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Remote sensing data assimilation using coupled radiative transfer models
Physics and Chemistry of the Earth 28 (2003) 3–13 www.elsevier.com/locate/pce
Remote sensing data assimilation using coupled radiative transfer models Wout Verhoef
a,*
, Heike Bach
b
a
b
National Aerospace Laboratory NLR, P.O. Box 153, 8300 AD Emmeloord, The Netherlands VISTA Geowissenschaftliche Fernerkundung GmbH, Anton-Ferstl-Strasse 11, D-82234 Wessling, Germany
Abstract This paper discusses data assimilation of biophysical parameters retrieved from optical remote sensing images in land surface process models by means of image simulation and model inversion. Two different approaches are presented. The first is based on model inversion of atmospherically corrected Landsat TM surface reflectance images and assimilation of the retrieved parameters in a crop growth model. In the second approach top-of-atmosphere (TOA) hyperspectral radiance images have been simulated for the future ESA mission SPECTRA. In this case only the simulation of the images has been executed in order to demonstrate the feasibility of this task with existing software running on a PC. The radiative transfer models that have been used are PROSPECT (leaf level), GeoSAIL (canopy level) and MODTRAN4 (atmosphere). Coupling of this chain of models to land use information of the area can be used to generate TOA radiance images. Comparison of simulated images with actual remote sensing data can be applied to retrieve biophysical parameters and in turn these can be employed to update process models of crop growth. 2003 Elsevier Science Ltd. All rights reserved. Keywords: Canopy reflectance; Vegetation; BRDF; Atmospheric effect; Image simulation; Hyperspectral sensors
1. Introduction Data assimilation of biophysical parameters retrieved from remotely sensed imagery in land surface process models can be considered both a challenge and a natural way of stimulating the integration and use of remote sensing data in dynamic geographic information systems (GIS). Monitoring processes at the land surface requires the regular supply of actual information derived from earth observation images and other sources. However, the assimilation of these data in a GIS environment is hampered by the fact that remote sensing imagery originates from a large variety of sensors that may differ widely (Fig. 1) in spectral and spatial resolution, sensor type, angular geometry of observation, local solar times, temporal frequency, etc. Observation modeling might be used to compensate these differences. In a remote sensing observation model the available information on object properties stored in the GIS can be used to generate images for the given *
Corresponding author. Tel.: +31-527-24-82-53; fax: +31-527-2482-10. E-mail address: [email protected] (W. Verhoef). URL: http://www.vista-geo.de.
sensor and observational conditions. Next, these images are compared to actual remote sensing images, and from the differences one can draw conclusions about the assumptions on object properties that have been made initially. This may lead to adjusted properties which fit the actual observations better, and thus it forms a natural way of updating and improving the information in a GIS, as illustrated in Fig. 2. In this paper two approaches of observation modeling in a feedback loop will be discussed, of which one is based on using surface spectral reflectance inputs obtained after atmospheric correction of Landsat TM images, and the other on top-of-atmosphere (TOA) radiances. In the latter case the atmosphere is considered as just one component of a generic remote sensing observation model. This approach is illustrated in Fig. 3. In the first approach also the coupling to a process model (a crop growth model) was incorporated. In this way data assimilation of biophysical parameters in the process model is possible, but also in another sense a synergy is established, as the growth model can help improve the model inversion by supplying plausible ranges of canopy parameters that can next be used to constrain the model inversion or to provide good starting guesses.
1474-7065/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S1474-7065(03)00003-2
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W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
Actual image
Satellite X Sensor Y Day D Time t Direction r
Sensor def.
Objectsensor geom.
Updating strategy
Atmos. prop.
Observed site
Fig. 1. Problem of remote sensing data assimilation for a given geographical site.
GIS info
RS MODEL
GROUND PROCESSING
TOA radiance images
TOA radiance images
Fig. 2. Remote sensing data assimilation by means of observation modeling in a GIS environment.
In the following sections both methodologies will be discussed and in both cases examples of results will be shown. 2. Approach 1: the GeoBIRD project 2.1. Methodology In the ESA-project GeoBIRD (Geo-Biophysical Information retrieval from Remote sensing Data, Bach et al., 2001) the retrieval of biophysical parameters and their assimilation in a crop growth model was studied by applying model inversion to atmospherically corrected Landsat TM images of sites in Germany. The crop growth model used was PROMET-V (Schneider and Mauser, 2000), and this model was run for crop classes
Simulated image
GIS data
Object prop.
Generic RS observation model Fig. 3. A generic RS observation modeling approach to support the assimilation of actual earth observation data.
like grass, wheat, maize and barley. It was fed with spatial fields of interpolated meteorological data and a land use map. Atmospheric correction of the Landsat TM images was achieved by means of PULREF (Procedure Using Lowtran for REFlectance), which is based on LOWTRAN7 (Kneizys et al., 1988) simulations and includes correction of the adjacency effect (Bach, 1995). The canopy reflectance model used for inversion of the surface reflectance data was GeoSAIL (Bach et al., 2001). This model is a two-layer version of the model SAILH (Verhoef, 1998) that was developed in the project to mimic the vertical leaf color gradient often seen in canopies like wheat. The structural properties in both layers (leaf angle distribution and leaf size) are assumed to be identical in both layers, but the leaf area indexes (LAIs) for green and brown leaves may differ. The leaf angle distribution is described by two parameters, a and b, of which a determines the average leaf slope, and b expresses the so-called bimodality of the distribution (Verhoef, 1998). The division of the LAI for both types of leaf over both layers is governed by the parameters fraction brown leaves fB and the so-called dissociation factor D. For the extreme values D ¼ 1 (complete dissociation) and D ¼ 0 (homogeneous mixture) Fig. 4 illustrates the division of green and brown leaves over both layers when two thirds of the leaf area is brown. The GeoSAIL model should not be confused with a model of nearly the same name (GeoSail), which is a hybrid geometrical/turbid medium version of the SAIL model (Huemmrich, 2001). In the GeoSAIL model the reflectance of the soil is included and the effect of surface soil moisture on the soilÕs reflectance spectrum is incorporated by means of a special submodel described in Bach (1995). The input parameters of GeoSAIL are coming from various sources. The image acquisition conditions pro-
W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
D=1
D=0
fG = 1/3
fB = 2/3
Fig. 4. Modeling of leaf color gradient by means of a dissociation factor D in the GeoSAIL model.
vide the solar zenith angle and the viewing angle. The fraction diffuse incident irradiance from the sky in the TM bands was taken from the corresponding LOWTRAN7 modeling data. Land use and crop phenology determined the leaf angle distribution parameters, the layer dissociation factor, and the leaf optical properties of green and brown leaves, which for each crop class were taken from literature data. Dry soil reflectance spectra were taken from atmospherically corrected Landsat TM images of the region and field measurement data. The crop growth process model PROMET-V also models local hydrology and thus it can also provide estimates of soil moisture. The LAIs of living and dead leaves were used to determine the total LAI and fraction brown leaf area as inputs for GeoSAIL. The hot spot parameter q was slaved to total LAI by means of a simple inversely proportional relationship that is plausible for cereal crops. Fig. 5 summarizes how all required inputs for GeoSAIL were obtained. The strategy that was applied in order to assimilate retrieved biophysical parameters in the process model PROMET-V is illustrated in Fig. 6. Optical remote
PROMET-V
LAI, fraction brown leaves, soilmoisture, q (slaved to LAI)
Literature
( ρ,τ ) leaf, green & brown dry soil reflectance LIDF (a,b) Dissociation factor D
Acquisition
fraction diffuse sky irradiance solar zenith angle, viewing angle
Landuse
Fig. 5. Interfacing of crop growth model PROMET-V with canopy reflectance model GeoSAIL.
