Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China DOI Creative Commons
Xinyu Liu, Jian Wang, Xiaodong Song

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(7), P. 1847 - 1847

Published: March 30, 2023

The accurate mapping of soil organic carbon (SOC) distribution is important for sequestration and land management strategies, contributing to mitigating climate change ensuring agricultural productivity. Heihe River Basin in China an region that has immense potential SOC storage. Phenological variables are effective indicators vegetation growth, hence closely related SOC. However, few studies have incorporated phenological prediction, especially alpine areas such as the Basin. This study used random forest (RF) extreme gradient boosting (XGBoost) effects (e.g., Greenup, Dormancy, etc.) obtained from MODIS (i.e., Moderate Resolution Imaging Spectroradiometer) product (MCD12Q2) on content prediction middle upper reaches current also identified dominating compared model performance using a cross validation procedure. results indicate that: (1) when were considered, R2 (coefficient determination) RF XGBoost 0.68 0.56, respectively, consistently outperforms various experiments; (2) environmental MAT, MAP, DEM NDVI play most roles prediction; (3) can account 32–39% spatial variability both models, factor among five categories predictive variables. proved introduction significantly improve prediction. They should be indispensable accurately modeling studies.

Language: Английский

Digital mapping of soil organic carbon using remote sensing data: A systematic review DOI

Nastaran Pouladi,

Asa Gholizadeh, Vahid Khosravi

et al.

CATENA, Journal Year: 2023, Volume and Issue: 232, P. 107409 - 107409

Published: July 27, 2023

Language: Английский

Citations

34
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 DOI
Xingnan Liu, Mingchang Wang, Ziwei Liu

et al.

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

Published: Feb. 3, 2025

Language: Английский

Citations

1
Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases DOI Creative Commons
Jiao Tan, Jianli Ding, Lijing Han

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(4), P. 1066 - 1066

Published: Feb. 15, 2023

One reason for soil degradation is salinization in inland dryland, which poses a substantial threat to arable land productivity. Remote-sensing technology provides rapid and accurate assessment salinity monitoring, but there lack of high-resolution remote-sensing spatial estimations. The PlanetScope satellite array high-precision mapping surface monitoring through its 3-m resolution near-daily revisiting frequency. This study’s use the new attempt estimate drylands. We hypothesized that field observations, data, spectral indices derived from data using partial least-squares regression (PLSR) method would produce reasonably regional maps based on 84 ground-truth various parameters, like band reflectance, published indices. results showed newly constructed red-edge yellow indices, we were able develop several inversion models maps. Different algorithms, including Boruta feature preference, Random Forest algorithm (RF), Extreme Gradient Boosting (XGBoost), applied variable selection. (YRNDSI YRNDVI) had best Pearson correlations 0.78 −0.78. also found proportions bands accounted large proportion essential strategies three with preference at 80%, RF XGBoost 60%, indicating these two contributed more estimation results. PLSR model different XGBoost-PLSR coefficient determination (R2), root mean square error (RMSE), ratio performance deviation (RPD) values 0.832, 12.050, 2.442, respectively. These suggest has potential significantly advance research by providing wealth fine-scale information.

Language: Английский

Citations

20
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

6
Improving the Accuracy of Soil Organic Carbon Estimation: CWT-Random Frog-XGBoost as a Prerequisite Technique for In Situ Hyperspectral Analysis DOI Creative Commons
Jixiang Yang,

