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Advances in Satellite Image Analysis and Applications for Earth Observation
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Advances in Satellite Image Analysis and Applications for Earth Observation

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 5049

Special Issue Editors

Dr. Daniele Oxoli

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, Italy
Interests: geographic information system; digital mapping; spatial analysis mapping; python; spatial statistics; geoinformation; satellite image processing; satellite image analysis; geospatial science
Dr. Maria Alicandro

E-Mail Website
Guest Editor
Institute INSIT, School of Business and Engineering Vaud, University of Applied Sciences and Arts Western Switzerland, 1400 Yverdon-les-Bains, Switzerland
Interests: geographic information science; geospatial artificial intelligence; citizen science; open data; geospatial web; spatio-temporal modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Maryam Lotfian

E-Mail Website
Guest Editor
Institute INSIT, School of Business and Engineering Vaud, University of Applied Sciences and Arts Western, 1400 Yverdon-les-Bains, Switzerland
Interests: geographic information science; geospatial artificial intelligence; citizen science; open data; geospatial web; and spatio-temporal modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Peng

E-Mail Website
Guest Editor
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: geographical information science; spatial and temporal information modelling; complex network analysis; knowledge graph
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maria Antonia Brovelli

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Politecnico di Milano, P.zza Leonardo da Vinci, 32, Building 3, 20133 Milano, Italy
Interests: geospatial web; geodata science; citizen science; open science; open data; open geospatial software; geospatial artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The vibrant evolution of satellite technology has inaugurated a novel epoch in Earth Observation (EO), yielding unparalleled insights into the dynamic processes of our planet. The new array of EO satellite missions, coupled with advancing spectral, spatial, and temporal resolutions and accuracies in satellite imagery, enables the extensive analysis and monitoring of complex ecosystems, climatic dynamics, and human activities. In parallel, Geospatial Artificial Intelligence (GeoAI) techniques are gaining momentum by increasing the analysis automation of extensive EO datasets by boosting the identification of patterns, anomalies, and trends on a global scale.

This Special Issue delves into the latest EO satellite image analysis advancements and explores their manifold applications across various scientific disciplines. From monitoring environmental changes and assessing natural disasters to informing urban planning and agricultural practices, the contributions within this collection will showcase cutting-edge methodologies and innovative approaches that harness the power of satellite EO data for a better understanding of Earth’s complex systems. This Special Issue emphasizes the transformative potential of machines and deep learning in enhancing the accuracy and efficiency of EO data management and image analysis.  

We welcome the submission of original research articles and reviews that focus on, but are not restricted to, the following themes:

  • Advanced techniques for satellite-based urban environment monitoring;
  • Satellite EO data application for ecosystem and biodiversity conservation;
  • Satellite EO application to climate pattern analysis;
  • Novel approaches for disaster management using EO satellite imagery;
  • Satellite EO application in smart farming and smart cities;
  • Satellite-based analysis of human activities and their impact on the environment;
  • Satellite imaging sensors’ data fusion;
  • Machine and deep learning techniques for satellite imagery content retrieval;
  • Evaluations of cutting-edge EO software and platforms;
  • Big satellite EO data analytics.

Overall, satellite technology continues to advance, and this Special Issue aims to serve as a comprehensive reference on state-of-the-art techniques, tools, and applications propelling the field of EO satellite imagery into the future.

Dr. Daniele Oxoli
Dr. Maria Alicandro
Dr. Maryam Lotfian
Dr. Peng Peng
Prof. Dr. Maria Antonia Brovelli
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 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

  • image analysis
  • environmental monitoring
  • image-based change detection
  • urban planning
  • earth observation
  • GeoAI
  • multi-scale and multi-sensor
  • earth observation analytics

