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Sensor-Based Geoinformatics Solutions for Sustainable Agricultural and Water/Forest Resources Management
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Sensor-Based Geoinformatics Solutions for Sustainable Agricultural and Water/Forest Resources Management

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 10045

Special Issue Editors

Dr. Pulakesh Das

E-Mail Website
Guest Editor
Madhya Pradesh State Electronics Development Corporation, Bhopal, India
Interests: remote sensing; forest; agriculture; water resources; resilience; sustainable landscape management
Special Issues, Collections and Topics in MDPI journals
Dr. Satiprasad Sahoo

E-Mail Website
Guest Editor
International Center for Agricultural Research in the Dry Areas (ICARDA), 2 Port Said, Victoria Sq, Ismail El-Shaaer Building, 15A Maadi, Cairo 11728, Egypt
Interests: environment; spatial analysis; satellite image analysis; hydrology; remote sensing; geology; geographic information system; mapping; environmental impact assessment; vegetation mapping
Dr. Madhumita Sahoo

E-Mail Website
Guest Editor
School of Mechanical, Aerospace and Civil Engineering, University of Sheffield, Sheffield, UK
Interests: water resources management; remote sensing
Dr. Shafique Matin

E-Mail Website
Guest Editor
Madhya Pradesh Agency For Promotion of Information Technology, Bhopal 462011, India
Interests: satellite remote sensing and GIS; landscape modelling; land system studies and desertification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Incorporating geoinformatics technology into agricultural practices and water and forest resource management has become increasingly vital for sustainable environmental stewardship in recent years. Progress in satellite, airborne, and terrestrial sensors has yielded enormous data for insights into agricultural landscapes, water resources, and forest ecosystem dynamics. Remote sensing facilitates real-time observation and data amalgamation, while artificial intelligence techniques enable robust predictive modeling development, enhancing decision-making for sustainable resource management. With the escalating challenges of natural resource management, such as climate extremes, various disasters, urbanization, and resource over-exploitation, novel geoinformatics systems have become essential for comprehending and addressing these problems.

The Sensors journal is a global, peer-reviewed, open-access platform focused on the science and technology of sensors. The journal advances cutting-edge research in sensor technology, facilitating progress in diverse areas such as agricultural monitoring, environmental evaluation, and forest management. This perfectly aligns with the focus of the Special Issue, which highlights the application of geoinformatics in monitoring agricultural practices and water and forestry resources.

Dr. Pulakesh Das
Dr. Satiprasad Sahoo
Dr. Madhumita Sahoo
Dr. Shafique Matin
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 250 words) can be sent to the Editorial Office for assessment.

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. Sensors 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 2600 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

  • remote sensing
  • crop monitoring
  • hydrological modeling
  • forest change monitoring
  • machine learning (ML)
  • deep learning (DL)

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Published Papers (3 papers)

