International Journal of Remote Sensing Vol. 26, No. 8, 20 April 2005, 1589–1610
A whole image approach using field measurements for transforming EO1 Hyperion hyperspectral data into canopy reflectance spectra E. RAMSEY III* and G. NELSON US Geological Survey, National Wetlands Research Center, 700 Cajundome Boulevard, Lafayette, Louisiana 70506, USA (Received 7 May 2003; in final form 20 September 2004 ) To maximize the spectral distinctiveness (information) of the canopy reflectance, an atmospheric correction strategy was implemented to provide accurate estimates of the intrinsic reflectance from the Earth Observing 1 (EO1) satellite Hyperion sensor signal. In rendering the canopy reflectance, an estimate of optical depth derived from a measurement of downwelling irradiance was used to drive a radiative transfer simulation of atmospheric scattering and attenuation. During the atmospheric model simulation, the input whole-terrain background reflectance estimate was changed to minimize the differences between the model predicted and the observed canopy reflectance spectra at 34 sites. Lacking appropriate spectrally invariant scene targets, inclusion of the field and predicted comparison maximized the model accuracy and, thereby, the detail and precision in the canopy reflectance necessary to detect low percentage occurrences of invasive plants. After accounting for artifacts surrounding prominent absorption features from about 400 nm to 1000 nm, the atmospheric adjustment strategy correctly explained 99% of the observed canopy reflectance spectra variance. Separately, model simulation explained an average of 88%¡9% of the observed variance in the visible and 98%¡1% in the near-infrared wavelengths. In the 34 model simulations, maximum differences between the observed and predicted reflectances were typically less than ¡1% in the visible; however, maximum reflectance differences higher than ¡1.6% (,¡2.3%) at more than a few wavelengths were observed at three sites. In the near-infrared wavelengths, maximum reflectance differences remained less than ¡3% for 68% of the comparisons (¡1 standard deviation) and less than ¡6% for 95% of the comparisons (¡2 standard deviation). Higher reflectance differences in the visible and near-infrared wavelengths were most likely associated with problems in the comparison, not in the model generation.
1.
Introduction
We are developing remote sensing tools to map the localized occurrences and regional distribution of a widespread and gregarious invasive species, Chinese tallow tree (Triadica sebifera). This species is actively advancing inland in the south and south-east of the United States, and is causing extensive disruption by displacing native communities (Ramsey et al. 2002). Operational resource management of this aggressively expanding invasive plant requires regional, repetitive, and cost-effective mapping; however, appropriate mapping technologies do not exist. *Corresponding author. Email:
[email protected] International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2005 US Government http://www.tandf.co.uk/journals DOI: 10.1080/0431160512331326729
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The sole attempt to map Chinese tallow with remote sensing data used airborne colour-infrared photography (1:12 000) to simulate low spectral but high spatial resolution satellite and airborne image and digital video data (Ramsey et al. 2002). Mapping occurred when the senescing Chinese tallow with red and yellow leaves presented a high spectral contrast within the native landscapes (Everitt et al. 2000). Classification accuracy higher than 95% confirmed that high spatial resolution data can be used to monitor Chinese tallow infestations, but the mapping was labourintensive, and coverage was severely limited (,5 ha), providing neither operationally repetitive nor regional mapping. Furthermore, we found that in the period of infestation studied, pure stands of Chinese tallow trees rarely occurred, and where they did, the stands were narrow and fragmented with dimensions rarely more than about 10 m. Tallow trees were ubiquitous and were mixed intricately and at a low density within the matrix of upland and wetland native vegetation (figure 1, Ramsey et al. 2002). The necessary trade-off was between the need for high spatial resolution and the need for regional coverage. To overcome this challenge, high spectral resolution data that offered subtle spectral discrimination were used to compensate for the use of moderate spatial
Figure 1. region.
