Soil Loss Estimation by Water Erosion in Agricultural Areas Introducing Artificial Intelligence Geospatial Layers into the RUSLE Model

Land, Journal Year: 2024, Volume and Issue: 13(2), P. 174 - 174

Published: Feb. 1, 2024

The existing digital soil maps are mainly characterized by coarse spatial resolution and not up to date; thus, they unable support the physical process-based models for improved predictions. overarching objective of this work is oriented toward a data-driven approach datacube-based tools (Soil Data Cube), leveraging Sentinel-2 imagery data, open access databases, ground truth data Artificial Intelligence (AI) architectures provide enhanced geospatial layers into Revised Universal Soil Loss Equation (RUSLE) model, improving both reliability final map. proposed methodology was implemented in agricultural area Imathia Regional Unit (northern Greece), which consists mountainous areas lowlands. Enhanced Organic Carbon (SOC) texture were generated at 10 m through time-series analysis satellite an XGBoost (eXtrene Gradinent Boosting) model. model trained 84 samples (collected from fields) taking account also additional environmental covariates (including elevation climatic data) following Digital Mapping (DSM) approach. introduced RUSLE’s erodibility factor (K-factor), producing erosion layer with high resolution. Notable prediction accuracy achieved AI R2 0.61 SOC 0.73, 0.67 0.63 clay, sand, silt, respectively. average annual loss unit found be 1.76 ton/ha/yr 6% total suffering severe (>11 ton/ha/yr), border regions, showing strong influence mountains fields. overall could strongly regional decision making planning policies such as European Common Agricultural Policy (CAP) Sustainable Development Goals (SDGs).

Language: Английский

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Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review

Agriculture, Journal Year: 2024, Volume and Issue: 14(7), P. 1005 - 1005

Published: June 26, 2024

This review focuses on digital soil organic carbon (SOC) mapping at regional or national scales in spatial resolutions up to 1 km using open data remote sensing sources, emphasizing its importance achieving United Nations’ Sustainable Development Goals (SDGs) related hunger, climate action, and land conservation. The literature was performed according scientific studies indexed the Web of Science Core Collection database since 2000. analysis reveals a steady rise total 2000, with SOC accounting for over 20% these 2023, among which SDGs 2 (Zero Hunger) 13 (Climate Action) were most represented. Notably, countries like States, China, Germany, Iran lead research. shift towards machine deep learning methods has surged post-2010, necessitating environmental covariates topography, climate, spectral data, are cornerstones prediction methods. It noted that available primarily restrict resolution km, typically requires downscaling harmonize topography (up 30 m) multispectral 10–30 m). Future directions include integration diverse development advanced algorithms leveraging learning, utilization high-resolution more precise mapping.

Language: Английский

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Spatial and temporal evolution of soil organic matter and its response to dynamic factors in the Southern part of Black Soil Region of Northeast China

Soil and Tillage Research, Journal Year: 2025, Volume and Issue: 248, P. 106475 - 106475

Published: Feb. 3, 2025

Language: Английский

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High-Accuracy Mapping of Soil Organic Carbon by Mining Sentinel-1/2 Radar and Optical Time-Series Data with Super Ensemble Model

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 678 - 678

Published: Feb. 17, 2025

Accurate digital soil organic carbon mapping is of great significance for regulating the global cycle and addressing climate change. With advent remote sensing big data era, multi-source multi-temporal techniques have been extensively applied in Earth observation. However, how to fully mine time-series high-accuracy SOC remains a key challenge. To address this challenge, study introduced new idea mining data. We used 413 topsoil samples from southern Xinjiang, China, as an example. By (Sentinel-1/2) 2017 2023, we revealed temporal variation pattern correlation between Sentinel-1/2 SOC, thereby identifying optimal time window monitoring using integrating environmental covariates super ensemble model, achieved Southern China. The results showed following aspects: (1) windows were July–September July–August, respectively; (2) modeling accuracy sensor integrated with was superior single-source alone. In model based on data, cumulative contribution rate Sentinel-2 51.71% higher than that Sentinel-1 data; (3) stacking model’s predictive performance outperformed weight average simple models. Therefore, covariates, driven represents strategy mapping.

Language: Английский

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Potential of satellite hyperspectral imaging technology in soil health analysis: A step towards environmental sustainability

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(3)

Published: Feb. 19, 2025

Language: Английский

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A critical systematic review on spectral-based soil nutrient prediction using machine learning

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(8)

Published: July 4, 2024

Language: Английский

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Assessing the potential of multi-source remote sensing data for cropland soil organic matter mapping in hilly and mountainous areas

CATENA, Journal Year: 2024, Volume and Issue: 245, P. 108312 - 108312

Published: Aug. 12, 2024

Language: Английский

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A brief history of remote sensing of soybean

Agronomy Journal, Journal Year: 2025, Volume and Issue: 117(1)

Published: Jan. 1, 2025

Abstract The last 20 years have been a period of significant advancement in the tools available for remote sensing soybean [ Glycine max (L.) Merr.] terms price, ease use, quality information provided, and range research applications. This review article posits that now is an appropriate time to reflect on previous two decades effort devoted gain appreciation how far field has come, while also acknowledging much work remains be performed. Structured by management activities, this based selected works culled from broad search. These contributed meaningful knowledge specific or elucidated key points not presented those more intentionally focused soybean. While there were many successes varied applications research, taking 20‐year perspective exposed areas unmet expectations. Advances are hampered systemic challenges with inconsistent results confounding factors imposed settings. There potential address these tempering expectations what possible addressing reporting standards data needs, specifically related machine learning. future bright, but concerted community needed continue advance state into next years.

Language: Английский

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Improving SOC estimation in low-relief farmlands using time-series crop spectral variables and harmonic component variables based on minimum sample size

International Soil and Water Conservation Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Scale effects on the accuracy and result of soil nitrogen mapping in coastal areas of northern China

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124233 - 124233

Published: Jan. 29, 2025

Language: Английский

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