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Machine Learning for Spatiotemporal Remote Sensing Data (2nd Edition)
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Machine Learning for Spatiotemporal Remote Sensing Data (2nd Edition)

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

Deadline for manuscript submissions: 29 August 2025 | Viewed by 3669

Special Issue Editors

Prof. Dr. Lianfa Li

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Guest Editor
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing 100101, China
Interests: spatial statistics; machine learning; spatiotemporal data mining; remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaomei Yang

E-Mail Website
Guest Editor
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing 100101, China
Interests: coastal remote sensing and GIS; monitoring and assessment; coastal hazards and resilience
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yong Ge

E-Mail Website
Guest Editor
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing 100101, China
Interests: information extraction; uncertainty assessment; image processing and analysis; spatial statistics; classification
Special Issues, Collections and Topics in MDPI journals
Dr. Nikolaos L. Tsakiridis

E-Mail Website
Guest Editor
Laboratory of Remote Sensing, Spectroscopy, and Geographical Information Systems, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, 54636 Thessaloniki, Greece
Interests: soil science; infrared spectroscopy; big data; remote sensing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring spatiotemporal changes in geospatial features such as land cover, land use, and meteorology is critical for practical applications of remotely sensed data. However, spatiotemporal modeling of remote sensing data is challenging due to massive missing values caused by​ clouds or other issues with the high reflectivity, inconsistency, and heterogeneity of spatiotemporal dependencies among geospatial features. Although traditional machine learning methods can include temporal variables in the model to account for temporal variance, due to the lack or limitation of explicit spatiotemporal dependencies in these methods, it may introduce confounding bias by mixing spatial and temporal covariates, especially for classification by remote sensing data. Modern deep learning offers us new opportunities, including flexible network structures such as 3D CNN, CNN-LSTM, CovLSTM, and CNN-Transformer, for explicit spatiotemporal interdependent modeling and efficient parallel computing for processing massive spatiotemporal data input. Whereas deep learning has been widely applied in spatiotemporal predictions in computer vision, natural language processing, meteorology, etc., due to the particularity and complexity of geospatial features, there are many issues to be explored in its use in the spatiotemporal prediction of remote sensing data.

This Special Issue aims to cover machine learning methods and applications in various fields for spatiotemporal regression and classification of remote sensing data. Topics may cover anything from data structure and processing, spatiotemporal fusion, and spatiotemporal interdependent modeling to mechanisms and prediction interpretation. In particular, deep learning methods and their comparisons with other machine learning methods for spatiotemporal modeling are welcome. Articles may address, but are not limited, to the following topics:

  • Spatiotemporal modeling by remote sensing;
  • Monitoring of land use or land cover by remote sensing;
  • Spatiotemporal inversion of geospatial parameters; 
  • Spatiotemporal deep learning in remote sensing;
  • Predictions by remote sensing;
  • Weather forecast by remote sensing.

Prof. Dr. Lianfa Li
Prof. Dr. Xiaomei Yang
Prof. Dr. Yong Ge
Dr. Nikolaos L. Tsakiridis
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

  • spatiotemporal modeling
  • spatiotemporal dependency
  • spatiotemporal prediction
  • spatiotemporal fusion
  • forecast
  • machine learning
  • deep learning
  • regression
  • classification
  • remote sensing
  • forecast

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Related Special Issue

Published Papers (5 papers)