The study area location. The 34 field sites were distributed throughout the stippled
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resolution data that offered regional coverage. The hyperspectral Hyperion sensor on board the Earth Observing 1 (EO1) satellite provided the high spectral resolution data necessary, and a spectral unmixing tool provided the subpixel Chinese tallow detection. Successful unmixing applications require that the canopy spectral information be related directly to changes in the canopy composition (e.g. Miller 2002), where composition refers to the major canopy components, such as the types of trees or grasses and ground surface (e.g. litter, water, mud). Our challenge is to relate the normally low-percentage occurrence of Chinese tallow to subtle and meaningful spectral differences within the spectral matrix of native plants and variable canopy compositions and structures. Creating this meaningful relationship first required a consistent and accurate method to subdue or eliminate influences that affect the signal recorded at the sensor and that are not directly related to the canopy spectral composition. Atmospheric influences modify the reflected signal from the target to the sensor, in one sense decreasing relative spectral differences within the sensor record (reflectance peaks and troughs) and in another sense, particular to high spectral resolution image collections, adding fine atmospheric absorption features to the spectral record. These additions confuse the extraction of subtle canopy spectral features, and thereby diminish the ability to determine subtle changes in canopy composition (Ramsey et al. 1992, Ben-Dor et al. 1994, Ramsey and Jensen 1995, Vermote and Vermeulen 1999). Removal of atmospheric effects allows extraction of the canopy reflectance from the sensor signal and creates a more spatially and temporally extendable and comparable dataset that maximizes the spectral distinctiveness (information) of the canopy reflectance spectra (Sjoberg and Horn 1983, Ben-Dor et al. 1994, Richards 1999, Smith and Milton 1999). We anticipated that to successfully map changes in canopy reflectance resulting from low-percentage occurrences of tallow within the 30-m pixel, the canopy reflectance retrieval from the Hyperion image data should be within 2% or less of the actual reflectance in the visible wavelengths. This accuracy limit was estimated from field canopy reflectance spectra pairs; two hardwood stands containing about 0% and 17% and two pine stands containing 2% and 17% red tallow canopy compositions (figure 2, Ramsey et al. 2005). We calculated that about 2% (¡1 standard deviation) average reflectance difference shown in the critical red wavelength region of both spectra sets would correspond to a canopy spatial area about 12 m612 m, equivalent to a small number of senescing tallow trees within a 30-m pixel spatial resolution. These examples indicate that at a minimum, the estimated mean and maximum differences in the observed and predicted canopy reflectance spectra in the visible wavelengths should provide the capability to detect a few mature tallow trees grouped or scattered within each Hyperion pixel. To achieve at least this accuracy level while maintaining spatially extensive coverage, our objective was to produce an accurate rendition of the hyperspectral canopy reflectance that accounted for atmospheric influences (the view and sun geometry) and illumination of the canopy. 1.1
Atmospheric correction and normalization
Many methods exist for adjusting hyperspectral image data for scattering and attenuation, ranging from those that rely solely on image-based inputs to those that rely solely on radiative transfer models driven by pertinent atmospheric measurements and estimates and generalized datasets (e.g. Goetz et al. 1997, Staenz et al.
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Figure 2. (a) Hardwood forest sites with 0% (solid line) and 17% (dashed line) red Chinese tallow. (b) Pine forest sites with 17% (solid line) and 2% (dashed line) red Chinese tallow.
