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# SAIL-thermique, a model to simulate land surface emissivity (LSE) spectra

Albert Olioso, Frédéric Jacob, Audrey Lesaignoux

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Albert Olioso, Frédéric Jacob, Audrey Lesaignoux. SAIL-thermique, a model to simulate land surface emissivity (LSE) spectra. 1. International Surface Working Group, Jul 2017, Moss Landing, United States. hal-01607899

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# SAIL-thermique, a model to simulate land surface emissivity (LSE) spectra

Albert Olioso,
INRA, UMR EMMAH (INRA-UAPV), Avignon, France

Frédéric Jacob, Audrey Lesaignoux
IRD, UMR LISAH, Montpellier, France

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INRA
SCIENCE & IMPACT

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IRD
Institut de recherche
pour le développement

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UMR 1114 INRA-UAPV

Environnement Méditerranéen et
Modélisation des Agro-Hydrosystèmes
**Introduction**

☐ Land Surface Emissivity (LSE) is required for deriving Land Surface Temperature (LST)

☐ Emissivity spectra are required for:

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

☐ There is very few available information on LSE and spectral LSE except from multispectral TIR sensors as ASTER and few leaf samples in spectral libraries (measurements are complex and up to recently only geologists were interested)

☐ Few models of LSE accounting for vegetation canopy characteristics exist (eg. Anton and Ross 1990, Olioso 1992, Snyder and Wan, 1998, 4SAIL Verhoef et al. 2007...)

☐ None (?) of them was evaluated against data
# Recent progress

## ☐ Hyperspectral and multispectral sensors

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

## ☐ Spectral libraries

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

## ☐ LSE models

→ coherent results between different LSE models

But still no evaluation against data and not used for extracting LSE or build LSE maps
## Hypothesis

* It is possible to use “standard” Radiative Transfer models for simulating land surface emissivity in presence of vegetation canopy

## But

* Previous model evaluation were only done by comparison to other models
(ex: François et al., 1997, Guillevic et al., 2003, Sobrino et al., 2005, Ren et al. 2015...)

* We have to evaluate modeled land surface emissivity against in-situ measurements

## In this talk:

* Presentation of the SAIL-Thermique land surface emissivity model

* Test of the model against ground data

* One example of application of the model for the (re-)evaluation of the Temperature-Emissivity-Separation algorithm (TES)
SAIL model

(Verhoef 1984-1985)

sun

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

(Verhoef 1984-1985)

<figure><img src="image_11.png" /></figure>
SAIL model (Verhoef 1984-1985)

<figure><img src="image_12.png" /></figure>
SAIL model

(Verhoef 1984-1985)

<figure><img src="image_13.png" /></figure>

SAIL-Thermique model

directional emissivity
## Inputs and outputs of the SAIL and the SAIL-Thermique models

<table><thead><tr><th></th><th>SAIL</th><th>SAIL-Thermique</th></tr></thead><tbody><tr><th>Inputs</th><td><ul><li>LAI</li><li>mean leaf angle</li><li>leaf/stem reflectances</li><li>leaf transmittance</li><li>soil reflectance</li><li>geometry of observation</li><li>Sun position</li></ul></td><td><ul><li>LAI</li><li>mean leaf angle</li><li>leaf/stem reflectances</li><li>leaf transmittance</li><li>soil reflectance</li><li>geometry of observation</li></ul></td></tr><tr><th>Outputs</th><td><ul><li>spectral reflectances</li><li>NDVI</li><li>absorption of solar radiation (and albedo)</li></ul></td><td><ul><li>land surface emissivity</li><li>absorption of thermal radiation by vegetation and soil</li></ul></td></tr></tbody></table>

One (strong) hypothesis: horizontally homogeneous canopy

$$ \text{but} \quad \left\{ \begin{array}{rcl} \rightarrow & \text{several superposed horizontal layers} & \\ \rightarrow & \text{several vegetation elements can be mixed} & \end{array} \right. $$
Test of the model: datasets -> LSE data

☐ **Bibliographic data:**
van de Griend and Owe (1993), Valor and Caselles (1996), Sugita et al. (1996)

☐ Available **database** including **ground measurements of land surface emissivity**:
DAISEX, Alpilles-ReSEDA, WATERMED

☐ Original data in Avignon and Camargue (HyEurope 2007)

☐ Most measurements done using the box method
(few by using 'TES')

→ 8-14 or 8-13 µm
→ 1, 4 or 6 bands

-> overall: ~100 individual data points !!

