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Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 20277

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Special Issue Editors

Dr. Christoph Jörges

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Guest Editor

Department of Geography, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
Interests: machine learning; advanced statistical analysis; agriculture remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Aaron Moody

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Guest Editor

Department of Geography and Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Interests: environmental conservation; biogeography; remote sensing; quantitative methods in geography; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Earth observation through remote sensing provides an essential source of continuous spatio-temporal data. Artificial intelligence, in turn, can use these big data to gain new insights and information, as well as find correlations and patterns. Nevertheless, these data-driven algorithms sometimes lack interpretability and physical accuracy, which could be enhanced by combining machine learning approaches with process-based physical modeling.

This Special Issue on “Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing” aims to gather high-quality state-of-the-art research contributions on recent applications to support sustainable agricultural practices or new methods for vegetation monitoring, among others.

Manuscript submissions are encouraged to cover a broad range of related topics, including but not limited to the following:

Data- and process-driven model integration for agriculture and vegetation applications;
Physics-informed neural networks (PINNs);
Digital twin with a focus on agriculture/vegetation;
Potentials and limitations of AI algorithms and methods for agriculture/vegetation applications;
AI for agricultural decision making;
Data fusion and super-resolution;
Time series analysis;
Image processing, classification, semantic segmentation, and object detection;
Hyperspectral imaging for agriculture/vegetation (e.g., protein quantification, soil carbon content);
Change detection and agriculture/vegetation monitoring;
Urban heat islands and green spaces;
Drought monitoring;
Pest and disease monitoring;
Smart farming and agriculture.

All proposals related to the application of AI to remote sensing data in agriculture and vegetation will also be evaluated.

Dr. Christoph Jörges
Dr. Aaron Moody
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
machine learning
deep learning
remote sensing
data mining
water–energy–food nexus
crop yield prediction
agriculture
climate change
sustainable irrigation and fertilization

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Related Special Issue

Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing (Second Edition) in Remote Sensing

Published Papers (11 papers)

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25 pages, 1223 KB

Open AccessArticle

UAV-Based Multispectral Phenotyping and Machine-Learning Modeling Reveals Early Canopy Traits as Strong Predictors of Yield and Weed Competitiveness in Oat (Avena sativa L.)

by Dilshan Benaragama, Mujahid Hussain, Brianna Senetza, Steve Shirtliffe and Chris Willenborg

Remote Sens. 2026, 18(8), 1211; https://doi.org/10.3390/rs18081211 - 17 Apr 2026

Viewed by 215

Abstract

Understanding how oat (Avena sativa L.) cultivars differ in canopy development and competitive ability is essential for improving yield stability under increasing weed pressure. This study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars grown under weed-free and weedy conditions across two locations for two years. Weedy conditions involved natural weed populations and pseudo-weeds where canola (Brassica napus) seeded as a weed. Weekly drone imaging was carried out using a multispectral sensor, which provided vegetation indices (NDVI, NDRE, ExG) and canopy metrics (ground cover, height, volume). Logistic and Gompertz models were fitted to cultivar traits to describe growth trajectories and obtain dynamic growth parameters. Cultivars showed clear differences in early canopy expansion, maximum NDVI, and canopy volume, with forage types expressing aggressive growth and several grain types combining high early growth rate with high yield potential. Machine-learning models integrating static and dynamic UAV-derived plant traits identified early ground cover and NDRE at three weeks after planting as the strongest predictors of grain yield. Models accurately predicted both weed-free (MAE = 262, R² = 0.90) and weedy yield (MAE = 258, R² = 0.90), demonstrating that early-season UAV traits capture the physiological and structural characteristics associated with competitive ability and grain yield. These findings show that high-throughput UAV phenotyping can reliably identify traits linked to yield formation and weed tolerance, providing a scalable approach for selecting competitive oat cultivars without relying solely on labor-intensive weedy field trials. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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20 pages, 2528 KB

