Geometric accuracy assessment of coarse-resolution satellite datasets: a study based on AVHRR GAC data at the sub-pixel level

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).