Resolution Effect of Soil Organic Carbon Prediction in a Large-Scale and Morphologically Complex Area DOI
Ting Wu,

J. Y. Chen,

Youfu Li

et al.

Eurasian Soil Science, Journal Year: 2023, Volume and Issue: 56(S2), P. S260 - S275

Published: Oct. 30, 2023

Language: Английский

Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran DOI

Kamran Azizi,

Younes Garosi, Shamsollah Ayoubi

et al.

Soil and Tillage Research, Journal Year: 2023, Volume and Issue: 229, P. 105681 - 105681

Published: Feb. 27, 2023

Language: Английский

Citations

33
A comparative study of empirical and machine learning approaches for soil thickness mapping in the Joshimath region (India) DOI
Kunal Gupta, Neelima Satyam, Samuele Segoni

et al.

CATENA, Journal Year: 2024, Volume and Issue: 241, P. 108024 - 108024

Published: April 11, 2024

Language: Английский

Citations

9
A critical systematic review on spectral-based soil nutrient prediction using machine learning DOI
Shagun Jain, Divyashikha Sethia, K. C. Tiwari

et al.

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

Published: July 4, 2024

Language: Английский

Citations

7
On the impact of soil texture on local scale organic carbon quantification: From airborne to spaceborne sensing domains DOI Creative Commons
Vahid Khosravi, Asa Gholizadeh, Daniel Žížala

et al.

Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 241, P. 106125 - 106125

Published: April 26, 2024

Soil organic carbon (SOC) distribution and interaction with light is influenced by soil texture parameters (clay, silt sand), which makes SOC prediction complicated, especially in samples considerable pedological variability. Hence, understanding the relationship between important within context of using remote sensing data. The main objective this study was to find impact on performance local models that were developed Sentinel-2 (S2) multispectral CASI/SASI (CS) hyperspectral airborne data as predictor variables. One approach lowering variance stratification samples. Therefore, collected from four agricultural sites Czech Republic segregated based i) site-based ii) texture-based strategies. Random forest (RF) then all stratified classes without considering variables results compared those obtained RF non-stratified (NS) Both strategies provided more homogeneous classes, enhanced accuracy prediction, NS In addition, yielded higher predictions than ones. Except sand, adding predictors improved models, so highest a model clay added CS Overall, could significantly enhance when S2

Language: Английский

Citations

6
Quantifying the impact of factors on soil available arsenic using machine learning DOI
Zhaoyang Han, Jun Yang,

Yunxian Yan

et al.

Environmental Pollution, Journal Year: 2024, Volume and Issue: 359, P. 124572 - 124572

Published: July 17, 2024

Language: Английский

Citations

4
Optimal Mapping of Soil Erodibility Factor (K) Using Machine Learning Models in a Semi-arid Watershed DOI Creative Commons
Mohammad Sajjad Ghavami, Na Zhou, Abdolhossein Ayoubi

et al.

Earth Systems and Environment, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Language: Английский

Citations

0
Improving spatial prediction of soil organic matter in central Vietnam using Bayesian-enhanced machine learning and environmental covariates DOI Creative Commons
Nguyen Huu Ngu, Trung Hieu Nguyen,

Hitoshi Shinjo

et al.

Archives of Agronomy and Soil Science, Journal Year: 2025, Volume and Issue: 71(1), P. 1 - 17

Published: Jan. 6, 2025

Soil organic matter (SOM) has a vital role in maintaining soil quality and ecosystem functions. However, predicting its spatial distribution remains challenging task since it was affected by various environmental covariates. To address this limitation, novel approach integrating Bayesian technique into the random forest (RF) algorithm proposed research. A total of 94 surficial samples from top 30 cm eight key covariates were utilized for training testing with 70:30 ratio. According to results, enhanced RF model demonstrated significant improvement accuracy (RMSE = 0.31%; MAE 0.25%, R2 0.79, Acc 0.81) compared traditional 0.66%; 0.48%, 0.10, 0.61). The four including rainfall, distance sea, water bodies, altitude explained 74.07%, 75.37% variability SOM content models, respectively. Locations high characterized abundant greater proximity rivers, low elevations. These findings introduce reliable context complex changes.

Language: Английский

Citations

0
Spatio-temporal changes in subsurface soil salinity based on electromagnetic induction and environmental covariates at the Tarim River Basin, southern Xinjiang, China DOI
Fei Wang, Wei Yang, Shengtian Yang

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110108 - 110108

Published: Feb. 12, 2025

Language: Английский

Citations

0
SpatialFormer: A Model to Estimate Soil Organic Carbon Content Using Spectral and Spatial Information DOI
Zhaohong Tong, Lanfa Liu

Journal of soil science and plant nutrition, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

Language: Английский

Citations

0
Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms DOI Creative Commons

S. Ajith,

S Vijayakumar,

N. Elakkiya

et al.

Discover Food, Journal Year: 2025, Volume and Issue: 5(1)

Published: March 20, 2025

Language: Английский

Citations

0