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A special issue of Earth (ISSN 2673-4834). This special issue belongs to the section "AI and Big Data in Earth Science".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 9077

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Dr. Hossein Bonakdari

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Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: water resources management; hydrological modeling; artificial intelligence; sustainable development; time series
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of artificial intelligence (AI), machine learning (ML), and big data analytics is revolutionizing how we observe, model, and understand the Earth system. With the growing availability of high-resolution satellite data, sensor networks, and large-scale simulations, Earth science is becoming increasingly data-intensive. These emerging technologies enable the extraction of hidden patterns, improve forecasting accuracy, and enhance our ability to assess and mitigate environmental risks.

At the same time, the increasing availability of diverse remote sensing data, such as radar, optical, and LiDAR acquired through multiplatform (drone and satellite) and multi-resolution systems, is reshaping how Earth processes are observed, analyzed, and modeled. The integration of heterogeneous datasets with AI-driven methodologies offers new opportunities to develop more accurate, interpretable, and scalable models of environmental dynamics.

This Special Issue welcomes manuscripts addressing, but not limited to, the following themes:

AI and machine learning applications in geoscience, climate, and environmental modeling.
Big data analytics and data fusion for remote sensing and Earth observation.
Integration of radar, optical, and LiDAR data from multiplatform and multi-resolution systems.
Open-source and cloud-based frameworks for large-scale environmental data analysis.
Interpretable AI, uncertainty quantification, and model validation in Earth system studies.

Dr. Hossein Bonakdari
Guest Editor

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. Earth 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 1400 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

artificial intelligence
machine learning
big data
remote sensing
radar
optical
LiDAR
multiplatform observation
multi-resolution analysis
earth system modeling

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Published Papers (7 papers)

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Research

28 pages, 18790 KB

Open AccessArticle

Evaluating Landsat Water Indices and Monitoring Long-Term Surface-Water Dynamics in Lake Nasser and the Tushka Lakes in a Hyper-Arid Environment Using Google Earth Engine

by Bosy A. El-Haddad, Ahmed M. Youssef, Alaa Ramadan, El-Sayed M. Robaa and Shaymaa Rizk

Earth 2026, 7(4), 112; https://doi.org/10.3390/earth7040112 - 5 Jul 2026

Viewed by 492

Abstract

Long-term monitoring of surface-water dynamics in hyper-arid reservoir systems requires consistent remote-sensing methods that can distinguish open water from bright desert surfaces, shallow water, wet sand, and mixed shoreline pixels. This study evaluates Landsat-derived spectral water indices for delineating surface water in Lake Nasser and the adjacent Tushka Lakes, generates a multi-decadal record of surface-water extent using Google Earth Engine, and places the resulting surface-water patterns in the context of available hydrogeological observations. Landsat TM and OLI surface reflectance imagery was used to compare seven commonly applied water indices (NDWI, EWI, NDX, WRI, AWEI_nsh, TCW, and NWI) based on mapped water area, relative area differences, and classification accuracy metrics derived from 1000 stratified reference samples. Among the tested indices, NDWI provided stable water–land separation (overall accuracy ≈ 93.6%; κ ≈ 0.898) and was selected for long-term mapping. The NDWI-based workflow was implemented in Google Earth Engine to generate quarterly composites of surface-water extent for the period 1987–2026. The resulting time series reveals stable, persistent surface water in the central and southern sectors of Lake Nasser, in contrast to pronounced seasonal and interannual variability in the shallow, intermittently connected Tushka basins. Total mapped water area increased from 2631 km² in 1987 to 8923 km² in early 2026, with Lake Nasser ranging from 2411 to 6060.7 km² and the Tushka Lakes expanding from no mapped water before 1998 to more than 3300 km² during 2025. To assess possible surface–subsurface interaction, daily lake-stage records (1965–2014) and monthly groundwater levels from 44 observation wells were used to estimate potential seepage losses from Lake Nasser to the Nubian Sandstone Aquifer System using Darcy’s law. Annual seepage estimates ranged from 15.58 × 10⁶ to 36.68 × 10⁶ m³/year, suggesting spatial variability in potential lake–aquifer seepage along the western lake margin. The combined remote-sensing and hydrogeologic results provide complementary, non-causal evidence for interpreting where surface-water persistence and estimated seepage may co-occur. Because spatial correlation analysis, calibrated ground-water modeling, full water-budget analysis, and independent field validation were not performed, the inferred seepage–surface-water relation should be regarded as a cautious hypothesis rather than proof of causality. Full article

(This article belongs to the Special Issue Feature Papers for AI and Big Data in Earth Science)

