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Remote Sensing Applications in Agricultural, Earth and Environmental Science, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 20 May 2026 | Viewed by 5137

Special Issue Editors

Dr. Romano Lottering

E-Mail Website
Guest Editor
School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Durban 4041, South Africa
Interests: forest health; invasive species; classification; climate change; drought; image texture; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Kabir Peerbhay

E-Mail Website
Guest Editor
SAPPI Forests Southern Africa, Shaw Research Centre (SRC) R107, Off the R103, Howick 3290, South Africa
Interests: forestry; invasive species; classification; climate change; soil organic carbon; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Samuel Adelabu

E-Mail Website
Guest Editor
Department of Geography, Faculty of Natural and Agricultural Sciences, Unversity of The Free State, PO Box 339, Bloemfontein 9300, South Africa
Interests: forest health; ecological monitoring; disaster management; climate change; classification; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in remote sensing technology have improved the detection and mapping of features on the earth’s surface. These advances include refined sensor fidelity, which further elucidates processes that affect spatial phenomena when compared to earlier sensors. In addition, new developments in machine and deep learning approaches improve the prediction and accuracy of research outputs over conventional techniques. Such advances support research developments in many fields, opening new prospects in agriculture, earth, and environmental sciences toward a more sustainable future.  

Therefore, this Special Issue calls for high-quality novel research and review articles on remote sensing applications in agriculture, earth, and environmental sciences, focusing particularly on the recent advances in these fields.   

Dr. Romano Lottering
Dr. Kabir Peerbhay
Dr. Samuel Adelabu
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. Applied Sciences 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 2400 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

  • agriculture
  • earth observation
  • environmental sciences
  • machine learning
  • deep learning

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Related Special Issue

Published Papers (4 papers)

