, Western, Eastern parts region while majorly located central part. Compared past surveys 1980 s, there no significant change surveyed can provide important implications guidance decisions on heavy metal pollution remediation, precision agriculture, prevention acidification.

Язык: Английский

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Digital mapping of soil properties in the high latitudes of Russia using sparse data

Geoderma Regional, Год журнала: 2024, Номер 36, С. e00776 - e00776

Опубликована: Фев. 2, 2024

Язык: Английский

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Towards spatially continuous mapping of soil organic carbon in croplands using multitemporal Sentinel-2 remote sensing

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2022, Номер 193, С. 187 - 199

Опубликована: Сен. 26, 2022

Язык: Английский

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Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

Journal of Integrative Agriculture, Год журнала: 2024, Номер 23(8), С. 2820 - 2841

Опубликована: Янв. 9, 2024

Faced with increasing global soil degradation, spatially explicit data on cropland organic matter (SOM) provides crucial for carbon pool accounting, quality assessment and the formulation of effective management policies. As a spatial information prediction technique, digital mapping (DSM) has been widely used to map at different scales. However, accuracy SOM maps is typically lower than other land cover types due inherent difficulty in precisely quantifying human disturbance. To overcome this limitation, study systematically assessed framework "information extraction-feature selection-model averaging" improving model performance using 462 samples collected Guangzhou, China 2021. The results showed that dynamic extraction, feature selection averaging could efficiently improve final predictions (R2: 0.48 0.53) without having obviously negative impacts uncertainty. Quantifying environment was an efficient way generate covariates are linearly nonlinearly related SOM, which improved R2 random forest from 0.44 extreme gradient boosting 0.37 0.43. FRFS recommended when there relatively few environmental (<200), whereas Boruta many (>500). granger-ramanathan approach average When structures initial models similar, number did not have significantly positive effects predictions. Given advantages these selected strategies over great potential high-accuracy any scales, so can provide more reliable references conservation policy-making.

Язык: Английский

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Integrating high-resolution data and species-level traits for enhanced ecosystem projections using a dynamic vegetation model: Case study in Wallonia, Belgium

Journal of Environmental Management, Год журнала: 2025, Номер 375, С. 124329 - 124329

Опубликована: Янв. 31, 2025

Язык: Английский

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Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning

Geoderma, Год журнала: 2025, Номер 455, С. 117225 - 117225

Опубликована: Фев. 21, 2025

Язык: 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, Год журнала: 2025, Номер unknown

Опубликована: Март 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

Язык: Английский

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National-scale mapping topsoil organic carbon of cropland in China using multitemporal Sentinel-2 images

Geoderma, Год журнала: 2025, Номер 456, С. 117272 - 117272

Опубликована: Март 30, 2025

Язык: Английский

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