☐ Comparison to LES simulated using 4SAIL (Verhoef et al. 2007)
# Test of the model: datasets

- LAI (measured or 'approximated' by NDVI)

In most cases:

- no leaf angle

- no leaf optical properties

=> stochastic simulations

$$ 
\left\{
\begin{array}{rcl}
& & \rightarrow \text{direction of observation = nadir} \\
& & \rightarrow \text{LAI: uniform distribution } [0-6] \\
& & \rightarrow \text{mean leaf angle: uniform distribution } [45^\circ - 70^\circ] \\
\text{leaf properties:} & & \begin{array}{l} - \text{normal distribution of reflectances with} \\ \quad [\text{mean 0.029 and std 0.011}] \\ \quad - \text{transmittance set to 0} \end{array}
\end{array}
\right.
$$
Test of the model: datasets

→ leaf reflectance : normal distribution with
[mean 0.029 and std 0.011]

leaf optical property data:

- ASTER spectral library

- MODIS spectral library

- Fuchs and Tanner 1966

- Idso et al. 1969

- Wang et al. 1994

- Coll et al. 2001

<figure><img src="image_14.png" /></figure>
## Test of the model: datasets

→ leaf reflectance : normal distribution with
[mean 0.029 and std 0.011]

leaf optical property data:
- ASTER spectral library
- MODIS spectral library
- Fuchs and Tanner 1966
- Idso et al. 1969
- Wang et al. 1994
- Coll et al. 2001

new leaf optical property data:
- Gerber et al. 2012
- Fabre et al. 2012

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>
## Test of the model: datasets

→ leaf reflectance : normal distribution with
[mean 0.029 and std 0.011]

<figure><img src="image_18.png" /></figure>

leaf optical property data:

<figure><img src="image_19.png" /></figure>

- ASTER spectral library

- MODIS spectral library

- Fuchs and Tanner 1966

- Idso et al. 1969

- Wang et al. 1994

- Coll et al. 2001

new leaf optical property data:

- Gerber et al. 2012

- Fabre et al. 2012

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>
## Test of the model: datasets

→ leaf reflectance : normal distribution with
[mean 0.029 and std 0.011]

leaf optical property data:
- ASTER spectral library
- MODIS spectral library
- Fuchs and Tanner 1966
- Idso et al. 1969
- Wang et al. 1994
- Coll et al. 2001

new leaf optical property data:
- Gerber et al. 2012
- Fabre et al. 2012

<figure><img src="image_23.png" /></figure>

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<figure><img src="image_25.png" /></figure>
Evaluation of SAIL-Thermique: Alpilles + Barrax (8-14 µm) [crops]

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Evaluation of SAIL-Thermique: van de Griend and Owe 1993 (8-14 µm)
[herbaceous, shrubs]

<figure><img src="image_27.png" /></figure>
Evaluation of SAIL-Thermique: van de Griend and Owe 1993 (8-14 µm)
[herbaceous, shrubs]

<figure><img src="image_28.png" /></figure>
Evaluation of SAIL-Thermique:

Marrakech 2003 (8-13 µm)

[dry cereals]

<figure><img src="image_29.png" /></figure>
Impact of leaf drying

→ leaf emissivity is lower for dry leaf

<figure><img src="image_30.png" /></figure>
All data together

simulation for mean values (leaf emissivity =0.97 (wet) and 0.91 (dry), spherical canopy)

<figure><img src="image_31.png" /></figure>
Simulation of land surface emissivity spectra

LSE spectra are used as background in many methods to estimate LSE value

ex: 
* the TES method for deriving surface temperature from multispectral data
* the spectral LSE atlas developed at University of Wisconsin

There is no measurements of hyperspectral LSE really available for documentation or evaluation

we propose to use SAIL-Thermique

SAIL-Thermique is a monospectral model

but it may be used for simulating emissivity at any TIR wavelength as soon as leaf and soil spectral properties are known
Simulation of land surface emissivity spectra

