HYPERSPECTRAL REFLECTANCE OF SUB-BOREAL FORESTS MEASURED BY /PROBA AND AIRBORNE SPECTROMETER Andres Kuusk, Joel Kuusk, Mait Lang, Tõnu Lükk, and Tiit Nilson Tartu Observatory, 6162 Tõravere, Estonia ABSTRACT The Mode 3 scenes of the forest test site Järvselja, Estonia were atmospherically corrected, and compared to airborne measurements and to model simulations with forest reflectance model. The comparison of and airborne data hints that the instrument seems to have calibration problems in NIR spectral bands. Updated calibration coefficients of the sensor are provided. The revised reflectances supported by forestry database and spectral signatures of ground vegetation may serve as a database for the next phase of intercomparison of radiative transfer models RAMI. Key words: ; forest; hyperspectral reflectance. 1. INTRODUCTION Three rounds of intercomparisons of vegetation radiative transfer models (RAMI) have been carried out using simulated data sets [11, 14]. In such comparisons some features of light scattering which can not be considered by all models are excluded from the consideration (e.g. specular reflection on leaves, non-lambertian soil reflectance). A challenge for the next phase of RAMI is the use of field data. The forest test site at Järvselja, Estonia, is well studied, available are PROBA/ images [1], airborne measured reflectance spectra, reflectance spectra of ground vegetation, and forestry database. images were atmospherically corrected and compared to airborne measurements and to model simulations with forest reflectance model [8]. Comparison of and airborne measured data affirms that the applied procedure of atmospheric correction has high precision in green to NIR spectral regions. The spectrometer seems to have calibration problems in NIR spectral bands. Atmospherically corrected NIR spectra of forests systematically deviate from airborne measurements, and also from data of other satellite instruments and model simulations with forest reflectance model. Updated calibration coefficients of the sensor are provided. Revised reflectance data, both reflectance spectra and angular course of reflectance of ma- Table 1. Observation parameters. Parameter \ Scene 573 575 577 Time, GMT 9:43:39 9:44:28 9:45:18 Zenith angle, deg 7.62 37.23 56.71 Azimuth angle (1), deg -22.46 19.79 23.43 Grid resolution, m 17 17 21 19 28 19 (1) relative to Sun azimuth ture stands are compared to model simulations. We see both: rather good agreement of simulated and data, as well significant deviations from each other for some stands in some spectral bands. 2. IMAGES A set of three Mode 3 image cubes - 18 spectral bands selected for vegetation studies - of the sub-boreal forest test site at Järvselja in Estonia (27.3E, 58.3N, 4 m asl) was acquired on July 1, 25. Detailed description of the test site can be found in [7]. Weather conditions during acquisition were excellent, Sun zenith angle was 36.6. Image parameters are listed in Tab. 1. 3. AIRBORNE MEASUREMENTS Unfortunately we could not carry out airborne measurements at the test site simultaneous to the acquisition. Later, a special airborne spectrometer UAVSPEC was designed, and several stands in the scenes were measured on July 26, 26. The heart of the spectrometer is the 256 band NIR enhanced miniature spectrometer MMS-1 by Carl Zeiss Jena GmbH. The spectrometer is controlled by an Atmel microcontroller ATmega88. The fore-optics restricts the field-of-view to 2. The spectrometer system comprised web camera and Magellan SporTrak Pro GPS receiver for position estimation. The spectrometer was mounted at the chassis of Proc. Envisat Symposium 27, Montreux, Switzerland 23 27 April 27 (ESA SP-636, July 27)
