Improving the estimation of alfalfa yield based on multi-source satellite data and the synthetic minority oversampling strategy

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 236, С. 110497 - 110497

Опубликована: Май 9, 2025

Язык: Английский

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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery

Remote Sensing, Год журнала: 2025, Номер 17(10), С. 1759 - 1759

Опубликована: Май 18, 2025

Climate change is threatening the sustainability of crop yields due to an increasing frequency extreme weather conditions, requiring timely agricultural monitoring. Remote sensing facilitates consistent and continuous monitoring field crops. This study aimed estimate alfalfa height through satellite images machine learning methods within Google Earth Engine (GEE) Python API. Ground measurements for this were collected over three years in four Canadian provinces. We utilized Sentinel-2 data obtain imagery corresponding same timeframe location as ground measurements. Three algorithms employed plant from images: random forest (RF), support vector regression (SVR), gradient boosting (XGB). The efficacy these has been assessed compared. Several widely used vegetation indices, instance normalized difference index (NDVI), enhanced (EVI), red-edge (NDRE), selected study. RF feature importance was determine ranking features most least significant. selection strategies compared with situation where all are used. demonstrated that XGB surpassed SVR when assessing test performance. Our findings showed could predict R2 0.79 a mean absolute error (MAE) around 4 cm indicated exhibited lowest accuracy among tested, 0.69 MAE 4.63 cm. analysis important red edge (NDRE) water (NDWI) variables determining height. results also using strategies, can be estimated comparably high accuracy. Given models fully trained developed (v. 3.10), they readily implemented decision system deliver near real-time estimations farmers throughout Canada.

Язык: Английский

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Identification of Plastic Film Mulched Farmland in the Core Area of the Beijing-Tianjin Sand Source Region Using Multi-Temporal Remote Sensing Features

Remote Sensing Applications Society and Environment, Год журнала: 2025, Номер unknown, С. 101600 - 101600

Опубликована: Май 1, 2025

Язык: Английский

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Classification of Potato Crops in the High Andean Zone Through Sentinel -1/2 Image Fusion

Опубликована: Янв. 1, 2024

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Язык: Английский

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Estimation of All-Sky High-Resolution Gross Primary Production Across Different Biome Types Using Active Microwave Satellite Images and Environmental Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Год журнала: 2024, Номер 17, С. 12969 - 12982

Опубликована: Янв. 1, 2024

Gross primary production (GPP) measures the amount of carbon fixed by plants and, thus, plays a significant role in terrestrial cycle and global food security, especially context climate change neutrality. Currently, all-sky high-resolution (<100 m) GPP is increasingly needed for better understanding food–carbon–water–energy nexus. However, previous studies usually used optical satellites to estimate clear-sky at kilometer-scale resolution. Due missing estimates under cloudy-sky conditions, monitoring spatio–temporal changes from would suffer some uncertainties. Moreover, one issue that they only satellite images or environmental data rather than jointly integrating them biome types. To address these challenges, this study attempts use active microwave Sentinel-1 synthetic aperture radar (SAR) 10 m resolution GPP. measurements across nine types North America were employed develop SAR-based model. Meanwhile, an optical-based model with Landsat-8 was also proposed comparison. The results revealed that, first, SAR can be utilized By images, data, types, optimal showed high accuracy estimating daily coefficient determination (R ² ) = 0.764, root-mean-square error (RMSE) 1.976 gC/m /d, mean absolute (MAE) 1.308 /d. Second, had reasonable validation 0.809, RMSE 1.762 MAE 1.165 /d). Third, contributed more model, while contribution higher Fourth, performance GPP, two models consistency 0.730 1.858 /d) together. Therefore, demonstrated provides important source advancing our cycle, change.

Язык: Английский

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Corn phenology and productivity with PlanetScope images

Опубликована: Июль 15, 2024

Язык: Английский

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THEORY OF CLASSICAL AND MODIFIED SPACEBORNE SYNTHETIC APERTURE RADAR IMAGING

Advances in Space Research, Год журнала: 2024, Номер unknown

Опубликована: Сен. 1, 2024

Язык: Английский

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Enhancing Tree Species Mapping in Arkansas' Forests through Machine Learning and Satellite Data Fusion: A Google Earth Engine-Based Approach

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 2, 2024

Arkansas' subtropical climate nurtures extensive forested regions, particularly within the Ozark- St. Francis and Ouachita National Forests. Despite this, state lacks an up-to-date, high-resolution map detailing distribution of tree species its forests. This study harnesses power machine learning, specifically Random Forest (RF), Gradient Tree Boosting (GTB), Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) classifiers, Google Earth Engine (GEE) framework. These classifiers are applied to classify in forests by integrating data from various sources, including Sentinel-1/-2, Landsat-8, Agriculture Imagery Program (NAIP). The evaluates classification accuracy single-sensor images against fused composites, revealing that Landsat-8 Sentinel-1 achieve highest validation at 0.8875. is closely followed which yield accuracies 0.8863 0.8859, respectively. Among RF demonstrates accuracy, GTB, K-NN, SVM when images. incorporates Shapley Additive Explanations (SHAP) elucidate feature importance introduces a weighted ensemble method, resulting remarkably accurate with score 0.9772. research highlights efficacy combining learning algorithms fusing satellite significantly enhance accuracy. Moreover, capitalizes on explainable AI (XAI) principles leverages cloud computing capabilities GEE create more precise, cover regional scale.

Язык: Английский

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Satellite based estimation of forest biomass for structural resource planning using gaussian processes and sentinel-2 imagery

i-manager’s Journal on Structural Engineering, Год журнала: 2024, Номер 13(3), С. 34 - 34

Опубликована: Янв. 1, 2024

This study presents a replicable, cost-efficient method for estimating forest biomass critical sustainable structural material sourcing using Sentinel-2 satellite imagery and Gaussian Process Regression. A simplified inventory method, coupled with spectral data in the visible to mid-infrared bands, enables accurate quantification across diverse structures Mediterranean climates. Compared traditional LiDAR-based techniques, this approach offers faster, lower-cost deployment without significant trade-off accuracy, making it suitable applications construction timber forecasting, infrastructure planning, environmental assessments. The has been validated several types is packaged freely accessible programming tool direct integration into engineering planning workflows.

Язык: Английский

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