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AI-Driven Satellite Data for Global Environment Monitoring (Second Edition)
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AI-Driven Satellite Data for Global Environment Monitoring (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 9343

Special Issue Editor

Prof. Dr. Yang-Won Lee

E-Mail Website
Guest Editor
Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of Korea
Interests: artificial intelligence; semantic segmentation; remote sensing of disaster; applications in agriculture, forest, hydrology, and meteorology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the 2nd edition of the Special Issue “AI-Driven Satellite Data for Global Environment Monitoring”.

The acceleration of environmental changes on Earth may significantly affect the global atmosphere, oceans, agriculture, forests, and water. Indeed, the Earth belongs to our descendants, not to us, and so we must deliver a safe and clean Earth to them. Satellite remote sensing data are essential material for the spatially and temporally continuous observation of the Earth. Moreover, recent technological developments ensure higher resolution and broader coverage to monitor disasters, meteorology, air quality, vegetation, hydrology, and polar regions. AI is a powerful tool for creating high-quality satellite images and for observation of the Earth’s environmental phenomena using advanced computing power. In addition to the classical algorithms, various state-of-the-art models can help improve AI-driven satellite data for global environmental monitoring. We invite colleagues’ insights and contributions to various research areas involving remote sensing combined with an AI approach. Papers can be focused on, but are not limited to, the following:

  • Deep learning-based object detection from satellite images for the environmental monitoring of Earth;
  • Semantic segmentation of satellite images for the environmental monitoring of Earth;
  • Super-resolution techniques for the environmental monitoring of Earth;
  • AI-based spatiotemporal image fusion for the environmental monitoring of Earth;
  • AI-based change detection for the environmental monitoring of Earth;
  • Satellite-based disaster management using AI models;
  • AI-based retrieval algorithm for the satellite products in atmosphere, meteorology, ocean, and air quality;
  • AI-based retrieval algorithm for the satellite products in agriculture, forests, hydrology, and ecology;
  • AI-driven novel methods for Earth’s environmental monitoring with satellite images.

Prof. Dr. Yang-Won Lee
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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
  • semantic segmentation
  • remote sensing of disaster
  • applications in agriculture, forest, hydrology, and meteorology

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Related Special Issue

Published Papers (4 papers)

