CHLOROPHYLL RETRIEVAL FROM CANOPY REFLECTANCE OVER ORCHARDS USING HYPERSPECTRAL TECHNIQUES

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1 CHLOROPHYLL RETRIEVAL FROM CANOPY REFLECTANCE OVER ORCHARDS USING HYPERSPECTRAL TECHNIQUES P. Kempeneers a, S. De Backer b, P. J. Zarco-Teada c, S. Delalieux d, G. Sepulcre- Cantó c, F. Morales f, Ruth Sagardoy f, J.A.N. van Aardt e, P. Coppin d, P. Scheunders b a Flemish Institute for Technological Research (VITO), Boeretang 00, B-400 Mol, Belgium b University of Antwerpen, Groenenborgerlaan 171, B-00 Antwerpen, Belgium c Instituto de Agricultura Sostenible (IAS-CSIC), Córdoba, Spain d Katholieke Universiteit Leuven, Celestinenlaan 00E, B-3001 Heverlee, Belgium e CSIR - NRE Ecosystems Earth Observations, P.O. Box 395, Pretoria 0001, South Africa f Estación Experimental Aula Dei (EEAD-CSIC), Zaragoza, Spain pieter.kempeneers@vito.be ABSTRACT This paper presents the retrieval of foliar chlorophyll from canopy reflectance, measured with the AHS hyperspectral airborne sensor over a peach orchard. First, it is shown that nutrient deficiencies that caused stress can be detected on hyperspectral spectra. Second, the chlorophyll retrieval via model inversion is found dependent on viewing and illumination conditions. Finally, a methodology is presented for a robust chlorophyll retrieval via inverse modelling using multiple angular information. During an extensive field campaign, foliar and crown reflectance have been measured with a portable field spectroradiometer. Airborne hyperspectral imagery was acquired over the orchard with the AHS hyperspectral sensor with different viewing conditions. Stress on the peach orchard was treated with iron chelates to recover from iron chlorosis conditions. Blocks of trees treated with iron chelates created a dynamic range of chlorophyll concentration as measured in leaves. A relationship was established between the measured spectra and estimated biochemical parameters via inversion of a linked directional homogeneous canopy reflectance model (ACRM) and the PROSPECT leaf model. Numerical model inversion was conducted by minimizing the difference between the measured reflectance samples and modelled values. Results were compared with a simple linear regression analysis, linking chlorophyll to the reflectance measured at the Top of Canopy. 1 INTRODUCTION Quantifying photosynthetic pigments in agricultural crops is important to assess their physiological state. More in particular, crop stress and chlorosis detection at an early stage are important for precision agriculture practice. Timely and efficient agricultural management of orchards can improve yield and fruit quality (Cordeiro, 1995 and Tagliavini, 001). Hyperspectral remote sensing has the potential to estimate leaf biochemical constituents such as chlorophyll concentration (C ab ) from airborne imagery. Differences in reflectance between healthy and stressed vegetation due to changes in chlorophyll concentration are expressed in the visible region of the spectrum. In this work, we show the ability to detect chlorosis stress from canopy reflectance spectra. Moreover, we demonstrate the dependence of illumination and viewing geometry on the accuracy of a quantitative chlorophyll retrieval at the canopy level. A simple linear regression analysis was applied. The optimal single band was automatically selected for this purpose. However, regression techniques require training data and the predictive algorithm is not generally applicable (Grossman, 1996) The selected bands depend on the dataset at hand, influenced by n.1