2002). Methods solely dependent on image-based inputs, such as Internal Average Relative Reflectance (IARR) and Flat Field (FF), can either normalize the sensor responses to the image mean or reference spectra or normalize to a spectrally flat target (Ben-Dor et al. 1994, Smith and Milton 1999). Although no field data are required, these methods have not proven to be either reliable (possibly because of their sensitivity to the spectral complexity of the landscape) or practical to implement (as there is difficulty in locating appropriate image-based references); in fact, they may suppress or remove canopy spectral features (Smith and Milton 1999, Ben-Dor and Levin 2000). Another primarily image-based method applies an empirical line (EL) adjustment to the sensor responses by generating regression parameters from the covariation of field or library reflectance spectra with their associated image-based sensor response spectra. These regression parameters are then used to adjust the image. In its simplest application, the EL method is based on bright and generated or estimated dark target reflectance spectra (Kruse et al. 1990, Ben-Dor and Levin 2000, Moran et al. 2001, Miller 2002), although additional midintensity targets are used to better account for possible nonlinear relationships and provide validation assessment (Smith and Milton 1999). At the other extreme, methods dependent solely on atmospheric retrieval with radiative transfer methods rely on measured or estimated values of atmospheric variables at the time of the image collection and simulate the interaction of radiation with the atmosphere and the surface (Smith and Milton 1999). These methods may produce more reliable simulations of atmospheric scattering and attenuation, but they can also enhance sensitivity to image noise, and often their reliance on unavailable atmospheric variables limits their usefulness (Farrand et al. 1994, Smith and Milton 1999). Between these two adjustment extremes are methods that couple image-based inputs and radiative transfer models. One method uses image-extracted, dark-target reflectance to produce an estimate of atmospheric path radiance, and horizontal visibility to produce an estimate of the atmospheric optical depth in conjunction with atmospheric scattering and absorption and aerosol distribution functions as inputs into a radiative transfer model (Ahern et al. 1977, Chavez 1988, Moran et al. 1992, Quaidrari and Vermote 1999). As in the EL method, the adjustment produced
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is applied to the sensor response as a constant throughout the image replication of the scene. Another set of adjustment methods also couples a radiative transfer model with similar inputs as the dark-target method; however, extraction of column water vapour directly from the image data allows a pixel-by-pixel adjustment of the image (Ben-Dor and Levin 2000, Miller 2002). In contrast to the EL and dark-target methods, pixel-by-pixel adjustment methods such as ATmospheric REMoval (ATREM) (software from University of Colorado) and more recently Atmospheric CORection Now (ACORN) (ImSpec LLC) are not directly amenable to broadband atmospheric adjustments but rely more on high spectral resolution image data to correctly determine the water absorption magnitude. Unlike ATREM, however, ACORN is not public domain software and was not considered within this study. In a comparison of ATREM-modelled and field spectra, correspondence was within 10% in most of the visible and near-infrared wavelengths, while in a comparison of overlap spectra on adjacent frames, EL and ATREM produced nearly comparable results, reducing differences to less than 5% (Perry et al. 2000). In another study, EL performed superior to ATREM (Ben-Dor and Levin 2000), and in another combining calibration and image classification, EL performed equal to or better than ATREM (Miller 2002). At six sites containing various canopy compositions, we compared field and canopy reflectance spectra generated from the Hyperion image data with ATREM (a horizontal visibility estimate was input) and a post-processing enhancement (empirical flat field optimal reflectance transform, EFFORT). Reflectance differences averaged 20.0151¡0.0058 (¡1 standard deviation) in the visible (425–690 nm, n527 wavelengths) and 0.0009¡0.0208 in the near-infrared (700–1094 nm, n540) wavelengths. Maximum reflectance differences ranged up to about 4% (except for one difference around 10%) in the visible, and although a number of higher differences were found (up to about 20%, n55), maximum differences most often were less than 5% in the near-infrared wavelengths. We pursued alternate methods of atmospheric adjustment for several reasons: (1) the possibility of higher than acceptable errors (,2% reflectance error in the visible) in vegetated canopy reflectance retrieval with ATREM; (2) documented problems in IARR consistency and spectral feature suppression; (3) unavailable spectrally invariant targets of sufficient spatial extent for application of FF (and similarly dark-target subtraction); and (4) detailed atmospheric measurements for implementing