Spatially Aware Deep Learning for Microclimate Prediction from High-Resolution Geospatial Imagery

Abstract

Microclimate models are essential for linking climate to ecological processes, yet most physically based frameworks estimate temperature independently for each spatial unit and rely on simplified representations of lateral heat exchange. As a result, the spatial scales over which surrounding environmental conditions influence local microclimates remain poorly quantified. Here, we show how remote sensing can help quantify the contribution of spatial context to microclimate temperature predictions. Building on convolutional neural network principles, we designed a task-specific deep neural network and trained a series of models in which the spatial extent of input data was systematically varied. Drone-derived spatial layers and meteorological data were used to predict ground temperature at a focal location, allowing direct assessment of how prediction accuracy changes with increasing spatial context. Our results show that incorporating spatially adjacent information substantially improves prediction accuracy, with diminishing returns beyond spatial extents of approximately 5-7 m. This characteristic scale indicates that ground temperatures are influenced not only by local surface properties, but also by horizontal heat transfer and radiative interactions operating across neighboring microhabitats. The magnitude of spatial effects varied systematically with time of day, microhabitat type, and local environmental characteristics, highlighting context-dependent spatial coupling in microclimate formation. By treating deep learning as a diagnostic tool rather than solely a predictive one, our approach provides a general and transferable method for quantifying spatial dependencies in microclimate models and informing the development of hybrid mechanistic-data-driven approaches that explicitly account for spatial interactions while retaining physical interpretability.

Figures (6)

• Figure 1: Flowchart of the data and modeling pipeline used in our study. Drone mapping missions were conducted to simultaneously collect RGB and thermal imagery, which were processed into orthophoto, Digital Surface Model (DSM), and thermal maps. All maps were standardized to a 15 cm² resolution, and five submaps were randomly sampled from each. From these, five feature maps were generated—Triangular Greenness Index (TGI), shade, height, skyview, and solar radiation—and segmented into small tiles. Each tile was paired with the temperature at its central pixel to train a modified ResNet model. Separate models were trained for different tile sizes to evaluate how spatial context influences model performance. Model accuracy was assessed across varying microhabitats, times of day, and environmental conditions.
• Figure 2: Illustration of tiles at different spatial scales used in the study. Each panel shows a tile of a specific size, ranging from 9×9 to 63×63 pixels. Larger tiles retain broader spatial context and environmental gradients, while smaller tiles capture more localized information with less surrounding context. Each panel displays ground-truth thermal maps.
• Figure 3: Convolutional neural network architecture for microclimate prediction. Five spatial input tiles are processed through successive convolutional and pooling layers, flattened, and concatenated with eight meteorological variables. The combined features are then passed through a fully connected layer and a nonlinear activation function to produce the final prediction, representing the temperature at the center of the tiles. Colors: green – input tiles; blue – convolutional layers; orange – pooling layers; grey – fully connected layer; red – predicted temperature.
• Figure 4: Progression of squared prediction error ($^\circ$C$^2$) with increasing tile size. (a) -- the reference RGB map. (b) through (h) -- the spatial distribution of squared prediction errors for models utilizing tile sizes ranging from $5\times5$ to $63\times63$ pixels, respectively.
• Figure 5: Model performance improved with increased spatial resolution, highlighting the value of spatial context in predicting ground temperature. Each panel shows how tile size affects prediction error (mean square error) across three dayparts: (a) morning, (b) noon, and (c) evening. Colored points represent predictions in open (orange) and shaded (black) microhabitats, and lines show smooth GAM fits with shaded confidence intervals.