5
sensing images, in this case from the Landsat Thematic Mapper, are atmospherically corrected, producing surface reflectance data. These are compared to simulated reflectances produced by GeoSAIL. The free parameters that could be adjusted in the model inversion procedure were total LAI, fraction brown leaves and soil moisture. As Landsat TM data contain optical reflectance data from only six spectral bands, the number of free parameters also has to be kept rather small. The other parameters were held fixed at their values such as determined according to Fig. 5. The model inversion tries to minimize the difference between observed and simulated reflectance spectra. In this case a simple 3D golden section algorithm was used to adjust the free parameters. An advantage of this algorithm is that the range of variation for each free parameter can be given on input, and this possibility was used to impose these as plausible ranges that were obtained from PROMET-V simulations under extreme circumstances. Thus, a synergy between radiative transfer model and dynamical process model is established, as the process model helps constraining the model inversion to achieve quick convergence, and the model inversion result helps to adjust the process model so that it follows the correct path through time. In the GeoBIRD project only one control parameter of the growth model was adjusted in order to match the courses of LAI. For cereal crops this was the plant density, and for grass the most recent cutting date. All other inputs of the growth model were taken from local meteorological data, the land use and agricultural practices. The strategy illustrated in Fig. 6 can be called a two-step data assimilation scheme. In the first step the biophysical parameters are retrieved from spectral observations using a radiative transfer model, and in the second step the time course of the retrieved parameters is used to adjust a process model until both match sufficiently well. In Fig. 7 this two-step data assimilation is illustrated on a more abstract level. Here also the numbers of input parameters and output parameters involved at each level of model inversion are emphasized. A careful tuning of the numbers of parameters to be retrieved to the quality and quantity of the observed data in the spectral and temporal domains is mandatory for a successful result, as otherwise the ill-posedness of the model inversion problem could easily lead to unreliable outputs. 2.2. Results The accuracy of the results can be measured by looking at the errors in the simulated remote sensing observations, in the retrieved biophysical parameters, and in final process model outcomes, such as (in this case) yield figures. In Fig. 8 a comparison between observed and simulated Landsat TM reflectance spectra is
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W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
Calibration& atmospheric correction
OpticalRS images
Meteo-data, landuse, GIS-Info
Geobiophysical maps biomass, yield, height, ...
Plausible ranges of parameters
Observed reflectance spectrum
Vegetation growth
model
GeoSAIL inversion
Simulated total LAI
AdjustLAI, faction brown,r soilmoisture
Observed reflectance spectrum
Adjust density/ cuttingdate
Canopy reflectance modeling GeoSAIL
RS-retrieved totalLAI
Fig. 6. Geo-biophysical parameter retrieval and data assimilation by a spatially distributed radiative transfer model coupled to a crop growth process model (Bach et al., 2000).
shown for maize in the Upper Rhine Valley on five dates in 1995. Except for the bare soil case, which probably is poorly simulated due to a bad estimation of soil drying after rainfall, these simulations are quite well in agreement with the observations. Spatial details of the model inversion/image simulation results for one date, 23rd August 1995, are shown in Fig. 9, which displays the input land use map of the area, the Landsat TM image in bands 2, 3 and 4 (B, G, R), the retrieved parameters total LAI, fraction brown leaf area, and soil moisture, and finally the RMS error of the image simulation. The latter is a measure of the confidence one may have in the quality of the retrieval results. Application of a crop growth model that is updated by means of spatially detailed remote sensing observa-
RS data >> biophys. param. >> process model
# RS parameters (bands, directions) # biophys. parameters
# image acquisitions # process variables updated
6 TM bands / LAI, fraction brown, soil moisture
5 dates / plant density, cutting date (grass)
GeoBIRD
Fig. 7. Illustration of RS data assimilation by means of two feedback loops.
Measured TM spectra 50
Simulated with PROMET-V/GeoSAIL 50
3 May 95 20 June 95
3 May 95
40
20 June 95 40
22 July 95
Simulated Reflectances [%]
Measured Reflectance [%]
6 July 95
23 August 95
30
20
10
6 July 95 22 July 95 23 August 95
30
20
10
0
0
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Wavelength [µm]
1.8
2.0
2.2
2.4
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Wavelength [µm]
Fig. 8. Actual and simulated TM reflectance spectra of maize at Upper Rhine Valley test site (Bach et al., 2000).
2.2
2.4
W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
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Fig. 9. Examples of Upper Rhine Valley spatially distributed GeoSAIL model inversion results.
tions during the growing season can lead to improved yield predictions and to a better identification of local anomalies within agricultural fields. Fig. 10 illustrates that yield predictions based on growth model outputs gain a lot in spatial detail by using feedback from remote sensing observations. Also field-averaged yield figures are likely to become more accurate, since remote sensing inputs are used to adjust the model-predicted growth curves.
3. Approach 2: the DAASCEES project 3.1. Methodology The second approach that one can follow for the assimilation of biophysical parameters retrieved from remote sensing observations has already been illustrated in Figs. 2 and 3 of Section 1. In this case everything related to the actual observations is included in a generic
Fig. 10. Comparing grain yield prediction results obtained by means of the crop growth model with and without using feedback from remote sensing images.
