Improving spatial prediction of soil organic matter in typical black soil area of Northeast China using structural equation modeling integration framework

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 236, P. 110404 - 110404

Published: April 24, 2025

Language: Английский

---

Deep Learning-Driven Soil Texture Classifier using Landsat 8 Images

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 101568 - 101568

Published: April 1, 2025

Language: Английский

---

Integration of Sentinel-1 and 2 for estimating soil organic carbon content in reclaimed coastal croplands with novel indices

Soil and Tillage Research, Journal Year: 2025, Volume and Issue: 252, P. 106629 - 106629

Published: May 2, 2025

Language: Английский

---

Exploring the Impacts of Data Source, Model Types and Spatial Scales on the Soil Organic Carbon Prediction: A Case Study in the Red Soil Hilly Region of Southern China

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(20), P. 5151 - 5151

Published: Oct. 15, 2022

Rapid and accurate mapping of soil organic carbon (SOC) is great significance to understanding the spatial patterns fertility conducting cycle research. Previous studies have dedicated considerable efforts prediction SOC content, but few systematically quantified effects environmental covariates selection, scales model types on accuracy. Here, we spatially predicted content through digital (DSM) based 186 topsoil (0–20 cm) samples in a typical hilly red region southern China. Specifically, first determined an optimal covariate set from different combinations multiple variables, including multi-sensor remote sensing images (Sentinel-1 Sentinel-2), climate variables DEM derivatives. Furthermore, evaluated impacts resolution (10 m, 30 90 250 m 1000 m) (three linear three non-linear machine learning techniques) prediction. The results performance analysis showed that combination Sentinel-1/2-derived topographic predictors generated best predictive performance. Among all covariates, especially Sentinel-2-derived predictors, were identified as most important explanatory controlling variability content. Moreover, accuracy declined significantly with increased achieved highest using XGBoost at 10 resolution. Notably, learners yielded superior capability contrast models predicting SOC. Overall, our findings revealed predictor modeling techniques could considerably improve Particularly, freely accessible Sentinel series satellites potential high-resolution properties.

Language: Английский

---

Soil organic carbon stock prediction using multi-spatial resolutions of environmental variables: How well does the prediction match local references?

CATENA, Journal Year: 2023, Volume and Issue: 229, P. 107197 - 107197

Published: May 11, 2023

Language: Английский

---

Predicting soil organic carbon in cultivated land across geographical and spatial scales: Integrating Sentinel-2A and laboratory Vis-NIR spectra

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 203, P. 1 - 18

Published: July 27, 2023

Language: Английский

---

Soil organic carbon: measurement and monitoring using remote sensing data

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 395 - 409

Published: Jan. 1, 2024

Language: Английский

---

Continental-scale mapping of soil pH with SAR-optical fusion based on long-term earth observation data in google earth engine

Ecological Indicators, Journal Year: 2024, Volume and Issue: 165, P. 112246 - 112246

Published: June 14, 2024

Language: Английский

---

Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment

International Journal of Remote Sensing, Journal Year: 2022, Volume and Issue: 43(18), P. 6856 - 6880

Published: Sept. 17, 2022

Accurate mapping of soil organic carbon (SOC) and inorganic (SIC) contents at regional scales can be very important for sustainable agriculture management. Low variation in terrain attributes (classically used digital mapping) low relief areas calls additional spatial data to explain variability. The main objective this study was evaluate the predictive capability Sentinel-1 (radar) Sentinel-2 (optical) time series statistics, summarized as multi-temporal features (MTF) improve predictions SOC SIC Ghorveh plain, located Kurdistan Province, Western Iran. A systematic grid sampling then employed collect 150 surface samples (0–30 cm) measurements. We applied boosted regression trees (BRT) random forest (RF) predict by using covariate sets compiled from radar optical topographic attributes. Model performance, evaluated 10-fold cross-validation, showed that RF set containing Sentinel-1, performed best predicting (RMSE = 0.23, ME 0.005, R2 0.29). On other hand, SIC, MTF ranked with BRT 0.77, ME= −0.001, 0.48). indicates multiple dates remote sensing results improved predictions. However, model performance moderate poor, respectively. Therefore more substantial studies would required verify if computational effort is likely justified an increase accuracy general.

Language: Английский

---

Reducing location error of legacy soil profiles leads to improvement in digital soil mapping

Geoderma, Journal Year: 2024, Volume and Issue: 447, P. 116912 - 116912

Published: May 29, 2024

Digital soil mapping relies on statistical relationships between profile observations and environmental covariates at the sample locations. However, inherent limitations of legacy profiles, such as inaccurate georeferencing, could frequently introduce location errors into these profiles that affect quality digital mapping. To address this challenge, study focuses reducing error evaluating resulting impact We improved agreement detailed descriptive information relatively accurate (such elevation, slope, land use) to reduce profiles. Quantile regression forest models were constructed predict properties their uncertainty using before after correction. Our results demonstrate for majority variables, correcting positional in some extent enhances accuracy The largest improvement was found organic carbon 0–5 cm depth interval, with 21 % increase MEC. reduced is particularly noteworthy regions characterized by complex terrain. In addition, details predicted maps errors, which better represent spatial variation across China. Besides, we also elevation primary controlling factor This research presents a step towards producing high-resolution high-quality datasets, can provide essential support management ensure future security.

Language: Английский

---