an adjustment based solely on radiative transfer retrieval. While we had collected field canopy spectra at 34 sites concurrent with EO1 Hyperion image data, the range and nature of the field reflectance data did not fit within the necessary constraints required for application of the widely applied and highly successful EL or darktarget adjustment methods (Chavez 1988, Smith and Milton 1999, Ben-Dor and Levin 2000, Moran et al. 2001, Miller 2002). Canopy field reflectance spectra did cover a variety of canopy types values, but near-Lambertian targets (e.g. nonvegetated) of high (e.g. extensive bare and preferably horizontal) or low reflectance (e.g. clear lakes) and of acceptable spatial extent were unavailable. Lacking appropriate field data for EL calibration, we chose a strategy that combined concurrent surface downwelling irradiance and nonoptimized field canopy radiance spectra as inputs into a radiative transfer model. Constrained within the radiative transfer model, results would be more generalized, interpretable and comparable to other strategies that incorporate a radiative transfer solution. Further, even though field reflectance data were not optimal, the ability to include
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field measurements in the model calibration (e.g. Chavez 1988) and more directly to include a correction for adjacency influences should improve the radiative transfer model performance. In addition, the developed strategy provided estimates of predicted accuracy and the range and nature of specific spectral differences by comparing canopy reflectance spectra extracted from the Hyperion image data to field canopy reflectance spectra. In creating a highly accurate rendition of canopy reflectance spectra from the Hyperion image data, our goal was to prevent errors in the Hyperion image interpretation and provide accurate image classification (e.g. Ben-Dor et al. 1994, Vermote and Vermeulen 1999). 1.2
Field measurements and radiative transfer
Operationally, the reduction in the amount and quality of image information can be generally accounted for by three main atmospheric effects: (1) modification of the irradiance at the top of the atmosphere to produce the target surface irradiance (mainly by changes in skylight illumination); (2) attenuation of radiation transferred from the surface to the sensor; and (3) addition of an extraneous component of scattered radiance or path radiance to the transferred component (Turner et al. 1971). If the target reflectance is perfectly diffuse, the radiance upwelling from the target (Lu) at a certain wavelength can be written as follows: Lu ~ðri =pÞHOBS
ð1Þ
where HOBS5the total direct plus skylight irradiance at the target (W m22 mm21), and ri5the estimated target reflectance. As written, equation (1) assumes the upwelling radiance from the target is independent of the sensor observation angle and, thus, depends only on the downwelling irradiance and the target reflectance. In most cases, the target is not inherently diffuse, and the reflectance will vary with changes in the viewing and illumination geometry (Turner and Spencer 1972, Quaidrari and Vermote 1999, Smith and Milton 1999, Vermote and Vermeulen 1999). The distribution function describing this angular variation (bi-directional reflectance distribution function, BRDF) depends on the target characteristics. Because of the highly varied landscape, the inability to provide a priori estimates of the surface BRDF, and a minimal off-nadir Hyperion view (2.7u), a simple diffuse surface estimate was used (constant p). Thus the radiance within a spectral band observed by a sensor can be expressed as: La ~ððri =pÞðHOBS ÞÞTv zLp ,
ð2Þ
where La5apparent radiance at the sensor platform, Tv5slant transmittance from the target surface to the sensor, and Lp5path radiance not originating from the target pixel being scattered into the sensor field of view. Optical depth and whole-terrain background reflectances are implicit in the model generation of Tv and Lp. 2.
Study area and image collection
The study area was located in a subsiding coastal-to-upland transition region near the Texas and Louisiana border (figure 1). Upland and bottomland hardwood forests, marsh, rangeland (grazed prairie), agriculture, and water make up the dominant land covers. This area was chosen because of the known occurrence of
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Chinese tallow. The collection date was chosen to capture the high spectral contrast of senescing Chinese tallow within the surrounding forest and grassland landscapes. Ground-based and helicopter-based observations confirmed the occurrence of tallow senescence at the time of Hyperion image collection. The 5 November 2001 cloud-free Hyperion image was 256 pixels wide (i.e. covering a 7.65-km width swath) by 6925 rows in length. All data were acquired with 42.55 m radians along track instantaneous-field-of-view defining at an aboveground level of 705 km at nadir a nominal ground resolution of approximately 30 m. The Hyperion image was not georeferenced to avoid spectral interpolations. To find the 34 field sites on the Hyperion image, the sites were first located on a rectified Landsat Thematic Mapper image with their GPS coordinates and these centre locations were transformed to Hyperion image row and column indexes. At the time of collection (11:30 local standard time (LST) or 16:33 universal coordinate time (UCT)), the sun zenith was nearly 50u and the sun azimuth was 153u. The meteorological visibility (MVn) was obtained from the Lake Charles, Louisiana, airport weather station as 16 km at the time of the Hyperion data collection and was related to surface visual range (Vn) following Bowker and Davis (1987) as: Vn ~ð1:3+0:3ÞMVn :