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optical remote sensing model. In this way one is able to take account of all effects related to sensor properties (e.g. spectral bands, resolutions in the spectral and spatial domains), the atmosphere and the angular suntarget-sensor configuration, so that for the quantitative interpretation of the data the user can focus on object properties. Incorporation of the atmosphere in a complete observation model that includes non-Lambertian reflectance of a heterogeneous landscape has the advantage that atmospheric correction is no longer necessary. Furthermore, atmospheric correction methods are mostly based on the assumption of Lambertian surface reflectances, and therefore are inherently inconsistent, as most land objects are significantly different from Lambertian. In the ESA project DAASCEES (Data Assimilation And Scaling for the Candidate Earth Explorer core mission SPECTRA) the actual data assimilation by means of the comparison of simulated with actual images could not be studied yet, as the SPECTRA mission is still a candidate Earth Explorer Mission, possibly scheduled for launch towards the end of this decade. However, simulated imagery for this sensor could already be generated by assembling a generic observation model that comprised all necessary components. These consist of • The leaf level radiative transfer model PROSPECT (Jacquemoud and Baret, 1990) • The canopy level radiative transfer model GeoSAIL (Bach et al., 2001) • The atmospheric radiative transfer model MODTRAN4 (Berk et al., 2000) • Sensor-specific data like spectral range and resolution, spatial sampling and modulation transfer function (MTF) The SPECTRA mission will be featured by an imaging spectroradiometer covering the solar reflective spectral range from 450 to 2350 nm and two thermal infrared bands co-registered with the optical bands, at
the same spatial resolution of 50 m nominal under nadir view. Along-track as well as across-track pointing capabilities will be provided in order to allow angular sampling of the bidirectional reflectance distribution function (BRDF) and to achieve a revisit time of three days. Simulation of all these aspects has been accomplished for the Barrax site in Spain for the date of 28 June 2000. For this, first a Landsat TM image of the region on the same day had been classified into the main crop classes (alfalfa, maize, barley and dry stubble) and bare soil by the University of Valencia (Alonso-Chorda, 2001). Similar to the procedure applied in the GeoBIRD project (Section 2), the Landsat image was atmospherically corrected to produce six-band surface reflectance images that were used as input for model inversion with GeoSAIL in order to obtain realistic spatially varying fields of the parameters total LAI, fraction brown leaves and surface soil moisture. The input parameters of PROSPECT and the remaining input parameters of GeoSAIL were estimated by fitting leaf optical spectra to literature data for the various crop classes, and by using in situ photographs taken at the Barrax site. A summary of the fixed input parameters is given in Table 1. The PROSPECT parameters are: N ¼ leaf mesophyll parameter Cab ¼ chlorophyll contents Cw ¼ water contents Cdm ¼ dry matter contents Cs ¼ senescent material (brown pigments) contents The complete modeling chain applied for the SPECTRA image simulations is illustrated in Fig. 11. The left block (land surface) shows the generation of the necessary input parameters for GeoSAIL. In order to model the radiative interaction of a non-Lambertian heterogeneous land surface with the overlying atmosphere at one wavelength it is not only necessary to compute for each pixel the targetÕs bidirectional reflectance for the given angular geometry, but also three other reflectance components. The four surface reflec-
Table 1 Input parameters of the PROSPECT and GeoSAIL simulations Parameter PROSPECT N Cab Cw Cdm Cs GeoSAIL fB range D a b
Corn
Barley
Alfalfa
Dry crops
Brown leaves
1.48 50 0.011 0.005 0
1.8 70 0.011 0.005 0
2 70 0.014 0.005 0
1.8 70 0.014 0.005 0
3 0 0 0.01 0.7
0.0–0.0 0.8 0.65 0.15
0.6–1.0 0.8 0.85 0.15
0.0–0.0 0.8 0.00 0.15
1.0–1.0 0.8 0.0 0.0
– – – –
W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
Land surface
9
Satellite
Landuse Soilmap Groundmeas.
Sun-target-sensor geometry
LUT
Sensor resolution
SPECTRA images
Atmosphere
LUT
Leaf properties
Canopy & Soil param.
PROSPECT model
Leaf spectra
GeoSAIL model
Visibility etc.
MODTRAN4 model
Radiance spectra
Effective atm. par.
Response analysis
Stimuli Surface BRDF images
Adjacency effect
Surface filtered images
4-stream interaction
TOA radiance images
Fig. 11. Modeling of hyperspectral TOA radiance images.
tance fields so obtained are the minimum that is required to model the interaction with the atmosphere with the so-called four-stream approximation (Verhoef, 1985, 1998). Fortunately, the GeoSAIL model delivers these four reflectances directly as outputs, a consequence of the fact that internally in GeoSAIL also a four-stream approximation is used. In order to simulate the adjacency effect, two of the four GeoSAIL output fields have to be spatially filtered. These are the diffuse hemispherical reflectances for direct solar and diffuse incidence, respectively. The directional target reflectance
fields for direct solar and diffuse incidence remain unaffected by this operation. Fig. 12 illustrates the four reflectance components for the Barrax site after the spatial filtering associated with the adjacency effect has been applied. In order to model the four-stream interaction of the atmosphere with the surface, the atmosphere must be described by at least six ÔeffectiveÕ parameters, of which two are reflectances and four are transmittances. These parameters can be derived from MODTRAN4 outputs by applying a so-called interrogation technique. In this
Fig. 12. Simulated images of surface BRDF components after taking into account the adjacency effect. Band combination R, G, B ¼ 840, 730 and 660 nm.
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W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
method MODTRAN is treated like a black box, and one performs three successive model runs over the whole spectrum for the assumed atmospheric and geometrical conditions, and with spectrally flat surface albedos of 0.0, 0.5 and 1.0, respectively. Similar approaches have been reported in Bach (1995) and De Haan and Kokke (1996). From the MODTRAN radiance outputs it is possible to derive all six atmospheric parameters for each wavelength, and the spectra of these parameters can be applied to model the four-stream radiation interaction with the earthÕs surface pixel by pixel. The final result is the TOA spectral radiance at each pixel position and at each wavelength. Fig. 11 illustrates how the atmospheric modeling block stands relatively off-line from the main chain, and that the actual interaction with the heterogeneous surface is modeled by means of the six effective parameters. The last step in the image simulation chain is formed by applying realistic spatial resolutions and samplings. The Landsat images forming the basis of the simulations have 30 m resolution and have been resampled to 25 m during geometric correction. The basic surface reflectance and TOA radiance fields have also been produced in this sampling grid. The expected spatial resolution of the SPECTRA missionÕs imaging spectroradiometer has been simulated by means of spatial filtering in accordance with view angle dependent MTF data provided by ESA. Nevertheless, all output images have been resampled to a common sampling distance of 50 m in order to facilitate the intercomparison of simulated images under different directions. 3.2. Results Validation results of the hyperspectral surface reflectance modeling with GeoSAIL for alfalfa and corn crops are presented in Fig. 13. Here, the measured surface reflectance spectra have been obtained from HyMap airborne imaging spectrometer data recorded one day after the Landsat TM image acquisition. The HyMap data have been atmospherically corrected, so they can represent surface reflectance measurements in the field. It appears that in general simulated and observed reflectance spectra agree reasonably well, except in the red region of the spectrum, where systematic differences seem to be present. This may be due to incorrect modeling of the fraction brown leaves for these crops. An example of bottom-of-atmosphere (BOA) reflectance modeling results, together with sample reflectance spectra for four classes, is shown in Fig. 14. The wavelengths chosen for the image are 660, 730 and 840 nm and they represent the strong red-NIR transition seen in green vegetation. An example of the atmospheric modeling by means of MODTRAN4 for a visibility of 40 km resulting into
Fig. 13. Surface reflectance modeling validation results for alfalfa and corn. Blue curves show HyMap-derived surface reflectance spectra, red curves the corresponding simulated spectra.
Fig. 14. Simulated SPECTRA scene showing BOA directional reflectances in three bands and complete spectra from four pixels of alfalfa (green), corn (blue), dry stubble (red) and bare soil (brown).
W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
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Table 2 Input parameters and options for MODTRAN4
1 0.9 0.8 0.7 0.6
τ ss τ oo ρso ρdd τ sd τ do
0.5 0.4 0.3 0.2 0.1 0 400
700
1000
1300 1600 wavelength (nm)
1900
2200
Fig. 15. Spectra of six optical atmospheric parameters derived from MODTRAN4 outputs using interrogation technique.
spectra of the six effective atmospheric parameters is shown in Fig. 15. The spectra cover the range from 450 to 2350 nm at 10 nm resolution. The two top curves represent the direct atmospheric transmittances in the direction of the sun and in the viewing direction. The other curves represent contributions related to combinations of specular and diffuse scattering in forward and backward directions. A demonstration of the effect of the atmosphere on the TOA radiance spectra for a barley pixel under different viewing directions and two visibilities is shown in Fig. 16. Here a series of seven along-track viewing directions has been simulated for 40 km (clear) and 5 km (hazy) visibilities. From this simulation it appears that especially below 800 nm the atmospheric visibility has a large impact on the radiance spectra. Details on the atmospheric modeling with MODTRAN4 are summarized in Table 2.