☐ SAIL-Thermique is a monospectral model

☐ but it may be used for simulating emissivity at any TIR wavelength as soon as leaf and soil spectral properties are known

<figure><img src="image_32.png" /></figure>
Simulation of land surface emissivity spectra

☐ SAIL-Thermique is a monospectral model

☐ but it may be used for simulating emissivity at any TIR wavelength as soon as leaf and soil spectral properties are known

<figure><img src="image_33.png" /></figure>
Simulation of land surface emissivity spectra

☐ SAIL-Thermique is a monospectral model

☐ but it may be used for simulating emissivity at any TIR wavelength as soon as leaf and soil spectral properties are known

<figure><img src="image_34.png" /></figure>
Comparison to standard linear mixing with soil and leaf samples

<figure><img src="image_35.png" /><figcaption>linear mixing from samples</figcaption></figure>

<figure><img src="image_36.png" /></figure>
Comparison to standard linear mixing with soil and leaf samples

Radiative transfer calculations

<figure><img src="image_37.png" /></figure>

linear mixing from samples

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>
Comparison to standard linear mixing with soil and leaf samples

Radiative transfer calculations

<figure><img src="image_40.png" /></figure>

linear mixing from samples

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>
Comparison to standard linear mixing with soil and leaf samples

Radiative transfer calculations

<figure><img src="image_43.png" /></figure>

linear mixing from samples

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

Cavity effect
Comparison to standard linear mixing with soil and leaf samples

<figure><img src="image_46.png" /><figcaption>linear mixing from full canopy value</figcaption></figure>

<figure><img src="image_47.png" /></figure>
Comparison to standard linear mixing with soil and leaf samples

Radiative transfer calculations

<figure><img src="image_48.png" /></figure>

linear mixing from full canopy value

<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>
Comparison to standard linear mixing with soil and leaf samples

Radiative transfer calculations

<figure><img src="image_51.png" /></figure>

linear mixing from full canopy value

<figure><img src="image_52.png" /></figure>

Homogeneous pixel

<figure><img src="image_53.png" /></figure>
Comparison to standard linear mixing with soil and leaf samples

Radiative transfer calculations

<figure><img src="image_54.png" /></figure>

linear mixing from full canopy value

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>

Heterogeneous pixel
# Application to the Temperature – Emissivity Separation (TES) algorithm

Jacob et al., RSE, 2017

<figure><img src="image_57.png" /></figure>

* Simulation of land surface emissivity spectra for vegetation surfaces

<figure><img src="image_58.png" /></figure>

* Training of the MMD - ε<sub>min</sub> relationship used in the TES algorithm for ASTER

<figure><img src="image_59.png" /></figure>

* Evaluation of the new algorithm for emissivity and temperature retrievals

<figure><img src="image_60.png" /><figcaption>(Jacob et al. 2017)</figcaption></figure>

<figure><img src="image_61.png" /></figure>
# Application to the TES algorithm

## Evaluation of the new algorithm for emissivity and temperature retrievals

<table><thead><tr><th>RMSE<br>(Bias)</th><th>Channel 1<br>8.28 µm</th><th>Channel 2<br>8.63 µm</th><th>Channel 3<br>9.07 µm</th><th>Channel 4<br>10.65 µm</th><th>Channel 5<br>11.28 µm</th><th>Ts</th></tr></thead><tbody><tr><th scope="row">Schmugge et al.</th><td>0.015<br>(0.009)</td><td>0.013<br>(0.008)</td><td>0.011<br>(0.007)</td><td>0.011<br>(0.008)</td><td>0.011<br>(0.008)</td><td>0.60 K<br>(-0.37 K)</td></tr><tr><th scope="row">New</th><td>0.008</td><td>0.007</td><td>0.006</td><td>0.006</td><td>0.006</td><td>0.33 K</td></tr></tbody></table>

When accounting of canopy impact on LSE

- error on LSE and LST retrieval were reduced by a factor 2

- retrieved LSE were increased

- retrieved LST were decreased
Concluding remarks

☐ Yes, it is possible to use a "standard" Radiative Transfer models for simulating land surface emissivity in presence of vegetation canopy

☐ The SAIL-Thermique model is a simple model that can be applied to homogeneous canopy

☐ The model may be used for helping in the analysis of TIR multispectral data
• variation of emissivity as a function of plant canopy structure and sensor characteristics
• in an inverse mode for deriving plant component spectra

☐ The model was used for deriving a new version of the TES algorithm by improving the representativity of the MMD – ε<sub>min</sub> relationship for vegetated areas.
Can be applied to any multispectral sensors (MODIS, MISTIGRI, HyspIRI, MASTER, HyTES...)