ð3Þ
The surface visibility was estimated as 21 km. Two monochrometer gratings covered spectral ranges from about 400 nm to 1000 nm and 900 nm to 2500 nm, referred to as the visible-to-near-infrared (VNIR) and shortwave-infrared (SWIR) regions, respectively. The spectral overlap of the VNIR and SWIR includes the spectral range 892–926 nm. Preflight measurements of the spectral frequency response per wavelength at multiple locations across the different focal planes were used to specify the centre wavelengths and bandwidths for the focal plane (e.g. table 1). The distance between centre wavelengths and bandwidths was approximately 10 nm. The preflight calibration also uncovered a variation of the centre wavelength across the focal plane (i.e. field-of-view, 7.65 km) reaching up to 23.5 nm in the VNIR but only about 20.5 nm in the SWIR at the most extreme pixel field-of-view (EO1/Hyperion Science Data User’s Guide 2001). Visible inspection found that good-quality Hyperion image data extended from about 425 nm to 1300 nm, including some poorer quality bands on either side of the band overlap region and excluding a block of bands in the higher near-infrared region (table 1). Severe streaking and poor image contrast depicted the lowest wavelength bands in the shortwave region, while good-quality bands extended from about 1500 nm to 1770 nm and 2284 nm to 2300 nm. We restricted analyses to wavelengths below 1000 nm and, with the exception of nine bands surrounding the spectral overlap region, used only bands of visibly high quality (table 1). Pre-launch and on-orbit calibrations have reported the signal to noise ratio (SNR) in the VNIR ranged from about 140 to 190 (Barry et al. 2002). With respect to a 30% uniform albedo target (Pearlman et al. 2000), these ratios yield an estimated noise equivalent delta reflectance in the VNIR equal to or less than about 0.2%. In addition, a series of lunar and solar calibration collections found VNIR repeatability ,1% (Barry et al. 2002). Both measures are well below the 2% upper limit estimated for detecting a few senescing tallow trees. Finally, the detection of red tallow leaves relied on spectral variation arising in the green-to-red-to-near-infrared wavelength; thus, limiting the spectral region of our analyses to the visible-to-near-infrared wavelength region did not adversely influence the attainment of the study objectives.
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3. 3.1
Hyperion band centres (nm).
Methods Collection of canopy reflectance spectra
Upwelling radiance (about 400–1000 nm, 252 channels, Spectron Engineering, Inc., n.d.) from a helicopter platform was measured at 34 sites on 5 November 2001 during the same day as the Hyperion image data collection (at 11:30 LST) (see §2). Upwelling radiance recordings began at about 11:15 LST (16:19 UCT) and ended at about 14:17 LST (19:21 UCT). At the first site, the sun zenith was about 50u, then decreased to a minimum of about 41u at about 12:17 LST, and again reached about 50u at the last site occupied. At all but one site, sun zeniths were within 4u of the sun zenith at the time of the Hyperion collection. Although reported to have nominal bandwidths around 2.6 nm, Spectron Engineering radiometer bandwidths are actually nearer 10 nm (Markham et al. 1995). Up to seven near nadir upwelling radiance spectra collected over 1–2 minutes at each site (except at one site where the reading took about 20 min) were normalized by simultaneous collections of downwelling sunlight irradiance (300–1100 nm at 2 nm bandwidths, LICOR 1984), thus obtaining a site mean field reflectance spectra (ri, best termed hemispherical-directional reflectance following Nicodemus et al. 1977) (figure 3). Intensity compatibility of the radiometers used to obtain the upwelling and downwelling light was guaranteed by calibrating both instruments to the same bench top light calibration instrument (LICOR 1984). Band alignment was performed within 1 year of the collection. Before normalization, spectra were resampled to common spectral band centres and bandwidths. Even though
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Figure 3. LICOR-measured downwelling irradiance at the time of the Hyperion image data collection (circles). Spectron Engineering-measured target upwelling intensity before (solid line) and after (dashed line) calibration to radiance (from helicopter platform). Absorption features associated with water (about 930–960 nm) and oxygen (around 690 nm and 760 nm) are shown in the recorded spectra.
