摘要：AVHRR Global Area Coverage (GAC) data provide dailyglobal coverage of the Earth, which are widely used for global environmentaland climate studies. However, their geolocation accuracy has not beencomprehensively evaluated due to the difficulty caused by onboard resamplingand the resulting coarse resolution, which hampers their usefulness invarious applications. In this study, a correlation-based patch matchingmethod (CPMM) was proposed to characterize and quantify the geo-locationaccuracy at the sub-pixel level for satellite data with coarse resolution,such as the AVHRR GAC dataset. This method is neither limited to landmarks norsuffers from errors caused by false detection due to the effect of mixedpixels caused by a coarse spatial resolution, and it thus enables a more robustand comprehensive geometric assessment than existing approaches. Data ofNOAA-17, MetOp-A and MetOp-B satellites were selected to test the geocodingaccuracy. The three satellites predominately present west shifts in theacross-track direction, with average values of −1.69, −1.9, −2.56 kmand 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 partlyinduced by the larger satellite zenith angles (SatZs) and partly due to theterrain effect, which is related to SatZ and becomes apparent in the case oflarge SatZs. It is thus suggested that GAC data with SatZs less than40∘ should be preferred in applications. The along-trackgeolocation accuracy is clearly improved compared to the across-trackdirection, with average shifts of −0.7,−0.02 and 0.96 km and standarddeviations 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).