☐ Would this be useful for TIR hyperspectral atmospheric sounders ?

☐ We have to go further in understanding leaf and soil optical properties in the thermal infrared domain (e.g. response to drying) and we need measurements on vegetation
Evaluation of SAIL-Thermique against 4SAIL

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>
Evaluation of SAIL-Thermique against 4SAIL

$$
\text{SAIL-Thermique:} \qquad \varepsilon = 1 - \rho_{sd} - \frac{\tau_{ss} r_{sd} + \tau_{sd} r_{dd}}{1 + \rho_{dd} r_{dd}} \tau_{dd}
$$

$$
\text{4SAIL:} \quad \varepsilon_o = \gamma_o + \frac{(\tau_{do} + \tau_{oo})}{1 - r_s \rho_{dd}} r_s \gamma_d + \frac{(\tau_{do} + \tau_{oo})}{1 - r_s \rho_{dd}} \varepsilon_s.
$$

$$
\gamma_d = 1 - \rho_{dd} - \tau_{dd}
$$

$$
\gamma_o = 1 - \rho_{do} - \tau_{do} - \tau_{oo}.
$$
Evaluation with multispectral measurements

Soybean data in Avignon, with different LAI

Leaf and soil spectra and 6 band radiometer measurements above canopy

<figure><img src="image_64.png" /></figure>
<figure><img src="image_65.png" /><figcaption>Simulations</figcaption></figure>

LAI

<figure><img src="image_66.png" /></figure>

## Evaluation with multispectral measurements

Soybean data in Avignon, with different LAI

TES method applied from radiometetric measurements above canopy in ASTER bands

<figure><img src="image_67.png" /></figure>
<figure><img src="image_68.png" /></figure>

ELSEVIER

Available online at www.sciencedirect.com

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ScienceDirect

Remote Sensing of Environment xx (2007) xxx–xxx

Remote Sensing
of
Environment

www.elsevier.com/locate/rse

# Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0–14.0 µm) ☆

Beatriz Ribeiro da Luz *, James K. Crowley

U. S. Geological Survey, MS 954, 12201 Sunrise Valley Drive, Reston, VA 20192, United States

Received 23 October 2006; received in revised form 18 January 2007; accepted 20 January 2007

Remote Sensing of Environment 114 (2010) 404–413

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ELSEVIER

Contents lists available at ScienceDirect

Remote Sensing of Environment

journal homepage: www.elsevier.com/locate/rse

Remote Sensing
Environment

<figure><img src="image_71.png" /></figure>

# Identification of plant species by using high spatial and spectral resolution thermal infrared (8.0–13.5 µm) imagery ☆

Beatriz Ribeiro da Luz *, James K. Crowley
# Influence of Water Content on Spectral Reflectance of Leaves in the 3–15-µm Domain

Sophie Fabre, Audrey Lesaignoux, Albert Olioso, and Xavier Briottet

<figure><img src="image_72.png" /></figure>

ELSEVIER

Contents lists available at ScienceDirect

Remote Sensing of Environment

journal homepage: www.elsevier.com/locate/rse

<figure><img src="image_73.png" /></figure>

Modeling directional-hemispherical reflectance and transmittance of fresh and dry leaves from 0.4 µm to 5.7 µm with the PROSPECT-VISIR model

F. Gerber<sup>a,b</sup>, R. Marion<sup>b</sup>, A. Olioso<sup>c</sup>, S. Jacquemoud<sup>a,*</sup>, B. Ribeiro da Luz<sup>d</sup>, S. Fabre<sup>e</sup>

*International Journal of Remote Sensing*
Vol. 34, No. 7, 10 April 2013, 2268–2285

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Taylor & Francis
Taylor & Francis Group

