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Updated: Jun 3, 2026

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

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Published on: July 4, 2014

GeoAI for polar vegetation mapping and hydrological interactions: A systematic review.

Narmilan Amarasingam¹, Mariapina Vomero², Arthur Platel³

¹Securing Antarctica's Environmental Future (SAEF), University of Wollongong, Wollongong, New South Wales, 2522, Australia; Environmental Futures, University of Wollongong, Wollongong, New South Wales, 2522, Australia.

The Science of the Total Environment

|June 1, 2026

Summary

Artificial intelligence (AI) and remote sensing (RS) are revolutionizing polar ecological monitoring. This review synthesizes 116 studies, highlighting a shift towards integrated AI-enhanced RS frameworks for tracking climate-driven changes in vegetation and hydrology.

Keywords:

Antarctica Arctic Deep learning Drone Machine learning Satellite Uncrewed aerial vehicle (UAV)

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Last Updated: Jun 3, 2026

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

Published on: July 4, 2014

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

Area of Science:

* Environmental Science, Remote Sensing, Artificial Intelligence, Polar Ecology.
* Utilizes bibliometric and conceptual-network analyses for systematic synthesis.

Background:

* Logistical and environmental challenges in polar regions necessitate advanced monitoring techniques.
* Remote sensing (RS) and Artificial Intelligence (AI) offer novel solutions for ecological assessment.
* Previous reviews have not systematically evaluated the scope and quality of AI-enhanced RS in polar environments.

Purpose of the Study:

* To conduct the first systematic review of AI-enhanced RS (GeoAI) in Arctic and Antarctic environments (2005-2025).
* To analyze trends, methodologies, and identify research gaps in polar GeoAI applications.
* To establish a foundation for future polar monitoring strategies.

Main Methods:

* Systematic literature search adhering to PRISMA 2020 guidelines, analyzing 116 studies.
* Bibliometric and conceptual-network analyses to map publication trends and research connections.
* Evaluation of methodologies, including machine learning, deep learning, and spectral-index applications.

Main Results:

* Significant expansion of GeoAI research since 2018, driven by UAVs, multispectral imaging, and deep learning (DL).
* Shift from isolated monitoring to integrated data-fusion frameworks linking vegetation, hydrology, and climate.
* Emergence of DL, particularly convolutional neural networks, for fine-scale analysis; persistent gaps in UAV-to-satellite integration and validation.
* Increasing use of advanced spectral indices beyond greenness for physiological insights.

Conclusions:

* GeoAI is crucial for understanding and managing polar ecosystems under rapid climate change.
* Future research should focus on hierarchical UAV-to-satellite fusion, open datasets, and explainable AI.
* These advancements are essential for scalable, climate-adaptive conservation in Earth's sensitive polar regions.