Snowpack water-equivalent estimates from satellite and aircraft remote-sensing measurements of the Red River basin, north-central U.S.A.

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Abstract
Most algorithms to extract dry snowpack water equivalent (SWE) from satellite passive-microwave observations are based on point measurements of SWE or extrapolation of point measurements to the 30 km footprint of the satellite observations. SWE observations on a scale comparable to the satellite observations can be obtained from airborne gamma-ray attenuation techniques from flight lines that are approximately 10 km long. During the winter of 1989, the NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC) flew 92 of these night lines over a 200 × 250 km area of the Red River basin which is located in the north-central part of the United States of America. These observations provide a unique dataset of snow water-equivalent determinations on spatial scales similar to the satellite passive-microwave observations as acquired by the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) F-8 satellite. Land-classification determinations from the Advanced Very High Resolution Radiometer (AVHRR) show that the eastern part of the region contains a coniferous forest of varying coverage, while the remainder is farmland or prairie. SSM/I data, including observations from a no-snow case in the preceding fall, the flight-line data and the AVHRR data were all co-registered to a common 20 km grid. The resulting dataset was analyzed using linear regression, artificial intelligence and general linear models. The results showed that the passive-microwave response was similar to the response predicted by Mie scattering theory. A comparison of the three techniques found that the artificial intelligence technique and the general linear model explained significantly more of the variance in the dataset, as evidenced byR2values of 0.97 compared to 0.88 for the linear multiple-regression analysis. Hence, a neural network approach which was continually trained on new datasets as they became available, could provide better estimates of snowpack water equivalent than algorithms based on linear-regression techniques.

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