## Influence of soil moisture content on spectral reflectance of bare soils in the 0.4–14 µm domain

Audrey Lesaignoux<sup>a,b*</sup>, Sophie Fabre<sup>a</sup>, and Xavier Briottet<sup>a</sup>
<figure><img src="image_75.png" /></figure>

ELSEVIER

Contents lists available at ScienceDirect

Remote Sensing of Environment

journal homepage: www.elsevier.com/locate/rse

<figure><img src="image_76.png" /></figure>

Remote Sensing
Environment

<figure><img src="image_77.png" /></figure>

# Reassessment of the temperature-emissivity separation from multispectral thermal infrared data: Introducing the impact of vegetation canopy by simulating the cavity effect with the SAIL-Thermique model

<figure><img src="image_78.png" /></figure>

CrossMark

Frédéric Jacob a,*, Audrey Lesaignoux a, Albert Olioso b, Marie Weiss b, Karine Caillault c, Stéphane Jacquemoud d,
Françoise Nerry e, Andrew French f, Thomas Schmugge g, Xavier Briottet h, Jean-Pierre Lagouarde i
<figure><img src="image_79.png" /><figcaption>Fig. 3. DHR (black) and DHT (grey) of fresh (plain line) and dry (dashed line) *Catalpa* leaves from the USGS dataset.</figcaption></figure>
<figure><img src="image_80.png" /><figcaption>Fig. 6. Emissivity of fresh (plain black line) and dry (dashed black line) *Catalpa* leaves from the USGS dataset. The spectral ranges of interest are marked by lines (3.5–5.7 µm, 3–5 µm, and 8–14 µm). See Table 2 for emissivity statistics for these ranges.</figcaption></figure>
<table><caption>**Table 2**

Statistics about leaf emissivity in three spectral domains.</caption><thead><tr><th>Spectral range</th><th>Minimum</th><th>Maximum</th><th>Mean</th><th>Standard deviation</th></tr></thead><tbody><tr><td><em>Fresh leaves</em></td><td></td><td></td><td></td><td></td></tr><tr><td>3.5–5.7 µm</td><td>0.89</td><td>0.97</td><td>0.95</td><td>0.02</td></tr><tr><td>3–5 µm</td><td>0.92</td><td>0.97</td><td>0.95</td><td>0.01</td></tr><tr><td>8–14 µm</td><td>0.93</td><td>0.98</td><td>0.96</td><td>0.01</td></tr><tr><td><em>Dry leaves</em></td><td></td><td></td><td></td><td></td></tr><tr><td>3.5–5.7 µm</td><td>0.67</td><td>0.90</td><td>0.79</td><td>0.06</td></tr><tr><td>3–5 µm</td><td>0.75</td><td>0.97</td><td>0.90</td><td>0.06</td></tr><tr><td>8–14 µm</td><td>0.89</td><td>0.96</td><td>0.94</td><td>0.02</td></tr></tbody></table>
<figure><img src="image_81.png" /><figcaption>Fig. 10. Simulated (dashed line) vs. measured (plain line) reflectance and transmittance of fresh (top left: *Catalpa*, top right: *Tillia*) and dry (bottom left: *Catalpa*, bottom right: *Acer platanoides*) leaves.</figcaption></figure>
<figure><img src="image_82.png" /><figcaption>Fig. 2. Spectral reflectances of leaves for several water contents in the thermal domain. (a) Adaxial and (b) abaxial sides of cherry tree, (c) adaxial side of sunflower, and (d) sorghum leaf samples. Arrow labels locate a specific behavior, detailed in the document.</figcaption></figure>
<figure><img src="image_83.png" /><figcaption>Fig. 3. Leaf reflectances at (a) 4.1 and (b) 11 µm as a function of LWC (wavelengths chosen in atmospheric window only).</figcaption></figure>
<figure><img src="image_84.png" /></figure>

<figure><img src="image_85.png" /></figure>

<figure><img src="image_86.png" /></figure>

<figure><img src="image_87.png" /><figcaption>Figure 3. Spectral signatures of three soil samples (the so-called samples 1, 2, and 3) depending on SMC (values in %), in the solar (a) and the thermal (b) domains.</figcaption></figure>