Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas

Agriculture, Год журнала: 2023, Номер 13(5), С. 976 - 976

Опубликована: Апрель 28, 2023

The problem of salinization/spreading saline soils is becoming more urgent in many regions the world, especially context climate change. monitoring salt-affected soils’ properties a necessary procedure land management and irrigation planning aimed to obtain high crop harvest reduce degradation processes. In this work, machine learning method was applied for modeling spatial distribution topsoil (0–20 cm) properties—in particular: soil organic carbon (SOC), pH, salt content (dry residue). A random forest (RF) approach used combination with environmental variables predict semi-arid area (Trans-Ural steppe zone). Soil, salinity, texture maps; topography attributes; remote sensing data (RSD) were as predictors. coefficient determination (R2) root mean square error (RMSE) estimate performance RF model. cross-validation result showed that model achieved an R2 0.59 RMSE 0.68 SOM; 0.36 0.65, respectively, pH; 0.78 1.21, respectively dry residue prediction. SOC ranged from 0.8 2.8%, average value 1.9%; pH 5.9 8.4, 7.2; varied greatly 0.04 16.8%, 1.3%. variable importance analysis indicated (salinity indices NDVI) dominant prediction parameters. RSD evaluating their explained by absorption characteristics/reflectivity visible near-infrared spectra. Solonchak are distinguished crust on surface and, result, reduced contents vegetation biomass. However, change non-saline over short distance mosaic structure cover requires high-resolution or aerial images obtained unmanned vehicle/drones successful digital mapping presented results provide effective landscapes further management/reclamation degraded arid regions.

Язык: Английский

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Spatial prediction of soil properties using random forest, k-nearest neighbors and cubist approaches in the foothills of the Ural Mountains, Russia

Modeling Earth Systems and Environment, Год журнала: 2023, Номер 9(3), С. 3461 - 3471

Опубликована: Янв. 25, 2023

Язык: Английский

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Tree-structured parzen estimator optimized-automated machine learning assisted by meta–analysis for predicting biochar–driven N2O mitigation effect in constructed wetlands

Journal of Environmental Management, Год журнала: 2024, Номер 354, С. 120335 - 120335

Опубликована: Фев. 17, 2024

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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Multiple Environmental Variables as Covariates to Improve the Accuracy of Spatial Prediction Models for SOM on Karst Aera

Land Degradation and Development, Год журнала: 2025, Номер unknown

Опубликована: Янв. 12, 2025

ABSTRACT Aims accurately predicting the spatial distribution of soil organic matter (SOM) is essential for environmental management and carbon storage estimation. However, diversity sources variables poses a challenge in studying SOM. Methods order to address this issue, we propose leveraging multiple employing machine learning models, specifically Lightweight gradient boosting (LightGBM) random forest (RF), SOM distribution. 128 samples were collected from Caohai National Nature Reserve, their content was measured. Results study found that average 36.75 g/kg. Compared traditional linear regression models such as ordinary kriging (OK), least squares (OLS), geographically weighted (GWR), based on nonlinear regression, LightGBM RF, demonstrated higher cross‐validated coefficients determination ( R 2 ) 0.62 0.60, respectively, outperforming other models. Additionally, RF exhibited lower mean absolute error (MAE) root square (RMSE), indicating stability generalization capability. The among showed consistency, with observed southern near‐Caohai Lake regions northern farther Lake. Shapley additive explanations (SHAP) model highlighted agricultural land (AL), pH, Elevation (ELV) primary covariates influencing Conclusions provides valuable insights support estimation karst plateau region.

Язык: Английский

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Over Time Efficiency of Predictive Models Based on Proximal Sensing to Assess the Dynamics of Soil Fertility Attributes

Journal of soil science and plant nutrition, Год журнала: 2025, Номер unknown

Опубликована: Фев. 3, 2025

Язык: Английский

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

Earth system science data, Год журнала: 2025, Номер 17(2), С. 517 - 543

Опубликована: Фев. 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).

Язык: Английский

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Unveiling soil salinity patterns in soda saline-alkali regions using Sentinel-2 and SDGSAT-1 thermal infrared data

Remote Sensing of Environment, Год журнала: 2025, Номер 322, С. 114708 - 114708

Опубликована: Март 14, 2025

Язык: Английский

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Cadmium accumulation in tropical island paddy soils: From environment and health risk assessment to model prediction

Journal of Hazardous Materials, Год журнала: 2023, Номер 465, С. 133212 - 133212

Опубликована: Дек. 12, 2023

Язык: Английский

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