converted to a common spectral base, the difference in bandwidths associated with the Spectron and LICOR produced a slight artifact in the generated reflectance spectra surrounding the oxygen absorption peak around 760 nm. As part of the reflectance spectra generation, the Spectron Engineering upwelling spectra were shifted during the normalization by one or two bandwidths (diodes) until the artifact was minimized. An average above-ground-level (AGL) of about 190 m and a fixed radiometer entrance slit size of 6u resulted in an average ground-instantaneous fieldof-view (GIFOV) of about 20 m. Slight movements of the helicopter platform while recording multiple scans (normally 6–7 per site), however, increased the actual ground area imaged. The averaged canopy reflectance probably represented an area nearer the 30-m nominal spatial resolution of the EO1 Hyperion and ALI sensors and the Landsat 7 ETM+ sensor, providing good spatial ground-extent concurrence between the field and satellite image datasets. In addition, even though collections at 190-m AGL included some atmosphere, these light recordings were considered to be the top-of-canopy estimates. 3.2
Generation of atmospheric parameters
A radiative transfer model developed by Turner and Spencer (1972) allowed the estimation of atmospheric path radiance and optical thickness within each band of remotely sensed data and, consequently, generation of target reflectance spectra. Alan Kortesoja at ERIM, Ann Arbor, Michigan, wrote the computer program that was provided to the Bureau of Reclamation by Mr A. J. Richardson of the USDA/ ARS Remote Sensing research in Weslaco, Texas. Jim Verdin and Gerry Teter at the USBR Remote Sensing Section, Denver Federal Center, Denver, Colorado, provided a modified version to us that we further modified to run as part of an optimization routine. The radiative transfer model requires sensor and sun
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geometry, target field and Hyperion reflected radiances (figures 3 and 4, respectively), background reflectances (figure 5), and an estimate of the total atmospheric optical depth. Assumptions and simplifications of the Turner–Spencer atmospheric model operation pertinent to this study were: (1) Between 270 nm and 1000 nm, light extinction is caused solely by Rayleigh and aerosol scattering (Elterman 1970). Aerosol scattering is sufficiently modelled by using a single-scattering phase function representing a typical land aerosol (Turner and Spencer 1972). (2) The atmosphere is assumed to be a semi-infinite, plan-parallel, homogeneous, isotropic air mass. Variations occur only in the vertical direction, and the target is illuminated by direct solar radiation at the solar zenith angle Oz with respect to an outward normal (Turner and Spencer 1972) and by a uniform, hemispherical, diffuse sky component (Turner 1978). (3) The surface reflectance is assumed to be perfectly diffuse or Lambertian. The target radiance is then independent of the sensor observation angle and depends only on the total downwelling irradiance (direct plus diffuse) and the surface reflectance. (4) The phase function used in the model represents average atmospheric conditions but not necessarily a single event (Turner and Spencer 1972). Systematic differences have been found between field-measured path radiance and path radiance calculated by using the Turner–Spencer model (Ahern et al. 1977). The differences were not explained, but a split aerosol optical depth, instead of the exponential decrease of the well-mixed aerosol layer (Sjoberg and Horn 1983), or a different scattering phase function than that provided with the Turner–Spencer atmospheric model (Bowker and Davis 1987) may be more applicable in some cases.
Figure 4. Measured downwelling irradiance as in figure 3 (dashed line) and the Hyperion target radiance associated with the same site illustrated in figure 3 (solid line).
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Figure 5. Initial whole-terrain background reflectance (dashed line) and whole-terrain background reflectance output during the atmospheric model simulation (solid line).
