Explainable GeoAI-Based Multi-Temporal Remote Sensing Framework for Early Detection of Climate-Induced Land Cover Transformation

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 02 | Page :
    By

    Bibhu Prasad Ganthia,

  • Subash Ranjan Kabat,

  1. Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
  2. Professor and Principal, Department of Electrical Engineering, Radhakrishna Institute of Technology and Engineering, Bhubaneswar, Odisha, India

Abstract

Climate change has emerged as one of the primary drivers of rapid land cover transformation, affecting ecosystems, agricultural productivity, biodiversity, and regional sustainability. Traditional remote sensing approaches often face challenges in detecting subtle and early-stage land cover changes due to limitations in temporal analysis and model interpretability. This study proposes an Explainable GeoAI-based multi-temporal remote sensing framework for the early detection of climate-induced land cover transformation using multi-source satellite imagery and geospatial datasets. The framework integrates advanced deep learning architectures with explainable artificial intelligence techniques to identify spatial and temporal patterns associated with vegetation degradation, urban expansion, desertification, wetland loss, and changes in agricultural landscapes. Multi-temporal observations from optical, hyperspectral, and synthetic aperture radar datasets are processed through feature extraction and spatio-temporal learning modules to improve detection accuracy under varying climatic conditions. Explainability methods such as SHAP and attention visualization are incorporated to provide transparent decision-making and to identify the dominant climatic and environmental factors influencing land cover dynamics. Experimental evaluation demonstrates that the proposed framework enables earlier detection of environmental changes, enhances classification reliability, and supports policymakers and environmental agencies in implementing proactive climate adaptation and land management strategies through scalable, robust, interpretable, data-driven, sustainable, region-specific, predictive environmental monitoring systems globally.

Keywords: Explainable GeoAI, Multi-Temporal Remote Sensing, Land Cover Transformation, Climate Change Detection, Geospatial Artificial Intelligence

How to cite this article:
Bibhu Prasad Ganthia, Subash Ranjan Kabat. Explainable GeoAI-Based Multi-Temporal Remote Sensing Framework for Early Detection of Climate-Induced Land Cover Transformation. International Journal of Satellite Remote Sensing. 2026; 04(02):-.
How to cite this URL:
Bibhu Prasad Ganthia, Subash Ranjan Kabat. Explainable GeoAI-Based Multi-Temporal Remote Sensing Framework for Early Detection of Climate-Induced Land Cover Transformation. International Journal of Satellite Remote Sensing. 2026; 04(02):-. Available from: https://journals.stmjournals.com/ijsrs/article=2026/view=249991