Open AccessArticle

Utilizing Multi-Source Remote Sensing Data and the CGAN to Identify Key Drought Factors Influencing Maize Across Distinct Phenological Stages

by Hui Zhao, Jifu Guo, Jing Jiang, Funian Zhao and Xiaoyang Yang

Remote Sens. 2026, 18(7), 1085; https://doi.org/10.3390/rs18071085 - 3 Apr 2026

Viewed by 367

Abstract

Drought is one of the major disasters constraining crop production. The accurate identification of the dominant environmental factors that drive drought stress at different growth stages of maize is essential for developing stage-specific and precise water management strategies, enhancing drought resistance, and ensuring food security. However, a key challenge is quantifying the nonlinear interactions among multiple environmental factors. This study focuses on the rain-fed agricultural region of Northwest China. To address the limited availability of drought event samples in this region and the inadequacy of traditional statistical methods in capturing complex inter-factor relationships, we integrate a small-sample modeling framework based on an improved Conditional Generative Adversarial Network (CGAN) with an attribution framework that employs SHapley Additive exPlanations (SHAP) for interpretability analysis. We incorporate ten environmental factors derived from multi-source remote sensing: temperature (T_max, T_min, T_mean), precipitation (P), evapotranspiration (ET), soil moisture at 0–10 cm (SM_0–10) and at 10–40 cm (SM_10–40), and solar-induced chlorophyll fluorescence (SIF_max, SIF_min, SIF_mean). Sample sets were established for different maize phenological stages. The CGAN model was employed to achieve high-precision estimation of maize drought severity levels, while the SHAP method was used to quantitatively analyze the dominant factors and their contributions at each phenological stage. The results show that the CGAN model achieved coefficients of determination (R²) of 0.963, 0.972, and 0.979 for the seedling, jointing–tasseling, and maturity stages, respectively, demonstrating excellent nonlinear modeling capability under small samples. SHAP analysis reveals a clear dynamic evolution of dominant factors across phenological stages. Evapotranspiration (ET) dominated in the seedling stage, reflecting the primary role of surface water–heat balance, while the jointing–tasseling stage transitioned to a co-dominance of ET, topsoil moisture (SM_0–10), and minimum SIF, indicating intensified crop transpiration and physiological stress under the meteorological drought framework, and the maturity stage shifted to an absolute dominance centered on mean temperature (T_mean), highlighting the critical impact of heat stress. This study provides a data-driven quantitative perspective for understanding maize drought mechanisms and offers a scientific basis for formulating differentiated drought management strategies for different growth stages. Furthermore, it demonstrates the potential of integrating CGAN with SHAP for agricultural remote sensing and drought attribution research in data-scarce regions. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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34 pages, 20591 KB

Open AccessArticle

Estimating Grazing Land Acres Across the Contiguous United States Using Machine Learning Methods

by Mingyue Hu, Cindy Yu, Zhengyuan Zhu, Sarah McCord and Loretta J. Metz

Remote Sens. 2026, 18(7), 1050; https://doi.org/10.3390/rs18071050 - 31 Mar 2026

Viewed by 407

Abstract

Quantifying the extent of rangeland and pastureland (collectively termed grazing lands herein) in the US is a critical first step in many grazing lands assessments. This research presents a model-assisted framework to estimate grazing land acreage within arbitrary geographic boundaries by integrating high quality survey data with satellite-based raster geospatial data. Leveraging the image photo interpretation data from the USDA Natural Resources Conservation Service (NRCS) National Resources Inventory (NRI) survey as a reference dataset, we use machine learning to fuse NRI point level data with auxiliary data from the satellite-based Cropland Data Layer (CDL) to enhance the precision of acreage estimates of grazing lands. The methodology includes three steps: (1) modeling the relationship between NRI rangeland and pastureland indicators and CDL variables; (2) generating a high-resolution rangeland and pastureland probabilities map across the contiguous US; and (3) summarizing these probabilities to calculate the acreage of rangeland and pastureland for specific areas of interest. This approach provides researchers and land managers with a scalable tool to define grazing land extents within a self-selected study area, ensuring that subsequent resource characteristics or condition assessments are representative and spatially accurate. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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23 pages, 153696 KB

