Abstract
Abstract. AVHRR Global Area Coverage (GAC) data provide daily global coverage of the Earth, which are widely used for global environmental and climate studies. However, their geolocation accuracy has not been comprehensively evaluated due to the difficulty caused by onboard resampling and the resulting coarse resolution, which hampers their usefulness in various applications. In this study, a correlation-based patch matching method (CPMM) was proposed to characterize and quantify the geo-location accuracy at the sub-pixel level for satellite data with coarse resolution, such as the AVHRR GAC dataset. This method is neither limited to landmarks nor suffers from errors caused by false detection due to the effect of mixed pixels caused by a coarse spatial resolution, and it thus enables a more robust and comprehensive geometric assessment than existing approaches. Data of NOAA-17, MetOp-A and MetOp-B satellites were selected to test the geocoding accuracy. The three satellites predominately present west shifts in the across-track direction, with average values of −1.69, −1.9, −2.56 km and standard deviations of 1.32, 1.1, 2.19 km for NOAA-17, MetOp-A, and MetOp-B, respectively. The large shifts and uncertainties are partly induced by the larger satellite zenith angles (SatZs) and partly due to the terrain effect, which is related to SatZ and becomes apparent in the case of large SatZs. It is thus suggested that GAC data with SatZs less than 40∘ should be preferred in applications. The along-track geolocation accuracy is clearly improved compared to the across-track direction, with average shifts of −0.7, −0.02 and 0.96 km and standard deviations of 1.01, 0.79 and 1.70 km for NOAA-17, MetOp-A and MetOp-B, respectively. The data can be accessed from https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002 (Stengel et al., 2017) and https://doi.org/10.5067/MODIS/MOD13A1.006 (Didan, 2015).
Highlights
Advanced Very High Resolution Radiometer (AVHRR) data provide valuable data sources with a near-daily global coverage to support a broad range of environmental monitoring research, including weather forecasting, climate change, ocean dynamics, atmospheric soundings, land cover monitoring, search and rescue, forest fire detection, and many other applications (Van et al, 2008)
Based on the idea of the coregistration method, this study proposes a method named correlation-based patch matching method (CPMM), which is capable of quantifying the geometric accuracy of coarse-resolution satellite data available as fundamental climate data records (FCDRs) for global applications (Hollmann et al, 2013)
We show the procedure based on AVHRR Global Area Coverage (GAC) data, which are compiled for the ESA CCI cloud project (Stengel et al, 2017) and are used for the ESA CCI+ snow project
Summary
Advanced Very High Resolution Radiometer (AVHRR) data provide valuable data sources with a near-daily global coverage to support a broad range of environmental monitoring research, including weather forecasting, climate change, ocean dynamics, atmospheric soundings, land cover monitoring, search and rescue, forest fire detection, and many other applications (Van et al, 2008). AVHRR data are rarely used at the full spatial resolution for global monitoring due to the limited data availability (Pouliot et al, 2009; Fontana et al, 2009). The Global Area Coverage (GAC) AVHRR dataset with a reduced spatial resolution is generally employed in long-term studies at a global or regional perspective (Hori et al, 2017; Delbart et al, 2006; Stöckli and Vidale, 2004; Moulin et al, 1997). X. Wu et al.: Geometric accuracy assessment of coarse-resolution satellite datasets results in errors in the along-track direction (Devasthale et al, 2016). An uncertainty of 1 s approximately induces an error of 8 km in this direction. The spatial misplacement of the GAC scene caused by these factors can be up to 25–30 km occasionally (Devasthale et al, 2016)
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