Interpretable geospatial machine learning for seasonal land surface temperature prediction using multi-source earth observation data

Understanding Land Surface Temperature Patterns in Istanbul: The Role of Blue–Green and Built-Environment Predictors Using Explainable Machine Learning

Rapid urbanization and the replacement of vegetated surfaces by impervious materials can substantially alter urban thermal environments, while water bodies, topography, and coastal setting may further modify these patterns. Understanding how these factors contribute to land surface temperature (LST), particularly acros...

Machine Learning-Based Reconstruction of Missing Meteorological Observations Using Reanalysis and Satellite Data in West Africa

A machine learning-based framework for reconstructing missing hourly observations from automatic weather stations by integrating in situ AWS measurements with ERA5-Land reanalysis fields and Global Precipitation Measurement (GPM) satellite-derived products is presented.

Integrating Satellite Data with Ground-Based Low-Cost Sensors for Hourly Fine-Scale Land Surface Temperature Mapping: A Case Study in Bentley, Western Australia

Climate change and rapid urbanisation are intensifying the urban heat island effect, increasing thermal stress, degrading air quality, and leading to rising energy demand. Monitoring neighbourhood-scale heat requires Land Surface Temperature (LST) observations at fine spatial and temporal resolutions, yet satellite the...

Planetary Boundary Layer Height Prediction over Karst Plateau: A Study Integrating Multi-Source Remote Sensing and Machine Learning

The planetary boundary layer height (PBLH) is a critical parameter for air quality modeling, weather forecasting, and climate studies, yet accurately estimating it over complex terrain remains a major challenge. This study investigates whether multi-source ground-based remote sensing combined with machine learning can...

Bias correction of monthly air temperature from the NASA POWER gridded meteorological dataset over Bangalore Urban, India: a comparative evaluation of statistical and machine-learning methods

Spectral index and machine learning-based evaluation of coastal urban thermal dynamics using google earth engine

This present study introduces a cloud-based geospatial and machine learning framework for assessing coastal urban heat island (UHI) dynamics in Thoothukudi coastal region, Southern India, using 12 seasonal Landsat composites (2010–2025) processed through Google Earth Engine (GEE). Seasonal classification followed I...