2 species, canopy structure, illumination and viewing geometry. A more general approach was therefore used based on radiative transfer models. When inverted, they allow estimation of leaf biochemistry from remote sensing data. METHODOLOGY Biochemical parameters of crops can be estimated more accurately with the introduction of hyperspectral sensors. The large number of narrow bands allow more precise measurements of absorption features. For chlorophyll, absorptions are located in the visible region of the spectrum (Curran, 1989). Several methodologies exist to estimate the concentration of chlorophyll from remote sensing data..1 Regression The chlorophyll value measured in situ can be used as the response value in a regression analysis. The explanatory variable is the reflectance value in one (simple linear regression) or more bands (multiple regression). Wessman (1988), Curran (199) and Martin (1997) derived predictive algorithms for chlorophyll from a training data set by estimating the statistical relationship between the chlorophyll concentration and the specific bands of a leaf or canopy reflectance. If properly trained, regression methods can be very useful for a specific dataset at hand. A simple linear regression was used in this study since no improvements were found using multiple linear regression. Model inversion The predictive algorithm obtained from regression, trained on a specific site and crop, is not reliable for other conditions (Grossman, 1996). The selected bands depend on the dataset at hand, influenced by species, canopy structure, illumination and viewing geometry. Several techniques have been introduced to increase the robustness of biochemical parameter estimation, including continuum-removal (Kokaly, 1999), band ratios (Chapelle, 199; Blackmer, 1996) and normalized differences (Daughtry, 000). Another technique to retrieve chlorophyll from reflectance data is through numerical inversion of leaf and canopy reflectance models. Inversion is usually performed with an iterative optimisation method (section.), though neural networks are also used. Foliar chlorophyll can be retrieved from leaf reflectance by inverting a leaf model such as PROSPECT (Jacquemoud, 1996). To estimate biochemical parameters from canopy reflectance, the leaf model must be coupled with a canopy model. The radiative transfer canopy model, ACRM (Kuusk, 1995a, Kuusk, 1995b, Kuusk, 003), was used for this purpose. The numerical inversion minimizes a merit or cost function. This is typically the sum of squared differences between the measured and modelled canopy spectral reflectance in each spectral band i=1 d: ( R Rˆ ( P) ) = d (1) i= 1 i i The modelled reflectance depends on the model parameters P. The optimisation thus consists of finding the optimal model parameters P that minimize the merit function. Modifications of this merit function exist, for example by weighing the contributions of the individual wavelengths. Zarco (001) based the merit function on an optical index R750/R 750 focussing on a single band ratio, rather than the entire spectrum. The authors showed superior results, especially if reflectance signals included shadowed pixels. We filtered the modelled reflectance to obtain a simulated reflectance signature that is consistent to the reflectance measured by the airborne hyperspectral sensor. It is important to match the modelled spectrum to the sensor specifications, especially in cases such as the AHS sensor which has a varying bandwidth. Fig. 1 shows the modelled spectrum before and after filtering. Rˆ Rˆ n.

3 results from destructive chemical analysis in the laboratory for a subset of leaf samples. SPAD measurements and laboratory analysis exhibited good correlation (R =0.8). Figure 1: The modeled canopy spectrum before (solid) and after (dots) filtering according to the specifications of the AHS sensor The merit function was then extended to incorporate multi-observation data with different viewing geometry. For different observations R, a ˆR modelled reflectance is calculated using a parameter set P with appropriate viewing and illumination geometry. The remaining biophysical and biochemical parameters in the set P are identical for all observations. The new merit function that is minimized takes the error of all observed and modelled reflectances into account: d ( = R i Rˆ i ( P )) () i= 1.3 Experimental set up The peach orchard consists of trees ordered in 35 rows and 6 columns. The total number of trees is 05 instead of 10, due to 5 missing trees. Iron chlorosis was induced in columns, while trees were treated in groups of 3. The iron chelate was applied with different concentrations: 0 g/tree, 60 g/tree, 90 g/tree and 10 g/tree..4 Field data collection Fresh leaves were sampled for each tree and measured with the ASD spectrometer. Leaf reflectance was measured for 716 leaves, using a Leaf Clip. Chlorophyll was measured for each leaf with a SPAD-50 Minolta Chlorophyll Meter. Mean values were calculated per tree to facilitate comparison with parameters obtained at canopy level. The SPAD measurements were calibrated to obtain chlorophyll concentration, using the.5 Airborne hyperspectral data Imagery for the peach orchard was acquired with the airborne hyperspectral sensor AHS in four tracks with different viewing and illumination conditions. The AHS sensor has 63 bands covering the visual and near infrared part of the spectrum (450nm-500nm). Image data were processed to top of canopy level, using in-house developed software for atmospheric correction (based on MODTRAN). The orchard has a plant spacing of 4 meters, with an average per-tree crown diameter similar to the ground resolution of the sensor (.5m). This complicated tree identification from the image since individual tree pixels inevitably exhibited spectral mixtures with neighbouring trees, understorey and shadow. 3 RESULTS Leaf biochemical parameters were first obtained by inversion of PROSPECT applied to the leaf spectra. All five PROSPECT parameters were estimated simultaneously by the optimisation routine. Their mean values and variances are shown in table 1. Foliar chlorophyll was obtained at an acceptable accuracy (R =0.8, RMSE=6.5 µg/cm ). Table 1: Means and variances of estimated PROSPECT parameters over all leaves Parameter Mean Variance Water equivalent thickness Leaf protein content Leaf pigment concentration Leaf structure parameter Chlorophyll concentration It is clear from table 1 that apart from chlorophyll, there were small variation in biochemical parameters. As a result, we fixed those parameters to their mean value for further analysis at canopy level. A total of 05 canopy spectra were derived from the AHS image. Each spectrum corresponded to a single tree. Chlorophyll was then derived from canopy reflectance through inversion of PROSPECT and ACRM. The sun and view angles were set to the actual viewing geometry during the flight for each pixel. The leaf n.3