Open AccessArticle

Fine Mapping of Sparse Populus euphratica Forests Based on GF-2 Satellite Imagery and Deep Learning Models

by Hao Li, Jiawei Zou, Qinyu Zhao, Suhong Liu and Qingdong Shi

Remote Sens. 2026, 18(6), 902; https://doi.org/10.3390/rs18060902 - 15 Mar 2026

Viewed by 409

Abstract

Populus euphratica is a critical constructive species in arid desert regions, serving as a “natural barrier” for oasis protection. The sustainable management of Populus euphratica forests is directly related to regional ecological security, and the fine identification of sparse Populus euphratica forests is essential for the conservation of natural Populus euphratica forests. Currently, most mapping studies on Populus euphratica distribution focus on the extraction of dense, contiguous Populus euphratica forests, with insufficient attention paid to the identification of sparse Populus euphratica forests. This study utilizes Gaofen-2 (GF-2) satellite imagery as the data source and takes a typical sparse Populus euphratica forests distribution area in the Tarim River Basin as the study site. It systematically evaluates the performance of nine mainstream deep learning models, including U-Net, DeepLabV3+, and SegFormer, in the task of sparse Populus euphratica forests identification. The results indicate that: (1) The false-color sample set, synthesized from near-infrared, red, and green bands, contributes to improved model accuracy. Compared to the true-color (red, green, blue bands) dataset, the average Intersection over Union (IoU) of the nine models shows a relative improvement of approximately 20%. (2) For the sparse Populus euphratica forests identification task based on the false-color dataset, four models—U-Net, U-Net++, MA-Net, and DeepLabV3+—exhibited excellent performance, with IoU exceeding 75%. (3) Using U-Net as the baseline model, this study integrated the max-pooling indices mechanism, atrous spatial pyramid pooling, and residual connection modules to construct a semantic segmentation network tailored for sparse Populus euphratica forests, named Sparse Populus euphratica Segmentation Network (SPS-Net). This model achieved an IoU of 80%, a relative improvement of approximately 6.3% over the baseline model, and demonstrated good stability in large-scale classification tests. The identification scheme for sparse Populus euphratica forests constructed using GF-2 imagery and deep learning models proposed in this study can provide effective technical support for the refined monitoring and protection of natural Populus euphratica forests. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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28 pages, 37662 KB

Open AccessArticle

First Agriculture Land Use Map in Vietnam Using an Adaptive Weighted Combined Loss Function for UNET++

by Ta Hoang Trung, Nguyen Vu Ky, Duong Cao Phan, Duong Binh Minh, Ho Nguyen and Kenlo Nishida Nasahara

Remote Sens. 2026, 18(3), 430; https://doi.org/10.3390/rs18030430 - 29 Jan 2026

Cited by 1 | Viewed by 954

Abstract

Accurate and timely agricultural mapping is essential for supporting sustainable agricultural development, resource management, and food security. Despite its importance, Vietnam lacks detailed and consistent large-scale agricultural maps. In this study, we produced the first national-scale agricultural map of Vietnam for 2024 using a UNet++ deep learning architecture that integrates multi-temporal Sentinel-1 and Sentinel-2 imagery with Global-30 DEM data. The resulting product includes 15 land-cover categories, eight of which represent the most popular agricultural types in Vietnam. We further evaluate the model’s transferability by applying the 2024 trained model to generate a corresponding map for 2020. The approach achieves overall classification accuracies of

83.01 \pm 1.37 %

(2020) and

80.09 \pm 0.76 %

(2024). To address class imbalance within the training dataset, we introduced an adaptive weight combined loss function that automatically adjusts the weight of dice loss and cross-entropy loss within a combined loss function during the model training process. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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27 pages, 4718 KB

Open AccessArticle

Data Augmentation and Interpolation Improves Machine Learning-Based Pasture Biomass Estimation from Sentinel-2 Imagery

by Blessing N. Azubuike, Anna Chlingaryan, Martin Correa-Luna, Cameron E. F. Clark and Sergio C. Garcia

Remote Sens. 2025, 17(23), 3787; https://doi.org/10.3390/rs17233787 - 21 Nov 2025