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Research

29 pages, 48363 KiB  
Article
Comparison of Artificial Intelligence Algorithms and Remote Sensing for Modeling Pine Bark Beetle Susceptibility in Honduras
by Omar Orellana, Marco Sandoval, Erick Zagal, Marcela Hidalgo, Jonathan Suazo-Hernández, Leandro Paulino and Efrain Duarte
Remote Sens. 2025, 17(5), 912; https://doi.org/10.3390/rs17050912 - 5 Mar 2025
Viewed by 983
Abstract
The pine bark beetle is a devastating forest pest, causing significant forest losses worldwide, including 25% of pine forests in Honduras. This study focuses on Dendroctonus frontalis and Ips spp., which have affected four of the seven native pine species in Honduras: Pinus [...] Read more.
The pine bark beetle is a devastating forest pest, causing significant forest losses worldwide, including 25% of pine forests in Honduras. This study focuses on Dendroctonus frontalis and Ips spp., which have affected four of the seven native pine species in Honduras: Pinus oocarpa, P. caribaea, P. maximinoi, and P. tecunumanii. Artificial intelligence (AI) is an essential tool for developing susceptibility models. However, gaps remain in the evaluation and comparison of these algorithms when modeling susceptibility to bark beetle outbreaks in tropical conifer forests using Google Earth Engine (GEE). The objective of this study was to compare the effectiveness of three algorithms—random forest (RF), gradient boosting (GB), and maximum entropy (ME)—in constructing susceptibility models for pine bark beetles. Data from 5601 pest occurrence sites (2019–2023), 4000 absence samples, and a set of environmental covariates were used, with 70% for training and 30% for validation. Accuracies above 92% were obtained for RF and GB, and 85% for ME, along with robustness in the area under the curve (AUC) of up to 0.98. The models revealed seasonal variations in pest susceptibility. Overall, RF and GB outperformed ME, highlighting their effectiveness for implementation as adaptive approaches in a more effective forest monitoring system. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring (Second Edition))
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19 pages, 21922 KiB  
Article
Spatial Downscaling of Satellite Sea Surface Wind with Soft-Sharing Multi-Task Learning
by Yinlei Yue, Jia Liu, Yongjian Sun, Kaijun Ren, Kefeng Deng and Ke Deng
Remote Sens. 2025, 17(4), 587; https://doi.org/10.3390/rs17040587 - 8 Feb 2025
Viewed by 666
Abstract
Sea surface wind (SSW) plays a pivotal role in numerous research endeavors pertaining to meteorology and oceanography. SSW fields derived from remote sensing have been widely applied; however, regional and local studies require higher-spatial-resolution SSW fields to identify refined details. Most of the [...] Read more.
Sea surface wind (SSW) plays a pivotal role in numerous research endeavors pertaining to meteorology and oceanography. SSW fields derived from remote sensing have been widely applied; however, regional and local studies require higher-spatial-resolution SSW fields to identify refined details. Most of the existing studies based on deep learning have constructed mappings from low-resolution inputs to high-resolution downscaled estimates. However, these methods have failed to capture the relationships between multiple variables as revealed by physical processes. Therefore, this paper proposes a spatial downscaling approach for satellite sea surface wind that employs soft-sharing multi-task learning. Sea surface temperature and water vapor are included as auxiliary variables for SSW, considering the close correlation revealed by physical principles and data availability. The spatial downscaling of auxiliary variables is designed as an auxiliary task and integrated into a multi-task learning network with generative adversarial network and dual regression structures. The proposed multi-task downscaling network achieves flexible parameter sharing and information exchange between tasks through a soft-sharing mechanism and bridge modules. Comprehensive experiments were conducted with WindSat SSW products at 0.25° from Remote Sensing Systems. The experimental results validate the outstanding downscaling capability of the proposed methodology with respect to precision in comparison with buoy measurements and reconstruction quality. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring (Second Edition))
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18 pages, 5858 KiB  
Article
Automatic Multi-Temporal Land Cover Mapping with Medium Spatial Resolution Using the Model Migration Method
by Ruijun Chen, Xidong Chen and Yu Ren
Remote Sens. 2025, 17(1), 37; https://doi.org/10.3390/rs17010037 - 26 Dec 2024
Viewed by 748
Abstract
Accurate land cover mapping plays a critical role in enhancing our understanding of Earth’s energy balance, carbon cycle, and ecosystem dynamics. However, existing methods for producing multi-epoch land cover products still heavily depend on manual intervention, limiting their efficiency and scalability. This study [...] Read more.
Accurate land cover mapping plays a critical role in enhancing our understanding of Earth’s energy balance, carbon cycle, and ecosystem dynamics. However, existing methods for producing multi-epoch land cover products still heavily depend on manual intervention, limiting their efficiency and scalability. This study introduces an automated approach for multi-epoch land cover mapping using remote sensing imagery and the model migration strategy. Landsat ETM+ and OLI images with a 30 m resolution were utilized as the primary data sources. An automatic training sample extraction method based on prior multi-source land cover products was first utilized. Then, based on the generated training dataset and a random forest classifier, local adaptive land cover classification models of the reference year were developed. Finally, by migrating the classification model to the target epoch, multi-epoch land cover products were generated. Yuli County in Xinjiang and Linxi County in Inner Mongolia were used as test cases. The classification models were first generated in 2020 and then migrated to 2010 to test the effectiveness of automated land cover classification over multiple years. Our mapping results show high accuracy in both regions, with Yuli County achieving 92.52% in 2020 and 88.33% in 2010, and Linxi County achieving 90.28% in 2020 and 85.28% in 2010. These results demonstrate the reliability of our proposed automated land cover mapping strategy. Additionally, the uncertainty analysis of the model migration strategy indicated that land cover types such as water bodies, wetlands, and impervious surfaces, which exhibit significant spectral changes over time, were the least suitable for model migration. Our results can offer valuable insights for medium-resolution, multi-epoch land cover mapping, which could facilitate more efficient and accurate environmental assessments. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring (Second Edition))
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35 pages, 31461 KiB  
Article
Detection of Floating Algae Blooms on Water Bodies Using PlanetScope Images and Shifted Windows Transformer Model
by Jihye Ahn, Kwangjin Kim, Yeji Kim, Hyunok Kim and Yangwon Lee
Remote Sens. 2024, 16(20), 3791; https://doi.org/10.3390/rs16203791 - 12 Oct 2024
Cited by 2 | Viewed by 6363
Abstract
The increasing water temperature due to climate change has led to more frequent algae blooms and deteriorating water quality in coastal areas and rivers worldwide. To address this, we developed a deep learning-based model for identifying floating algae blooms using PlanetScope optical images [...] Read more.
The increasing water temperature due to climate change has led to more frequent algae blooms and deteriorating water quality in coastal areas and rivers worldwide. To address this, we developed a deep learning-based model for identifying floating algae blooms using PlanetScope optical images and the Shifted Windows (Swin) Transformer architecture. We created 1,998 datasets from 105 scenes of PlanetScope imagery collected between 2018 and 2023, covering 14 water bodies known for frequent algae blooms. The methodology included data pre-processing, dataset generation, deep learning modeling, and inference result generation. The input images contained six bands, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), enhancing the model’s responsiveness to algae blooms. Evaluations were conducted using both single-period and multi-period datasets. The single-period model achieved a mean Intersection over Union (mIoU) between 72.18% and 76.47%, while the multi-period model significantly improved performance, with an mIoU of 91.72%. This demonstrates the potential of our model and highlights the importance of change detection in multi-temporal images for algae bloom monitoring. Additionally, the padding technique proposed in this study resolved the border issue that arises when mosaicking inference results from individual patches, providing a seamless view of the satellite scene. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring (Second Edition))
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