Accuracy and uncertainty of thermal-infrared remote sensing of stream temperatures at multiple spatial scales

Stream temperature is an important indicator of water quality, particularly in regions where endangered fish populations are sensitive to elevated water temperature. Regional assessment of stream temperatures from the ground is limited by sparse sampling in both space and time. Remotely sensed thermal-infrared (TIR) images are able to make spatially distributed measurements of the radiant skin temperature of streams. We quantify and discuss the accuracy and uncertainty limits to recovering stream temperatures in the Pacific Northwest for a range of stream widths (10–500 m), and TIR pixel sizes (5–1000 m) from remotely sensed airborne and satellite TIR images. Among locations with more than three pixels across the stream, the image temperature overestimated the in-stream temperature on average by 1.2 -C, which is 7% of the in-stream temperature (standard error (SE) of 0.2 -C, n=21). The corresponding uncertainty (band weighted standard deviation in image temperature) for these locations averaged T0.3 -C (SE<0.1 -C, n=21) which is 2% of in-stream temperatures. This overestimation by the image temperatures is likely to be due to thermal stratification between the stream surface and the location of the in-stream temperature measurements deeper in the water column. For streams with one to three pixels across, mixing with bank elements increased the overestimation by image temperatures to 2.2 -C (SE=0.3 -C, n=23) on average (13% of in-stream temperatures), and the uncertainty increased to T0.4 -C (SE=0.1 -C, n=23) which is 2% of in-stream temperatures. For a fraction of a pixel across the stream the overestimation by image temperatures was 7.6 -C (SE=1.2 -C, n=23) on average (45% of in-stream temperatures), and the uncertainty was T0.5 -C (SE=0.1 -C, n=23) which is 3% of in-stream temperatures. These results show that reliable satellite TIR measurement of stream temperatures is limited to large rivers (¨180-m across for Landsat ETM+), unless novel unmixing algorithms are used effectively. D 2005 Published by Elsevier Inc.