Neural Networks with PyTorch — SnowEx Hackweek 2024

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Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign

Abstract. The high temporal resolution of thermal infrared imagery from the geostationary GOES-R satellites presents an opportunity to observe mountain snow and forest temperatures over the full diurnal cycle. However, the off-nadir views of these imagers may impact or bias surface temperature observations, especially when viewing a surface composed of both snow and forests. We used GOES-16 and -17 thermal infrared brightness temperature observations of a flat snow and forest-covered study site at Grand Mesa, Colorado, USA, to characterize how forest coverage and view angle impact these observations. These two geostationary satellites provided views of the study area from the southeast (134.1° azimuth, 33.5° elevation) and southwest (221.2° azimuth, 35.9° elevation) respectively. Coincident ground-based and airborne IR observations collected as part of the NASA SnowEx field campaign in February 2020 provided a rich dataset for comparison. Observations over the course of two cloud-free days spanned the entire study site. The surface temperature observations from each dataset were compared to find their relative differences, and how those differences may have varied over time or as a function of varying forest cover across the study area. GOES-16 and -17 surface brightness temperatures were found to match the diurnal cycle and temperature range within ~1 hour and ± 3 °C of ground-based observations. GOES-16 and -17 were both biased warmer than nadir-looking airborne IR and ASTER observations. The warm biases were higher at times when the sun-satellite phase angle was near its daily minimum, and the warm biases seen in GOES-16 were greater for pixels that contained more forest coverage. The observations suggest that a “thermal infrared shadow-hiding” effect may be occurring, where the geostationary satellites are preferentially seeing the warmer sunlit sides of trees at different times of day. These biases are important to understand for applications using GOES-R ABI for surface temperatures over areas with surface roughness features, such as forests, that could exhibit a thermal infrared shadow-hiding effect.

NASA GISS: Climate Change Research Initiative