Science Inventory

Evaluating satellite and modeled lake surface water temperature across the contiguous United States

Citation:

Schaeffer, B., H. Ferriby, W. Salls, N. Reynolds, Jeff Hollister, B. Kreakie, S. Shivers, B. Johnson, O. Cronin-Golomb, K. Meyers, AND M. Beal. Evaluating satellite and modeled lake surface water temperature across the contiguous United States. HYDROBIOLOGIA. Springer, New York, NY, 853:3715–3737, (2026). https://doi.org/10.1007/s10750-026-06178-z

Impact/Purpose:

This study provides a novel contribution to lake temperature monitoring relevant to cyanoHAB forecasting. Validation of the Landsat derived surface water temperature and development of random forest models demonstrate methods to generate continuous temperature datasets for 2,192 of the largest U.S. lakes that are resolved by Sentinel-3 OLCI. The random forest models using in situ temperature measurements were not biased from cloud processing and demonstrated better model performance. This work emphasized the need for accurate and reliable temperature data for cyanoHAB forecasting, where combining in situ data with models fills critical spatio-temporal gaps supporting the ability to not only predict lake surface water temperature but also predict cyanoHAB events to protect aquatic ecosystems.

Description:

We developed a model to predict surface water temperature across U.S. lakes using satellite remote sensing and in situ observations to enhance cyanobacterial harmful algal bloom (cyanoHAB) forecasting. The study focused on Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensor resolved lakes. We developed random forest models using both Landsat-derived and in-situ-measured surface water temperature. Landsat models offered broad spatial and temporal coverage of all OLCI resolved lakes, but they were sensitive to cloud cover and required filtering to minimize error. In contrast, the in situ model represented fewer OLCI resolved lakes, but yielded lower mean absolute error and bias. The models predicted lake surface temperature across the entire calendar year, with best performance (RMSEapplied = 1.11; biasapplied = 0.01; MAEapplied = 0.77) from the in situ model. This approach allowed for the continuous prediction of lake surface temperatures from 1.1 to 31.6 °C for unfrozen, open¿water conditions critical for improving the accuracy of cyanoHAB forecasting. A key strength of this study was the use of an extensive dataset and model validation against in situ observations, which improved predictive accuracy throughout the year across all seasons. The predictive model offers a water resource tool for management, ecosystem protection, and public health.