TOA Radiances [W/m² sr µm]
Visibility = 40 km 160 140 120 100 80
Case: Along-track Pointing Variable observation geometry zen 60, azi 10 (forward) zen 45, azi 10 zen 25, azi 10 zen 0 Nadir zen 25, azi 190 zen 45, azi 190 zen 60, azi 190 (backwards)
Clear atmosphere
60 40 20 0 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength [µm]
Visibility = 5 km TOA Radiances [W/m² sr µm]
160 140 120 100 80
Case: Along-track Pointing Variable observation geometry zen 60, azi 10 (forward) zen 45, azi 10 zen 25, azi 10 zen 0 Nadir zen 25, azi 190 zen 45, azi 190 zen 60, azi 190 (backwards)
Hazy atmosphere
60 40 20 0
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength [µm]
Fig. 16. Simulated barley spectra of TOA radiances for a clear (top) and hazy (bottom) atmosphere.
Parameter/option
Value
Atmospheric profile Use DISORT Azimuth dependence Number of streams CO2 mixing ratio Aerosol extinction Stratospheric aerosol
Mid-latitude summer Yes Yes 8 360 ppmv Rural Background profile and extinction 40 and 5 km 700 km 0.7 km Internal Mie-generated 181 39.05 deg. N 2.10 deg. W 11:00 h 450–2350 nm 10 nm 12 nm Gaussian
Visibility Sensor altitude Target altitude Aerosol phase functions Day of year Target latitude Target longitude GMT time Spectral range Interval FWHM Slit function
Along-track pointing of the instrument for a satellite in a descending node sun-synchronous orbit leads to a viewing azimuth angle of about 10 or 190 degrees. At 11:00 h local solar time for the Barrax site on 28 June the solar azimuth is about 130 degrees, so that in this case there is a 60 or 120 degrees difference of the viewing azimuth with the principal plane (the plane of the sun). This configuration makes observation of the hot spot, which lies in the principal plane, impossible. However, by a combination of along-track with across-track pointing it is possible to make observations in the principal plane, including the hot spot. In the hot spot, viewing is exactly along the sunrays, so that no shadows are observed, and therefore the reflectance in that direction is relatively high. Simulation results for a series of observation in the principal plane are shown in Fig. 17. The series of viewing zenith angles is the same as in Fig. 16, except that the 25 degree viewing angle was replaced by 22 degrees in order to capture the exact hot spot. A corresponding HyMap image in approximately the same spectral bands is shown at the bottom. This image shows the hot spot as a bright horizontal line, as the flight line was perpendicular to the principal plane of the sun. The series of images show the BOA reflectances (top row) and the TOA radiances (second row) of the Barrax site for seven directions in the principal plane. Note the bright appearance of the hot spot images and also their different color hue. Similar brightness and color changes are observable in the HyMap image. As a relative intensity scaling was applied (separately for BOA reflectance and TOA radiance) the effect of the atmosphere does not seem to be that obvious, but as shown in Fig. 16, the numerical effect of the atmosphere is substantial.
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W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
Fig. 17. Simulation of a series of SPECTRA scenes for acquisitions in the principal plane and the red edge band combination. Top rows surface reflectance and TOA radiances. Below a HyMap image in the same spectral bands is shown illustrating the hot spot as a bright line.
4. Conclusions The synergy of a vegetation growth model and a remote sensing observation model can be exploited to improve the retrieval of geo-biophysical information. GeoSAIL model inversion results obtained from atmospherically corrected Landsat TM images could be used to update internal model parameters of the growth model PROMET-V. Through this, dry biomass and grain yield of maize could be retrieved with improved accuracy and high spatial resolution. By coupling of the leaf model PROSPECT, the canopy model GeoSAIL and the atmospheric model MODTRAN4 simulated radiance images for the future ESA hyperspectral multidirectional Earth Explorer Mission SPECTRA have been generated. In a user-oriented GIS-driven approach simulated RS images are compared to actual RS images in order to derive new information by means of a feedback loop, implementing model inversion at the image level and direct assimilation of the results in a GIS environment. The combination GIS data––RS observation model facilitates the assimilation of RS data from various sensors and allows to take better account of factors like landscape heterogeneity, terrain topography and nonLambertian surface reflection.
Acknowledgements The results presented were obtained in the framework of the ESA studies GeoBIRD (Geo-Biophysical Information retrieval from Remote sensing Data, ESAESTEC contract no. 12950/98/NL/GD, study manager
Maurice Borgeaud) and DAASCEES (Data Assimilation And Scaling for the Candidate Earth Explorer core mission SPECTRA, ESA-ESTEC contract no. 15164/ 01/NL/SF, study manager Mike Rast). The authors want to express their sincere thanks to Karl Schneider, presently at the University of Cologne, and Wolfram Mauser (University of Munich) for providing the PROMET-V model and to Jose Moreno, Luis Alonso-Chorda and Alfonso Calera of the University of Valencia, for their support regarding the ground truth and airborne data of the Barrax site. Frederic Baret of INRA, Avignon, kindly provided us with the latest PROSPECT code plus leaf optical parameters. Umberto Del Bello of ESA-ESTEC, Noordwijk, provided data on the expected spatial resolution of the SPECTRA missionÕs optical instrument.
References Alonso-Chorda, L., 2001, personal communication. Bach, H., 1995. Die Bestimmung hydrologischer und landwirtschaftlicher Oberfl€achenparameter aus hyperspektralen Fernerkundungsdaten, Ph. D. Thesis University of Munich, M€ unchener Geographische Abhandlungen, 21, 175 p. Bach, H., Schneider, K., Verhoef, W., Stolz, R., Mauser, W., Van Leeuwen, H., Schouten, L., Borgeaud, M., 2001. Retrieval of geoand biophysical information from remote sensing through advanced combination of a land surface process model with inversion techniques in the optical and microwave spectral range. Proceedings of the 8th International Symposium ‘‘Physical measurements and signature in remote sensing’’, Aussois, CNES, pp. 639–647. Bach, H., Verhoef, W., Schneider, K., 2000. Coupling remote sensing observation models and a growth model for improved retrieval of (geo) biophysical information from optical remote sensing data, Remote Sensing for Agriculture. Ecosyst. Hydrol., SPIE 4171, 1– 11.
W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13 Berk A., Anderson, G.P., Acharya, P.K., Chetwynd, J.H., Bernstein, L.S., Shettle, E.P., Matthew, M.W., Adler-Golden, S.M., 2000. MODTRAN4 USERÕS MANUAL. Air Force Research Laboratory, Space Vehicles Directorate. Air Force Materiel Command, Hanscom AFB, MA 01731-3010, 97 p. De Haan, J.F., Kokke, J.M.M., 1996. Remote sensing algorithm development Toolkit 1. Operationalization of atmospheric correction methods for tidal and inland waters. BCRS Report NRSP-2 96-16, ISDN 90 5411 204 2, 91 p. Huemmrich, K.F., 2001. The GeoSail model: a simple addition to the SAIL model to describe discontinuous canopy reflectance. Rem. Sens. Environ. 75, 423–431. Jacquemoud, S., Baret, F., 1990. A model of leaf optical properties spectra. Rem. Sens. Environ. 34, 75–91.
