Leaf Area Index retrieval by inverting SCOPE model using hyperspectral data over floodplain meadow Suvarna Punalekara Anne Verhoefa Christiaan van der Tolb Kevin Whitea a University of Reading, UK b ITC, University of Twente, Netherlands

FIELD SITE: Yarnton Mead located towards the Northwest side of Oxford city, UK The site is declared as Site of Special Scientific Interest Highly bio-diverse grassland which is being managed traditionally for almost 1000 years. Variability in the biophysical parameters such as LAI, leaf biochemicals, leaf angles is crucial to understand the functional dynamics of these communities. Coupled canopy radiative transfer models And SVAT models could be highly beneficial for ecosystem research SCOPE model Source : Google Earth

LIDFa is coefficient determining leaf angel geometry Radiative transfer module of SCOPE PROSAIL = PROSPECT + SAIL SAIL model: Canopy radiative transfer model (Verhoef W., 1985) Parameters: Leaf Area Index (LAI) Leaf inclination distribution function (LIDFa) Solar geometry View geometry - Leaf reflectance and transmittance (PROSPECT) Canopy hot spot parameter (q) Soil reflectance LIDFa is coefficient determining leaf angel geometry LIDFa varies from -0.5 to 0.5 -0.5 ~ erect leaves 0.5 ~ planar leaves q ~ leaf width / canopy height Output: Outgoing spectrum of radiation Hemispherical and in viewing direction - Net radiation at every surface element

PROSPECT: Parameters: Output of Prospect: Leaf radiative transfer model (Jacquemoud and Baret, 1990) Parameters: - Leaf chlorophyll content (μg cm-2) : Lchl Leaf dry matter content (g cm-2) : Ldmc Leaf water content (cm) : Lwc Leaf senescent matter content (g cm-2) : Lsc - Leaf thickness parameter (N) VIS NIR SWIR Output of Prospect: Leaf reflectance and transmittance over a range of wavelength

Radiative model inversion : Look-Up Table (LUT) method General issues: Which parameters to fix and which one to vary as free parameters? What should be the range of parameters? How to select best fit solution? LUT dimensions? Computational time? Which part of spectrum to be used? Homogeneous / Heterogeneous target canopies?? Forward model runs Measured spectra LUT Statistical test used for inversion : Root mean square error (RMSE) ill-posed nature of retrieval Parameters for best fit solution

Field Spectro-radiometer SVC HR-1024i : - 350 nm- 2500 nm - Spectral Resolution: ≤ 3.5 nm,  700 nm, ≤ 9.5 nm,  1500 nm, ≤ 6.5 nm,  2100 nm - 8o IFOV lens at 1 m height above ground Retrieval has also been done using airborne hyperspectral data obtained by FENIX

Sampling strategy 3 main communities based on dominating species. 10 locations in every community 16 spectra in 1X1m plot at every location Data processed: FSF toolbox Carex acuta (CA) Juncus acutiflorus (JA) Sanguisorba Officinalis (SO) SO JA CA

plageophile - near planar Performance of LUT based retrieval strongly depends on in situ information (Darvishzadeh et al., 2008 ; Combal et al., 2002) Field information is highly important in defining parameter space and LUT dimensions FREE parameters : LAI, Leaf chlorophyll content (Lchl) Leaf dry matter content (Ldmc) Leaf water content (Lwc) LIDFa Hotspot parameter (q) FIXED parameters: soil reflectance Leaf thickness parameter (N) Leaf senescent matter content (Lsc) Sunscan canopy analyser Field measurements (13 June 2014) Lchl Ldmc Lwc LAI q LIDFa sps (µg cm-2) (g cm-2) cm m2 m-2 m1 m-1 Unit less SO 32.6 (2.3) 0.006 (0.0003) 0.013 (0.001) 7.88 (1.52) 0.1 plageophile - near planar CA 31.5 (3.9) 0.010 (0.001) 0.018 (0.002) 6.69 (1.03) 0.04 erectophile JA 23.5 (5.1) 0.011 (0.002) 0.033 (0.005) 5.88 (0.94) 0.03

Effect of number of combinations selected RMSE LAI measured

Comparison between modelled ‘best-fit’ and average measured spectra All measured wavelengths between 400 and 2470 nm were used Average of 200 best combinations have been used Wavelength (nm)

Effect of wavelength selection 400:010:2400 nm 400:020:2400 nm All measured wavelengths used 400:030:2400 nm Literature based LAI meas. LAI meas. LAI meas.

Effect of fixing one parameter from the original five free parameters except LAI Change in RMSE of LAI estimation is compared with the FREE run RMSE (0.78 m2 m-2) Fixing Lchl and q seem to have improved RMSE Fixing LIDFa and Ldmc, Lwc seem to make estimations worst.

Spectral index based approach for LAI estimation RMSE for LUT approach : 0.78 m2 m-2 All possible combinations of wavelengths were Used to calculate NDVI-like index

Mask for cloud shadow/target covered areas FENIX based maps LAI, (m2 m-2) Mask for cloud shadow/target covered areas

Conclusion LUT based inversion of PROSAIL model proved to be useful for LAI estimation for biodiverse floodplain meadow. Average of 200 ‘best’ solutions retrieved LAI with good RMSE Reducing number of wavelengths for retrieval did not prove beneficial for the inversion Fixing Lchl or q improved the RMSE of LAI retrieval, while fixing LIDFa, Ldmc and Lwc made it worse. LUT based approach proved better than spectral index based approach for LAI retrieval.

Thanks !!! Thanks to …… Alasdair Mac Arthur David Macdonald Ben Marchant Students and technical staff from University of Reading - Anne Verhoef - Paul Morris - Irina Tatarenko - Christiaan van der Tol Azin Howells