Cited by 2 | Viewed by 1388

Abstract

Accurate pasture biomass (PB) estimation is critical for tactical grazing management, yet traditional satellite-derived vegetation indices such as Normalised Difference Vegetation Index (NDVI) saturate when canopy density exceeds about 3 t DM ha⁻¹. This limits predictive accuracy because the spectral signal plateaus under dense vegetation, masking further biomass increases. To address this limitation, this study integrated multiple data sources to improve PB estimation in dairy systems. The dataset combined Sentinel-2 spectral bands, rising plate-meter (RPM) PB measurements, daily weather data, and paddock management features. A total of 3161 paired RPM–satellite observations were collected from 80 paddocks across 16 New South Wales dairy farms between November 2021 and July 2024. Eight regression algorithms and four predictor configurations were evaluated using robust cross-validation, including an 80:20 farm/paddock-stratified train–test-set split. The XGBoost model using full-band reflectance and concurrent weather data achieved strong baseline performance (R² = 0.63; MAE = 243 kg DM ha⁻¹) on non-interpolated data, outperforming NDVI-based models. To address temporal gaps between field readings and satellite imagery, Multiquadric interpolation was applied to RPM data, adding roughly 30% new observations. This enhanced dataset improved test performance to R² = 0.70 and MAE = 216 kg DM ha⁻¹, with gains maintained on external validations (R² = 0.41/0.48; MAE = 267/235 kg DM ha⁻¹). A progressive training strategy, which refreshed model parameters with seasonally aligned data, further reduced errors by 30% compared to static models and sustained performance even when farms or seasons were excluded. This fortified Sentinel-2 modelling workflow, combining RPM interpolation and progressive calibration, achieved accuracy comparable to the commercial Pasture.io platform (R² = 0.66; MAE = 240 kg DM ha⁻¹) which uses satellite imagery with higher temporal and spatial resolution, demonstrating potential for automated recalibration and near real-time, paddock-level decision support in pasture-based dairy systems. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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32 pages, 6841 KB

Open AccessArticle

Integration of UAV and Remote Sensing Data for Early Diagnosis and Severity Mapping of Diseases in Maize Crop Through Deep Learning and Reinforcement Learning

by Jerry Gao, Krinal Gujarati, Meghana Hegde, Padmini Arra, Sejal Gupta and Neeraja Buch

Remote Sens. 2025, 17(20), 3427; https://doi.org/10.3390/rs17203427 - 13 Oct 2025

Cited by 3 | Viewed by 3109

Abstract

Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study centers on maize, training deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease. The performance of multiple convolutional neural networks, such as ResNet-50, DenseNet-121, etc., is evaluated by their ability to classify maize diseases such as Northern Leaf Blight, Gray Leaf Spot, Common Rust, and Blight using UAV drone data. Remotely sensed MODIS satellite data was used to generate spatial severity maps over a uniform grid by implementing time-series modeling. Furthermore, reinforcement learning techniques were used to identify hotspots and prioritize the next locations for inspection by analyzing spatial and temporal patterns, identifying critical factors that affect disease progression, and enabling better decision-making. The integrated pipeline automates data ingestion and delivers farm-level condition views without manual uploads. The combination of multiple remotely sensed data sources leads to an efficient and scalable solution for early disease detection. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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23 pages, 10375 KB

Open AccessArticle

Extraction of Photosynthetic and Non-Photosynthetic Vegetation Cover in Typical Grasslands Using UAV Imagery and an Improved SegFormer Model

by Jie He, Xiaoping Zhang, Weibin Li, Du Lyu, Yi Ren and Wenlin Fu

Remote Sens. 2025, 17(18), 3162; https://doi.org/10.3390/rs17183162 - 12 Sep 2025

Cited by 1 | Viewed by 1243

Abstract

Accurate monitoring of the coverage and distribution of photosynthetic (PV) and non-photosynthetic vegetation (NPV) in the grasslands of semi-arid regions is crucial for understanding the environment and addressing climate change. However, the extraction of PV and NPV information from Unmanned Aerial Vehicle (UAV) remote sensing imagery is often hindered by challenges such as low extraction accuracy and blurred boundaries. To overcome these limitations, this study proposed an improved semantic segmentation model, designated SegFormer-CPED. The model was developed based on the SegFormer architecture, incorporating several synergistic optimizations. Specifically, a Convolutional Block Attention Module (CBAM) was integrated into the encoder to enhance early-stage feature perception, while a Polarized Self-Attention (PSA) module was embedded to strengthen contextual understanding and mitigate semantic loss. An Edge Contour Extraction Module (ECEM) was introduced to refine boundary details. Concurrently, the Dice Loss function was employed to replace the Cross-Entropy Loss, thereby more effectively addressing the class imbalance issue and significantly improving both the segmentation accuracy and boundary clarity of PV and NPV. To support model development, a high-quality PV and NPV segmentation dataset for Hengshan grassland was also constructed. Comprehensive experimental results demonstrated that the proposed SegFormer-CPED model achieved state-of-the-art performance, with a mIoU of 93.26% and an F1-score of 96.44%. It significantly outperformed classic architectures and surpassed all leading frameworks benchmarked here. Its high-fidelity maps can bridge field surveys and satellite remote sensing. Ablation studies verified the effectiveness of each improved module and its synergistic interplay. Moreover, this study successfully utilized SegFormer-CPED to perform fine-grained monitoring of the spatiotemporal dynamics of PV and NPV in the Hengshan grassland, confirming that the model-estimated f_PV and f_NPV were highly correlated with ground survey data. The proposed SegFormer-CPED model provides a robust and effective solution for the precise, semi-automated extraction of PV and NPV from high-resolution UAV imagery. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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25 pages, 7899 KB