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Kneizys, F.X., Shettle, E.P., Galler, W.O., Chetwynd, J.D., Abreu, L.W., Selby, J.E.A, Clough, S.A., Anderson, G.P., 1988. Users Guide to LOWTRAN-7, Air Force Geophysical Laboratory, Hanscom AFB, Env. Res. Pap., 1010, AFGL-TR-88-0177, 146 p. Schneider, K., Mauser, W., 2000. Using Remote Sensing Data to Model Water, Carbon and Nitrogen Fluxes with PROMET-V, Remote Sensing for Agriculture. Ecosyst. Hydrol., SPIE 4171, 12– 23. Verhoef, W., 1985. Earth observation modeling based on layer scattering matrices. Rem. Sens. Environ. 17, 165–178. Verhoef, W., 1998. Theory of radiative transfer models applied in optical remote sensing of vegetation canopies, Ph.D. Thesis, Wageningen Agricultural University, 310 p.
Remote sensing data assimilation using coupled radiative transfer models Wout Verhoef
a,*
, Heike Bach
b
a
b
National Aerospace Laboratory NLR, P.O. Box 153, 8300 AD Emmeloord, The Netherlands VISTA Geowissenschaftliche Fernerkundung GmbH, Anton-Ferstl-Strasse 11, D-82234 Wessling, Germany
Abstract This paper discusses data assimilation of biophysical parameters retrieved from optical remote sensing images in land surface process models by means of image simulation and model inversion. Two different approaches are presented. The first is based on model inversion of atmospherically corrected Landsat TM surface reflectance images and assimilation of the retrieved parameters in a crop growth model. In the second approach top-of-atmosphere (TOA) hyperspectral radiance images have been simulated for the future ESA mission SPECTRA. In this case only the simulation of the images has been executed in order to demonstrate the feasibility of this task with existing software running on a PC. The radiative transfer models that have been used are PROSPECT (leaf level), GeoSAIL (canopy level) and MODTRAN4 (atmosphere). Coupling of this chain of models to land use information of the area can be used to generate TOA radiance images. Comparison of simulated images with actual remote sensing data can be applied to retrieve biophysical parameters and in turn these can be employed to update process models of crop growth. 2003 Elsevier Science Ltd. All rights reserved. Keywords: Canopy reflectance; Vegetation; BRDF; Atmospheric effect; Image simulation; Hyperspectral sensors
1. Introduction Data assimilation of biophysical parameters retrieved from remotely sensed imagery in land surface process models can be considered both a challenge and a natural way of stimulating the integration and use of remote sensing data in dynamic geographic information systems (GIS). Monitoring processes at the land surface requires the regular supply of actual information derived from earth observation images and other sources. However, the assimilation of these data in a GIS environment is hampered by the fact that remote sensing imagery originates from a large variety of sensors that may differ widely (Fig. 1) in spectral and spatial resolution, sensor type, angular geometry of observation, local solar times, temporal frequency, etc. Observation modeling might be used to compensate these differences. In a remote sensing observation model the available information on object properties stored in the GIS can be used to generate images for the given *
Corresponding author. Tel.: +31-527-24-82-53; fax: +31-527-2482-10. E-mail address: [email protected] (W. Verhoef). URL: http://www.vista-geo.de.
sensor and observational conditions. Next, these images are compared to actual remote sensing images, and from the differences one can draw conclusions about the assumptions on object properties that have been made initially. This may lead to adjusted properties which fit the actual observations better, and thus it forms a natural way of updating and improving the information in a GIS, as illustrated in Fig. 2. In this paper two approaches of observation modeling in a feedback loop will be discussed, of which one is based on using surface spectral reflectance inputs obtained after atmospheric correction of Landsat TM images, and the other on top-of-atmosphere (TOA) radiances. In the latter case the atmosphere is considered as just one component of a generic remote sensing observation model. This approach is illustrated in Fig. 3. In the first approach also the coupling to a process model (a crop growth model) was incorporated. In this way data assimilation of biophysical parameters in the process model is possible, but also in another sense a synergy is established, as the growth model can help improve the model inversion by supplying plausible ranges of canopy parameters that can next be used to constrain the model inversion or to provide good starting guesses.
1474-7065/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S1474-7065(03)00003-2
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W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
Actual image
Satellite X Sensor Y Day D Time t Direction r
Sensor def.
Objectsensor geom.
Updating strategy
Atmos. prop.
Observed site
Fig. 1. Problem of remote sensing data assimilation for a given geographical site.
GIS info
RS MODEL
GROUND PROCESSING
TOA radiance images
TOA radiance images
Fig. 2. Remote sensing data assimilation by means of observation modeling in a GIS environment.
In the following sections both methodologies will be discussed and in both cases examples of results will be shown. 2. Approach 1: the GeoBIRD project 2.1. Methodology In the ESA-project GeoBIRD (Geo-Biophysical Information retrieval from Remote sensing Data, Bach et al., 2001) the retrieval of biophysical parameters and their assimilation in a crop growth model was studied by applying model inversion to atmospherically corrected Landsat TM images of sites in Germany. The crop growth model used was PROMET-V (Schneider and Mauser, 2000), and this model was run for crop classes
Simulated image
GIS data
Object prop.
Generic RS observation model Fig. 3. A generic RS observation modeling approach to support the assimilation of actual earth observation data.
like grass, wheat, maize and barley. It was fed with spatial fields of interpolated meteorological data and a land use map. Atmospheric correction of the Landsat TM images was achieved by means of PULREF (Procedure Using Lowtran for REFlectance), which is based on LOWTRAN7 (Kneizys et al., 1988) simulations and includes correction of the adjacency effect (Bach, 1995). The canopy reflectance model used for inversion of the surface reflectance data was GeoSAIL (Bach et al., 2001). This model is a two-layer version of the model SAILH (Verhoef, 1998) that was developed in the project to mimic the vertical leaf color gradient often seen in canopies like wheat. The structural properties in both layers (leaf angle distribution and leaf size) are assumed to be identical in both layers, but the leaf area indexes (LAIs) for green and brown leaves may differ. The leaf angle distribution is described by two parameters, a and b, of which a determines the average leaf slope, and b expresses the so-called bimodality of the distribution (Verhoef, 1998). The division of the LAI for both types of leaf over both layers is governed by the parameters fraction brown leaves fB and the so-called dissociation factor D. For the extreme values D ¼ 1 (complete dissociation) and D ¼ 0 (homogeneous mixture) Fig. 4 illustrates the division of green and brown leaves over both layers when two thirds of the leaf area is brown. The GeoSAIL model should not be confused with a model of nearly the same name (GeoSail), which is a hybrid geometrical/turbid medium version of the SAIL model (Huemmrich, 2001). In the GeoSAIL model the reflectance of the soil is included and the effect of surface soil moisture on the soilÕs reflectance spectrum is incorporated by means of a special submodel described in Bach (1995). The input parameters of GeoSAIL are coming from various sources. The image acquisition conditions pro-
W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
D=1
D=0
fG = 1/3
fB = 2/3
Fig. 4. Modeling of leaf color gradient by means of a dissociation factor D in the GeoSAIL model.