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Research

18 pages, 7704 KiB  
Article
A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau
by Ritu Wu, Zhimin Hong, Wala Du, Yu Shan, Hong Ying, Rihan Wu and Byambakhuu Gantumur
Remote Sens. 2025, 17(9), 1485; https://doi.org/10.3390/rs17091485 - 22 Apr 2025
Viewed by 252
Abstract
Grassland fires are one of the main disasters in the temperate grasslands of the Mongolian Plateau, posing a serious threat to the lives and property of residents. The occurrence of grassland fires is affected by a variety of factors, including the biomass and [...] Read more.
Grassland fires are one of the main disasters in the temperate grasslands of the Mongolian Plateau, posing a serious threat to the lives and property of residents. The occurrence of grassland fires is affected by a variety of factors, including the biomass and humidity of fuels, the air temperature and humidity, the precipitation and evaporation, snow cover, wind, the elevation and topographic relief, and human activities. In this paper, MCD12Q1, MCD64A1, ERA5, and ETOPO 2022 remote sensing data products and other products were used to obtain the relevant data of these factors to predict the occurrence of grassland fires. In order to achieve a better prediction, this paper proposes a generalized geographically weighted boosted regression (GGWBR) method that combines spatial heterogeneity and complex nonlinear relationships, and further attempts the generalized spatiotemporally weighted boosting regression (GSTWBR) method that reflects spatiotemporal heterogeneity. The models were trained with the data of grassland fires from 2019 to 2022 in the Mongolian Plateau to predict the occurrence of grassland fires in 2023. The results showed that the accuracy of GGWBR was 0.8320, which was higher than generalized boosted regression models’ (GBM) 0.7690. Its sensitivity was 0.7754, which is higher than random forests’ (RF) 0.5662 and GBM’s 0.6927. The accuracy of GSTWBR was 0.8854, which was higher than that of RF, GBM and GGWBR. Its sensitivity was 0.7459, which is higher than that of RF and GBM. This study provides a new technical approach and theoretical support for the disaster prevention and mitigation of grassland fires in the Mongolian Plateau. Full article
(This article belongs to the Special Issue Machine Learning for Spatiotemporal Remote Sensing Data (2nd Edition))
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21 pages, 25781 KiB  
Article
Formal Quantification of Spatially Differential Characteristics of PSI-Derived Vertical Surface Deformation Using Regular Triangle Network: A Case Study of Shixi in the Northwest Xuzhou Coalfield
by Cunfa Zhao, Langping Li, Huiyong Yin, Guanhua Zhao, Wei Wang, Jianxue Huang and Qi Fan
Remote Sens. 2025, 17(8), 1388; https://doi.org/10.3390/rs17081388 - 14 Apr 2025
Viewed by 304
Abstract
This study addresses the challenge of quantifying spatially differential vertical surface deformation (SDVSD). Traditional approaches using persistent scatterer interferometry (PSI) data often focus on bulk vertical surface deformation (VSD) but overlook directional variability and struggle with irregularly distributed persistent scatterer (PS) points, limiting [...] Read more.
This study addresses the challenge of quantifying spatially differential vertical surface deformation (SDVSD). Traditional approaches using persistent scatterer interferometry (PSI) data often focus on bulk vertical surface deformation (VSD) but overlook directional variability and struggle with irregularly distributed persistent scatterer (PS) points, limiting comprehensive SDVSD analysis. This study proposes a regular triangle network (RTN)-based framework that tessellates the study area into uniform triangular units, enabling the systematic quantification of the SDVSD direction, magnitude and rate while mitigating spatial biases from uneven PS distributions. Applied to the Shixi area in China’s Northwest Xuzhou Coalfield, the RTN-based framework revealed that (1) the SDVSD directionality aligned with the coal strata dip and working face distribution, contrasting with VSD’s focus on the magnitude and rate alone; (2) SDVSD exhibited seasonal rate fluctuations suggesting environmental influences, and, unlike VSD, it has a non-additivity property in temporal evolution; (3) there was spatial divergence between SDVSD and VSD, i.e., high VSD rates did not necessarily correlate with high SDVSD rates, emphasizing the need for an independent spatial gradient analysis. This study demonstrates that the RTN-based framework effectively disentangles the directional and magnitude (rate) components of SDVSD, offering a robust tool for the identification of deformation hotspots and linking surface dynamics to subsurface processes. By formalizing the quantification of PSI-derived SDVSD, this study advances InSAR deformation monitoring, providing actionable insights for infrastructure risk mitigation and sustainable land management in mining regions and beyond. Full article