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33 pages, 16386 KB

Open AccessEditor’s ChoiceArticle

Deep Learning-Based Classification of Aerial Imagery for Monitoring Climate Change Effects in the Maritime Alps

by Chiara Graziani, Francesca Matrone and Andrea Maria Lingua

Earth 2026, 7(3), 99; https://doi.org/10.3390/earth7030099 - 10 Jun 2026

Viewed by 610

Abstract

Mountain ecosystems are highly sensitive to climate change and require spatially explicit monitoring tools to support adaptive management. Within the framework of the Interreg-ALCOTRA “ACLIMO” project, this study investigates land cover dynamics in the Gesso Valley (Maritime Alps, Italy) over the period 2010–2021 using deep learning–based classification of high-resolution aerial orthophotos integrated with climate data analysis. Multi-temporal RGB and NIR imagery (2010, 2018, 2021) was classified using convolutional neural networks (U-Net and MMSegmentation) in ArcGIS Pro, with CORINE Land Cover datasets used for training. The best-performing model, based on CLC + Backbone 2018, achieved an overall accuracy of 82%, increasing to 87% after fine-tuning. Change detection revealed a general shift towards increased vegetation cover, while climate analysis based on regional weather stations (1990–2021) identified a warming trend of +0.4 °C/decade and recent drier conditions. Logistic regression highlighted significant associations between land cover transitions and climate anomalies, with temperature positively influencing change probability (OR = 1.40). The study demonstrates the potential of operational GIS-integrated deep learning workflows for climate change monitoring in complex alpine environments under real-world data constraints. Full article

(This article belongs to the Special Issue Feature Papers for AI and Big Data in Earth Science)

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14 pages, 1696 KB

Open AccessEditor’s ChoiceArticle

Machine Learning-Based Estimation of Daily Reference Evapotranspiration in Vojvodina, Serbia

by Milica Stajić, Dejan Mirčetić, Atila Bezdan, Radovan Savić, Sanja Antić, Nikola Santrač, Andrea Salvai, Milena Lakićević and Boško Blagojević

Earth 2026, 7(3), 88; https://doi.org/10.3390/earth7030088 - 26 May 2026

Viewed by 956

Abstract

Reference evapotranspiration (ET₀) is most commonly estimated using the FAO-56 Penman–Monteith (PM) equation. However, its application is often limited by the lack of required meteorological parameters. Due to their flexibility, ability to operate with limited input, and high accuracy in estimating ET₀, machine learning models have become increasingly relevant in scientific research, offering a practical alternative under limited data conditions. In this study, artificial neural networks (ANNs) were applied to estimate daily ET₀ using meteorological data from the Novi Sad station in Vojvodina (Serbia). The dataset consisted of eight meteorological variables relevant to evapotranspiration processes. Analysis showed that some variables had a stronger influence on ET₀ prediction than others. To evaluate their combined effect, a series of ANN models with different input combinations were developed and tested. The random forests, gradient boosting and k-nearest neighbors models were used as a benchmark, and model performance was evaluated using R², NSE, RMSE, and MAE. The highest accuracy was achieved when all variables were included, providing the model with maximum information. The best performance was obtained using a two-hidden-layer architecture with 32 and 16 neurons, resulting in R² = 0.97, NSE = 97.07%, RMSE = 0.23 mm/day, and MAE = 0.21 mm/day. The results showed that a limited number of input variables can be used to estimate ET₀ with high accuracy, achieving an R² value of 0.95 using only three input variables. Therefore, the findings of this study may contribute to more accurate and cost-effective irrigation scheduling and water balance estimation, providing practical benefits for agricultural water management and farmers in Serbia. Full article

(This article belongs to the Special Issue Feature Papers for AI and Big Data in Earth Science)

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46 pages, 86403 KB

Open AccessArticle

Seismic Shake-e 2.1 App to Contribute to Mitigating the Seismic Risk

by Armando Aguilar-Meléndez, Josep De la Puente, Marisol Monterrubio-Velasco, Alejandro García-Elías, Jesús Huerta-Chua and Armando Aguilar-Campos

Earth 2026, 7(3), 78; https://doi.org/10.3390/earth7030078 - 11 May 2026

Viewed by 889

Abstract

Seismic Shake-e is a free app that provides valuable data and tools related to earthquakes, covering the stages before, during, and after seismic events. In this text, we describe the main features of the Seismic Shake-e 2.1 (SSe) app, the considerations that guided its development, examples of its use, and the challenges for future versions. Version 1.0 of this app was awarded as one of the winners of EOVALUE: Call for Innovative Apps in environmental and social fields, a project by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. SSe recognizes two user levels: basic and intermediate/advanced. There are six modules for each level. The main topics of these modules for both user types are: (1) Accelerometer Networks (AN), (2) Seismograms Analyzer-e (SAe), (3) Seismic Design of Buildings (SDB), (4) Earthquake Preparedness (EP), (5) Earthquake Early Warning Systems (EEWS) & Tsunami Warning Systems (TWS), and (6) Earthquake Emergency Response & Recovery. The two key modules are AN and SAe: the first explains how to obtain seismic records, and the second provides tools for their analysis. We include some applications of SSe, along with their results and discussion. We also list the advantages of the main modules and discuss potential future developments and improvements. The uniqueness of this work is that we highlight the software’s essential features and demonstrate its applications. Full article