vide the solar zenith angle and the viewing angle. The fraction diffuse incident irradiance from the sky in the TM bands was taken from the corresponding LOWTRAN7 modeling data. Land use and crop phenology determined the leaf angle distribution parameters, the layer dissociation factor, and the leaf optical properties of green and brown leaves, which for each crop class were taken from literature data. Dry soil reflectance spectra were taken from atmospherically corrected Landsat TM images of the region and field measurement data. The crop growth process model PROMET-V also models local hydrology and thus it can also provide estimates of soil moisture. The LAIs of living and dead leaves were used to determine the total LAI and fraction brown leaf area as inputs for GeoSAIL. The hot spot parameter q was slaved to total LAI by means of a simple inversely proportional relationship that is plausible for cereal crops. Fig. 5 summarizes how all required inputs for GeoSAIL were obtained. The strategy that was applied in order to assimilate retrieved biophysical parameters in the process model PROMET-V is illustrated in Fig. 6. Optical remote
PROMET-V
LAI, fraction brown leaves, soilmoisture, q (slaved to LAI)
Literature
( ρ,τ ) leaf, green & brown dry soil reflectance LIDF (a,b) Dissociation factor D
Acquisition
fraction diffuse sky irradiance solar zenith angle, viewing angle
Landuse
Fig. 5. Interfacing of crop growth model PROMET-V with canopy reflectance model GeoSAIL.
5
sensing images, in this case from the Landsat Thematic Mapper, are atmospherically corrected, producing surface reflectance data. These are compared to simulated reflectances produced by GeoSAIL. The free parameters that could be adjusted in the model inversion procedure were total LAI, fraction brown leaves and soil moisture. As Landsat TM data contain optical reflectance data from only six spectral bands, the number of free parameters also has to be kept rather small. The other parameters were held fixed at their values such as determined according to Fig. 5. The model inversion tries to minimize the difference between observed and simulated reflectance spectra. In this case a simple 3D golden section algorithm was used to adjust the free parameters. An advantage of this algorithm is that the range of variation for each free parameter can be given on input, and this possibility was used to impose these as plausible ranges that were obtained from PROMET-V simulations under extreme circumstances. Thus, a synergy between radiative transfer model and dynamical process model is established, as the process model helps constraining the model inversion to achieve quick convergence, and the model inversion result helps to adjust the process model so that it follows the correct path through time. In the GeoBIRD project only one control parameter of the growth model was adjusted in order to match the courses of LAI. For cereal crops this was the plant density, and for grass the most recent cutting date. All other inputs of the growth model were taken from local meteorological data, the land use and agricultural practices. The strategy illustrated in Fig. 6 can be called a two-step data assimilation scheme. In the first step the biophysical parameters are retrieved from spectral observations using a radiative transfer model, and in the second step the time course of the retrieved parameters is used to adjust a process model until both match sufficiently well. In Fig. 7 this two-step data assimilation is illustrated on a more abstract level. Here also the numbers of input parameters and output parameters involved at each level of model inversion are emphasized. A careful tuning of the numbers of parameters to be retrieved to the quality and quantity of the observed data in the spectral and temporal domains is mandatory for a successful result, as otherwise the ill-posedness of the model inversion problem could easily lead to unreliable outputs. 2.2. Results The accuracy of the results can be measured by looking at the errors in the simulated remote sensing observations, in the retrieved biophysical parameters, and in final process model outcomes, such as (in this case) yield figures. In Fig. 8 a comparison between observed and simulated Landsat TM reflectance spectra is
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W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
Calibration& atmospheric correction
OpticalRS images
Meteo-data, landuse, GIS-Info
Geobiophysical maps biomass, yield, height, ...
Plausible ranges of parameters
Observed reflectance spectrum
Vegetation growth
model
GeoSAIL inversion
Simulated total LAI
AdjustLAI, faction brown,r soilmoisture
Observed reflectance spectrum
Adjust density/ cuttingdate
Canopy reflectance modeling GeoSAIL
RS-retrieved totalLAI
Fig. 6. Geo-biophysical parameter retrieval and data assimilation by a spatially distributed radiative transfer model coupled to a crop growth process model (Bach et al., 2000).
shown for maize in the Upper Rhine Valley on five dates in 1995. Except for the bare soil case, which probably is poorly simulated due to a bad estimation of soil drying after rainfall, these simulations are quite well in agreement with the observations. Spatial details of the model inversion/image simulation results for one date, 23rd August 1995, are shown in Fig. 9, which displays the input land use map of the area, the Landsat TM image in bands 2, 3 and 4 (B, G, R), the retrieved parameters total LAI, fraction brown leaf area, and soil moisture, and finally the RMS error of the image simulation. The latter is a measure of the confidence one may have in the quality of the retrieval results. Application of a crop growth model that is updated by means of spatially detailed remote sensing observa-
RS data >> biophys. param. >> process model
# RS parameters (bands, directions) # biophys. parameters
# image acquisitions # process variables updated
6 TM bands / LAI, fraction brown, soil moisture
5 dates / plant density, cutting date (grass)
GeoBIRD
Fig. 7. Illustration of RS data assimilation by means of two feedback loops.
Measured TM spectra 50
Simulated with PROMET-V/GeoSAIL 50
3 May 95 20 June 95
3 May 95
40
20 June 95 40
22 July 95
Simulated Reflectances [%]
Measured Reflectance [%]
6 July 95
23 August 95
30
20
10
6 July 95 22 July 95 23 August 95
30
20
10
0
0
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Wavelength [µm]
1.8
2.0
2.2
2.4
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Wavelength [µm]
Fig. 8. Actual and simulated TM reflectance spectra of maize at Upper Rhine Valley test site (Bach et al., 2000).
2.2
2.4
W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
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Fig. 9. Examples of Upper Rhine Valley spatially distributed GeoSAIL model inversion results.
tions during the growing season can lead to improved yield predictions and to a better identification of local anomalies within agricultural fields. Fig. 10 illustrates that yield predictions based on growth model outputs gain a lot in spatial detail by using feedback from remote sensing observations. Also field-averaged yield figures are likely to become more accurate, since remote sensing inputs are used to adjust the model-predicted growth curves.
3. Approach 2: the DAASCEES project 3.1. Methodology The second approach that one can follow for the assimilation of biophysical parameters retrieved from remote sensing observations has already been illustrated in Figs. 2 and 3 of Section 1. In this case everything related to the actual observations is included in a generic
Fig. 10. Comparing grain yield prediction results obtained by means of the crop growth model with and without using feedback from remote sensing images.