a Robinson R-22 helicopter and looked vertically downward. Data form the spectrometer were collected by a laptop PC 3-5 times per second, and from the web camera and the GPS receiver once per second. Measurements were done from the height of 1 m above ground level, the flight speed was 6 km/h. The spectrometer was calibrated by measuring a gray Spectralon panel at the test site just before the airborne measurements. Downward spectral diffuse and total fluxes were measured at the test site with the FieldSpec Pro spectrometer. reflectance.8.6.4 ρ = 1.7 ρ UAVSPEC +.42 4. ATMOSPHERIC CORRECTION.2 Atmospheric correction of images was performed in two stages. First, with the 6S atmospheric radiative transfer model by Vermote et al. [13] a look-up-table (LUT) of top-of-atmosphere (TOA) radiances was generated for every band varying ground vegetation parameters in model simulations. Optical parameters of the atmosphere were estimated from AERONET Sun photometer measurements at Tartu Observatory, 45 km far from the test site, and in situ measurements of the diffuse-to-total ratio of downward spectral fluxes at the test site during the acquisition. The created LUTs were used for the conversion of TOA radiances to top-of-canopy reflectance. This procedure was applied separately to every pixel in every spectral image. Second, the adjacency effect in image sets 573 and 575 (view angle 7.6 and 37.2, respectively) was corrected by 2-D deconvolution. The point spread function (PSF) of the atmosphere as a function of aerosol optical depth was estimated by Liang et al. [1] in numerical simulations. However, the horizontal range of the adjacency effect seems to be overestimated in [1]. To avoid the overcorrection we had to re-calibrate the horizontal range of the PSF by a factor of 1/5. The darkest targets in visible bands in the scenes are mature spruce stands. The nadir reflectance of seven spruce stands in 11 visible bands (bands 1-11) from and helicopter measurements is compared in Fig. 1. We see that the reflectances are systematically higher than the airborne measured reflectances. At the same time the differences are small in most visible bands (bands 3-11), and according to t-test at level.5 non-significant in bands 6-1. Blue radiance of dark spruce stands is very low and most of the signal in these bands is coming from the atmosphere. It is challenging to use blue spectral region for the extraction of aerosol optical thickness directly from the imagery as it was done in several previous works. However, the blue spectral region is also the most problematic in remote sensing for several reasons. As most of the signal is coming from the atmosphere, even small errors in atmospheric parameter values affect significantly atmospherically corrected reflectance values. Signal-to-noise ratio of silicon-based sensors in blue spectral region is less than in other visible bands. Also, the instrument has had calibration problems of blue bands [3]. In bands 3-11 and airborne...2.4.6.8 UAVSPEC reflectance Figure 1. Top of canopy reflectance of spruce stands from and airborne measurements in visible bands. measured reflectances are linked with regression, Fig. 1 ρ = 1.74 ρ UAVSPEC +.42. (1) Correlation of top-of-canopy visible reflectances and airborne measured visible reflectance values is high, r 2 =.96, and reflectance values are only.4% higher. This is the evidence of the good quality of applied atmospheric correction. 5. CALIBRATION REVISED The comparison of atmospherically corrected spectra to top-of-canopy measurements reveals problems in radiometric calibration. In Fig. 2 reflectance spectra of spruce stands from images, helicopter measurements at Järvselja, and helicopter measurements with a GER-26 spectrometer in Sweden by Syrén P. & Alm G. [12] are compared. Begiebing & Bach [2] and Guanter et al. [4] reported problems in NIR calibration, at wavelengths over 8 nm radiances were low. They suggested to revise NIR calibration and that was done by team in 25 [3]. Our data show that the revised calibration data are overcorrected. spectra of several homogeneous stands in the scene 573 were measured with airborne spectrometer UAVSPEC on July 26, 26. Weather and illumination conditions during helicopter measurements were similar to those during acquisition in 25. Altogether 615 recorded spectra over 197 pixels over 23 stands give us correction factors for the calibration coefficients (Fig. 3 and Tab. 2). All three sets of spectral images were radiometrically rescaled using these correction factors.