LI Xin-guo,

Xiaofei Ma

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(22), P. 5294 - 5294

Published: Nov. 9, 2023

Rapid and accurate measurement of the soil organic carbon (SOC) content is a pre-condition for sustainable grain production land development, contributes to neutrality in agricultural industry. To provide technical support development utilization resources, SOC can be estimated using Vis-NIR diffuse reflectance spectroscopy. However, spectral redundancy co-linearity issues spectra pose extreme challenges analysis model construction. This study compared effects different pre-processing methods feature variable algorithms on estimation content. this end, situ hyperspectral data samples were collected from lakeside oasis Bosten Lake Xinjiang, China. The results showed that combination continuous wavelet transform (CWT)-random frog could rapidly estimate with excellent accuracy (R2 0.65–0.86). selection algorithm effectively improved (average improvement (0.30–0.48); based their ability improve average, ranked as follows: particle swarm optimization (PSO) > ant colony (ACO) random Boruta simulated annealing (SA) successive projections (SPA). CWT-XGBoost best results, R2 = 0.86, RMSE 2.44, RPD 2.78. bands accounted only 0.57% bands, most important sensitive distributed at 755–1195 nm, 1602 1673 2213 nm. These findings are significance extraction precise information oases arid areas, which would aid achieving human–land sustainability.

Language: Английский

Citations

11
Spatial Estimation of Soil Organic Carbon Content Utilizing PlanetScope, Sentinel-2, and Sentinel-1 Data DOI Creative Commons
Ziyu Wang, Wei Wu, Hongbin Liu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(17), P. 3268 - 3268

Published: Sept. 3, 2024

The accurate prediction of soil organic carbon (SOC) is important for agriculture and land management. Methods using remote sensing data are helpful estimating SOC in bare soils. To overcome the challenge predicting under vegetation cover, this study extracted spectral, radar, topographic variables from multi-temporal optical satellite images (high-resolution PlanetScope medium-resolution Sentinel-2), synthetic aperture radar (Sentinel-1), digital elevation model, respectively, to estimate content arable soils Wuling Mountain region Southwest China. These were modeled at four different spatial resolutions (3 m, 20 30 80 m) eXtreme Gradient Boosting algorithm. results showed that modeling resolution, combination multi-source data, temporal phases all influenced performance. models generally yielded better a medium (20 resolution than fine coarse (80 resolutions. PlanetScope, Sentinel-2, topography factors gave satisfactory predictions dry (R2 = 0.673, MAE 0.107%, RMSE 0.135%). addition Sentinel-1 indicators best paddy field 0.699, 0.114%, 0.148%). values R2 optimal improved by 36.0% 33.4%, compared entire area. winter played dominant role both land. This offers valuable insights into effectively properties cover various scales data.

Language: Английский

Citations

4
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
Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models DOI Creative Commons

Zhibo Cui,

Songchao Chen, Bifeng Hu

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(7), P. 2184 - 2184

Published: March 30, 2025

Despite extensive use of Sentinel-2 (S-2) data for mapping soil organic carbon (SOC), how to fully mine the potential time-series S-2 still remains unclear. To fill this gap, study introduced an innovative approach mining data. Using 200 top samples as example, we revealed temporal variation patterns in correlation between SOC and subsequently identified optimal monitoring time window SOC. The integration environmental covariates with multiple ensemble models enabled precise arid region southern Xinjiang, China (6109 km2). Our results indicated following: (a) exhibited both interannual monthly variations, while July August is SOC; (b) adding properties texture information could greatly improve accuracy prediction models. Soil contribute 8.85% 61.78% best model, respectively; (c) among different models, stacking model outperformed weight averaging sample terms performance. Therefore, our proved that spectral from window, integrated has a high accurate mapping.

Language: Английский

Citations

0
Machine learning-based estimation of soil organic carbon in Thailand’s cash crops using multispectral and SAR data fusion combined with environmental variables DOI Creative Commons

Ousaha Sunantha,

Zhenfeng Shao,

Phodee Pattama

et al.

Geo-spatial Information Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 23

Published: April 4, 2025

Language: Английский

Citations

0
Integration of Sentinel-1 and 2 for estimating soil organic carbon content in reclaimed coastal croplands with novel indices DOI
Jianjun Wang, Jingjing Huang, Yun Zhang

et al.

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

Published: May 2, 2025

Language: Английский

Citations

0