Prediction and Monitoring of Soil pH Using Field Reflectance Spectroscopy and Time-series Sentinel-2 Remote Sensing Imagery

GEOMATICA, Год журнала: 2025, Номер unknown, С. 100053 - 100053

Опубликована: Фев. 1, 2025

Язык: Английский

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Quantification techniques of soil organic carbon: an appraisal

Analytical Sciences, Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

This review provides an overview of the analytical methods utilized across laboratory, field, landscape, and regional scales for assessing soil organic carbon (SOC) in agricultural soils. The significance depth SOC estimation underscores importance selecting appropriate sampling designs, depths, methods, baseline selection accurate stock estimation. Traditional such as wet digestion dry combustion (DC) remain prevalent routine laboratory analysis, with DC considered standard reference method, surpassing accuracy reliability. Recent advancements spectroscopic techniques enable measurement both settings situ, even at greater depths. Aerial spectroscopy, which employs multispectral hyperspectral sensors, unmanned aerial vehicles (UAVs), or satellites, facilitates surface measurement. While current precision levels these may be limited, forthcoming sensors enhanced signal‒to‒noise ratios are expected to significantly increase prediction accuracy. Furthermore, global level, satellite remote sensing have considerable potential Regardless whether traditional novel approaches utilized, determination depends on available resources research requirements, each plays a distinct role climate research. paper various scale-dependent measuring soil, along its limitations.

Язык: Английский

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A two-stage algorithm for regional-scale SOC prediction: Eliminating the spatial scale effect between multi-source remote sensing data

Soil and Tillage Research, Год журнала: 2025, Номер 251, С. 106552 - 106552

Опубликована: Март 18, 2025

Язык: Английский

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A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data

Land, Год журнала: 2025, Номер 14(4), С. 677 - 677

Опубликована: Март 23, 2025

Digital soil organic carbon (SOC) mapping is used for ecological protection and addressing global climate change. Sentinel-1 (S-1) microwave radar remote sensing data offer critical insights into SOC dynamics through tracking variations in moisture vegetation characteristics. Despite extensive studies using S-1 mapping, most focus on either single or multi-date periods without achieving satisfactory results. Few have investigated the potential of time-series high-accuracy mapping. This study utilized from 2017 to 2021 analyze temporal correlation between southern Xinjiang, China. The primary objective was determine optimal monitoring period SOC. Within this period, feature subsets were extracted variable selection algorithms. performance partial least squares regression, random forest, convolutional neural network–long short-term memory (CNN-LSTM) models evaluated a 10-fold cross-validation approach. findings revealed following: (1) exhibited both interannual monthly variations, with July October. volume reduced by 73.27% relative initial dataset when determined. (2) Introducing significantly improved CNN-LSTM model (R2 = 0.80, RPD 2.24, RMSE 1.11 g kg⁻1). Compared single-date 0.23) 0.33) data, R2 increased 0.57 0.47, respectively. (3) newly developed vertical–horizontal maximum mean annual cumulative indices made significant contribution (17.93%) Therefore, integrating selection, deep learning offers enhancing accuracy digital

Язык: Английский

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National-scale mapping topsoil organic carbon of cropland in China using multitemporal Sentinel-2 images

Geoderma, Год журнала: 2025, Номер 456, С. 117272 - 117272

Опубликована: Март 30, 2025

Язык: Английский

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