A GENERIC MACHINE LEARNING-BASED FRAMEWORK FOR PREDICTIVE MODELING OF LAND SURFACE TEMPERATURE

8 citations as recorded by crossref.

1. Machine Learning Approaches for Geospatial Modeling of Urban Land Surface Temperature: Assessing Geographical Compactness, Interpretability, and Causal Inference N. Hoang https://doi.org/10.3390/s25175380
2. Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models H. Miao et al. https://doi.org/10.3390/rs16132324
3. Research on the coupling mechanism between green space morphology and the urban heat island effect based on machine learning: a case study of Dali City, China J. Peng et al. https://doi.org/10.1038/s41598-025-30327-4
4. Integrating remote sensing and meteorological data for AI-based land surface temperature prediction with feature selection approaches T. Fong et al. https://doi.org/10.1186/s12302-025-01222-6
5. Integrating Remote Sensing and Ground-Based Data for Enhanced Spatial–Temporal Analysis of Heatwaves: A Machine Learning Approach T. Chongtaku et al. https://doi.org/10.3390/app14103969
6. Machine learning-based estimation of land surface temperature variability over a large region: a temporally consistent approach using single-day satellite imagery N. Rengma & M. Yadav https://doi.org/10.1007/s10661-024-12856-z
7. What if transformers revolutionize geospatial forecasting? ConvLSTM-Transformer-ARIMA framework for LST forecasting H. Zhang et al. https://doi.org/10.1016/j.scs.2025.106794
8. Assessment and prediction of land surface temperature effects on human thermal comfort in the city of Oran, Algeria A. Bendib & M. Boutrid https://doi.org/10.1007/s00704-024-05097-0