Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topsoil data

Geoderma, Journal Year: 2025, Volume and Issue: 454, P. 117199 - 117199

Published: Feb. 1, 2025

Language: Английский

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Exploring the untapped potential of hand-feel soil texture data for enhancing digital soil mapping: Revealing hidden spatial patterns from field observations

Geoderma, Journal Year: 2024, Volume and Issue: 441, P. 116769 - 116769

Published: Jan. 1, 2024

Digital soil mapping (DSM) is commonly conducted using input attributes derived from laboratory analyses of geographically referenced samples. Field observations are often abundant and can offer a dense source data that has the potential to enhance DSM predictions. However, they not widely used due concerns about subjectivity quality. This study investigates usefulness hand-feel texture (HFST) for DSM. We processed HFST obtained forest soils in France two inventory campaigns: (i) determination systematic 1 km2 grid utilizing specialized triangle, (ii) survey samples, different triangle. Both sets were as variables, with same covariates, predicting topsoil In sizable, forested area through method. By employing independent sampling selected areas, we uncovered measurement bias one datasets. intriguingly, these biased identified subtle yet highly specific unexpected patterns sands terraces alluvial deposits along small rivers. Thus, field observations, even if biased, should be dismissed solely based on their overall predictive performance. It essential carefully examine predicted maps covariates determine whether may have pedological and/or lithological origins pertinent enhancing predictions, process understanding, meeting requirements end users. Numerous available worldwide, datasets usually disregarded Here contend efforts put recovering data, deepening our understanding processes.

Language: Английский

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National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France

Geoderma Regional, Journal Year: 2024, Volume and Issue: 37, P. e00801 - e00801

Published: April 20, 2024

Accurate soil property and class predictions through spatial modelling necessitate a thoughtful selection of explanatory variables sample size, as their choice greatly impacts model performance. Within the framework Global Soil Nutrient Budgets maps (GSNmap), FAO Partnership (GSP) launched country-driven digital mapping (DSM) approach. The GSP asked countries if they could implement DSM prediction ten properties, using national point data set widely available covariates (GSP_Cov). In this study, we examined effect including additional national-based observations on performance models mainland France pilot. learning dataset was based systematic 16-to-16 km grid. For subset also assessed repeated k-fold cross-validation adding to many other irregularly spread measurements. GSP_Cov included common that represented information about terrain, climate, organisms. second consisted GSP_Cov, extended extra at level, such previously existing maps, geological remote sensing products others. Random Forest approach in combination with Boruta method employed for properties: organic carbon (SOC), pH (water), total nitrogen (N), phosphorus (P), potassium (K), cation exchange capacity (CEC), bulk density (BD), texture (clay, silt, sand). results revealed noteworthy enhancements more than half although, some them, improvements were negligible. most significant obtained pH, CEC texture, where previous map significantly contributed increase accuracy. Adding numerous points (around 25,000) improved particle-size fractions predictions. By broadening spectrum better covering feature geographical spaces considered models, research underscores importance implementing diverse range scale densifying enlarge multidimensional soil/covariates combinations. This should be taken into account continental endeavours.

Language: Английский

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Soil Texture and pH Mapping Using Remote Sensing and Support Sampling

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 12685 - 12705

Published: Jan. 1, 2024

Soil pH and texture are valuable information for agriculture, supporting the achievement of high productivity low environmental impact, which is basis sustainable agricultural production. In this study, we present novel soil mapping techniques that integrate high-spatial-resolution satellite ground data, surpassing traditional methods in precision reliability. By synergizing remote sensing including polarimetric synthetic aperture multispectral imagery, with climate terrain information, alongside coarse-resolution achieved accuracy, an average error less than 6%, predicting parameters. Notably, approach allows detailed at pixel level, revealing nuanced variability within 10×10 m field pixels. Considering method establishes itself as a benchmark management guidelines integrating sampling approach, offering actual spatial resolution crucial practices. This holistic new opportunities to revolutionize practices, facilitating variable rate applications, moisture, fertilization ultimately enhancing agri-environmental sustainability.