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optical remote sensing model. In this way one is able to take account of all effects related to sensor properties (e.g. spectral bands, resolutions in the spectral and spatial domains), the atmosphere and the angular suntarget-sensor configuration, so that for the quantitative interpretation of the data the user can focus on object properties. Incorporation of the atmosphere in a complete observation model that includes non-Lambertian reflectance of a heterogeneous landscape has the advantage that atmospheric correction is no longer necessary. Furthermore, atmospheric correction methods are mostly based on the assumption of Lambertian surface reflectances, and therefore are inherently inconsistent, as most land objects are significantly different from Lambertian. In the ESA project DAASCEES (Data Assimilation And Scaling for the Candidate Earth Explorer core mission SPECTRA) the actual data assimilation by means of the comparison of simulated with actual images could not be studied yet, as the SPECTRA mission is still a candidate Earth Explorer Mission, possibly scheduled for launch towards the end of this decade. However, simulated imagery for this sensor could already be generated by assembling a generic observation model that comprised all necessary components. These consist of • The leaf level radiative transfer model PROSPECT (Jacquemoud and Baret, 1990) • The canopy level radiative transfer model GeoSAIL (Bach et al., 2001) • The atmospheric radiative transfer model MODTRAN4 (Berk et al., 2000) • Sensor-specific data like spectral range and resolution, spatial sampling and modulation transfer function (MTF) The SPECTRA mission will be featured by an imaging spectroradiometer covering the solar reflective spectral range from 450 to 2350 nm and two thermal infrared bands co-registered with the optical bands, at
the same spatial resolution of 50 m nominal under nadir view. Along-track as well as across-track pointing capabilities will be provided in order to allow angular sampling of the bidirectional reflectance distribution function (BRDF) and to achieve a revisit time of three days. Simulation of all these aspects has been accomplished for the Barrax site in Spain for the date of 28 June 2000. For this, first a Landsat TM image of the region on the same day had been classified into the main crop classes (alfalfa, maize, barley and dry stubble) and bare soil by the University of Valencia (Alonso-Chorda, 2001). Similar to the procedure applied in the GeoBIRD project (Section 2), the Landsat image was atmospherically corrected to produce six-band surface reflectance images that were used as input for model inversion with GeoSAIL in order to obtain realistic spatially varying fields of the parameters total LAI, fraction brown leaves and surface soil moisture. The input parameters of PROSPECT and the remaining input parameters of GeoSAIL were estimated by fitting leaf optical spectra to literature data for the various crop classes, and by using in situ photographs taken at the Barrax site. A summary of the fixed input parameters is given in Table 1. The PROSPECT parameters are: N ¼ leaf mesophyll parameter Cab ¼ chlorophyll contents Cw ¼ water contents Cdm ¼ dry matter contents Cs ¼ senescent material (brown pigments) contents The complete modeling chain applied for the SPECTRA image simulations is illustrated in Fig. 11. The left block (land surface) shows the generation of the necessary input parameters for GeoSAIL. In order to model the radiative interaction of a non-Lambertian heterogeneous land surface with the overlying atmosphere at one wavelength it is not only necessary to compute for each pixel the targetÕs bidirectional reflectance for the given angular geometry, but also three other reflectance components. The four surface reflec-
Table 1 Input parameters of the PROSPECT and GeoSAIL simulations Parameter PROSPECT N Cab Cw Cdm Cs GeoSAIL fB range D a b
Corn
Barley
Alfalfa
Dry crops
Brown leaves
1.48 50 0.011 0.005 0
1.8 70 0.011 0.005 0
2 70 0.014 0.005 0
1.8 70 0.014 0.005 0
3 0 0 0.01 0.7
0.0–0.0 0.8 0.65 0.15
0.6–1.0 0.8 0.85 0.15
0.0–0.0 0.8 0.00 0.15
1.0–1.0 0.8 0.0 0.0
– – – –
W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
Land surface
9
Satellite
Landuse Soilmap Groundmeas.
Sun-target-sensor geometry
LUT
Sensor resolution
SPECTRA images
Atmosphere
LUT
Leaf properties
Canopy & Soil param.
PROSPECT model
Leaf spectra
GeoSAIL model
Visibility etc.
MODTRAN4 model
Radiance spectra
Effective atm. par.
Response analysis
Stimuli Surface BRDF images
Adjacency effect
Surface filtered images
4-stream interaction
TOA radiance images
Fig. 11. Modeling of hyperspectral TOA radiance images.
tance fields so obtained are the minimum that is required to model the interaction with the atmosphere with the so-called four-stream approximation (Verhoef, 1985, 1998). Fortunately, the GeoSAIL model delivers these four reflectances directly as outputs, a consequence of the fact that internally in GeoSAIL also a four-stream approximation is used. In order to simulate the adjacency effect, two of the four GeoSAIL output fields have to be spatially filtered. These are the diffuse hemispherical reflectances for direct solar and diffuse incidence, respectively. The directional target reflectance
fields for direct solar and diffuse incidence remain unaffected by this operation. Fig. 12 illustrates the four reflectance components for the Barrax site after the spatial filtering associated with the adjacency effect has been applied. In order to model the four-stream interaction of the atmosphere with the surface, the atmosphere must be described by at least six ÔeffectiveÕ parameters, of which two are reflectances and four are transmittances. These parameters can be derived from MODTRAN4 outputs by applying a so-called interrogation technique. In this
Fig. 12. Simulated images of surface BRDF components after taking into account the adjacency effect. Band combination R, G, B ¼ 840, 730 and 660 nm.
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W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
method MODTRAN is treated like a black box, and one performs three successive model runs over the whole spectrum for the assumed atmospheric and geometrical conditions, and with spectrally flat surface albedos of 0.0, 0.5 and 1.0, respectively. Similar approaches have been reported in Bach (1995) and De Haan and Kokke (1996). From the MODTRAN radiance outputs it is possible to derive all six atmospheric parameters for each wavelength, and the spectra of these parameters can be applied to model the four-stream radiation interaction with the earthÕs surface pixel by pixel. The final result is the TOA spectral radiance at each pixel position and at each wavelength. Fig. 11 illustrates how the atmospheric modeling block stands relatively off-line from the main chain, and that the actual interaction with the heterogeneous surface is modeled by means of the six effective parameters. The last step in the image simulation chain is formed by applying realistic spatial resolutions and samplings. The Landsat images forming the basis of the simulations have 30 m resolution and have been resampled to 25 m during geometric correction. The basic surface reflectance and TOA radiance fields have also been produced in this sampling grid. The expected spatial resolution of the SPECTRA missionÕs imaging spectroradiometer has been simulated by means of spatial filtering in accordance with view angle dependent MTF data provided by ESA. Nevertheless, all output images have been resampled to a common sampling distance of 50 m in order to facilitate the intercomparison of simulated images under different directions. 3.2. Results Validation results of the hyperspectral surface reflectance modeling with GeoSAIL for alfalfa and corn crops are presented in Fig. 13. Here, the measured surface reflectance spectra have been obtained from HyMap airborne imaging spectrometer data recorded one day after the Landsat TM image acquisition. The HyMap data have been atmospherically corrected, so they can represent surface reflectance measurements in the field. It appears that in general simulated and observed reflectance spectra agree reasonably well, except in the red region of the spectrum, where systematic differences seem to be present. This may be due to incorrect modeling of the fraction brown leaves for these crops. An example of bottom-of-atmosphere (BOA) reflectance modeling results, together with sample reflectance spectra for four classes, is shown in Fig. 14. The wavelengths chosen for the image are 660, 730 and 840 nm and they represent the strong red-NIR transition seen in green vegetation. An example of the atmospheric modeling by means of MODTRAN4 for a visibility of 40 km resulting into
Fig. 13. Surface reflectance modeling validation results for alfalfa and corn. Blue curves show HyMap-derived surface reflectance spectra, red curves the corresponding simulated spectra.
Fig. 14. Simulated SPECTRA scene showing BOA directional reflectances in three bands and complete spectra from four pixels of alfalfa (green), corn (blue), dry stubble (red) and bare soil (brown).