4 area index (LAI) was measured for 10 trees with the LAI-000 instrument. The mean value (1.71) was used for fixing LAI in the canopy model. The remaining parameters were fixed to the average value of the model range (Angstrom turbidity factor), or obtained by trial and error (leaf angle distribution, clumping parameter and the refractive index of leaf scattering layers). The chlorophyll was the only parameter to be inverted, without any constraints. An overview of the fixed parameters for the canopy reflectance model is shown in table. Table : Parameters used for the ACRM canopy reflectance model Parameter Value Angstrom turbidity factor 0.1 Leaf Area Index 1.71 Leaf size 0.03 Clumping parameter 0.8 Log eccentricity term for LAD.3 Mean leaf angle of elliptical LAD 45 Refractive index of leaf scattering layers 1 First, a model inversion was performed on the canopy reflectance from a single observation angle. Results varied significantly for each track as shown in table 3. A better result was obtained when all angular information was combined by taking the different observations in the merit function (1) into account. A simple linear regression was performed as a reference for our results from the inverse modelling approach (table 3). The regression was fitted to data from each individual track from this particular site and crop. Inverse modelling using only a single observation showed a distinct improvement over the simple regression. Moreover, results were stable, showing little difference between different observations (tracks). This indicated that there was no degradation in the subsequent observations due to pre-processing or other artefacts (such as mapping the individual trees in the different images). Results improved considerably as multiple observations were used in the merit function during the model inversion.the calculated chlorophyll values were averaged over all observations for regression, which explained the improvement in R and RMSE values. The scatterplots in Figure show the high dependence on viewing geometry on the model inversion results. The improvement when using all observations is shown in Figure 3. Table 3: Chlorophyll retrieval results from canopy model inversion using observations from single and multiple tracks Model inversion Regression Track R RMSE R RMSE All CONCLUSIONS AND FURTHER RESEARCH Leaf chlorophyll concentration was estimated from airborne hyperspectral imagery over a peach orchard. The results showed that chlorophyll content can be estimated from airborne hyperspectral data over a peach orchard for nutrient stress detection at the crown level. Model inversion of PROSPECT and ACRM were compared with a regression technique. The latter technique proved to be slightly better, but is crop, plot and observation specific. Furthermore, we showed that results from model inversion varied for different observational tracks over the same plot. A technique is presented to combine all observations during model inversion, making use of full geometry. Similar results to the regression approach were obtained when using the multiple angle technique. Further research will focus on the effect of geometry, including the use of a 3D canopy reflectance model. ACKNOWLEDGEMENTS We would like to acknowledge proect AGL and the Belgian Science Policy Office for financing this work. We also thank Drs. A. Kuusk and M. Disney for providing their code for the ACRM model. n.4

5 8 TH INTERNATIONAL SYMPOSIUM ON FLOW VISUALIZATION (1998) Figure : Chlorophyll retrieval from different observation geometries using model inversion and simple linear regression Figure 3: Chlorophyll retrieval using full geometry from 4 observations n.5

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