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Geomatics
Special Issues
Advanced Geospatial Intelligence for Sustainable Agriculture and Environmental Management
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Advanced Geospatial Intelligence for Sustainable Agriculture and Environmental Management

A special issue of Geomatics (ISSN 2673-7418).

Deadline for manuscript submissions: 30 April 2027 | Viewed by 516

Editors

Dr. Konstantinos X. Soulis

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
Interests: hydrology; environmental hydrology; hydrological modeling; hydrometry; WRM; irrigation; irrigation water management; soil hydrology; geographical information systems (GIS)
Special Issues, Collections and Topics in MDPI journals
Dr. Dionissios Kalivas

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece
Interests: land evaluation; site specific crop management; digital farming; GIS; remote sensing; spatial analysis; soil information systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Thomas Alexandridis

E-Mail Website
Guest Editor
Laboratory of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: agricultural and environmental resources monitoring and modeling using earth observation, and spatial analysis of geographic data in GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of spatial data collection technologies—spanning UAVs, satellite remote sensing, and IoT sensor networks—has revolutionized the management of agricultural and natural resources. When integrated with Geographical Information Systems (GISs) and Artificial Intelligence (AI), these technologies provide the capability to confront complex contemporary issues, from food security to environmental degradation.

This Special Issue aims to synthesize the latest research in Earth Observation and Geoinformatics. We seek to highlight how high-resolution, multitemporal, and multispectral data are being transformed into actionable intelligence for farmers, land managers, and policymakers. Particular focus is placed on the convergence of traditional geospatial methods with modern advancements such as Deep Learning, Cloud Computing, and Big Data analytics to improve risk assessment, forecasting, and decision-making.

We invite authors to submit original research and comprehensive reviews that explore the intersection of spatial analysis and sustainable management. Potential themes include, but are not limited to, the following:

  • Integration of Satellite Data, GNSS, and IoT in Agriculture.
  • Web-GIS and Cloud Services for Managing Big Spatial Data.
  • Spatial Statistics, Geostatistics, and AI/Neural Networks in Environmental Modeling.
  • Precision Agriculture, Smart Farming, and Digital Twins.
  • Crop Phenotyping, Yield Prediction, and Soil Fertility Management.
  • Pest, Disease, and Invasive Species Management.
  • Spatial Management of Livestock, Pastures, and Fisheries.
  • Land Suitability Analysis and Land Use Planning.
  • Water Resources Management and Hydrological Modeling.
  • Forestry Management and Ecosystem Restoration.
  • Geohazards Assessment (Floods, Droughts, Wildfires, and Landslides).
  • Food Security and Environmental Impact Assessment.

Dr. Konstantinos X. Soulis
Dr. Dionissios Kalivas
Prof. Dr. Thomas Alexandridis
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geomatics 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 1200 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

  • GIS
  • geoinformatics
  • spatial analysis
  • remote sensing
  • earth observation
  • agriculture
  • environment
  • digital twins
  • precision agriculture
  • natural resources
  • land use

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Related Special Issue

Published Papers (1 paper)

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Research

26 pages, 23307 KB  
Article
Spatiotemporal Modeling and Uncertainty Quantification of Reference Evapotranspiration Using Machine Learning and Bayesian Model Averaging in Benin
by Bienvenue Christela Finounou Mizele, Modeste Meliho, Vinasetan Ratheil Houndji, Semevo Arnaud R. M. Ahouandjinou and Collins A. Orlando
Geomatics 2026, 6(4), 73; https://doi.org/10.3390/geomatics6040073 - 2 Jul 2026
Viewed by 201
Abstract
Reference evapotranspiration (ET0) represents the atmospheric demand for water from a well-watered vegetated surface and is a key component of the hydrological cycle and agricultural water management. This study evaluated the performance of seven machine learning (ML) models: linear regression (LR), [...] Read more.
Reference evapotranspiration (ET0) represents the atmospheric demand for water from a well-watered vegetated surface and is a key component of the hydrological cycle and agricultural water management. This study evaluated the performance of seven machine learning (ML) models: linear regression (LR), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Decision Trees (DT), and Cubist, for predicting monthly FAO-56 Penman–Monteith ET0 in Benin. The target variable was calculated from data collected at six synoptic stations over the 2017–2021 period. Ten remote-sensing and topographic predictors were used: MODIS Land Surface Temperature (LST), six Sentinel-2 optical vegetation indices (NDVI, EVI, NDMI, NDWI, MSI, NDRE), elevation, and cyclic month encoding. Models were trained on the 2017–2019 period and evaluated on an independent temporal test set (2020–2021). All models showed positive predictive performance, with the BMA ensemble achieving the highest accuracy (RMSE = 7.0% of mean ET0, R2 = 0.802), followed by Cubist (RMSE = 7.3%, R2 = 0.787) and DT (RMSE = 7.5%, R2 = 0.776). The seven models were combined via Bayesian Model Averaging (BMA) with posterior weights estimated by the EM algorithm to produce 1 km monthly ET0 maps for Benin for 2025. BMA-derived inter-model standard deviation provided spatially explicit uncertainty estimates, revealing that prediction uncertainty is greatest in the northern Sudanian zone during the dry season. The ET0 target variable was constructed as a hybrid product combining station temperature observations with solar radiation, wind speed, and vapor pressure deficit extracted from the TerraClimate gridded reanalysis dataset; this methodological choice is discussed as a study limitation. Full article
(This article belongs to the Special Issue Advanced Geospatial Intelligence for Sustainable Agriculture and Environmental Management)
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