# Daily Evapotranspiration TRISHNA Level-2 products, EVASPA

Albert Olioso, Gilles Boulet, Philippe Gamet, Jean-Louis Roujean, Bimal Bhattacharya, Mark Irvine, Jérôme Ogée, Samuel Buis, Marie Weiss, Laurent Prevot, et al.

► To cite this version:

Albert Olioso, Gilles Boulet, Philippe Gamet, Jean-Louis Roujean, Bimal Bhattacharya, et al. Daily Evapotranspiration TRISHNA Level-2 products, EVASPA. International Science Workshop on High-Resolution Thermal Earth Observation, ISRO, Nov 2024, Ahmedabad, India. hal-05037175

HAL Id: hal-05037175

https://hal.inrae.fr/hal-05037175v1

Submitted on 16 Apr 2025

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Distributed under a Creative Commons Attribution 4.0 International License
TRISHNA

Daily Evapotranspiration
TRISHNA Level-2 products, EVASPA...

Albert Olioso, Gilles Boulet, Philippe Gamet, Jean-Louis Roujean, Bimal Bhattacharya, Mark Irvine, Jérôme Ogée, Samuel Buis, Marie Weiss, Laurent Prévot, Samuel Mwangi, Victor Penot, Nesrine Farhani, Aolin Jia, Blanca Matteo Herrera, Emmanuelle Sarrazin, Sébastien Marcq, Emilie Delogu, Renaud Binet, Philippe Maisongrande, Tian Hu, Kanishka Mallick, Jennifer Adams, Alexander Damm, Kathrin Naegeli, José Sobrino, Vincent Rivalland, Olivier Merlin, Olivier Hagolle, Jordi Etchanchu, Jérôme Demarty, Chloé Ollivier, Frédéric Jacob, ...

Presentation by Albert Olioso, albert.olioso@inrae.fr

URFM (INRAE), Avignon, France ; CESBIO, Toulouse, France ;
Indo-French Cell for Water Science, ICWaR, Indian Institute of Science, Bangalore, India)

ISPA, Bordeaux, France ;

LISAH, Montpellier, France ;

EMMAH, Avignon, France ;

CNES, Toulouse, France ; SAC/ISRO, India ;

IIST, Belvaux, Luxembourg ;

Université de Zurich, Suisse ;

Université de Valence, Espagne ;

HSM, Montpellier, France ;

...

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# TRISHNA Spectral bands and products

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## Spectral bands

<table><thead><tr><th>Band name</th><th>Wavelength Center (µm)</th><th>Bandwidth (nm)</th><th>Purpose</th></tr></thead><tbody><tr><td>Blue</td><td>0.485</td><td>70</td><td>Detection of low clouds</td></tr><tr><td>Green</td><td>0.555</td><td>70</td><td>Coastal, sediments, snow</td></tr><tr><td>Red</td><td>0.670</td><td>60</td><td>Vegetation (LAI, fCOVER, NDVI, ...)</td></tr><tr><td>NIR</td><td>0.860</td><td>40</td><td>Vegetation (LAI, fCOVER, NDVI, ...)</td></tr><tr><td>WV</td><td>0.910</td><td>30</td><td>Water vapour content estimation</td></tr><tr><td>Cirrus</td><td>1.380</td><td>30</td><td>Detection of thin cirrus clouds</td></tr><tr><td>SWIR</td><td>1.610</td><td>100</td><td>AOD, snow/cloud discrimination, vegetation stress, burnt areas</td></tr></tbody></table>

DAY-TIME

DAY-TIME
NIGHT-TIME

<table><tr><td>TIR 1</td><td>8.65</td><td>350</td><td>Temperature/emissivity separation</td></tr><tr><td>TIR 2</td><td>9.0</td><td>350</td><td>Temperature/emissivity separation</td></tr><tr><td>TIR 3</td><td>10.6</td><td>700</td><td>Split-window</td></tr><tr><td>TIR 4</td><td>11.6</td><td>1000</td><td>Split-window</td></tr></table>

## Distributed Products

### Level 1C

* ✓ TOA reflectances x7 VNIR/SWIR bands

* ✓ TOA radiances x4 LWIR bands

* ✓ Cloud mask

Radiometrically and geometrically calibrated

Orthorectified and resampled on a uniform spatial grid

(Sentinel-2 tiles, Copernicus DEM)

### Level 2A

* ✓ Surface reflectances x5 VNIR/SWIR bands

* ✓ LST, SST

* ✓ LSE x4 bands

* ✓ Cloud mask, TWVC, AOT

### Level 2B

* ✓ Vegetation variables, albedo

* ✓ Evapotranspiration and vegetation stress

### Level 3

* ✓ Time series of daily evapotranspiration
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TRISHNA

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ET level 2B

Daily evapotranspiration maps on days with TRISHNA acquisition
→ depends on satellite revisit and cloud cover

Target : data available with a 12h latency
(or before breakfast !)