W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
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Table 2 Input parameters and options for MODTRAN4
1 0.9 0.8 0.7 0.6
τ ss τ oo ρso ρdd τ sd τ do
0.5 0.4 0.3 0.2 0.1 0 400
700
1000
1300 1600 wavelength (nm)
1900
2200
Fig. 15. Spectra of six optical atmospheric parameters derived from MODTRAN4 outputs using interrogation technique.
spectra of the six effective atmospheric parameters is shown in Fig. 15. The spectra cover the range from 450 to 2350 nm at 10 nm resolution. The two top curves represent the direct atmospheric transmittances in the direction of the sun and in the viewing direction. The other curves represent contributions related to combinations of specular and diffuse scattering in forward and backward directions. A demonstration of the effect of the atmosphere on the TOA radiance spectra for a barley pixel under different viewing directions and two visibilities is shown in Fig. 16. Here a series of seven along-track viewing directions has been simulated for 40 km (clear) and 5 km (hazy) visibilities. From this simulation it appears that especially below 800 nm the atmospheric visibility has a large impact on the radiance spectra. Details on the atmospheric modeling with MODTRAN4 are summarized in Table 2.
TOA Radiances [W/m² sr µm]
Visibility = 40 km 160 140 120 100 80
Case: Along-track Pointing Variable observation geometry zen 60, azi 10 (forward) zen 45, azi 10 zen 25, azi 10 zen 0 Nadir zen 25, azi 190 zen 45, azi 190 zen 60, azi 190 (backwards)
Clear atmosphere
60 40 20 0 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength [µm]
Visibility = 5 km TOA Radiances [W/m² sr µm]
160 140 120 100 80
Case: Along-track Pointing Variable observation geometry zen 60, azi 10 (forward) zen 45, azi 10 zen 25, azi 10 zen 0 Nadir zen 25, azi 190 zen 45, azi 190 zen 60, azi 190 (backwards)
Hazy atmosphere
60 40 20 0
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength [µm]
Fig. 16. Simulated barley spectra of TOA radiances for a clear (top) and hazy (bottom) atmosphere.
Parameter/option
Value
Atmospheric profile Use DISORT Azimuth dependence Number of streams CO2 mixing ratio Aerosol extinction Stratospheric aerosol
Mid-latitude summer Yes Yes 8 360 ppmv Rural Background profile and extinction 40 and 5 km 700 km 0.7 km Internal Mie-generated 181 39.05 deg. N 2.10 deg. W 11:00 h 450–2350 nm 10 nm 12 nm Gaussian
Visibility Sensor altitude Target altitude Aerosol phase functions Day of year Target latitude Target longitude GMT time Spectral range Interval FWHM Slit function
Along-track pointing of the instrument for a satellite in a descending node sun-synchronous orbit leads to a viewing azimuth angle of about 10 or 190 degrees. At 11:00 h local solar time for the Barrax site on 28 June the solar azimuth is about 130 degrees, so that in this case there is a 60 or 120 degrees difference of the viewing azimuth with the principal plane (the plane of the sun). This configuration makes observation of the hot spot, which lies in the principal plane, impossible. However, by a combination of along-track with across-track pointing it is possible to make observations in the principal plane, including the hot spot. In the hot spot, viewing is exactly along the sunrays, so that no shadows are observed, and therefore the reflectance in that direction is relatively high. Simulation results for a series of observation in the principal plane are shown in Fig. 17. The series of viewing zenith angles is the same as in Fig. 16, except that the 25 degree viewing angle was replaced by 22 degrees in order to capture the exact hot spot. A corresponding HyMap image in approximately the same spectral bands is shown at the bottom. This image shows the hot spot as a bright horizontal line, as the flight line was perpendicular to the principal plane of the sun. The series of images show the BOA reflectances (top row) and the TOA radiances (second row) of the Barrax site for seven directions in the principal plane. Note the bright appearance of the hot spot images and also their different color hue. Similar brightness and color changes are observable in the HyMap image. As a relative intensity scaling was applied (separately for BOA reflectance and TOA radiance) the effect of the atmosphere does not seem to be that obvious, but as shown in Fig. 16, the numerical effect of the atmosphere is substantial.
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W. Verhoef, H. Bach / Physics and Chemistry of the Earth 28 (2003) 3–13
Fig. 17. Simulation of a series of SPECTRA scenes for acquisitions in the principal plane and the red edge band combination. Top rows surface reflectance and TOA radiances. Below a HyMap image in the same spectral bands is shown illustrating the hot spot as a bright line.
4. Conclusions The synergy of a vegetation growth model and a remote sensing observation model can be exploited to improve the retrieval of geo-biophysical information. GeoSAIL model inversion results obtained from atmospherically corrected Landsat TM images could be used to update internal model parameters of the growth model PROMET-V. Through this, dry biomass and grain yield of maize could be retrieved with improved accuracy and high spatial resolution. By coupling of the leaf model PROSPECT, the canopy model GeoSAIL and the atmospheric model MODTRAN4 simulated radiance images for the future ESA hyperspectral multidirectional Earth Explorer Mission SPECTRA have been generated. In a user-oriented GIS-driven approach simulated RS images are compared to actual RS images in order to derive new information by means of a feedback loop, implementing model inversion at the image level and direct assimilation of the results in a GIS environment. The combination GIS data––RS observation model facilitates the assimilation of RS data from various sensors and allows to take better account of factors like landscape heterogeneity, terrain topography and nonLambertian surface reflection.
Acknowledgements The results presented were obtained in the framework of the ESA studies GeoBIRD (Geo-Biophysical Information retrieval from Remote sensing Data, ESAESTEC contract no. 12950/98/NL/GD, study manager
Maurice Borgeaud) and DAASCEES (Data Assimilation And Scaling for the Candidate Earth Explorer core mission SPECTRA, ESA-ESTEC contract no. 15164/ 01/NL/SF, study manager Mike Rast). The authors want to express their sincere thanks to Karl Schneider, presently at the University of Cologne, and Wolfram Mauser (University of Munich) for providing the PROMET-V model and to Jose Moreno, Luis Alonso-Chorda and Alfonso Calera of the University of Valencia, for their support regarding the ground truth and airborne data of the Barrax site. Frederic Baret of INRA, Avignon, kindly provided us with the latest PROSPECT code plus leaf optical parameters. Umberto Del Bello of ESA-ESTEC, Noordwijk, provided data on the expected spatial resolution of the SPECTRA missionÕs optical instrument.
References Alonso-Chorda, L., 2001, personal communication. Bach, H., 1995. Die Bestimmung hydrologischer und landwirtschaftlicher Oberfl€achenparameter aus hyperspektralen Fernerkundungsdaten, Ph. D. Thesis University of Munich, M€ unchener Geographische Abhandlungen, 21, 175 p. Bach, H., Schneider, K., Verhoef, W., Stolz, R., Mauser, W., Van Leeuwen, H., Schouten, L., Borgeaud, M., 2001. Retrieval of geoand biophysical information from remote sensing through advanced combination of a land surface process model with inversion techniques in the optical and microwave spectral range. Proceedings of the 8th International Symposium ‘‘Physical measurements and signature in remote sensing’’, Aussois, CNES, pp. 639–647. Bach, H., Verhoef, W., Schneider, K., 2000. Coupling remote sensing observation models and a growth model for improved retrieval of (geo) biophysical information from optical remote sensing data, Remote Sensing for Agriculture. Ecosyst. Hydrol., SPIE 4171, 1– 11.
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