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

Applications of Machine Learning and Remote Sensing in Soil and Water Conservation DOI Creative Commons
Kwang Jin Kim,

Woo Hyeon Park,

Yongchul Shin

et al.

Hydrology, Journal Year: 2024, Volume and Issue: 11(11), P. 183 - 183

Published: Oct. 30, 2024

The application of machine learning (ML) and remote sensing (RS) in soil water conservation has become a powerful tool. As analytical tools continue to advance, the variety ML algorithms RS sources expanded, providing opportunities for more sophisticated analyses. At same time, researchers are required select appropriate technologies based on research objectives, topic, scope study area. In this paper, we present comprehensive review that been implemented advance conservation. key contribution paper is it provides an overview current areas within their effectiveness improving prediction accuracy resource management categorized subfields, including properties, hydrology resources, wildfire management. We also highlight challenges future directions limitations applications This aims serve as reference decision-makers by offering insights into fields

Language: Английский

Citations

1
Estimating soil organic carbon levels in cultivated soils from satellite image using parametric and data-driven methods DOI
Muhammed Halil Koparan, Hossein Moradi Rekabdarkolaee, Kunal Sood

et al.

International Journal of Remote Sensing, Journal Year: 2022, Volume and Issue: 43(9), P. 3429 - 3449

Published: May 3, 2022

Soil organic carbon (SOC) is one of the key soil components for cultivated soils. SOC regularly monitored and mapped to improve quality, health, productivity soil. However, traditional SOC-level monitoring expensive land managers farmers. Estimating using satellite imagery provides an easy, efficient, cost-effective way monitor surface levels. The objective this study was estimate distribution in selected soils Major Land Resource Areas (MLRA), 102A (Rolling Till Plain, Brookings County, SD), 103 (Central Iowa Minnesota Prairies, Lac qui Parle MN), with different resolutions (Landsat 8 PlanetScope). dominant area are Haplustolls, Calciustolls, Endoaquolls, which formed silty sediments, local alluvium, till. Landsat PlanetScope spectral bands were used develop prediction models. Parametric data-driven methods employed predict SOC. Multiple linear regression Linear Spatial Mixed Model (LSMM) on data. In addition parametric models, Regression Trees Random Forest also both results showed that reduced LSMM provided lowest RMSE, 0.401 0.367 PlanetScope, respectively. Furthermore, random forest has highest RPD RPIQ (RPD 2.67 2.49) 2.85 3.7). all cases, models obtained from better than those 8.

Language: Английский

Citations

7
Spatial heterogeneity and driving factors of aerosol in Western China: Analysis on multiangle implementation of atmospheric correction–aerosol optical depth in Xinjiang over 2001–2019 DOI
Wen Ma, Jianli Ding,

Xiaoye Jin

et al.

International Journal of Climatology, Journal Year: 2022, Volume and Issue: 43(4), P. 1993 - 2011

Published: Dec. 5, 2022

Abstract Knowledge of aerosol dynamics is essential to combating atmospheric pollution, and there a growing interest in changes their drivers. However, the effects interactions between natural anthropogenic drivers are not well understood. Here, we analyse optical depth (AOD) Xinjiang, China using multiangle implementation correction products over 2001–2019 investigate driving factors random forest (RF) geographical detector. The results show dominant AOD quasi‐period 3.21 months, 7.86 1.19 years, for seasonal, half‐year, interannual variations aerosols. increasing then decreasing nonlinear trends were observed variation during 19 years period. importance ranking two models indicated that meteorological dominated spatial distribution Xinjiang (72.73% RF 65.78% geographic detector), enhanced explanatory power changes. In addition, influence on was North precipitation population East Xinjiang. South basically constant time, showing spatially heterogeneous relationship This study emphasized heterogeneity small‐scale aerosols arid regions so can guide targeted air pollution prevention control local areas.

Language: Английский

Citations

6
An improved method for retrieving aerosol optical depth over Ebinur Lake Basin from Gaofen-1 DOI
Fangqing Liu, Zhe Zhang

Atmospheric Environment, Journal Year: 2023, Volume and Issue: 301, P. 119699 - 119699

Published: March 11, 2023

Language: Английский

Citations

2
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

Citations

2