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Evapotranspiration products, Level 2b = Daily evapotranspiration, index of water stress

> EVASPA: contextual Energy Budget ensemble platform -> LE
(Gallego-Elvira et al. 2013, Olioso et al. 2015, 2023, Allies et al. 2020)

> STIC: single pixel Energy Budget model -> LE (latent heat flux)
(Mallick et al., 2014, 2016, Hu et al. 2023)

> Scaling to daily ET by reference to incident solar radiation

> Uncertainty evaluation provided in the product

> Water Stress Index (SI) : - evaporative fraction at the time of acquisition : SI = EF = LE / (Rn – G)
- or at the daily scale : SI = 1 – ET / PET

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# Models

## EVASPA (EVApotranspiration from SPAce) Gallego-Elvira et al. 2013

* contextual model: triangle (fcover-Ts) and S-SEBI (albedo-Ts) methods

* Latent heat flux LE derived from the Evaporative Fraction EF:

$$LE = EF \cdot (Rn - G)$$

$$EF = LE / (Rn - G) = (Ts-Tmax)/(Tmin-Tmax)$$

Rn : net radiation (W m⁻²)

G : ground heat flux (W m⁻²)

* hypothesis : - variations in Ts are only related to variations in water status and vegetation density
  - presence of dry and wet areas

## References:

Allies A. et al. (2020).

Evapotranspiration Estimation in the Sahel Using a New Ensemble-Contextual Method.

Remote Sensing, 12, 380. doi:10.3390/rs12030380

Gallego-Elvira B. et al. (2013).

EVASPA (EVapotranspiration Assessment from SPAce) tool: an overview.

Procedia Environmental Sciences, 19, 303-310

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# Models

## EVASPA (EVApotranspiration from SPAce) Gallego-Elvira et al. 2013

* scaling to daily ET by reference to incident solar radiation

$$ET = LE / L \cdot Rgd / Rgi$$

ET : daily evapotranspiration (mm d⁻¹)

LE : latent heat flux at the time of satellite acquisition (W m⁻²)

Rgd : daily incoming solar radiation (W m⁻²)

Rgi : instantaneous incoming solar radiation (W m⁻²)

L : latent heat (kg J⁻¹)

* multi-data multi-model framework

=> ET as the ensemble mean

=> standard deviation uncertainty assessment

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EVASPA

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EVASPA

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Meteorological information :

- ECMWF analysis

- geostationnary satellite
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**EVASPA PRODUCTS :**
* L2B instant contextual EF
* L2B daily contextual ET
* L2B quality flags
* L2B water stress
Models

**STIC (Surface Temperature Initiated Closure) Mallick et al., 2014, 2016**

➤ single pixel model

➤ Penman-Monteith equation for the vegetation layer,
Shuttleworth-Walace resistance network

➤ complementarity equation (Bouchet, 1963)

Mallick K. et al. (2014). A Surface Temperature Initiated Closure (STIC) for surface energy balance fluxes.
*Remote sensing of Environment, 141, 243-261*

Mallick K. et al. (2016).
**Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin.**
*Hydrology and Earth System Sciences, 20, 4237-4264*

Hu T. et al. (2023).
**Evaluating European ECOSTRESS Hub Evapotranspiration Products Retrieved from Three Structurally Contrasting SEB Models over Europe.**
*Water Resources Research. 59, e2022WR034132.*

Upper boundary conditions

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TRISHINA

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Meteorological information :

- ECMWF analysis

- geostationnary satellite
Why this two models ?

> we have been working on them for sometime now
Why this two models ?

➤ we have been working on them for sometime now

> why are we working on them ?
# Why this two models ?

* we have been working on them for sometime now

* why are we working on them ?

* we want to limit the number of inputs that are complex to derive :

- meteorological forcing that is not accessible through remote sensing : Ta, RH, wind speed...

- surface aerodynamic parameters (roughness, vegetation height...)
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ET uncertainty break-down
# Why this two models ?

* we have been working on them for sometime now

* why are we working on them ?

* we want to limit the number of inputs that are complex to derive :

- meteorological forcing that is not accessible through remote sensing : Ta, wind speed...

- Surface aerodynamic parameters

* **EVASPA** : all inputs are coming from remote sensing data
  but "homogeneity hypothesis" and dry and wet limits must exist

* **STIC** : does not require aerodynamic parameters,
  but requires air temperature Ta and relative humidity RH
=> spatial distribution of Ta and RH
<figure><img src="image_20.png" /><figcaption>➤ automatic control of the validity of dry and wet limits -> Nesrine Farhani's talk</figcaption></figure>
Undergoing work

➤ automatic choice of the methods to compute the dry and the wet limits ->

Nesrine Farhani'talk

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Farhani et al., 2024. Spatially remotely sensed evapotranspiration estimates in Sahel regions using an ensemble contextual model with automated heterogeneity assessment. Submitted

EVASPA S-SEBI ensemble

AQUA and TERRA data

~20 methods for EF

West Senegal

Niakhar flux towers, Faidherbia ecosystem
Undergoing work

reducing uncertainty on G flux -> G / Rn ratio variability -> Gilles Boulet's talk

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## ET level 3

Most applications require continuous series of daily evapotranspiration

⇒ implementation of gap-filling methods

- interpollation of ET / Rg

- undergoing development accounting for rain and irrigation events
# Revisit

At equator

-> Landsat :

1/16 days

-> TRISHNA :

3/8 days

in Belgium

-> 1/16 days

-> 5/8 days

Lonzee,
Belgium

Latitude 50.55159° N
Longitude 4.74613° E

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Grosseto,
Italy

Latitude 42.82° N
Longitude 11.07° E

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Wankama,
Niger

Latitude 13.645° N
Longitude 2.63° E

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TRISHNA

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Gap filling

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Uncertainty related to gap-filling: one typical example

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TRISHNA vs. LANDSAT
Rendez vous in 2027 with the first data

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