| Title | Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies |
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| Author | Latifovic, R; Trishchenko, A ; Chen, J; Park, W B; Khlopenkov, K V;
Fernandes, R; Pouliot, D; Ungureanu, C; Luo, Y; Wang, S; Davidson, A; Cihlar, J |
| Source | Earth observation of Canada's landmass: results and future needs: a workshop in honour of Josef Cihlar on the occasion of his retirement; by Trishchenko, A (ed.); Chen, W (ed.); Canadian Journal of Remote Sensing vol. 31, no. 5, 2005 p. 324-346, https://doi.org/10.5589/m05-024 |
| Year | 2005 |
| Alt Series | Earth Sciences Sector, Contribution Series 2005581 |
| Publisher | Informa UK Limited |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Province | Canada; British Columbia; Alberta; Saskatchewan; Manitoba; Ontario; Quebec; New Brunswick; Nova Scotia; Prince Edward Island; Newfoundland and Labrador; Northwest Territories; Yukon; Nunavut |
| NTS | 1; 2; 3; 10; 11; 12; 13; 14; 15; 16; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 52; 53; 54; 55; 56; 57; 58; 59; 62; 63; 64; 65;
66; 67; 68; 69; 72; 73; 74; 75; 76; 77; 78; 79; 82; 83; 84; 85; 86; 87; 88; 89; 92; 93; 94; 95; 96; 97; 98; 99; 102; 103; 104; 105; 106; 107; 114O; 114P; 115; 116; 117; 120; 340; 560 |
| Lat/Long WENS | -141.0000 -50.0000 90.0000 41.7500 |
| Subjects | Nature and Environment; remote sensing; climate; data collections; satellites; Standards; Data processing; Climate change |
| Illustrations | tables; histograms; satellite images; graphs; bar graphs; location maps; time series |
| Program | Reducing Canada's Vulnerability to Climate Change
|
| Program | Canadian Space Agency, Government Related
Initiative Program (GRIP) |
| Released | 2014 06 02 |
| Abstract | Satellite data are an important component of the global climate observing system (GCOS). To serve the purpose of climate change monitoring, these data should satisfy certain criteria in terms of the
length of observations and the continuity and consistency between different missions and instruments. Despite the great potential and obvious advantages of satellite observations, such as frequent repeat cycles and global coverage, their use in
climate studies is hindered by substantial difficulties arising from large data volumes, complicated processing, and significant computer resources required for archiving and analysis. Successful examples of satellite earth observation (EO) data in
climate studies include, among others, analyses of the earth's radiation budget (Earth Radiation Budget Experiment (ERBE), Scanner for Radiation Budget (ScaRaB), and Cloud and the Earth's Radiant Energy System (CERES)), cloudiness (International
Satellite Cloud Climatology Project (ISCCP)), vegetation research (Global Inventory Modeling and Mapping Studies (GIMMS)), and the National Oceanic and Atmospheric Administration - National Aeronautics and Space Administration (NOAA-NASA) Pathfinder
Program. Despite several attempts, the great potential of the advanced very high resolution radiometer (AVHRR) 1 km satellite data for climate research remains substantially underutilized. To address this issue, the generation of a comprehensive
satellite data archive of AVHRR data and products at 1 km spatial resolution over Canada for 1981-2004 (24 years) has been initiated, and a new system for processing at level 1B has been developed. This processing system was employed to generate
baseline 1 day and 10 day year-round clear-sky composites for a 5700 km × 4800 km2 area of North America. This region is centred over Canada but also includes the northern United States, Alaska, Greenland, and surrounding ocean regions. The baseline
products include top-of-atmosphere (TOA) visible and near-infrared reflectance, TOA band 4 and band 5 brightness temperature, a cloud - clear - shadow - snow and ice mask, and viewing geometry. Details of the data processing system are presented in
the paper. An evaluation of the system characteristics and comparison with previous results demonstrate important improvements in the quality and efficiency of the data processing. The system can process data in a highly automated manner, both for
snow-covered and snow-free scenes, and for daytime and nighttime orbits, with high georeferencing accuracy and good radiometric consistency for all sensors from AVHRR NOAA-6 to AVHRR NOAA-17. Other processing improvements include the implementation
of advanced algorithms for clear sky - cloud - shadow - snow and ice scene identification, as well as atmospheric correction and compositing. At the time of writing, the assembled dataset is the most comprehensive AVHRR archive at 1 km spatial
resolution over Canada that includes all available observations from AVHRR between 1981 and 2004. The archive and the processing system are valuable assets for studying different aspects of land, oceans, and atmosphere related to climate variability
and climate change. |
| GEOSCAN ID | 221597 |
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