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Remote Sensing
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Spatial Calculation and Remote Sensing Diagnosis for Environmental Health
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Spatial Calculation and Remote Sensing Diagnosis for Environmental Health

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5416

Special Issue Editors

Dr. Min Xu

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: public health research based on spatial information technology
Special Issues, Collections and Topics in MDPI journals
Dr. Shaohua Wang

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: spatiotemporal big data analysis; GeoAI; remote sensing AI; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ran Tao

E-Mail Website
Guest Editor
School of Geosciences, University of South Florida, Tampa, FL 33620, USA
Interests: GIS; spatial statistics; spatial data mining; human mobility; geographic flow
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chunxiang Cao

E-Mail Website
Guest Editor
Center for Applications of Spatial Information Technologies in Public Health, Chinese Academy of Sciences, Beijing 100101, China
Interests: environmental health diagnosis based on remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental health directly affects people’s quality of life and is closely related to human physical health. Environmental problems such as air pollution, water source pollution, and soil pollution exacerbate the incidence of various diseases, including respiratory diseases and natural source diseases. Spatial calculation is a major area of research in big data and artificial intelligence (AI). Through virtual modeling of real scenes, spatial calculation constructs spatial perception networks and knowledge graphs of the host environment, exposure methods, propagation relationships, spatio-temporal paths, and other propagation chains, making it a powerful tool for precise spatial prevention and control of major environmental health events. Remote sensing can provide a comprehensive understanding of the environmental risks that life may face, effectively monitoring and diagnosing risk factors that affect health in the environment.

This Special Issue aims at the significant demand for environmental health monitoring and early warning, breaking through the natural habitat identification and human factor extraction technologies of satellite remote sensing and spatio-temporal big data analysis, and developing the theory and methods of disease transmission spatial calculation and risk early warning. Topics may cover anything from the diagnosis of environmental health by remote sensing, disease transmission spatial calculation and risk early warning, spatiotemporal big data analysis of geographic flow, and so on. Articles may address, but are not limited to, the following topics:

  • Diagnosis of environmental health by remote sensing;
  • Air, water, and soil pollution monitoring based on remote sensing;
  • Spatial calculation for public health;
  • Dynamic simulation of disease transmission;
  • Spatiotemporal big data analysis of geographic flow;
  • Disease multi temporal and spatial scale risk warning.

Dr. Min Xu
Prof. Dr. Shaohua Wang
Dr. Ran Tao
Prof. Dr. Chunxiang Cao
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

  • environmental health
  • spatial calculation
  • diagnosis by remote sensing
  • dynamic simulation
  • spatiotemporal big data analysis
  • geographic flow
  • disease transmission
  • risk warning

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

Published Papers (3 papers)