3.3
Optimizing the atmospheric model
Problems with our application of the Turner-Spencer model and use of field canopy reflectance spectra to drive the atmospheric correction of the Hyperion image data were linked mainly to three general issues. First, differences in times between the collection of field spectra and Hyperion images collection may have resulted in dramatic differences in atmospheric state, but more likely they resulted in differences in the percentage and distribution of canopy shadows related to changing sun zenith and azimuth angles. However, differences in canopy shading should have been minimized by the near-nadir collections associated with both datasets and by the collection of field upwelling recordings within a time period that encompassed the Hyperion image collection. Sun zeniths ranged less than 4u during 33 of the 34 field site collections and were within 4u of the sun zenith at the time of the Hyperion collection. Second, even though field sites were chosen to maximize canopy composition and structure uniformity in at least a 262 pixel area, site surroundings in some cases were fairly heterogeneous and canopy composition changed fairly abruptly over short ground distances. In these cases, even slight spatial incompatibility of the observed field and Hyperion-imaged areas could imply that different canopy characteristics were imaged by two sensors, increasing possible discrepancies between their associated reflectance spectra. Third, in more turbid atmospheres (high optical depths), the single scattering approximation may be too simplistic, and in other cases the Deirmendjian’s polydisperse continental aerosol distribution (as an atmospheric optical state estimator) may not have completely fit the conditions present during this event. Apart from differences in collection times and target locations and thereby coverage in the field and Hyperion collections, slight inappropriateness in the applied single scatter phase function and atmospheric optical state estimator was minimized by incorporating field data in the atmospheric simulation. In effect, we
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‘tuned’ the atmospheric model to the particular condition existent during the Hyperion image collection without concurrent and spatially extensive meteorological data. The model was tuned by implementing an optimizing technique that minimized the weighted sum of differences between the observed field canopy reflectances (ri) and the modelled Hyperion reflectances (rm) obtained from the Turner–Spencer model. The minimization was performed by incrementally modifying the whole-terrain background reflectance input (used to estimate adjacency effects) into the model calculation of path radiance. By including the background reflectance as an optimization variable in the path-radiance calculation, the apparent whole-terrain background reflectance used as an initial estimate was transformed into a more accurate depiction of the actual surface albedo. Additionally, slight discrepancies in the model simulation of the atmospheric state were aggregated into the background reflectance estimate, thus further refining the surface albedo estimate and maximizing the accuracy of the canopy reflectance extraction from the Hyperion image data. To illustrate how the field data and optimization methodology were incorporated into the atmospheric correction methodology, we provide our modification and addition within a very simple generalization of the Turner–Spencer model. Raw image brightness values (BV) were transformed into apparent radiance (mW m22 sr21mm21) by application of VNIR and SWIR gain factors as La(VNIR)5BV/43.2 and La(SWIR)5BV/94.4, respectively (EO1/Hyperion Science Date User’s Guide 2001). Next, the total optical depth (tT) was estimated from the downwelling irradiances measured at the surface at the time of the Hyperion image collection (HOBS) (figures 3 and 4) and from the solar irradiance at the top of the atmosphere (HTOP) as (Turner 1978, Elachi 1988): tT ~{LogðHOBS =HTOP d Þ cos Oz :
ð4Þ
Top-of-atmosphere irradiances were based on measurements by Neckel and Labs (1984) and modifications by Green and Gao (1993) (irradiance data taken from ATREM software, permission granted to copy and use ATREM for noncommercial purposes without fee, copyright by the Center for the Study of Earth from Space, University of Colorado at Boulder in 1992). The Earth-to-Sun distance (d ) was calculated by Bird and Riordan (1984) as: j~2pðday number of the yearÞ=365 d~1:00011z0:034221cosðjÞz0:00128sinðjÞ z0:000719cosð2jÞz0:000077sinð2jÞ:
ð5Þ
ð6Þ
The aerosol optical depth (tA) was subsequently calculated as the difference of the total (tT) and standard Rayleigh atmosphere (tR) (Turner and Spencer 1972). The fraction of energy scattered by the atmosphere into the forward hemisphere (EF) was calculated by Turner and Spencer (1972) as: EF~ð0:5tR z0:95tA Þ=tT
ð7Þ
