A machine learning model for estimating the temperature of small rivers using satellite-based spatial data

• Published in: Remote sensing of environment Vol. 311; p. 114271
• Main Authors: Philippus, Daniel, Sytsma, Anneliese, Rust, Ashley, Hogue, Terri S.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2024
• Subjects: Machine learning; Remote sensing; Stream temperature; Remote sensing; Machine learning; Stream temperature
• ISSN: 0034-4257
• DOI: 10.1016/j.rse.2024.114271

The influence of anthropogenic activity and land cover alteration on stream temperatures has major ecological implications, such as limiting fish survival. While these ecological impacts have been extensively studied at varying spatial scales for major rivers, our understanding of the range and complexity of this relationship across climates and geographies for smaller rivers (less than ∼60 m wide) remains limited. This is in part because although such rivers comprise a vast majority (∼97%) of total stream length, most smaller rivers lack routine temperature observations despite extensive gage networks. While existing gage networks in many regions are sufficient to support satellite remote sensing-based modeling of small stream temperatures, most large regions including the contiguous United States do not have modeled data products at high spatial and temporal resolution for small rivers. In this study, we developed a statistical model, TempEst (“temperature estimation”), to estimate monthly average water temperatures for smaller rivers and evaluated the model with rivers ranging from 3 m to 1000 m (1 km) in width, using land cover, topography, and satellite-based land surface temperature. The rivers chosen for development of the model are representative of a range of urban and rural land uses and cover eight US Environmental Protection Agency Level I ecoregions. The model was calibrated and validated using observed stream temperatures from the United States Geological Survey. TempEst is able to predict monthly average temperature for streams of any size with an overall median RMSE of about 1.5 °C and bias of 0%. Model accuracy has a consistent trend with training gauge network density, with median RMSE increasing to about 2.0 °C in more sparsely-gauged regions. As a simple demonstration, we used TempEst to estimate thermal suitability conditions for cutthroat trout in Rocky Mountain streams in the United States. The developed model, available as open-source R (model) and Google Earth Engine (data retrieval) scripts, will facilitate the study of stream temperature behaviors at higher resolutions than previously available across the contiguous United States and can be easily adapted to support global river systems. •A random forest model was built to estimate stream temperature from satellite data.•The model has a median estimation error across validation gages of 1.5 C.•The model performs consistently for all tested river widths including under 10 m.•The model performs consistently for natural and heavily altered locations.