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Remote Sensing
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Advances in the Spatial and Spatio-Temporal Modeling of Environmental Data
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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: 15 July 2025 | Viewed by 6526

Special Issue Editors

Prof. Dr. George Christakos

E-Mail Website
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
Dr. Junyu He

E-Mail Website
Guest Editor
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
Prof. Dr. Jiaping Wu

E-Mail Website
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

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

Published Papers (4 papers)

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Research

23 pages, 61232 KiB  
Article
High-Precision Remote Sensing Monitoring of Extent, Species, and Production of Cultured Seaweed for Korean Peninsula
by Shuangshuang Chen, Zhanjiang Ye, Runjie Jin, Junjie Zhu, Nan Wang, Yuhan Zheng, Junyu He and Jiaping Wu
Remote Sens. 2025, 17(7), 1150; https://doi.org/10.3390/rs17071150 - 24 Mar 2025
Viewed by 297
Abstract
Sustainable seaweed cultivation is crucial for marine environmental protection, ecosystem health, socio-economic development, and carbon sequestration. Accurate and timely information on the distribution, extent, species, and production of cultivated seaweeds is essential for tracking biomass production, monitoring ecosystem health, assessing environmental impacts, optimizing [...] Read more.
Sustainable seaweed cultivation is crucial for marine environmental protection, ecosystem health, socio-economic development, and carbon sequestration. Accurate and timely information on the distribution, extent, species, and production of cultivated seaweeds is essential for tracking biomass production, monitoring ecosystem health, assessing environmental impacts, optimizing cultivation planning, supporting investment decisions, and quantifying carbon sequestration potential. However, this important information is usually lacking. This study developed a high-precision monitoring approach by integrating Otsu thresholding features with random forest classification, implemented through Google Earth Engine using Sentinel-2 imagery (10-m). The method was applied to analyze spatiotemporal variations of seaweed cultivation across the Korean Peninsula from 2017 to 2023. Results showed that annual cultivation acreage in North Korea remained relatively stable between 1506 and 2033 ha, while it experienced a significant increase of 8209 ha in South Korea. By integrating spectral features, seaweed phenology, and field cultivation practices, we successfully differentiated the predominant species: laver (Pyropia) and kelp (Saccharina and Undaria). During the 2022–2023 cultivation season, South Korea’s farms comprised 78% laver and 22% kelp, while North Korea’s showed an inverse distribution. A strong correlation (r2 = 0.99) between acreage and seaweed production enabled us to estimate annual seaweed production in North Korea, effectively addressing data gaps in regions with limited statistics. Our approach demonstrates the potential for global seaweed cultivation monitoring, while the spatial analysis lays the foundation for identifying potential cultivation zones. Given the relatively low initial investment requirement of seaweed farming and significant economic return, this approach offers valuable insights for promoting economic development and food security, ultimately supporting sustainable aquaculture management. Full article
(This article belongs to the Special Issue Advances in the Spatial and Spatio-Temporal Modeling of Environmental Data)
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19 pages, 9773 KiB  
Article
Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates
by Mo Zhang, Yong Ge and Jianghao Wang
Remote Sens. 2024, 16(21), 3939; https://doi.org/10.3390/rs16213939 - 23 Oct 2024
Cited by 1 | Viewed by 973
Abstract
Accurate high-resolution soil moisture maps are crucial for a better understanding of hydrological processes and energy cycles. Mapping strategies such as downscaling and interpolation have been developed to obtain high-resolution soil moisture maps from multi-source inputs. However, research on the optimization performance of [...] Read more.
Accurate high-resolution soil moisture maps are crucial for a better understanding of hydrological processes and energy cycles. Mapping strategies such as downscaling and interpolation have been developed to obtain high-resolution soil moisture maps from multi-source inputs. However, research on the optimization performance of integrating downscaling and interpolation, especially through the use of mutual covariates, remains unclear. In this study, we compared four methods—two standalone methods based on downscaling and interpolation strategies and two combined methods that utilize soil moisture maps as mutual covariates within each strategy—in a case study of daily soil moisture mapping at a 1 km resolution in the Tibetan Plateau. We assessed mapping performance in terms of prediction accuracy and differences in spatial coverage. The results indicated that introducing interpolated soil moisture maps into the downscaling strategy significantly improved prediction accuracy (RMSE: −5.94%, correlation coefficient: +14.02%) but was limited to localized spatial coverage (6.9% of grid cells) near in situ sites. Conversely, integrating downscaled soil moisture maps into the interpolation strategy resulted in only modest gains in prediction accuracy (RMSE: −1.07%, correlation coefficient: +1.04%), yet facilitated broader spatial coverage (40.4% of grid cells). This study highlights the critical differences between downscaling and interpolation strategies in terms of accuracy improvement and spatial coverage, providing a reference for optimizing soil moisture mapping over large areas. Full article
(This article belongs to the Special Issue Advances in the Spatial and Spatio-Temporal Modeling of Environmental Data)
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23 pages, 8143 KiB  
Article
Enhancing Remote Sensing Water Quality Inversion through Integration of Multisource Spatial Covariates: A Case Study of Hong Kong’s Coastal Nutrient Concentrations
by Zewei Zhang, Cangbai Li, Pan Yang, Zhihao Xu, Linlin Yao, Qi Wang, Guojun Chen and Qian Tan
Remote Sens. 2024, 16(17), 3337; https://doi.org/10.3390/rs16173337 - 8 Sep 2024
Viewed by 2115
Abstract
The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build [...] Read more.
The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters. Full article
(This article belongs to the Special Issue Advances in the Spatial and Spatio-Temporal Modeling of Environmental Data)
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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
Cited by 2 | Viewed by 1930
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
(This article belongs to the Special Issue Advances in the Spatial and Spatio-Temporal Modeling of Environmental Data)
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