Assessing the spaceborne 183.31-GHz radiometric channel geolocation using high-altitude lakes, ice shelves, and SAR imagery
| Autor: | Mario Papa, Frank S. Marzano, Janja Avbelj, Vinia Mattioli |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Synthetic aperture radar
image geolocation error geography Ice cloud geography.geographical_feature_category Radiometer 0211 other engineering and technologies 02 engineering and technology synthetic aperture radar (SAR) imagery Ice shelf Geolocation Sea surface temperature surface landmarks SSMIS General Earth and Planetary Sciences Special sensor microwave/imager Environmental science Electrical and Electronic Engineering spaceborne millimeter-wave radiometers 021101 geological & geomatics engineering Remote sensing |
| Popis: | The goal of this work is to perform the geolocation error assessment of the channel imagery at 183.31 GHz of the Special Sensor Microwave Imager/Sounder (SSMIS). The frequency around 183.31 GHz still represents the highest channel frequency of current spaceborne microwave and millimeter-wave radiometers. The latter will be extended to frequencies up to 664 GHz, as in the case of EUMETSAT Ice Cloud Imager (ICI). This use of submillimeter observations unfortunately prevents a straightforward geolocation error assessment using landmark-based techniques. We used SSMIS data at 183.31 GHz as a submillimeter proxy to identify the most suitable targets for geolocation error validation in very dry atmospheric conditions, as suggested by radiative transfer modeling. Using a yearly SSMIS data set, three candidates’ landmark targets are selected: 1) high-altitude lakes and high-latitude bays using a coastline reference database and 2) Antarctic ice shelves using coastlines derived from Sentinel-1 Synthetic Aperture Radar (SAR) imagery. Data processing is carried out by using spatial cross correlation methods in the spatial frequency domain and performing a numerical sensitivity analysis to contour displacement. Cloud masking, based on a fuzzy-logic approach, is applied to automatically selected clear-air days. The results show that the average geolocation error is about 6.2 km for mountainous lakes and sea bays and 5.4 km for ice shelves, with a standard deviation of about 2.7 and 2.0 km, respectively. The results are in line with SSMIS previous estimates, whereas annual clear-air days are about 10% for mountainous lakes and sea bays and 18% for ice shelves. |
| Databáze: | OpenAIRE |
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