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Research

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19 pages, 8575 KiB  
Article
Comprehensive Validation of MODIS-Derived Instantaneous Air Temperature and Daily Minimum Temperature at Nighttime
by Wenjie Zhang, Jiarui Zhao, Wenbin Zhu, Yunbo Kong, Bingcheng Wan and Yilan Liao
Remote Sens. 2025, 17(10), 1732; https://doi.org/10.3390/rs17101732 - 15 May 2025
Viewed by 164
Abstract
Nighttime near-surface air temperature is a critical input for ecological, hydrological, and meteorological models and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived instantaneous nighttime near-surface air temperature (Ta) and daily minimum temperatures (Tmin) can provide spatially continuous monitoring. The MOD07 [...] Read more.
Nighttime near-surface air temperature is a critical input for ecological, hydrological, and meteorological models and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived instantaneous nighttime near-surface air temperature (Ta) and daily minimum temperatures (Tmin) can provide spatially continuous monitoring. The MOD07 Level-2 and MYD07 Level-2 atmospheric profile product provides air temperature at various altitude levels, facilitating a more direct estimation of Ta and Tmin. However, previous validations mainly focused on daytime, with a lack of validation for nighttime. Therefore, this study comprehensively evaluated the MOD07 Level-2 and MYD07 Level-2 derived Ta by 2168 hourly meteorological measurements over 5000 m altitude spanning in China. Furthermore, a detailed evaluation of their capability to estimate Tmin was also compared with MOD11 Level-2 and MYD11 Level-2 land surface temperature. Our results show that the highest available pressure method (HAP) estimated that, in instantaneous nighttime Ta, there was severe underestimation especially in high-altitude areas for both MOD07 (r = 0.95, Bias = −0.27 °C, and RMSE = 4.53 °C) and MYD07 data (r = 0.96, Bias = −0.17 °C, and RMSE = 3.73 °C). The adiabatic lapse rate (ALR) correction effectively reduced these errors, achieving optimal accuracy with MYD07 data (r = 0.97, Bias = −0.05 °C, and RMSE = 3.29 °C). However, the underestimation by the HAP method was still insufficient compared to Tmin estimation by land surface temperature (LST). The LST method offers improved accuracy (r = 0.98, Bias = −0.16 °C, RMSE = 2.89 °C). In general, MYD-based estimations consistently outperformed MOD-based estimations. However, seasonal and elevational variability was observed in all methods, with errors increasing notably in mountainous areas (RMSE rapidly increases to 5 °C and above when the altitude exceeds 2000 m). These findings can provide practical guidance for selecting appropriate inversion methods according to terrain and season and support the development of more accurate air temperature products for a range of climate- and environmental-related applications. Full article
(This article belongs to the Special Issue Spatial Calculation and Remote Sensing Diagnosis for Environmental Health)
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27 pages, 14721 KiB  
Article
Phenology-Optimized Drought Index Reveals the Spatio-Temporal Patterns of Vegetation Health and Its Attribution on the Loess Plateau
by Zichen Yue, Shaobo Zhong, Wenhui Wang, Xin Mei and Yunxin Huang
Remote Sens. 2025, 17(5), 891; https://doi.org/10.3390/rs17050891 - 3 Mar 2025
Viewed by 717
Abstract
Frequent droughts pose a severe threat to the ecological health and sustainable development of the Loess Plateau (LP). The accurate assessment of the impact of drought on vegetation is crucial for diagnosing ecological health. Traditional drought assessment methods often rely on coarse estimations [...] Read more.
Frequent droughts pose a severe threat to the ecological health and sustainable development of the Loess Plateau (LP). The accurate assessment of the impact of drought on vegetation is crucial for diagnosing ecological health. Traditional drought assessment methods often rely on coarse estimations based on averages of vegetation drought indices, overlooking the spatial differentiation of complex vegetation phenology. This study proposes a vegetative drought assessment method that considers vegetation phenological characteristics using MODIS EVI and LST data products. First, the start and end of the growing season timepoints were extracted from the Enhanced Vegetation Index (EVI) using Savitzky–Golay (S–G) filtering and the dynamic threshold method, determining the growing-time window for each pixel. Next, the Vegetation Health Index (VHI) series was calculated and extracted for each pixel within the growing season. The mean value of the VHI series was then used to construct the Growing Season Health Index (GSHI). Based on the GSHI, the long-term vegetation drought characteristics at LP were revealed. Finally, we integrated the Optimal Parameters-based Geographical Detector (OPGD) to identify and quantify the multiple driving forces of vegetation drought. The results showed that: (1) the spatio-temporal difference of vegetation phenology on the LP was significant, exhibiting distinct zonal characteristics; (2) the spatial distribution of growing season drought on the LP presented a “humid southeast, arid northwest” pattern, with the early 21st century being a period of high drought occurrence; (3) drought has been alleviated in large-scale natural areas, but the local drought effect under urbanization is intensifying; and (4) meteorology and topography influence vegetation drought by regulating water redistribution, while the drought effect of human activities is intensifying. Full article
(This article belongs to the Special Issue Spatial Calculation and Remote Sensing Diagnosis for Environmental Health)
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Review

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30 pages, 5328 KiB  
Review
Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review
by Shaohua Wang, Dachuan Xu, Haojian Liang, Yongqing Bai, Xiao Li, Junyuan Zhou, Cheng Su and Wenyu Wei
Remote Sens. 2025, 17(4), 698; https://doi.org/10.3390/rs17040698 - 18 Feb 2025
Cited by 1 | Viewed by 3806
Abstract
Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the [...] Read more.
Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques—including image classification, object detection, semantic segmentation, and change detection—to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture. Full article
(This article belongs to the Special Issue Spatial Calculation and Remote Sensing Diagnosis for Environmental Health)
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