EVASPA, a tool for mapping evapotranspiration from space

Albert Olioso, Malik Bahir, Belen Gallego Elvira, Sébastien Garrigues, Maria

Mira, Olivier Marloie, Gilles Boulet

► To cite this version:

Albert Olioso, Malik Bahir, Belen Gallego Elvira, Sébastien Garrigues, Maria Mira, et al.. EVASPA, a tool for mapping evapotranspiration from space. Living Planet Symposium 2016 (LSP16), May 2016, Prague, Czech Republic. , Proceedings ESA Living Planet Symposium 2016. hal-03590489

HAL Id: hal-03590489

https://hal.inrae.fr/hal-03590489

Submitted on 9 May 2022

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Distributed under a Creative Commons Attribution 4.0 International License
## Context and objectives:

Evapotranspiration (ET) is a fundamental variable of the hydrological cycle. There is a strong need of methods to monitor ET in space and time, e.g. for evaluating crop water use, improving knowledge in surface processes in hydrological or climate modelling, as well as for weather forecast.

We have developed the EVASPA tool to provide continuous mapping of daily ET from remote sensing (RS) data at spatial and temporal scales relevant to hydrological or agronomical studies (Gallego-Elvira et al. 2013).

Further, EVASPA has been designed for providing ET estimations together with estimation uncertainties.

In this poster, we provide examples of EVASPA results over the Cruau-Camargue test site (Lower Rhône valley) from MODIS TERRA and AQUA (1km resolution) and LANDSAT-7 ETM+ data (60 m resolution).

## Methods

EVASPA was developed with specific characteristics:

* ET is estimated using several algorithms derived from S-SEBI (Roerink et al. 2000), the 'triangle' method (Tang et al. 2010) and SEBS (Su 2002)

* various equations and data sources are used for estimating input information (net radiation, ground heat flux, evaporative fraction, climate variables...)

* it integrates various RS sensors (and can be easily adapted to new sensors)

* time integration/interpolation procedures are used for calculating daily ET and for providing ET on days without RS images

=> all together this provides a continuous estimation of ET associated to a day-to-day evaluation of the uncertainties in ET derivation (ensemble evaluation)

## Comparison of EVASPA estimates to ground data

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

Sansouire = saltmarsh
scrubs (in Camargue)

RMSE ranged between 0.6 mm.d⁻¹ and 1 mm.d⁻¹ depending on the ecosystem and the year.

Ground measurements over dry grassland site (4)
EVASPA average and standard deviation (17 models, 2 satellites)
Best model

<figure><img src="image_4.png" /></figure>

## Land use (~2000)

<figure><img src="image_5.png" /></figure>

## Monthly evapotranspiration (mm) for the main land use types

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

## ET monitoring over the Cruau aquifer area

<figure><img src="image_9.png" /></figure>

When aggregated over the aquifer area (550 km²) the ensemble annual mean ET was 239 hm³.

The range of ET estimated with the various models was 224 hm³ to 261 hm³

(while rain ranged between 260 hm³ (S) to 299 hm³ (N) and irrigation was estimated around 250-300 hm³).

## Comparison of ET estimations for different models and sensors

<figure><img src="image_10.png" /></figure>

MODIS TERRA and AQUA provided close ET, in particular for the S-SEBI - Triangle approaches.

SEBS and S-SEBI - Triangle approaches provided different ET depending on the ecosystem

Main differences were related to the high sensitivity of SEBS to aerodynamic + thermal roughness estimates and air temperature, while these inputs were not required in the S-SEBI - Triangle approaches

## Example of ET maps

Averaged (ensemble) estimation of
ET

<figure><img src="image_11.png" /></figure>

### References:

Boulet G. et al., 2012. An empirical expression to relate aerodynamic and surface temperatures for use within single-source energy balance models. *Agricultural and Forest Meteorology*, **161**, 148-155.

Gallego-Elvira B. et al., 2013. EVASPA (Evapotranspiration Assessment from SPACe) tool: An overview. *Procedia Environmental Sciences*, **19**, 303-310.

Roerink G.J. et al., 2000. S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. *Phys. Chem. Earth (B)*, **25**, 147-157.

Su Z., 2002. The surface energy balance system [SEBS] for estimation of turbulent fluxes. *Hydrol. Earth Symp. Sci.*, **6**, 85-99.

Tang R. et al., 2010. An application of the T-s-VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in and around and semi- and semi-arid regions: Implementation and validation. *Remote Sensing of Environment*, **114**, 540-551.