VALIDATION OF FINE RESOLUTION LAND-SURFACE ENERGY FLUXES DERIVED WITH COMBINED SENTINEL-2 AND SENTINEL-3 OBSERVATIONS IGARSS 2018 – 27.07.2018 Radoslaw Guzinski, Internal Research Fellow, ESA / DHI-GRAS H. Nieto, T. El-Madany, M. Migliavacca, A. Carrara

Outline What and why of evapotranspiration (ET) How to estimate ET Results obtained with Sentinel observations Conclusions

What and why of Evapotranspiration

Evapotranspiration http://www.esa.int/Our_Activities/Observing_the_Earth/SMOS/Earth_s_water_cycle

Transpiration through leaf stomata Control gas exchange between leaf and atmosphere Opened when sufficient light and water available Maximize photosynthesis Leaf temperature lowered Closed when resources are limited Conserve water Leaf temperature increased Credit: http://gfs.wur.nl

Water use in agriculture FAO. 2016. AQUASTAT website. Food and Agriculture Organization of the United Nations (FAO). Website accessed on 2018/04/23 World population increase - 4.4 Water withdrawal increase - 7.3 Percentage of withdrawals for agriculture – 60 – 70%

Water use in agriculture FAO. 2016. AQUASTAT website. Food and Agriculture Organization of the United Nations (FAO). Website accessed on 2018/04/23 Spain – 60% Italy – 44 % Greece – 89%

HOW to estimate ET

Evapotranspiration – energy perspective Rn – incoming solar and thermal radiation minus the reflected components G – ratio of Rn reaching the soil H – driven by temperature difference between surface/vegetation and air and controlled by wind speed and vegetation characteristics λE – driven by moisture difference between surface/vegetation and air and controlled by wind speed and vegetation characteristics Rn ≈ G + H + λE Source: http://nevada.usgs.gov/water/et/measured.htm

Modelling of evapotranspiration Two Source Energy Balance (TSEB) modelling scheme Introduced by Norman et al. (1995) Energy balance: Two source: github.com/hectornieto/pyTSEB Source: Mecikalski et al., 1999

Data requirements Optical remote sensing data Leaf Area Index (LAI) Fractional vegetation cover Fraction of vegetation that is green Albedo Thermal remote sensing data Land surface temperature (LST) Meteorological data Air temperature Incoming solar radiation Wind speed Ancillary data Vegetation height Leaf transmittance

Data Sources Sentinel-2 – 20 m Atmospheric correction with Sen2Cor LAI and fractional cover from SNAP Albedo based on L7 algorithm Sentinel-3 – 1000 m Land Surface Temperature (LST) - doi.org/10.1016/j.rse.2016.03.035 ECMWF ERA-5 reanalysis meteorological data Corine 2012 land cover map

Spatial sharpening Optical20 Thermal1000 Aggregate Optical1000 Establish relation Apply relation Thermal20 Implemented method based on Gao et al. (2012): A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land Homogenous sample selection Regression tree + leaf linear regression Local and global regressions Residual analysis and correction Bagging regressor github.com/radosuav/pyDMS

Results obtained with Sentinel OBSERVATIONS

Majadas - site Period: December 2016 – November 2017 Location: Central Spain Mediterranean savannah (dehesa)

Majadas - Results RMSE: 62 W/m2 RMSE: 60 W/m2 Relative error: 46 % Statistics for LE, shown in blue On the left using original resolution S3 LST On the right using sharpened (20 m) S3 LST RMSE: 62 W/m2 Relative error: 46 % Correlation: 0.64 RMSE: 60 W/m2 Relative error: 44 % Correlation: 0.69

True colour composite (20m - S2) Majadas - comparison True colour composite (20m - S2) Maps of the area surrounding the flux tower, indicated as black dot. Left panel shows Sentinel-2 true colour composite on 24.09.2017, while middle and right panels shows respectively fine scale and coarse scale instantaneous latent heat flux estimates obtained during Sentinel-3 overpass on 23.09.2017. Fine resolution ET (20 m - S2 & S3) Coarse resolution ET (1 km - S3)

Accuracy vs. revisit frequency Statistics for LE, shown as blue dots Using ERA5 EDA EM meteorological data and S3 images falling within +- 10 days of S2 image. RMSE: 95 W/m2 Relative error: 70 % Correlation: 0.36 No. Observations: 86 RMSE: 71 W/m2 Relative error: 50 % Correlation: 0.46 No. Observations: 57 RMSE: 64 W/m2 Relative error: 41 % Correlation: 0.60 No. Observations: 37

Accuracy vs. revisit frequency Statistics for LE, shown as blue dots Using ERA5 EDA EM meteorological data and S3 with VZA <= 55 and +- days difference between S2 and S3 images as indicated. RMSE: 68 W/m2 Relative error: 47 % Correlation: 0.48 No. Observations: 53 RMSE: 66 W/m2 Relative error: 45 % Correlation: 0.55 No. Observations: 43 RMSE: 53 W/m2 Relative error: 41 % Correlation: 0.63 No. Observations: 32

Previous study: agricultural site

Previous study: agricultural site - results Statistics for LE, shown as blue dots RMSE: 51 W/m2 Relative error: 18 % Correlation: 0.75 RMSE: 40 W/m2 Relative error: 14 % Correlation: 0.92 RMSE: 42 W/m2 Relative error: 15 % Correlation: 0.87

Previous study: agricultural site – spatial comparison Landsat LST Sharpened MODIS LST

Sentinels for Evapotranspiration (SEN-ET) Development of an efficient, open-source software implementation of a selected evapotranspiration methodology which exploits the synergies between the data collected by sensors on board of Sentinel-2 and Sentinel-3 satellites. Literature review- report available online Model selection: Two Source Energy Balance (TSEB) Once Source Contextual (Metric) Two Source Contextual (ESVEP) Two machine-learning sharpening methods Validation site selection and data collection: Agriculture, grassland, forests, savannah, wetlands esa-sen4et.org

Conclusion

Conclusion 20 m ET can be estimated by sharpening S3 LST with S2 reflectance Comparable, but not always same using high-res LST Possibly reduced dynamic range Method being refined and validated in SEN-ET ET estimated every 5-6 days when S3A and S3B are taken into account Frequency can be increased by reducing accuracy

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