IMAGE PIXELS OF RFI<0.2 ONLY Towards Improving our Understanding on the Retrievals of Key Parameters Characterising Land Surface Interactions from Space: Introduction and First Results from the PREMIER-EO Project Matthew R. North1, Gareth Ireland1, George P. Petropoulos1,*, Prashant K. Srivastava2,3, Crona Hodges4 1 Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, Wales, UK; 2NASA Goddard Space Flight Centre, Greenbelt, MA, USA; 3Earth System Science Interdisciplinary Centre, University of Maryland, Baltimore, MA, USA, 4Geo Smart Decisions Ltd, SY18 6BN, UK *Correspondence to: george.petropoulos@aber.ac.uk  INTRODUCTION 4. METHODS 5.2 SMOS Figure 4: Accuracy assessment of SMOS SMC estimates. Top scatterplots show estimates vs. measured SMC for all sites evaluated. Bottom scatterplots show results stratified by land cover type. Scatterplots on the left show all days results without an RFI threshold, scatterplots on the right show results excluding days with RFI > 0.2. Earth Observation (EO) has played a significant role in extending our capability to study the spatio-temporal variations of key state variables characterising Earth’s physical processes, including soil moisture content (SMC) and evapotranspiration (ET). At present, there is a suite of operationally distributed satellite products available today, providing at a global scale, operational estimates of numerous key state variables such as SMC or ET. Validation of these products for a range of climate and environmental conditions across continents is a fundamental step related to their practical use (Jia et al., 2010). Table 3: Summarises the statistical accuracy between satellite predicted SMC and in-situ observed SMC by season. Season Bias/MBE Scatter/MSD RMSE MAD/MAE   N ALL DATA Spring -0.073 0.067 0.099 0.087 0.607 77 Summer -0.047 0.071 0.085 0.324 505 Autumn -0.035 0.076 0.053 0.455 118 Winter -0.044 0.078 0.089 0.492 72 IMAGE PIXELS OF RFI<0.2 ONLY -0.065 0.056 0.086 0.075 0.699 29 -0.049 0.058 0.065 0.630 167 -0.046 0.051 0.069 0.655 32 -0.060 0.052 0.079 0.292 0.747 27 Figure 1: Overall methodology followed for the validation of SEVIRI ET product. 2. AIMS & OBJECTIVES Figure 2: Overall methodology followed for the validation of SMOS Soil Moisture product. PREMIER-EO aims to develop a fundamental understanding of the present ability of EO technology in deriving LE/H fluxes as well as of SMC at a global scale from operational EO products. This study presents the first results from the PREMIER-EO project, where a validation of SMOS SMC estimates and SEVIRI ET estimates have been conducted at several sites belonging to the CarboEurope and ISMN observational networks. 6. CONCLUSIONS Table 1: A description of the statistical metrics used in this study SEVIRI Qualitative comparison of the SEVIRI ET maps with maps of FVC showed good spatial agreement. Validation on retrieved SEVIRI estimates for all days was also good (bias = -0.015 mm h-1, 0.106 mm h-1, d-index of 0.755, RMSD = 0.107 mm h-1). Highest agreement for the “olive orchards” test site (d-index 0.893). Agreement over the “open shrubland” test site exhibited lowest error distribution (RMSD/MAE = 0.041/0.028 mm h-1). Poorest agreement was found for the “broadleaf forest” and coniferous forest” land covers types (d-index = 0.594/0.667 and RMSD = 0.172/0.152 mm h-1). SMOS SMOS algorithm performed better over short vegetation cover (“dehesa”, olive orchards and cropland, RMSE – 0.044, 0.061, 0.057 m3 m-3) and during the autumn season (RMSE – 0.076 m3 m-3). Filtering out RFI contaminated pixels resulted in a significant improvement in overall product accuracy (improvement in RMSE of the pooled datasets by ~30%.) The removal of RFI resulted in a minimum improvement of ~5% in all comparison scenarios. 3. DATASETS & STUDY SITES SEVIRI In total 55 days of the SEVIRI MET product were downloaded for a total of nine different Mediterranean sites belonging to CarboEurope. Cloud free conditions were selected and also quality of in-situ was checked by assessing the energy balance closure (EBC). LE measured from the in-situ data was converted to ET using the formula: SMOS In total 18 days representing different biophysical conditions from the CarboEurope network was used in the study. 5. RESULTS 5.1 SEVIRI Figure 3: Accuracy assessment of SEVIRI ET estimates. Scatterplots show estimated vs. measured ET for the 6 land cover types. Bottom Right indicates the correlation between observed ET and FVC for an area in Spain. Olive Orchards site Open Shrubland site Broadleaf Forest site Coniferous Forest site 7. FUTURE WORK Future work for PREMIER-EO is to extend the current validation of EO products into a range of different ecosystems globally using in-situ validated measurements acquired concurrently. Validation will be conducted for 2 years of continuous data to allow also evaluating seasonal effects. Validation will cover operational products of SMC and ET from SEVIRI, MODIS, SMOS & ASCAT. The research outcomes of this project will provide an important contribution towards addressing the knowledge gaps related to the operational estimation of ET and SM. This project results will also support efforts ongoing globally towards the operational development of related products using technologically advanced EO instruments which were launched recently or planned be launched in the next 1-2 years. Figure 1: a) Above, map of the Mediterranean sites chosen for MET validation, b) Below, location of the sites used in SMOS validation. Grasslands site Cropland site Table 2: The statistical accuracy between satellite predicted and in situ observed ET. Land Cover Type CarboEurope Flux Site Country N (30 Minute Intervals) N (Days) Bias Scatter RMSD MAE d- Index Olive Orchards ES_Lju SPAIN 309 7 -0.008 0.040 0.041 0.028 0.893 Open Shrubland ES_Agu/ES_Amo 547 12 -0.009 0.049 0.050 0.034 0.867 Grassland IT_Ca2/IT_Mbo ITALY 214 6 -0.002 0.072 0.046 0.880 Cropland IT_Ro3 299 8 -0.012 0.089 0.090 0.061 Broadleaf Forest IT_Ca1/IT_Col 470 10 -0.021 0.171 0.172 0.101 0.594 Coniferous Forest IT_Ren 126 3 -0.060 0.140 0.152 0.082 0.667 All Days - 1965 32 -0.015 0.106 0.107 0.058 0.755 References Z. Jia, et al., "Validation of remotely sensed evapotranspiration: a case study," in Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, 2010, pp. 2119-2122. F. Li, et al., "Utility of remote sensing-based two-source energy balance model under low-and high-vegetation cover conditions," Journal of Hydrometeorology, vol. 6, pp. 878-891, 2005.