TEMLI: A High-Resolution Air Temperature Estimation Using Machine Learning and Land Surface Data Across Morocco

Abstract Air temperature (Tair) is an important parameter to understand climate dynamics and support decision-making across various sectors. However, accurately estimating Tair at high spatial resolution remains challenging, particularly in data-sparse regions. This study addresses the research gap that arises from the scarcity of observed temperature data by developing a robust machine learning-based estimation model, TEMLI (Temperature Estimation with ML and Land Input). The approach leverages satellite-derived inputs such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), albedo, and wind speed. The objective is to improve the accuracy of Tair estimation by capturing the nonlinear relationships between these variables. Among the models tested within the TEMLI framework, the Multilayer Perceptron (MLP) demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of less than 1.6 °C. Notably, the model excelled in estimating extreme temperatures, especially in high-altitude regions. Trained using Moroccan weather station data, the model’s generalizability across diverse climatic conditions was also validated. This study concludes that integrating satellite-derived data with machine learning offers a scalable and reliable approach for Tair estimation, particularly in regions with limited in situ observations. The findings have significant implications for advancing climate modeling, monitoring extreme events, and informing decision-making in various climate-sensitive sectors. Graphical Abstract This visual summary offers a concise and accessible overview of the study’s main objectives, methodology, and findings. The TEMLI (Temperature Estimation with Machine Learning and Land Inputs) framework aims to improve air temperature (Tair) estimation at high spatial resolution (1 km) using satellite-derived inputs. The left panel introduces the research context, emphasizing the need for accurate temperature datasets in regions with sparse observations. The central panel outlines the methodological flowchart, detailing the various inputs (LST, NDVI, NDWI, albedo, wind speed), the machine learning models tested, and the selection of the optimal model (MLP), and the evaluation metrics and process. The right panel highlights key results, demonstrating TEMLI’s improved performance over ERA5 products with RMSE < 1.6 °C and R² > 0.9, particularly in high-altitude areas. It also summarizes practical applications, such as enhanced hydrological modeling, improved agricultural climate monitoring, and better temperature-based climate adaptation strategies.