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Advances in the Spatial and Spatio-Temporal Modeling of Environmental Data

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 939

Special Issue Editors


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Guest Editor
Department of Geography, San Diego State University, San Diego, CA 92182, USA
Interests: modern spatiotemporal geostatistics; Bayesian maximum entropy; risk assessment; chronotopologic stochastic modeling
Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316000, China
Interests: spatiotemporal data analysis; remote sensing; spatiotemporal geostatistics; artificial intelligence; blue carbon
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316000, China
Interests: coastal environment and ecosystem; coastal blue carbon; remote sensing; geospatial informatics

Special Issue Information

Dear Colleagues,

In recent years, thanks to advancements in sensing technologies and data collection methods, the field of environmental research has witnessed an unprecedented surge in the availability of spatial and spatiotemporal data. Such data, ranging from satellite imagery and remote sensing to ground-based observations, offer valuable insights into the complex interactions between natural processes and human activities. To harness the full potential of these datasets, sophisticated modeling techniques are required, capable of capturing the intricate spatial patterns and temporal dynamics inherent in environmental systems.

This Special Issue aims to bring together cutting-edge research and technological innovations in the development and application of spatial and spatiotemporal modeling approaches, including spatiotemporal geostatistical methods, chrono-topological analyses, artificial intelligence, temporal GIS, etc. This collection of articles serves as a platform for researchers, practitioners, and academics to share their expertise, techniques and findings, fostering a deeper understanding of environmental processes.

The scope of this Special Issue encompasses advances in a wide array of environmental domains, including but not limited to climate, ecology, geohydrology, ocean and marine sciences, atmospheric science, human exposure, and environmental health.

Prof. Dr. George Christakos
Dr. Junyu He
Prof. Dr. Jiaping Wu
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

  • multi-sourced data fusion
  • spatiotemporal geostatistics
  • artificial intelligence
  • remote sensing
  • high spatiotemporal resolution
  • uncertainty assessment

Published Papers (1 paper)

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Research

22 pages, 7305 KiB  
Article
Developing a Multi-Scale Convolutional Neural Network for Spatiotemporal Fusion to Generate MODIS-like Data Using AVHRR and Landsat Images
by Zhicheng Zhang, Zurui Ao, Wei Wu, Yidan Wang and Qinchuan Xin
Remote Sens. 2024, 16(6), 1086; https://doi.org/10.3390/rs16061086 - 20 Mar 2024
Viewed by 634
Abstract
Remote sensing data are becoming increasingly important for quantifying long-term changes in land surfaces. Optical sensors onboard satellite platforms face a tradeoff between temporal and spatial resolutions. Spatiotemporal fusion models can produce high spatiotemporal data, while existing models are not designed to produce [...] Read more.
Remote sensing data are becoming increasingly important for quantifying long-term changes in land surfaces. Optical sensors onboard satellite platforms face a tradeoff between temporal and spatial resolutions. Spatiotemporal fusion models can produce high spatiotemporal data, while existing models are not designed to produce moderate-spatial-resolution data, like Moderate-Resolution Imaging Spectroradiometer (MODIS), which has moderate spatial detail and frequent temporal coverage. This limitation arises from the challenge of combining coarse- and fine-spatial-resolution data, due to their large spatial resolution gap. This study presents a novel model, named multi-scale convolutional neural network for spatiotemporal fusion (MSCSTF), to generate MODIS-like data by addressing the large spatial-scale gap in blending the Advanced Very-High-Resolution Radiometer (AVHRR) and Landsat images. To mitigate the considerable biases between AVHRR and Landsat with MODIS images, an image correction module is included into the model using deep supervision. The outcomes show that the modeled MODIS-like images are consistent with the observed ones in five tested areas, as evidenced by the root mean square errors (RMSE) of 0.030, 0.022, 0.075, 0.036, and 0.045, respectively. The model makes reasonable predictions on reconstructing retrospective MODIS-like data when evaluating against Landsat data. The proposed MSCSTF model outperforms six other comparative models in accuracy, with regional average RMSE values being lower by 0.005, 0.007, 0.073, 0.062, 0.070, and 0.060, respectively, compared to the counterparts in the other models. The developed method does not rely on MODIS images as input, and it has the potential to reconstruct MODIS-like data prior to 2000 for retrospective studies and applications. Full article
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