Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 248, P. 106397 - 106397
Published: Dec. 5, 2024
Language: Английский
Published: Jan. 1, 2025
Language: Английский
Citations
0
Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 380, P. 125095 - 125095
Published: March 26, 2025
Language: Английский
Citations
0
Land Degradation and Development, Journal Year: 2025, Volume and Issue: unknown
Published: April 3, 2025
ABSTRACT Given that Sentinel‐2 (S2) multispectral images provide extensive spatial information and ground‐based hyperspectral data capture refined spectral characteristics, their integration can enhance both the comprehensiveness precision of surface acquisition. This study seeks to leverage these sources develop an optimized estimation model for accurately monitoring large‐scale soil organic carbon (SOC) content, thereby addressing current limitations in multi‐source fusion research. In this study, using mathematical transformation discrete wavelet transform process ground delta oasis Weigan Kuqa rivers Xinjiang, China, combination with S2 image, machine learning algorithms were employed construct models SOC content total variables characteristic variables, inversion oases was carried out. We found R ‐DWT‐H9 significantly correlation between ( p < 0.001). The accuracy constructed based on feature selected by SPA IRIV generally higher than variable models. IRIV‐RFR had highest stable capability. values 2 training validation sets 0.66 0.64, respectively. RMSE 1.5 g∙kg −1 , RPD > 1.4. interior oasis, mainly deficient (61.35%) or relatively (8.17%), while periphery it extremely (30.48%). Combine providing a reference evaluating fertility arid regions.
Language: Английский
Citations
0
International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 139, P. 104513 - 104513
Published: April 15, 2025
Language: Английский
Citations
0
Published: Jan. 1, 2024
Soil sensing enables rapid and cost-effective soil analysis. However, a single sensor often does not generate enough information to reliably predict wide range of properties. Within case-study, our objective was identify how many which combinations sensors prove be suitable for high-resolution mapping. On subplot an agricultural field showing high spatial variability, six in-situ proximal (PSSs) next remote (RS) data from Sentinel-2 were evaluated based on their capabilities set properties including: organic matter, pH, moisture as well plant-available phosphorus, magnesium potassium. The PSSs consisted ion-selective pH electrodes, capacitive sensor, apparent electrical conductivity measuring system passive gamma-ray-, X-ray fluorescence- near-infrared spectroscopy. All possible exhaustively ranked prediction performances. Over all properties, fusion demonstrated considerable increase in accuracy. Five out predicted with R2 ≥ 0.80 the best model. Nonetheless, improvement derived fusing increasing number subject diminishing returns. Sometimes adding more even decreased performances specific Gamma-ray spectroscopy most effective, both or combination other sensors. As RS outperformed three PSSs. showed especially potential but limited benefit when multiple fused.
Language: Английский
Citations
1
Land Degradation and Development, Journal Year: 2024, Volume and Issue: 35(13), P. 3981 - 3998
Published: July 8, 2024
Abstract Soil salinization is a critical environmental and socio‐economic concern with global implications, its severity expected to amplify under changing climate. The impact of climate change on in Central Asia still not fully understood. This study addresses this gap by employing digital soil mapping (DSM) framework. Cubist, random forest (RF), quantile regression forests (QRF) are utilized project variations surface salinity (0‐10 cm) from 2025 2100 two shared pathways (SSPs): SSP2‐4.5 SSP5‐8.5. These models developed using data 20 (GCMs) obtained the Coupled Model Intercomparison Project Phase 6 (CMIP6). results reveal that RF model exhibits superior predictive capability estimating salinity. performed calibration set coefficient determination ( R 2 ) 0.86, root mean square error (RMSE) 9.84 9.90 dS m −1 , ratio performance interquartile distance (RPIQ) 3.09 3.07, Nash–Sutcliffe efficiency (NSE) 0.86. multi‐GCM ensemble means revealed potential for varying degrees Asia, higher levels predominantly observed southeast southwest area, particularly downstream river lakeside areas. Temporal analysis evolution reveals an overall increase across region, more notable changes projected Specifically, rate was 0.0005 /year 0.01 Turkmenistan possessing highest regional average salinity, exception declining trend within area. remaining regions exhibit upward noteworthy SSP5‐8.5 scenario, where obvious. findings hold significant enhancing our understanding how responds change, advances toward sustainable development, enhances comprehension dynamics region.
Language: Английский
Citations
1
Geoderma, Journal Year: 2024, Volume and Issue: 450, P. 117017 - 117017
Published: Sept. 20, 2024
Language: Английский
Citations
1
Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 113 - 127
Published: Nov. 22, 2024
Language: Английский
Citations
0
Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 248, P. 106397 - 106397
Published: Dec. 5, 2024
Language: Английский
Citations
0