Open AccessArticle

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

by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman and Maxime Leduc

Remote Sens. 2025, 17(10), 1759; https://doi.org/10.3390/rs17101759 - 18 May 2025

Cited by 4 | Viewed by 2882

Abstract

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

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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21 pages, 2750 KB

Open AccessArticle

Comparison of Tree Typologies Mapping Using Random Forest Classifier Algorithm of PRISMA and Sentinel-2 Products in Different Areas of Central Italy

by Eros Caputi, Gabriele Delogu, Alessio Patriarca, Miriam Perretta, Giulia Mancini, Lorenzo Boccia, Fabio Recanatesi and Maria Nicolina Ripa

Remote Sens. 2025, 17(3), 356; https://doi.org/10.3390/rs17030356 - 22 Jan 2025

Cited by 3 | Viewed by 2938

Abstract

The continuous development of satellite imagery, coupled with advancements in machine learning technologies, allows detailed mapping of terrestrial landscapes. This study evaluates the classification performance of tree typologies using Sentinel-2 and PRISMA data, focusing on central Italy’s different areas. The purpose is to assess the role of spectral and spatial resolution in land cover classification, contributing to forest management and conservation efforts. Random Forest Classifier was applied to classify tree typologies across two study areas: the Roman Coastal region and the Lake Vico Basin. Ground truth (GT) data, collected from a trial citizen survey campaign, were used for training and validation. PRISMA datasets, particularly when processed with PCA, consistently outperformed Sentinel-2. The PRISMA PCA dataset achieved the highest overall accuracy with 71.09% for the Roman Coastal region and 87.15% for the Lake Vico Basin, emphasizing the value of spectral resolution. However, Sentinel-2 showed comparative strength in spatially heterogeneous areas. Tree typologies with more uniform distribution, such as hazelnut and chestnut, achieved higher classification accuracy compared to mixed-species forests. The study assesses that Sentinel-2 remains a viable alternative where spatial resolution is critical also considering the limited PRISMA images’ availability. Moreover, the work explores the potential of combining satellites and accurate GT for improved land cover mapping. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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27 pages, 5151 KB

Open AccessReview

Advancing Sparse Vegetation Monitoring in the Arctic and Antarctic: A Review of Satellite and UAV Remote Sensing, Machine Learning, and Sensor Fusion

by Arthur Platel, Juan Sandino, Justine Shaw, Barbara Bollard and Felipe Gonzalez

Remote Sens. 2025, 17(9), 1513; https://doi.org/10.3390/rs17091513 - 24 Apr 2025

Cited by 12 | Viewed by 4636

Abstract

Polar vegetation is a critical component of global biodiversity and ecosystem health but is vulnerable to climate change and environmental disturbances. Analysing the spatial distribution, regional variations, and temporal dynamics of this vegetation is essential for implementing conservation efforts in these unique environments. However, polar regions pose distinct challenges for remote sensing, including sparse vegetation, extreme weather, and frequent cloud cover. Advances in remote sensing technologies, including satellite platforms, uncrewed aerial vehicles (UAVs), and sensor fusion techniques, have improved vegetation monitoring capabilities. This review explores applications—including land cover mapping, vegetation health assessment, biomass estimation, and temporal monitoring—and the methods developed to address these needs. We also examine the role of spatial, spectral, and temporal resolution in improving monitoring accuracy and addressing polar-specific challenges. Sensors such as Red, Green, and Blue (RGB), multispectral, hyperspectral, Synthetic Aperture Radar (SAR), light detection and ranging (LiDAR), and thermal, as well as UAV and satellite platforms, are analysed for their roles in low-stature polar vegetation monitoring. We highlight the potential of sensor fusion and advanced machine learning techniques in overcoming traditional barriers, offering a path forward for enhanced monitoring. This paper highlights how advances in remote sensing enhance polar vegetation research and inform adaptive management strategies. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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