(This article belongs to the Special Issue Feature Papers for AI and Big Data in Earth Science)

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28 pages, 3802 KB

Open AccessArticle

Modeling Flood Susceptibility in Rwanda Using an AI-Enabled Risk Mapping Tool

by Yves Hategekimana, Valentine Mukanyandwi, Georges Kwizera, Fidele Karamage, Emmanuel Ntawukuriryayo, Fabrice Manzi, Gaspard Rwanyiziri and Moise Busogi

Earth 2026, 7(2), 53; https://doi.org/10.3390/earth7020053 - 21 Mar 2026

Cited by 1 | Viewed by 1839

Abstract

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

(This article belongs to the Special Issue Feature Papers for AI and Big Data in Earth Science)

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23 pages, 5440 KB

Open AccessArticle

Risk Assessment of Land Subsidence Hazard Due to Groundwater Depletion for Water Conservation

by Ni Made Pertiwi Jaya and Masahiko Nagai

Earth 2026, 7(1), 29; https://doi.org/10.3390/earth7010029 - 15 Feb 2026

Cited by 1 | Viewed by 1434

Abstract

Hazard risk monitoring of groundwater depletion and land subsidence due to excessive groundwater extraction is crucial for groundwater resource development, especially in densely populated, small-island developing sites. The island of Bali, Indonesia, represents such an urban environment at risk of land subsidence arising from groundwater depletion. The total percentage of groundwater depletion was calculated and interpolated spatially using measurements of groundwater level from 2008 to 2017 at 18 monitoring well sites available in the area. Furthermore, time-series synthetic-aperture radar (SAR) interferometry processing was applied to estimate the temporal change in land displacement using the Phased Array type L-band SAR (PALSAR) data from 2007 to 2010. The result of downward displacement, signifying subsidence, corresponded with the Global Navigation Satellite System (GNSS) data measurements at stations distributed in the observed subsided areas, i.e., CDNP and CPBI. The displacement varied consistently with changes in groundwater level. In regard to maintaining groundwater utilization, the hazard–risk relation of the groundwater depletion, i.e., low (<10%), moderate (10–25%), and high (>25%), and the presence/absence of subsidence were utilized to classify groundwater conservation into safe, vulnerable, critical, and damaged zones. This application can be considered effective in providing spatial information for sustainable groundwater management. Full article

(This article belongs to the Special Issue Feature Papers for AI and Big Data in Earth Science)

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21 pages, 4938 KB

Open AccessArticle

Impact of LULC Classification Methods on Runoff Simulation in an Arid Mountainous Watershed Using Remote Sensing and Machine Learning

by Ali Ibrahim, Ahmed Wageeh, Mohamed A. Hamouda, Alaa Ahmed and Ahmed Gad

Earth 2026, 7(1), 26; https://doi.org/10.3390/earth7010026 - 11 Feb 2026

Cited by 4 | Viewed by 1993

Abstract

Reliable hydrologic modeling in arid, topographically complex watersheds depends on accurate land-use/land-cover (LULC) representation. This study evaluates how different LULC categorization methods affect simulated runoff for the Wadi Hatta watershed (UAE) using a GIS-driven machine learning framework that combines high-resolution remote sensing with hydrologic modeling. LULC maps were generated in Google Earth Engine using Random Forest (RF) and Support Vector Machine (SVM) classifiers applied to Sentinel-2 (10 m) and Landsat 8/9 (30 m) imageries and compared with the 10 m ESRI predefined LULC dataset. The resulting LULC classifications were converted to SCS Curve Numbers and used in HEC-HMS hydrologic modeling to simulate runoff under a 50-year design storm, under consistent meteorological and physical conditions. Results show that Sentinel-2 + SVM achieved the highest classification accuracy (overall accuracy up to 0.86) and produced the earliest and highest simulated peak discharge (11.4 m³/s), reflecting improved detection of impervious surfaces. In contrast, the Landsat-9 + RF scenario yielded the lowest peak (7.5 m³/s), consistent with a higher proportion of pervious land covers. LULC change analysis between 2017 and 2024 showed increases in forest cover (1.0–3.3%) and built-up areas (6.0–7.9%) driven by afforestation and urban expansion. These results demonstrate that LULC input resolution and classifier selection significantly influence hydrologic model sensitivity and runoff estimates, underscoring the need for carefully selected, high-resolution LULC products in flood risk assessment and water resource planning in data-scarce arid environments. Full article

(This article belongs to the Special Issue Feature Papers for AI and Big Data in Earth Science)

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