Estimating leaf area index by inversion of

PROSAILH radiative transfer model on SPOT 6

imagery

SPOT 6 EO-Innovation workshop

Moses Azong Cho, Abel Ramoelo, Jenny Holloway

Earth Observation Group, Natural Resources and Environment, The Council for Scientific and

Industrial Research (CSIR), PO Box 395, Pretoria, South Africa

• To explore the utility of PROSAILH radiative

transfer model for estimating LAI from the SPOT 6

imagery.

• PROSAILH is a combination of PROSPECT (a leaf optical

properties model) and SAIL ( canopy bidirectional reflectance

model)

Aim of study

• Leaf area index (LAI) is an

important biophysical variable

that determines vegetation

photosynthetic capacity and other

plant and ecosystem processes

such as evapotranspiration,

surface energy balance. LAI is

therefore key to environmental

modelling of ecosystem

productivity, land degradation and

climate

Relevance of leaf area index (LAI)

• Statistical or empirical and

• Statistical models are limited in the sense that they are site, seasonal and sensor specific, i.e. an empirical model develop for one season or site cannot be used to assess LAI for another site or season.

• Physically-based modelling approaches.

• Based on the fundamental equations of radiative transfer

• In the forward modelling mode, the radiative transfer model is used to simulate synthetic spectra

• Inversion modelling is then used to predict LAI from measured reflectance.

Remote sensing of LAI

• Study site and field data collection

• The study was conducted in St Lucia,

Dukuduku forest area. The location of the

study area lies at 31°57’31.431”E,

27°51’12.406”S and 32°29’3.227”E,

28°33’6.838”S. The study area comprises

of various land cover types such as forest,

sugarcane and forest plantations, savanna

and grasslands.

• LAI was measured in the field using the licor

2200 (Plant canopy analyzer) for 91 sites

covering a much bigger area than the

specific area used in this study. Only 13 of

the plots were located in the specific area.

Materials and Methods

• The PROSAILH model parameterisation

• chlorophyll content (0 to 80 ug/cm2),

• carotenoid content (0 to 20 ug/cm2),

• total brown pigment (0 to 1),

• leaf water content, LAI (0 to 7.5),

• leaf thickness (0.0005 to 0.055),

• structural parameter (mean = 3 and Stdev = 0.02),

• average leaf angle (25 to 65),

• Hotspot (mean = 0.1, Stdev = 0.01)

• soil coefficient (const. at 1),

• biophysical and soil variables.

• The solar zenith (54.11 deg) and azimuth (47.68 deg) at the time of image acquisition were used.

• The fraction of diffuse illumination was fixed to 0.1, independent of wavelengths.

• Within specific ranges and distributions of each variable, 1000 parameter sets were randomly chosen to simulate the synthetic canopy reflectance spectra. The wavelengths were restricted to the SPOT 6 spectral configuration. The 1000 spectra and their corresponding parameter value sets were used to build a look-up-table.

• To predict the LAI for each pixel of the image, the root mean square error (RMSE) between each simulated spectrum and each pixel spectrum is calculated. The LAI value belonging to the simulated spectrum with the lowest RMSE is then assigned to the pixel. We have developed an IDL software that is implemented in ENVI for this purpose.

Materials and Methods

LAI radiative transfer model -

PROSAILH modelling Software

• Accuracy assessment

• The RMSE and coefficient of determination between the

measured and predicted LAI used to assess the predictive

capability of the PROSAILH radiative transfer model. Only

13 ground truthing points that fall within the SPOT 6

imagery were used for the evaluation. The SPOT 6 results

were compared with those of MODIS 250 m. MODIS 250

m has only two bands in the red and NIR. Inversion of the

MODIS 250 was done by inverting a quadratic regression

equation developed between the simulated reflectance

and their corresponding LAI on the MODIS imagery.

Materials and Methods

The inversion of PROSAILH radiative transfer model on the SPOT 6 data yielded a lower RMSE (0.85, n = 12) and R2 (0.78) for thirteen measured LAI when compared to MODIS 250 m imagery (RMSE = 1.26, R2 = 0.49) (Figure 1)

Results

Figure 1. Relationship between measured

and predicted LAI for (A) SPOT6 and (B)

MODIS 250 m imagery

A strong positive correlation was

observed between the predicted

SPOT 6 LAI and MODIS 250 m

y = 0.4766x + 2.180

Where y = MODIS LAI and x = SPOT LAI

What is the implication of this?

Results

Relationship between SPOT6 AND MODIS 250 m LAI

predicted using PROSAILH radiative transfer model.

Relationship between observed and predicted LAI

PROSAILH radiative transfer model on MODIS 250 m

for 91 plots in from Eastern cape, KZN and

Mpumalanga

LAI map

• This preliminary investigation shows the potential

of accurately estimating LAI from SPOT 6 using

physically-based modelling approach

• SPOT 6 LAI could be used to calibrate MODIS

LAI.

• SPOT 6 atmospheric correction module is

required.

Conclusions

Thank you

mcho@csir.co.za