Language: Английский

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Prediction of Groundwater Level Based on the Integration of Electromagnetic Induction, Satellite Data, and Artificial Intelligent

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 210 - 210

Published: Jan. 8, 2025

Groundwater level (GWL) in dry areas is an important parameter for understanding groundwater resources and environmental sustainability. Remote sensing data combined with machine learning algorithms have become one of the tools modeling. However, effectiveness above-based model plains arid zone remains underexplored. Fortunately, soil salinity moisture may provide optimized solution GWL prediction based on application apparent conductivity (ECa, mS/m) using electromagnetic induction (EMI). This has not been attempted previous studies oases regions. The study proposed two strategies to predict GWL, included ECa-based a remote sensing-based (RS_GWL), then compared explored their performances cooperation possibilities. To this end, first constructed ECa RS_GWL ensemble variables field observations (474 reads 436 from mountain–oasis–desert system, respectively). Subsequently, strategy improve accuracy was by comparing correlation between models. results showed that explains 30% 90% spatial variability value domain intervals, < 10 m > m, respectively. R2 modeling validation 79% 73%, Careful analysis scatter plots predicted revealed when varies 0–600 mS/m, 600–800 800–1100 >1100 fluctuation ranges corresponding correspond 0–31 0–15 0–10 0–5 m. Finally, combining distribution map, following optimization were finally established: 5 (in natural land 1100 mS/m), (occupied farmland RS_GWL) (from RS_GWL), 3 (speculated). In conclusion, demonstrated integration EMI technology significantly improved precision forecasting shallow oasis plain regions, outperforming outcomes achieved use alone.

Language: Английский

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A global soil spectral grid based on space sensing

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 968, P. 178791 - 178791

Published: Feb. 20, 2025

Language: Английский

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Water content intelligent measurement method of detection robot for deep soils within loess slopes

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117374 - 117374

Published: March 1, 2025

Language: Английский

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Transforming Soil Quality Index Predictions in the Nile River Basin Using Hybrid Stacking Machine Learning Techniques

Advances in Space Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Innovative soil classification approach for achieving global biodiversity framework utilizing integrated data fusion of EMIT and multispectral satellite observations: Case study of Imam Turki bin Abdullah Royal Reserve, Kingdom of Saudi Arabia

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103123 - 103123

Published: April 1, 2025

Language: Английский

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Assessment of soil fertility and nutrient distribution for enhanced soil health and field management through an innovative approach

Agrosystems Geosciences & Environment, Journal Year: 2025, Volume and Issue: 8(2)

Published: April 17, 2025

Abstract The principal agricultural region of alfalfa, maize, and rapeseed was examined for soil nutrients. Primary statistics a parameter were maximum, minimum, mean, standard deviation, coefficient variation, skewness, kurtosis. Some parameters had non‐normal distributions statistically significant. Sodium has 97% fluctuation, whereas pH 5%. Datasets acidity, organic matter, sand, silt are typically disseminated. available iron varied from 0.06 to 8.84 mg/kg, manganese, copper, zinc, lime 0.23 20.96 mg/kg. Total nitrogen ranged 0.02% 0.82%. Highly variable macronutrient variation coefficient. Thus, the critical limits elements physicochemical characteristics 4.5, 6, 0.7, 0.8 Soil nutrients may be mapped compare nutritional status indicate regional strengths weaknesses. These maps can prescribe fertilizers different crops without overusing them, incurring financial losses environmental harm. This study standardizes spatial distribution calculate evaluation index. In ArcGIS 10.8, fuzzy linear membership function used standardize these within range 0–1. index map is then categorized into four types using Jenks Natural Breaks. found severe iron, phosphorus deficiencies in Sharif Abad soil. Environmental human causes caused deficiency this region. Manganese shortages rare, while copper deficits widespread north, west, southwest, with 37% area below level.

Language: Английский

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