.3.25.2.15.1.5 UAVSPEC GER-26. 4 5 6 7 8 9 1 11 Figure 2. Top of canopy reflectance of spruce stands from and airborne measurements. radiances as provided by the team. ρ UAVSPEC / ρ 1.2 1.8.6.4.2 Table 2. Correction factors for radiances. Band λ, nm Coef STD 1 442.4.5438.563 2 49.2.661.624 3 53..8399.69 4 551.3.7738.67 5 57..885.765 6 631.4.8937.772 7 661.2.9145.88 8 674.6.9535.12 9 697.5 1.47.758 1 76.5.951.783 11 712.6.9178.87 12 741.8.886.92 13 752.1.988.972 14 781.1.937.952 15 872.3.8754.852 16 895.7.8634.816 17 91..8134.771 18 119.3.627.565 λ - central wavelength of the band Coef, STD - mean value and standard deviation of the correction factor 4 5 6 7 8 9 1 11 Figure 3. Correction factors for calibration coefficients. 6. REFLECTANCE SPECTRA.4.3.2.1. 1 2 3 4 5 6 7 8 4 Age, y 6 8 12 1.4.3.2.1. 6.1. Age dependence Age dependence of reflectance was derived for spruce and birch stands. of 227 spruce stands and 933 birch stands in the age range from 3 to 95 years was used for creating Figs. 4 and 5, respectively. In near infrared (NIR) bands the reflectance of birch stands decreases monotonously during the whole age period considered. NIR reflectance of young spruce stands decreases with stand age. Since the age of 4 years reflectance of spruce stands is almost constant throughout the whole spectral domain of the spectrometer. In visible bands reflectance both of spruce and birch stands is very low - between 3% and 4% in green and between 1.5% and 2% in red bands - and almost independent of stand age, Fig. 6. Green reflectance of birch stands almost copies the age course of NIR reflectance in a reduced scale. Figure 4. Age profile of reflectance for spruce stands..4.3.2.1. 1 8 2 3 4 5 6 6 7 8 4 Age, y 12 1 Figure 5. Age profile of reflectance for birch stands. 6.2. Spectral and angular signatures of mature stands A representative selection of pine, birch, spruce, and alder and aspen stands in the age range from 55 to 65.4.3.2.1.
Visible reflectance.6.18.4.2 spruce 781 nm birch 781 nm spruce 551 nm birch 551 nm spruce 672 nm birch 672 nm 1 2 3 4 5 6 7 8 9 Age, years.36.3.24 Figure 6. Age profile of reflectance for spruce and birch stands in red, green and NIR (the right axis) bands. years is in all three scenes. Angular and spectral signatures of this selection of stands are compared to simulations with forest reflectance model by Kuusk & Nilson [8]. of 12 pine, 8 spruce, 8 birch, and 5 alder (and aspen) stands was involved in the analysis, total pixel numbers 416, 1334, 121 and 492 in every image of the scene 573, respectively. In Figs. 7-1 we see both rather good accordance of model and data, and significant discrepancies for some species in some bands. Mean value of top-ofcanopy reflectance was calculated for every stand, error bars in x-y-plots are the standard deviation of mean values. The same in model simulations - reflectance spectra were simulated separately for every stand, and the plotted standard deviation is that of mean values over the set of stands for each species. In simulations the leaf/needle optical properties from the LOPEX database [5] were used so that the PROSPECT leaf optics model [6] was fitted to measured reflectance and transmittance (if available) spectra. Four components (dry matter, water, chlorophyll, and brown pigment) were used for leaves and five components (dry matter, water, chlorophyll, lutein, and base (constant) absorption) for conifer needles. The needle stack reflectance and no transmittance of needles are reported in the LOPEX database. Also, there is no data on the optical properties of Scots pine needles, here reflectance data of Bhutan pine needles are used instead. The available sets of the absorption spectra of biochemical leaf components do not allow a perfect fit of measured leaf/needle spectra with the PROSPECT model. Principal limitations of the PROSPECT model do not allow to reproduce the