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

32 pages, 7202 KiB  
Article
Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation
by Antonio Giganti, Sara Mandelli, Paolo Bestagini and Stefano Tubaro
Remote Sens. 2024, 16(21), 3963; https://doi.org/10.3390/rs16213963 - 24 Oct 2024
Viewed by 961
Abstract
Plants emit biogenic volatile organic compounds (BVOCs), such as isoprene, significantly influencing atmospheric chemistry and climate. BVOC emissions estimated from bottom-up (BU) approaches (derived from numerical simulations) usually exhibit denser and more detailed spatial information compared to those estimated through top-down (TD) approaches [...] Read more.
Plants emit biogenic volatile organic compounds (BVOCs), such as isoprene, significantly influencing atmospheric chemistry and climate. BVOC emissions estimated from bottom-up (BU) approaches (derived from numerical simulations) usually exhibit denser and more detailed spatial information compared to those estimated through top-down (TD) approaches (derived from satellite observations). Moreover, numerically simulated emissions are typically easier to obtain, even if they are less reliable than satellite acquisitions, which, being derived from actual measurements, are considered a more trustworthy instrument for performing chemistry and climate investigations. Given the coarseness and relative lack of satellite-derived measurements, fine-grained numerically simulated emissions could be exploited to enhance them. However, simulated (BU) and observed (TD) emissions usually differ regarding value range and spatiotemporal resolution. In this work, we present a novel deep learning (DL)-based approach to increase the spatial resolution of satellite-derived isoprene emissions, investigating the adoption of efficient domain adaptation (DA) techniques to bridge the gap between numerically simulated emissions and satellite-derived emissions, avoiding the need for retraining a specific super-resolution (SR) algorithm on them. For this, we propose a methodology based on the cycle generative adversarial network (CycleGAN) architecture, which has been extensively used for adapting natural images (like digital photographs) of different domains. In our work, we depart from the standard CycleGAN framework, proposing additional loss terms that allow for better DA and emissions’ SR. We extensively demonstrate the proposed method’s effectiveness and robustness in restoring fine-grained patterns of observed isoprene emissions. Moreover, we compare different setups and validate our approach using different emission inventories from both domains. Eventually, we show that the proposed DA strategy paves the way towards robust SR solutions even in the case of spatial resolution mismatch between the training and testing domains and in the case of unknown testing data. Full article
(This article belongs to the Special Issue Advances in Satellite Image Analysis and Applications for Earth Observation)
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17 pages, 9005 KiB  
Article
NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
by Dimitri Charrière, Loïc Francon and Gregory Giuliani
Remote Sens. 2024, 16(20), 3894; https://doi.org/10.3390/rs16203894 - 19 Oct 2024
Viewed by 1128
Abstract
Cold ecosystems are experiencing a warming rate that is twice as fast as the global average and are particularly vulnerable to the consequences of climate change. In mountain ecosystems, it is particularly important to monitor vegetation to understand ecosystem dynamics, biodiversity conservation, and [...] Read more.
Cold ecosystems are experiencing a warming rate that is twice as fast as the global average and are particularly vulnerable to the consequences of climate change. In mountain ecosystems, it is particularly important to monitor vegetation to understand ecosystem dynamics, biodiversity conservation, and the resilience of these fragile ecosystems to global change. Hence, we used satellite data acquired by Sentinel-2 to perform a comparative assessment of the Normalized Difference Vegetation Index (NDVI) and the Plant Phenology Index (PPI) in mountainous regions (canton of Valais-Switzerland in the European Alps) for monitoring vegetation dynamics of four types: deciduous trees, coniferous trees, grasslands, and shrublands. Results indicate that the NDVI is particularly noisy in the seasonal cycle at the beginning/end of the snow season and for coniferous trees, which is consistent with its known snow sensitivity issue and difficulties in retrieving signal variation in dense and evergreen vegetation. The PPI seems to deal with these problems but tends to overestimate peak values, which could be attributed to its logarithmic formula and derived high sensitivity to variations in near-infrared (NIR) and red reflectance during the peak growing season. Concerning seasonal parameters retrieval, we find close concordance in the results for the start of season (SOS) and end of season (EOS) between indices, except for coniferous trees. Peak of season (POS) results exhibit important differences between the indices. Our findings suggest that PPI is a robust remote sensed index for vegetation monitoring in seasonal snow-covered and complex mountain environments. Full article
(This article belongs to the Special Issue Advances in Satellite Image Analysis and Applications for Earth Observation)
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23 pages, 22713 KiB  
Article
Evaluation of Ecological Environment Quality Using an Improved Remote Sensing Ecological Index Model
by Yanan Liu, Wanlin Xiang, Pingbo Hu, Peng Gao and Ai Zhang
Remote Sens. 2024, 16(18), 3485; https://doi.org/10.3390/rs16183485 - 20 Sep 2024
Viewed by 954
Abstract
The Remote Sensing Ecological Index (RSEI) model is widely used for large-scale, rapid Ecological Environment Quality (EEQ) assessment. However, both the RSEI and its improved models have limitations in explaining the EEQ with only two-dimensional (2D) factors, resulting in [...] Read more.
The Remote Sensing Ecological Index (RSEI) model is widely used for large-scale, rapid Ecological Environment Quality (EEQ) assessment. However, both the RSEI and its improved models have limitations in explaining the EEQ with only two-dimensional (2D) factors, resulting in inaccurate evaluation results. Incorporating more comprehensive, three-dimensional (3D) ecological information poses challenges for maintaining stability in large-scale monitoring, using traditional weighting methods like the Principal Component Analysis (PCA). This study introduces an Improved Remote Sensing Ecological Index (IRSEI) model that integrates 2D (normalized difference vegetation factor, normalized difference built-up and soil factor, heat factor, wetness, difference factor for air quality) and 3D (comprehensive vegetation factor) ecological factors for enhanced EEQ monitoring. The model employs a combined subjective–objective weighting approach, utilizing principal components and hierarchical analysis under minimum entropy theory. A comparative analysis of IRSEI and RSEI in Miyun, a representative study area, reveals a strong correlation and consistent monitoring trends. By incorporating air quality and 3D ecological factors, IRSEI provides a more accurate and detailed EEQ assessment, better aligning with ground truth observations from Google Earth satellite imagery. Full article
(This article belongs to the Special Issue Advances in Satellite Image Analysis and Applications for Earth Observation)
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22 pages, 56379 KiB  
Article
Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images
by Weiming Xu, Juan Wang, Chengjun Wang, Ziwei Li, Jianchang Zhang, Hua Su and Sheng Wu
Remote Sens. 2024, 16(14), 2637; https://doi.org/10.3390/rs16142637 - 18 Jul 2024
Viewed by 804
Abstract
The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental [...] Read more.
The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental variations. To address these issues, this paper proposes DSTBA-Net, an end-to-end encoder–decoder architecture. Initially, we introduce a Dual-Stream Feature Extraction (DSFE) mechanism within the encoder, which consists of Residual Blocks and Boundary Feature Guidance (BFG) to separately process image and boundary data. The extracted features are then fused in the Global Feature Fusion Module (GFFM), utilizing Transformer technology to further integrate global and detailed information. In the decoder, we employ Feature Compensation Recovery (FCR) to restore critical information lost during the encoding process. Additionally, the network is optimized using a boundary-aware weighted loss strategy. DSTBA-Net aims to achieve high precision in agricultural parcel segmentation and accurate boundary extraction. To evaluate the model’s effectiveness, we conducted experiments on agricultural parcel extraction in Denmark (Europe) and Shandong (Asia). Both quantitative and qualitative analyses show that DSTBA-Net outperforms comparative methods, offering significant advantages in agricultural parcel extraction. Full article
(This article belongs to the Special Issue Advances in Satellite Image Analysis and Applications for Earth Observation)
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