Comprehensive accuracy assessment of long-term geostationary SEVIRI-MSG evapotranspiration estimates across Europe

This study quantifies the accuracy of evapotranspiration (ET) estimates from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) geostationary sensor onboard the Meteosat Second Generation (MSG) satellites, along seven key dimensions, i.e., diurnal cycle, daily, intra-annual, inter-annual, ecosystem, climate zone, and products intercomparison. In situ measurements were collected at 54 eddy covariance (EC) sites to evaluate the accuracy of SEVIRI actual ET products (diurnal and daily SEVIRI-ETa) as well as reference ET (daily SEVIRI-ET0) covering the period from 2004 to 2018 across Europe. SEVIRI-ETa is produced by the Tiled ECMWF Surface Scheme of Exchange processes at the Land surface (TESSEL) model, while SEVIRI-ET0 is estimated by a combination of a thermodynamically-based and an atmospheric boundary layer model. This evaluation is further separated according to the land cover heterogeneity of the SEVIRI pixels across all 54 EC sites, using MODIS land cover data. The Root Mean Squared Error (RMSE), along with the Kling-Gupta efficiency (KGE) and their respective decompositions, were employed to quantify the errors. For diurnal SEVIRI-ETa estimates, we found that the KGE (RMSE [mm hour- 1]) varied between -1.6 (0.04) to 0.8 (0.14), with a median value of 0.26 (0.07). Higher accuracy for diurnal SEVIRI-ETa was obtained in the summer and during the mid-day time. For daily SEVIRI-ETa, the KGE (RMSE [mm day- 1]) varied between -0.88 (0.43) to 0.93 (1.79), with a median value of 0.6 (0.77) and for daily SEVIRI-ET0 the KGE (RMSE [mm day- 1]) varied between 0.51 (0.40) to 0.94 (1.50), with a median value of 0.77 (0.57). For daily SEVIRI-ETa, intra-annual accuracy was low from January to March, increased in the mid-year, and then began to decline from November to December. Although accuracy remained relatively stable during the middle of the year, it varied considerably in the winter period. In the inter-annual dimension, the mid-year positive KGE values and distributions changed over time from 2004 to 2018. In spatial dimensions, the highest accuracy was in peat and grassland ecosystems, and the lowest in cropland ecosystem, with similar patterns observed in the boreal snow fully humid warm summer and warm temperate fully humid hot summer climate zones. Regarding SEVIRI-ET0 results, similar to SEVIRI-ETa, intra-annual accuracy was low in the first quarter of the year and the last one but high in the midyear. In the inter-annual dimension, unlike SEVIRI-ETa, almost an identical pattern was observed for the midyear positive KGE values, demonstrating only a slight change in SEVIRI-ET0 accuracy during 2004-2018. However, the highest accuracy was found in crop ecosystem, while the lowest was in forest ecosystem, reflecting similar trends in the warm temperate fully humid hot summer and warm temperate summer dry hot summer climate zones. The observed range of median RMSE changed between 0.4 and 1.5 mm day-1, also suggests a reasonable accuracy for SEVIRI-ET estimates in all spatial domains. Our results showed that the main trends in the accuracies (median KGEs) of SEVIRI-ET (both ETa and ET0) remained similar in separated homogeneous and heterogeneous sites and were comparable to combined sites among the dimensions. Through error decomposition, we discerned that SEVIRI-ET estimates performed particularly well in explaining inter-annual and spatial variabilities. Furthermore, the intercomparison of ET products revealed that SEVIRI satellite-derived ETa exhibited the strongest correlation with in situ ET measurements across all ecosystem types and climate zones, outperforming other products (such as MODIS, PML, GLEAM, and BESS). The ET estimates from other products exhibited lower standard deviation errors and were in closer agreement with the in situ measurements. This study provides the first comprehensive evaluation of the accuracy of SEVIRI diurnal and daily ET products across Europe, which may serve as a stimulus for further optimized selection of these products by potential users for various applications.