(This article belongs to the Special Issue Machine Learning for Spatiotemporal Remote Sensing Data (2nd Edition))
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26 pages, 141581 KiB  
Article
Analysis of Grassland Vegetation Coverage Changes and Driving Factors in China–Mongolia–Russia Economic Corridor from 2000 to 2023 Based on RF and BFAST Algorithm
by Chi Qiu, Chao Zhang, Jiani Ma, Cuicui Yang, Jiayue Wang, Urtnasan Mandakh, Danzanchadav Ganbat and Nyamkhuu Myanganbuu
Remote Sens. 2025, 17(8), 1334; https://doi.org/10.3390/rs17081334 - 8 Apr 2025
Viewed by 384
Abstract
Changes in grassland vegetation coverage (GVC) and their causes in the China–Mongolia–Russia Economic Corridor (CMREC) region have been a hot button issue regarding the ecological environment and sustainable development. In this paper, multi-source remote sensing (RS) data were used to obtain GVC from [...] Read more.
Changes in grassland vegetation coverage (GVC) and their causes in the China–Mongolia–Russia Economic Corridor (CMREC) region have been a hot button issue regarding the ecological environment and sustainable development. In this paper, multi-source remote sensing (RS) data were used to obtain GVC from 2000 to 2023 based on random forest (RF) regression inversion. The nonlinear characteristics such as the number of mutations, magnitude of mutations, and time of mutations were detected and analyzed using the BFAST model. Driving factors such as climatic factors were introduced to quantitatively explain the driving mechanism of GVC changes. The results showed that: (1) RF model is the optimal model for the inversion of GVC in this region. The R2 of the RF training set reached 0.94, the RMSE of the test set was 12.86%, the correlation coefficient between the predicted and actual values was 0.76, and the CVRMSE was 18.07%. (2) During the period of 2000–2023, the number of mutations in GVC ranged from 0 to 5, and there were at least 1 mutation in 58.83% of the study area. The years with the largest proportion of mutations was 2010, followed by 2016, accounting for 14.57% and 11.60% of all mutations, respectively. The month with the highest percentage of mutations was October, and followed by June, accounting for 31.73% and 22.19% of all mutations, respectively. (3) The sustained and stable positive effect was shown by precipitation on GVC before and after the maximum mutation. Wind speed was a negative effect on GVC in areas with more severe desertification, such as Inner Mongolia, China and parts of Mongolia. On the other hand, GVC was reduced by the wind speed before and after the maximum mutations. Therefore, to guarantee the ecological security of the CMREC, governments should formulate new countermeasures to prevent desertification in the region according to the laws of nature and strengthen international cooperation. Full article
(This article belongs to the Special Issue Machine Learning for Spatiotemporal Remote Sensing Data (2nd Edition))
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21 pages, 3643 KiB  
Article
Spatiotemporal Footprints of Surface Urban Heat Islands in the Urban Agglomeration of Yangtze River Delta During 2000–2022
by Yin Du, Jiachen Xie, Zhiqing Xie, Ning Wang and Lingling Zhang
Remote Sens. 2025, 17(5), 892; https://doi.org/10.3390/rs17050892 - 3 Mar 2025
Cited by 1 | Viewed by 599
Abstract
Compared with atmospheric urban heat islands, surface urban heat islands (SUHIs) are easily monitored by the thermal sensors on satellites and have a more stable spatial pattern resembling the urban and built-up lands across single cities, large metropolitans, and urban agglomerations; hence, they [...] Read more.