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Research

25 pages, 3222 KB  
Article
Progressive Attention-Enhanced EfficientNet–UNet for Robust Water-Body Mapping from Satellite Imagery
by Mohamed Ezz, Alaa S. Alaerjan, Ayman Mohamed Mostafa, Noureldin Laban and Hind H. Zeyada
Sensors 2026, 26(3), 963; https://doi.org/10.3390/s26030963 - 2 Feb 2026
Viewed by 619
Abstract
The sustainable management of water resources and the development of climate-resilient infrastructure depend on the precise identification of water bodies in satellite imagery. This paper presents a novel deep learning architecture that integrates a convolutional block attention module (CBAM) into a modified EfficientNet–UNet [...] Read more.
The sustainable management of water resources and the development of climate-resilient infrastructure depend on the precise identification of water bodies in satellite imagery. This paper presents a novel deep learning architecture that integrates a convolutional block attention module (CBAM) into a modified EfficientNet–UNet backbone. This integration allows the model to prioritize informative features and spatial areas. The model robustness is ensured through a rigorous training regimen featuring five-fold cross-validation, dynamic test-time augmentation, and optimization with the Lovász loss function. The final model achieved the following values on the independent test set: precision = 90.67%, sensitivity = 86.96%, specificity = 96.18%, accuracy = 93.42%, Dice score = 88.78%, and IoU = 79.82%. These results demonstrate improvement over conventional segmentation pipelines, highlighting the effectiveness of attention mechanisms in extracting complex water-body patterns and boundaries. The key contributions of this paper include the following: (i) adaptation of CBAM within a UNet-style architecture tailored for remote sensing water-body extraction; (ii) a rigorous ablation study detailing the incremental impact of decoder complexity, attention integration, and loss function choice; and (iii) validation of a high-fidelity, computationally efficient model ready for deployment in large-scale water-resource and ecosystem-monitoring systems. Our findings show that attention-guided segmentation networks provide a robust pathway toward high-fidelity and sustainable water-body mapping. Full article
(This article belongs to the Special Issue Sensor-Based Geoinformatics Solutions for Sustainable Agricultural and Water/Forest Resources Management)
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18 pages, 3716 KB  
Article
Analyzing the Influence of Anthropogenic Heat on Groundwater Using Remote-Sensing and In Situ Data
by Surya Deb Chakraborty, M. Sami Zitouni, Saeed Al Mansoori, P. Jagadeeswara Rao and K. Mruthyunjaya Reddy
Sensors 2025, 25(20), 6351; https://doi.org/10.3390/s25206351 - 14 Oct 2025
Viewed by 1220
Abstract
The continuous expansion of impervious surfaces replacing the vegetation cover and surface water areas increases urban heating. Such heating leads to downward heat transfer and latent heat flux from the surface to subsurface aquifers. This study used Landsat optical and thermal satellite data [...] Read more.
The continuous expansion of impervious surfaces replacing the vegetation cover and surface water areas increases urban heating. Such heating leads to downward heat transfer and latent heat flux from the surface to subsurface aquifers. This study used Landsat optical and thermal satellite data for land use/land cover (LULC), land surface temperature (LST), and anthropogenic heat flux (Has) change mapping in Bangalore City, India. The in situ sensor-based land surface temperature (LST) and groundwater temperature (GWT) measurements were used to validate the study outcome. A minor difference was observed between the satellite data and the in situ LST due to the differential data acquisition time. The built-up area increased from 7.61% to 28.78% from 1999 to 2017 at the cost of the green cover and the extent of waterbodies. Therefore, LST change was higher in green cover areas (~6 °C LST) than in urban areas (>3 °C). The anthropogenic heat fluxes increased significantly (above 65 W/m2) during the study period. The in situ GWT was strongly correlated with the Has (R2 = 0.83) and LST (R2 = 0.78). The study highlights the nature of urban expansion in Bangalore City, India, and its impact on LST, Has, and GWT. The observed changes in land use practices with urban heat indicators at 30 m scale can be used for sustainable land use planning to improve the thermal comfort of the city, preserving the urban ecosystems. The high collinearity between satellite-data-derived LST, Has, and GWT can be used for periodic monitoring at seasonal and annual scales using the Landsat data, which can be important inputs for land use planners and policymakers. Full article
(This article belongs to the Special Issue Sensor-Based Geoinformatics Solutions for Sustainable Agricultural and Water/Forest Resources Management)
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19 pages, 8547 KB  
Article
Development of an IoT-Based Flood Monitoring System Integrated with GIS for Lowland Agricultural Areas
by Sittichai Choosumrong, Kampanart Piyathamrongchai, Rhutairat Hataitara, Urin Soteyome, Nirut Konkong, Rapikorn Chalongsuppunyoo, Venkatesh Raghavan and Tatsuya Nemoto
Sensors 2025, 25(17), 5477; https://doi.org/10.3390/s25175477 - 3 Sep 2025
Cited by 2 | Viewed by 7408
Abstract
Disaster risk reduction requires efficient flood control in lowland and flood-prone areas, especially in agricultural areas like the Bang Rakam model area in Phitsanulok province, Thailand. In order to improve flood prediction and response, this study proposes the creation of a low-cost, real-time [...] Read more.
Disaster risk reduction requires efficient flood control in lowland and flood-prone areas, especially in agricultural areas like the Bang Rakam model area in Phitsanulok province, Thailand. In order to improve flood prediction and response, this study proposes the creation of a low-cost, real-time water-level monitoring integrated with spatial data analysis using Geographic Information System (GIS) technology. Ten ultrasonic sensor-equipped monitoring stations were installed thoughtfully around sub-catchment areas to provide highly accurate water-level readings. To define inundation zones and create flood depth maps, the sensors gather flood level data from each station, which is then processed using a 1-m Digital Elevation Model (DEM) and Python-based geospatial analysis. In order to create dynamic flood maps that offer information on flood extent, depth, and water volume within each sub-catchment, an automated method was created to use real-time water-level data. These results demonstrate the promise of low-cost IoT-based flood monitoring devices as an affordable and scalable remedy for communities that are at risk. This method improves knowledge of flood dynamics in the Bang Rakam model area by combining sensor technology and spatial data analysis. It also acts as a standard for flood management tactics in other lowland areas. The study emphasizes how crucial real-time data-driven flood monitoring is to enhancing early-warning systems, disaster preparedness, and water resource management. Full article
(This article belongs to the Special Issue Sensor-Based Geoinformatics Solutions for Sustainable Agricultural and Water/Forest Resources Management)
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