References

  1. Yuan Y, Yu JL, Pan Y, Zheng S. Low-resolution phase shifter-based hybrid precoding for mmWave massive MIMO. EURASIP Journal on Wireless Communications and Networking. 2024 Nov 5;2024(1):85.
  2. Zeng J, Liao B. Transmit and receive hybrid beamforming design for OFDM dual-function radar- communication systems. EURASIP journal on advances in signal processing. 2023 Mar 20;2023(1):37.
  3. Maeng SJ, Yapici Y, Güvenç I, Dai H, Bhuyan A. Hybrid precoding for mmWave massive MIMO with one-bit DAC. IEEE Communications Letters. 2020 Aug 10;24(12):2941-5.
  4. Morsali A, Haghighat A, Champagne B. Deep learning-based hybrid analog-digital signal processing in mmWave massive-MIMO systems. IEEE Access. 2022 Jul 5;10:72348-62.
  5. Kassam J, Miri M, Magueta R, Castanheira D, Pedrosa P, Silva A, Dinis R, Gameiro A. Two-step multiuser equalization for hybrid mmwave massive mimo gfdm systems. Electronics. 2020 Jul 29;9(8):1220.
  6. Xie H, Wang Y, Gao Z, Ganthia BP, Truong CV. Research on frequency parameter detection of frequency shifted track circuit based on nonlinear algorithm. Nonlinear Engineering. 2021 Jan 1;10(1):592-9.
  7. Gu J, Wang W, Yin R, Truong CV, Ganthia BP. Complex circuit simulation and nonlinear characteristics analysis of GaN power switching device. Nonlinear Engineering. 2021 Jan 1;10(1):555- 62.
  8. Rubavathy SJ, Venkatasubramanian R, Kumar MM, Ganthia BP, Kumar JS, Hemachandu P, Ramkumar MS. Smart grid based multiagent system in transmission sector. In2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) 2021 Sep 2 (pp. 1-5). IEEE.
  9. Priyadarshini L, Kundu S, Maharana MK, Ganthia BP. Controller design for the pitch control of an autonomous underwater vehicle. Engineering, Technology & Applied Science Research. 2022 Aug 7;12(4):8967-71.
  10. Zheng W, Mehbodniya A, Neware R, Wawale SG, Ganthia BP, Shabaz M. Modular unmanned aerial vehicle platform design: Multi-objective evolutionary system method. Computers and Electrical Engineering. 2022 Apr 1;99:107838.
  11. Ranjan S, Jaiswal S, Latif A, Das DC, Sinha N, Hussain SS, Ustun TS. Isolated and interconnected multi-area hybrid power systems: A review on control strategies. Energies. 2021 Dec 8;14(24):8276.
  12. Guo S, Li X, Guo Z, Zhao X, Meng S, Li Z. Polarization-independent optoelectronic modulator based on graphene ridge structure. Nanomaterials. 2021 Sep 29;11(10):2559.
  13. Deng X, Li Z, Cao F, Hong E, Fang X. Woven fibrous photodetectors for scalable UV optical communication device. Advanced Functional Materials. 2023 Jun;33(23):2213334.
  14. Guo FH, Hao LZ, Yu WZ, Li SQ, Liu GC, Hao JY, Liu YJ. High‐performance Si/VO2‐nanorod heterojunction photodetector based on photothermoelectric effect for detecting human radiation. Rare Metals. 2024 Mar;43(3):1177-85.
  15. Pellis S. Golden Fractals in Fluid Dynamics and Turbulence. Available at SSRN 5543579. 2025 Sep 28.
  16. Bibhu Prasad Ganthia and rosalin pradhan AI-Assisted Conversion and Optimization of Analog-to- Digital Signal Transmission forLow-Power IoT and Edge Devices. International Journal of Microelectronics and Digital integrated circuits. 2025; 11(02): -p.
  17. Baraa SM, Desa H, Mohammed KS, Al-Malaisi TA, Hussain AS, Majdi HS. Selective harmonic elimination in reduced-switch multilevel inverters for PV systems using the sparrow search algorithm. Journal of Robotics and Control (JRC). 2025 Feb 15;6(1):385-95.
  18. Sahoo LK, Mahanta S, Kumar S, Kaliappan S, Ganthia BP. Fast Transient Detection and Power Quality Improvement in Grid-Connected Photovoltaic Systems Using Spiking Neural Networks. In2026 International Conference on Electrical and Electronics for Sustainable Innovations (ICEESI) 2026 Mar 27 (pp. 1-6). IEEE.
  19. Mahanta S, Sahoo LK, Balaraju V, Kaliappan S, Ganthia BP. Design and Performance Analysis of a Distributed Modular Active Power Filter Network for Power Quality Improvement in PV System. In2026 International Conference on Electrical and Electronics for Sustainable Innovations (ICEESI) 2026 Mar 27 (pp. 1-8). IEEE..
  20. Ganthia BP, Barik SK, Nayak B, Priyadarshi N, Padmanaban S, Hiran KK, Holm-Nielson JB, Bansal RC. 2 Power control of modified type III DFIG-based wind turbine system using four-mode type I fuzzy logic controller. Artificial Intelligence and Internet of Things for Renewable Energy Systems. 2021 Nov 22;12:41.
Ahead of Print Subscription Review Article
Volume 04
02
Received 15/07/2026
Accepted 16/07/2026
Published 17/07/2026
Publication Time 2 Days
42

Login


My IP

PlumX Metrics