EXPLAIN IT TO ME – FACING REMOTE SENSING CHALLENGES IN THE BIO- AND GEOSCIENCES WITH EXPLAINABLE MACHINE LEARNING

22 citations as recorded by crossref.

1. Reliability Scores From Saliency Map Clusters for Improved Image-Based Harvest-Readiness Prediction in Cauliflower J. Kierdorf & R. Roscher https://doi.org/10.1109/LGRS.2023.3293802
2. Artificial intelligence for modeling and understanding extreme weather and climate events G. Camps-Valls et al. https://doi.org/10.1038/s41467-025-56573-8
3. Towards interpretable machine learning for observational quantification of soil heavy metal concentrations under environmental constraints Y. Sun et al. https://doi.org/10.1016/j.scitotenv.2024.171931
4. A Decade of Deep Learning in Remote Sensing: A Systematic Review of Reviews A. Safonova et al. https://doi.org/10.1109/ACCESS.2026.3705849
5. Better, Not Just More: Data-centric machine learning for Earth observation R. Roscher et al. https://doi.org/10.1109/MGRS.2025.3551556
6. Satellite Microwave Radiometry at L-Band for Monitoring Earth’s Essential Climate Variables: From Fundamental Physics to Sixteen Years of Global Climate Observations and Beyond Y. Zhou et al. https://doi.org/10.1109/MGRS.2026.3665669
7. Grad-SLAM: Explaining Convolutional Autoencoders’ Latent Space of Satellite Image Time Series T. Di Martino et al. https://doi.org/10.1109/LGRS.2023.3302906
8. Visual Interpretation of Machine Learning: Genetical Classification of Apatite from Various Ore Sources T. Zhou et al. https://doi.org/10.3390/min13040491
9. Uncertainty-Driven Modeling of Microporosity and Permeability in Clastic Reservoirs Using Random Forest M. Risha et al. https://doi.org/10.1007/s11053-026-10672-5
10. Water depth estimation from Sentinel-2 imagery using advanced machine learning methods and explainable artificial intelligence V. Saeidi et al. https://doi.org/10.1080/19475705.2023.2225691
11. Opening the Black Box: A systematic review on explainable artificial intelligence in remote sensing A. Höhl et al. https://doi.org/10.1109/MGRS.2024.3467001
12. Joint Characterization of Sentinel-2 Reflectance: Insights from Manifold Learning D. Sousa & C. Small https://doi.org/10.3390/rs14225688
13. The responses of ecosystem water use efficiency to CO2, nitrogen deposition, and climatic drivers across China X. Cai et al. https://doi.org/10.1016/j.jhydrol.2023.129696
14. Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing I. Kakogeorgiou & K. Karantzalos https://doi.org/10.1016/j.jag.2021.102520
15. Core-Set Selection for Data-Efficient Land Cover Segmentation K. Nogueira et al. https://doi.org/10.1109/ACCESS.2026.3659734
16. Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling A. Maxwell et al. https://doi.org/10.3390/rs13244991
17. Explainable AI for earth observation: A review including societal and regulatory perspectives C. Gevaert https://doi.org/10.1016/j.jag.2022.102869
18. Assessment of open-pit captive limestone mining areas using sentinel-2 imagery with spectral indices and machine learning algorithms V. C & U. G https://doi.org/10.3233/KES-230065
19. A Colourimetric Approach to Ecological Remote Sensing: Case Study for the Rainforests of South-Eastern Australia R. Aravena et al. https://doi.org/10.3390/rs13132544
20. Interpreting Conv-LSTM for Spatio-Temporal Soil Moisture Prediction in China F. Huang et al. https://doi.org/10.3390/agriculture13050971
21. Meta-learning to address diverse Earth observation problems across resolutions M. Rußwurm et al. https://doi.org/10.1038/s43247-023-01146-0
22. Better, Not Just More: Data-centric machine learning for Earth observation R. Roscher et al. https://doi.org/10.1109/MGRS.2024.3470986