Automatized Sentinel-2 mosaicking for large area forest mapping

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 127, P. 103659 - 103659

Published: Jan. 22, 2024

Creating maps of forest inventory variables is commonly taking advantage satellite images, which are mosaicked together for gaining larger coverage. Recently, mosaicking has increasingly shifted towards user friendly cloud-based online environments such as Google Earth Engine (GEE), equipped with huge image repositories and extensive processing capabilities. This enables the easy transferability workflows into new sets diversifies range methodological options mosaicking. The quality control output mosaic, ensuring that reflectance values representative to targeted land cover, however primarily based on certain assumptions or pre-set rules may not always produce an optimal result. Our study focuses assessing comparing performance three different algorithms predicting variables, set field data main site type, fertility class, volume biomass growing stock. One compared mosaics derives from manual selection, thus enabling rigorous visual control, two others resting GEE-assisted automatized methods include applying a percentile-based statistic over all input selecting best pixels using predefined indicators. results indicate generally providing relatively equal levels. Compared them, quality-based mosaic slightly lower accuracy particularly when continuous (i.e., stock) it suffers minor defects. For total stock, example, RMS errors 56.22 % manual, 56.33 percentile-based, 59.47 mosaics, respectively. These perspective large area mapping, automatically generated provide approximately similar manually controlled workflow at fraction workload.

Language: Английский

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Remotely sensed inter-field variation in soil organic carbon content as influenced by the cumulative effect of conservation tillage in northeast China

Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 243, P. 106170 - 106170

Published: May 30, 2024

Language: Английский

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A two-dimensional bare soil separation framework using multi-temporal Sentinel-2 images across China

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 134, P. 104181 - 104181

Published: Sept. 30, 2024

Language: Английский

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An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 123 - 123

Published: Jan. 2, 2025

Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances soil assessing and mapping. This study aimed prove the need apply spatial statistical models for processing data remote (RS), which appears be an important source of at multiple scales. A crucial problem facing us is fusion multi-source different natures characteristics, among there support size measurement that unfortunately little considered RS. approach both sample (point) grid (areal) proposed explicitly takes into account correlation change increasing (upscaling) decreasing (downscaling). The techniques block cokriging kriging downscaling were employed implementation such approach, respectively. method applied data, jointly analysed with hyperspectral measured laboratory, UAV, satellite (Planet Sentinel 2) olive grove after filtering pixels. Each type had its own was transformed same as so could applied. To demonstrate statistical, well practical, effectiveness a method, it compared by cross-validation test univariate predicting each property. positive results obtained should stimulate advanced more widely RS data.

Language: Английский

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Modeling Study on Optimizing Water and Nitrogen Management for Barley in Marginal Soils

Plants, Journal Year: 2025, Volume and Issue: 14(5), P. 704 - 704

Published: Feb. 25, 2025

Water and N availability are key factors limiting crop yield, particularly in marginal soils. This study evaluated the effects of water stress on barley grown soils using field trials AgroC model. Experiments from 2020 to 2022 Lithuania with spring cv. KWS Fantex under two fertilization treatments sandy soil provided data for model parameterization. The simulated growth assess yield potential gaps due stress. Potential grain yields (assuming no or stress) ranged 4.8 6.02 t DW ha−1, losses up 54.4% assuming only 59.2% stress, even N100 treatment (100 kg ha−1 yr−1). A synthetic case varying 0 200 yr−1 showed that increasing still enhanced but optimal rate 100–120 depended climatic conditions, leading uncertainty recommendations. underscores importance integrating advanced modeling techniques sustainable agricultural practices boost resilience Incorporating remote sensing capture variability is recommended improving simulation accuracy, contributing agriculture strategies Baltic–Nordic region.

Language: Английский

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Satellite Soil Observation (Satsoil): Extraction of Bare Soil Reflectance for Soil Organic Carbon Mapping on Google Earth Engine

Published: Jan. 1, 2025

Language: Английский

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

GEOMATICA, Journal Year: 2025, Volume and Issue: unknown, P. 100053 - 100053

Published: Feb. 1, 2025

Language: Английский

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Quantification techniques of soil organic carbon: an appraisal

Analytical Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: March 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.

Language: 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, Journal Year: 2025, Volume and Issue: 251, P. 106552 - 106552

Published: March 18, 2025

Language: 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, Journal Year: 2025, Volume and Issue: 14(4), P. 677 - 677

Published: March 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

Language: Английский

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