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Research

23 pages, 91075 KB  
Article
Improved Lightweight Marine Oil Spill Detection Using the YOLOv8 Algorithm
by Jianting Shi, Tianyu Jiao, Daniel P. Ames, Yinan Chen and Zhonghua Xie
Appl. Sci. 2026, 16(2), 780; https://doi.org/10.3390/app16020780 - 12 Jan 2026
Viewed by 406
Abstract
Marine oil spill detection using Synthetic Aperture Radar (SAR) is crucial but challenged by dynamic marine conditions, diverse spill scales, and limitations in existing algorithms regarding model size and real-time performance. To address these challenges, we propose LSFE-YOLO, a YOLOv8s-optimized (You Only Look [...] Read more.
Marine oil spill detection using Synthetic Aperture Radar (SAR) is crucial but challenged by dynamic marine conditions, diverse spill scales, and limitations in existing algorithms regarding model size and real-time performance. To address these challenges, we propose LSFE-YOLO, a YOLOv8s-optimized (You Only Look Once version 8) lightweight model with an original, domain-tailored synergistic integration of FasterNet, GN-LSC Head (GroupNorm Lightweight Shared Convolution Head), and C2f_MBE (C2f Mobile Bottleneck Enhanced). FasterNet serves as the backbone (25% neck width reduction), leveraging partial convolution (PConv) to minimize memory access and redundant computations—overcoming traditional lightweight backbones’ high memory overhead—laying the foundation for real-time deployment while preserving feature extraction. The proposed GN-LSC Head replaces YOLOv8’s decoupled head: its shared convolutions reduce parameter redundancy by approximately 40%, and GroupNorm (Group Normalization) ensures stable accuracy under edge computing’s small-batch constraints, outperforming BatchNorm (Batch Normalization) in resource-limited scenarios. The C2f_MBE module integrates EffectiveSE (Effective Squeeze and Excitation)-optimized MBConv (Mobile Inverted Bottleneck Convolution) into C2f: MBConv’s inverted-residual design enhances multi-scale feature capture, while lightweight EffectiveSE strengthens discriminative oil spill features without extra computation, addressing the original C2f’s scale variability insufficiency. Additionally, an SE (Squeeze and Excitation) attention mechanism embedded upstream of SPPF (Spatial Pyramid Pooling Fast) suppresses background interference (e.g., waves, biological oil films), synergizing with FasterNet and C2f_MBE to form a cascaded feature optimization pipeline that refines representations throughout the model. Experimental results show that LSFE-YOLO improves mAP (mean Average Precision) by 1.3% and F1 score by 1.7% over YOLOv8s, while achieving substantial reductions in model size (81.9%), parameter count (82.9%), and computational cost (84.2%), alongside a 20 FPS (Frames Per Second) increase in detection speed. LSFE-YOLO offers an efficient and effective solution for real-time marine oil spill detection. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Agricultural, Earth and Environmental Science, 2nd Edition)
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35 pages, 4409 KB  
Article
Hybrid Object-Based Augmentation and Histogram Matching for Cross-Domain Building Segmentation in Remote Sensing
by Chulsoo Ye and Youngman Ahn
Appl. Sci. 2026, 16(1), 543; https://doi.org/10.3390/app16010543 - 5 Jan 2026
Viewed by 403
Abstract
Cross-domain building segmentation in high-resolution remote sensing imagery underpins urban change monitoring, disaster assessment, and exposure mapping. However, differences in sensors, regions, and imaging conditions create structural and radiometric domain gaps that degrade model generalization. Most existing methods adopt model-centric domain adaptation with [...] Read more.
Cross-domain building segmentation in high-resolution remote sensing imagery underpins urban change monitoring, disaster assessment, and exposure mapping. However, differences in sensors, regions, and imaging conditions create structural and radiometric domain gaps that degrade model generalization. Most existing methods adopt model-centric domain adaptation with additional networks or losses, complicating training and deployment. We propose a data-centric framework, Hybrid Object-Based Augmentation and Histogram Matching (Hybrid OBA–HM), which improves cross-domain building segmentation without modifying the backbone architecture or using target-domain labels. The proposed framework comprises two stages: (i) object-based augmentation to increase structural diversity and building coverage, and (ii) histogram-based normalization to mitigate radiometric discrepancies across domains. Experiments on OpenEarthMap and cross-city transfer among three KOMPSAT-3A scenes show that Hybrid OBA–HM improves F1-scores from 0.808 to 0.840 and from 0.455 to 0.652, respectively, while maintaining an object-level intersection over union of 0.89 for replaced buildings. Domain-indicator analysis further reveals larger gains under stronger radiometric and geometric mismatches, indicating that the proposed framework strengthens cross-domain generalization and provides practical guidance by relating simple domain diagnostics (e.g., brightness/color and orientation mismatch indicators) to the expected benefits of augmentation and normalization when adapting to new domains. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Agricultural, Earth and Environmental Science, 2nd Edition)
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22 pages, 7441 KB  
Article
OpenBeePose: A Remote Sensing Framework for Bee Pose Estimation Using Deep Learning
by Sajad Sabzi, Ali Najar, Raziyeh Pourdarbani, Ginés García-Mateos, Ruben Fernandez-Beltran and Mohammad H. Rohban
Appl. Sci. 2026, 16(1), 303; https://doi.org/10.3390/app16010303 - 28 Dec 2025
Viewed by 481
Abstract
The application of remote sensing technology for pose estimation in beekeeping has the potential to transform colony management, improve bee health and mitigate the decline in bee populations. This paper presents a novel bee pose estimation method that integrates the accuracy and efficiency [...] Read more.
The application of remote sensing technology for pose estimation in beekeeping has the potential to transform colony management, improve bee health and mitigate the decline in bee populations. This paper presents a novel bee pose estimation method that integrates the accuracy and efficiency of two existing deep learning models: a variant of the classic VGG-19 network architecture for feature extraction and an adaptation of OpenPose for part detection and assembly. The proposed approach, OpenBeePose, is compared with state-of-the-art methods, including YOLO11 and the original OpenPose. The dataset used consists of 400 high-resolution images of the hive ramp (1080 × 1920 pixels) taken during daylight hours from eight different hives, totaling more than 3600 bee samples. Each bee is annotated in the YOLO format with two key points labeled: the stinger and the head. The obtained results show that OpenBeePose achieves a high level of accuracy, similar to those of other methods, with a success rate exceeding 99%. However, the most substantial advantage is its computational efficiency, which makes it the fastest method among those compared for 540 × 960 images, and it is almost twice as fast as OpenPose. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Agricultural, Earth and Environmental Science, 2nd Edition)
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21 pages, 9585 KB  
Article
Mapping Rice Cropping Systems in Data-Scarce Regions Using NDVI Time-Series and Dynamic Time Warping Clustering: A Case Study of Maliana, Timor-Leste
by Pedro Junior Fernandes and Masahiko Nagai
Appl. Sci. 2025, 15(23), 12544; https://doi.org/10.3390/app152312544 - 26 Nov 2025
Cited by 1 | Viewed by 2553
Abstract
Mapping of rice-cropping regimes is crucial for effective irrigation planning and yield monitoring, particularly in data-scarce regions. We analyzed 48 months of 3 m PlanetScope NDVI data, aggregated to a 25 m hexagonal grid, and used Dynamic Time Warping Clustering to segment phenological [...] Read more.
Mapping of rice-cropping regimes is crucial for effective irrigation planning and yield monitoring, particularly in data-scarce regions. We analyzed 48 months of 3 m PlanetScope NDVI data, aggregated to a 25 m hexagonal grid, and used Dynamic Time Warping Clustering to segment phenological patterns. Internal validation consistently identified two main clusters, indicating two dominant seasonality modes. Cluster 1 exhibited a higher mean NDVI, fewer low-canopy months, more vigorous growth periods, more peaks, and greater annual cycling, which suggests irrigated double cropping. Cluster 2 exhibited prolonged low NDVI values and a greater amplitude, consistent with single-rainfed systems. The rain–NDVI analysis supported these findings: Cluster 1 responded modestly to rainfall, whereas Cluster 2 exhibited a stronger and delayed response. Independent spatial checks confirmed these classifications. Off-season greenness, measured as NDVI above 0.50 from July to November, was concentrated near main and secondary canals and decreased with distance from intake points. This workflow combines DTW clustering with rainfall lag and off-season greenness analysis, effectively distinguishing between irrigated and rain-fed regimes using satellite time series. These findings are considered indicative rather than definitive, providing an assessment of cropping systems in Timor-Leste and demonstrating that DTW-based NDVI clustering offers a scalable approach in data-scarce regions. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Agricultural, Earth and Environmental Science, 2nd Edition)
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