Compared with atmospheric urban heat islands, surface urban heat islands (SUHIs) are easily monitored by the thermal sensors on satellites and have a more stable spatial pattern resembling the urban and built-up lands across single cities, large metropolitans, and urban agglomerations; hence, they are gaining more attention from scholars and urban planners worldwide in the search for reasonable urban spatial patterns and scales to guide future urban development. Traditional urban–rural dichotomies, being sensitive to the representative urban and rural areas and the diurnal and seasonal variations in the land surface temperature (LST), obtain inflated and varying SUHI spatial footprints of approximately 1.0–6.5 times the urban size from different satellite-retrieved LST datasets in many cities and metropolitan areas, which are not conducive to urban planners in developing reasonable strategies to mitigate SUHIs. Taking the Yangtze River Delta urban agglomeration of China as an example, we proposed an improved structural similarity index to quantify more reasonable spatial patterns and footprints of SUHIs from multiple LST datasets at an annual interval. We identified gridded LST anomalies (LSTAs) related to urbanization by adopting random forest models with climate, urbanization, geographical, biophysical, and topographical parameters. Using a structural similarity index of the LSTA annual cycle at a grid point relative to the urban reference LSTA annual cycle in terms of average values, variances, and shapes to characterize the SUHIs, cross-validated SUHI footprints ~1.06–2.45 × 104 km2 smaller than the urban size and clear transition zones between urban areas and the SUHI zone were obtained from multiple LST datasets for 2000–2022. Hence, urban planners can balance urbanization’s benefits with the adverse effects of SUHIs by enhancing the transition zone between urban areas and the SUHI zone in future urban design. Considering that urban areas rapidly transformed into SUHIs, with the ratio of the SUHI extent to the urban size increasing from 0.43 to 0.62 during 2000–2022, urban planners should also take measures to prevent the rapid expansion of high-density urban areas with an ISA density above 65% in future urban development. Full article
(This article belongs to the Special Issue Machine Learning for Spatiotemporal Remote Sensing Data (2nd Edition))
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37 pages, 76788 KiB  
Article
Machine Learning-Based Remote Sensing Inversion of Non-Photosynthetic/Photosynthetic Vegetation Coverage in Desertified Areas and Its Response to Drought Analysis
by Zichen Guo, Shulin Liu, Kun Feng, Wenping Kang and Xiang Chen
Remote Sens. 2024, 16(17), 3226; https://doi.org/10.3390/rs16173226 - 31 Aug 2024
Cited by 1 | Viewed by 1417
Abstract
Determining the responses of non-photosynthetic vegetation (NPV) and photosynthetic vegetation (PV) communities to climate change is crucial in illustrating the sensitivity and sustainability of these ecosystems. In this study, we evaluated the accuracy of inverting NPV and PV using Landsat imagery with random [...] Read more.
Determining the responses of non-photosynthetic vegetation (NPV) and photosynthetic vegetation (PV) communities to climate change is crucial in illustrating the sensitivity and sustainability of these ecosystems. In this study, we evaluated the accuracy of inverting NPV and PV using Landsat imagery with random forest (RF), backpropagation neural network (BPNN), and fully connected neural network (FCNN) models. Additionally, we inverted MODIS NPV and PV time-series data using spectral unmixing. Based on this, we analyzed the responses of NPV and PV to precipitation and drought across different ecological regions. The main conclusions are as follows: (1) In NPV remote sensing inversion, the softmax activation function demonstrates greater advantages over the ReLU activation function. Specifically, the use of the softmax function results in an approximate increase of 0.35 in the R2 value. (2) Compared with a five-layer FCNN with 128 neurons and a three-layer BPNN with 12 neurons, a random forest model with over 50 trees and 5 leaf nodes provides better inversion results for NPV and PV (R2_RF-NPV = 0.843, R2_RF-PV = 0.861). (3) Long-term drought or heavy rainfall events can affect the utilization of precipitation by NPV and PV. There is a high correlation between extreme precipitation events following prolonged drought and an increase in PV coverage. (4) Under long-term drought conditions, the vegetation in the study area responded to precipitation during the last winter and growing season. This study provides an illustration of the response of semi-arid ecosystems to drought and wetting events, thereby offering a data basis for the effect evaluation of afforestation projects. Full article
(This article belongs to the Special Issue Machine Learning for Spatiotemporal Remote Sensing Data (2nd Edition))
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