measured extremely low values of blue and red reflectance of spruce needles. At the same time, these low values may be partly caused by the measurement setup - by the shades in the stack of needles. In addition, the stack of needles has higher NIR reflectance than a single layer of needles. NIR reflectance Optical properties of ground vegetation in model simulations are from the database by Lang et al. [9], and correspond to the typical ground vegetation for the site type of every analyzed stand. Obviously, the whole variability of the ground vegetation is not accounted for in model simulations. A typical discrepancy is an overestimated green reflectance in model simulations. Also, the NIR reflectance is overestimated for all stands but alder. Some disagreement in the and simulated spectra can be explained by the mismatch of leaf spectra. In the scene 575 the view angle is very close to the Sun zenith angle. Nevertheless, the measurement was not very close to the hot spot - the azimuth difference was 19.8. 7. DISCUSSION Revised calibration coefficients for the Mode 3 spectral bands are derived using top-of-canopy reflectance spectra of homogeneous stands in the scene 573. Values of correction factors are between.8 and 1 for most bands, and variation is low. Low sensitivity both of the and UAVSPEC sensors in edge bands (blue and NIR - bands 1, 2 and 18) does not allow to trust correction factors for these three bands. Radiances in all three scenes of July 1, 25 were updated using the revised calibration data. Best agreement of model and data of nadir reflectance is in the red spectral region, the largest systematic deviations are in the NIR spectral domain. The most problematic seems to be the use of stack reflectance values of Bhutan pine needles for Scots pine at Järvselja. Angular reflectance course for broadleaf stands is predicted well. Angular dependence of red reflectance for coniferous stands is surprisingly even more expressive than in model simulations. Obviously some aspects of the stand structure are not adequate in the model (or in input parameters of the model?). level in band 8 (672 nm) is predicted well. As single scattering drives the red reflectance of stands, this result confirms that component radiances in red band and stand structure is modelled well in the model. Rather large relative variation of red reflectance of birch stands may be caused by differences in site types. Both leaf optical properties as well the reflectance of ground vegetation may vary depending on the site fertility. In Fig. 9 the average values over all site types are calculated. The position of the red edge is correct for all studied stands. The red edge in model simulations is only determined by chlorophyll absorption, i.e. by the absorption spectrum and amount of chlorophyll. The built-in chlorophyll absorption spectrum of the 1997 version of PROSPECT by Jacquemoud & Baret [6] was used for the simulation of leaf and needle reflectance. Situation is different in green and NIR bands. Green reflectance is overestimated to some extent in simulations for all stands. As the measured green reflectance is very low and consequently the role of multiple scat-
.4.5.35.3.4.4.25.2.15.1.5 (672 nm).3.2.1 (781 nm).3.2.1. 4 5 6 7 8 9 1 11-9 -75-6 -45-3 -15-9 -75-6 -45-3 -15 Figure 7. spectrum, and angular dependence of red and NIR reflectance, mature pine stands. Vertical blue line indicates the Sun zenith angle..4.5.35.3.25.2.15.1.5 (672 nm).4.3.2.1 (781 nm).4.3.2.1. 4 5 6 7 8 9 1 11-9 -75-6 -45-3 -15-9 -75-6 -45-3 -15 Figure 8. As Fig. 7, mature spruce stands..4.5.35.3.4.4.25.2.15.1.5 (672 nm).3.2.1 (781 nm).3.2.1. 4 5 6 7 8 9 1 11-9 -75-6 -45-3 -15-9 -75-6 -45-3 -15 Figure 9. As Fig. 7, mature birch stands..4.5.35.3.4.4.25.2.15.1.5 (672 nm).3.2.1 (781 nm).3.2.1. 4 5 6 7 8 9 1 11-9 -75-6 -45-3 -15-9 -75-6 -45-3 -15 Figure 1. As Fig. 7, mature aspen and alder stands.
tering non-significant, the problem must be in the overestimated leaf/needle green reflectance and/or branch reflectance. Indeed, the simulated green reflectance of deciduous leaves exceeds values in the LOPEX database, and NIR absorption of leaves in simulations is less than measured. As transmittance data of needles are missing, we have no information on NIR absorption in conifer needles. 8. CONCLUSIONS The created complex data set of the Järvselja forestry test site is a comprehensive database for the validation of forest radiative transfer models. The data set includes: Atmospherically corrected Mode 3 image sets 573, 575, 577 of July 1, 25. Airborne measured spectra of nadir reflectance in Mode 3 bands. Forestry database of Järvselja test site. Database of the reflectance spectra of ground vegetation. This data set can be used for the next phase of the intercomparison of radiative transfer models RAMI [11, 14]. ACKNOWLEDGEMENTS The image data presented in this paper are derived from the instrument, developed by Sira Technology Ltd., with support from the British National Space Centre, mounted onboard the European Space Agency s PROBA-1 platform, and provided by the European Space Agency. The Sun-photometer data are provided by the International AERONET Federation, we thank Drs. O. Kärner and M. Sulev for their effort in establishing and maintaining the Tõravere AERONET site. The study has been supported by Estonian Science Foundation, Grant no. 61. [4] Guanter, L., Alonso, L. & Moreno, J. (25). A method for the surface reflectance retrieval from PROBA/ data over land: Application to ESA SPARC campaigns. IEEE Trans. Geos. Remote Sens. 43(12), 298-2917. [5] Hosgood, B., Jacquemoud, S., Andreoli, G., Verdebout, J., Pedrini, A. & Schmuck, G. (1994). Leaf Optical Properties EXperiment 93 (LOPEX93). Report EUR 1695 EN, 7 pp. [6] Jacquemoud, S. & Baret, F. (199). PROSPECT: A model of leaf optical properties spectra. Remote Sens. Environ. 34, 75-91. [7] Kuusk, A., Lang, M. & Nilson, T. (25). Forest test site at Järvselja, Estonia. ESA Publication SP-593, 7 pp. [8] Kuusk, A. & Nilson, T. (2). A directional multispectral forest reflectance model. Remote Sens. Environ. 72, 244-252. [9] Lang, M., Kuusk, A., Nilson, T., Lükk, T., Pehk, M. & Alm, G. (22). spectra of ground vegetation in sub-boreal forests. Web page http://www.aai.ee/bgf/ger26/. [1] Liang, S.L., Fang, H.L. & Chen, M.Z. (21). Atmospheric correction of Landsat ETM+ land surface imagery - Part I: Methods. IEEE Trans. Geos. Remote Sens. 39(11), 249-2498. [11] Pinty, B., Widlowski, J.-L., Taberner, M., et al. (24). Radiation Transfer Model Intercomparison (RAMI) exercise: Results from the second phase. J. Geophys. Res. 19(D6): D621 1.129/ 23JD4252 25 March 24. [12] Syrén, P. & Alm, G. (1997). Personal communication. [13] Vermote, E.F., Tanre, D., Deuze, J.L., Herman, M. & Morcrette, J.J. (1997). Second simulation of the satellite signal in the solar spectrum, 6S - An overview. IEEE Trans. Geos. Remote Sens. 35(3), 675-686. [14] Widlowski, J.-L., Taberner, M., Pinty, B. et al. (27). The third RAdiation transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models. J. Geophys. Res., Forthcoming. REFERENCES [1] Barnsley, M.J., Settle, J.J., Cutter, M.A., Lobb, D.R. & Teston, F. (24). The PROBA/ mission: A low-cost smallsat for hyperspectral multiangle observations of the earth surface and atmosphere. IEEE Trans. Geos. Remote Sens. 42(7), 1512-152. [2] Begiebing, S. & Bach, H. (24). Analyses of hyperspectral and directional data for agricultural monitoring using a canopy reflectance model. ESA Publication SP-578, 8 pp. [3] Cutter, M. & Johns, L. (25). data products - latest issue. ESA Publication SP-593, 6 pp.