Initial estimates of the apparent whole-terrain background reflectance per band were calculated by extracting and averaging calibrated data within 868 pixels surrounding each site and then transforming the averages to apparent reflectance by dividing by the measured downwelling irradiance (figure 5). At this point, an
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adjustment was made to the initial background reflectance estimated from the image data. A weighting procedure was constructed as the product of the whole-terrain background reflectances (rBKG) and an optimization factor (bkWTG) as wrBKG ~rBKG zrBKG bkWTG :
ð8Þ
At each wavelength band, bkWTG was manipulated to minimize the sum of the differences between the observed reflectance (ri, field canopy) and modelled reflectance (rm, normalized and adjusted Hyperion image data) (figure 5). Energy scattered into the back hemisphere (EB512EF) was used to estimate and correct background reflectance (BKFAC) added to the target reflectance (recorded by the Hyperion sensor) following Turner and Spencer (1972) as: . BKFAC~ 2ðcos Oz Þ2 wrBKG ð1z2EBtT ð1{wrBKG ÞÞ: ð9Þ Transmittance from target to sensor altitude (estimated as above atmosphere) was calculated by Turner and Spencer (1972) as (figure 6): TV ~ expð{tT =cos OV Þ:
ð10Þ
After this point in the atmospheric correction, there were no further modifications of the original code. The angular distribution of radiation as described by the singlescattering phase function scattered through a scatter angle was calculated following Turner et al. (1971) and Turner and Spencer (1972). Subsequently, the intensity of scatter at the scatter angle was calculated by adjusting the aerosol scattered intensity (Deirmendjian’s polydisperse continental aerosol distribution) and the Rayleigh scattered intensity by the aerosol and Rayleigh optical depths, respectively, and then normalizing by the total optical depth. The angular distribution and the intensity of scatter were then combined to generate the forward and backward single-scattering
Figure 6. Path radiance (solid line) and atmospheric transmittance spectra output (dashed line) during the atmospheric model simulation.
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phase functions and were ultimately used to estimate the path radiance (Lp) not originating from the target pixel being scattered into the Hyperion sensor field of view and added to target radiance (Turner and Spencer 1972) (figure 6). 3.4
Creation of the reflectance images
Combining the measured downwelling irradiance with the generated Lp and Tv parameters, the image reflectances (rm) per wavelength were calculated as: rm ~ La {Lp p TV HOBS ð11Þ As in field reflectance (ri, §3.1) and as calculated in this study, the modeled Hyperion reflectance, rm, is best described as hemispherical-directional reflectance as defined in Nicodemus et al. (1977). 4.
Discussion of results
The modelled whole-terrain background reflectance at the Hyperion sensor was lower than the initial, apparent whole-terrain background estimate. As previously suggested, entering the background reflectance as the optimization variable into the iterative best-fit procedure as part of the path radiance calculation transformed the background reflectance into a more realistic depiction of the surface albedo. In the transformation, the apparent background reflectance was corrected in part for the addition of path radiance. The correction was indicated not only by the diminished overall magnitude but also by the more realistic depiction of the background reflectance, especially within the green wavelength region. Within the near-infrared wavelength region, the transformed whole-terrain background reflectance (figure 5) was highly responsive surrounding absorption peaks, as were the atmospheric transmission spectra (figure 6), which were also generated as part of the atmospheric state simulation. Also produced in the atmospheric state simulation, the path radiance exhibited an exponential decrease with increasing wavelength, although the relatively smooth decrease in the visible region was interrupted by higher variability in the near-infrared wavelength region (figure 6). In effect, the modelled whole-terrain background reflectance and path radiance variables were within the expected ranges and conformed to the expected patterns within the analysed visible and near-infrared wavelength regions. Ultimately, the validation of the model simulation is the comparability between the observed field and modelled Hyperion reflectances. The observed field and modelled canopy reflected radiance means per wavelength showed high correspondence, with slight deviations near the 940-nm water absorption peak (figure 7). Goodness-of-fit statistics describing the correspondence per wavelength between the observed and modelled reflectances indicated that the lowest model predictions were generally found in the blue wavelengths, surrounding the oxygen absorption peak centred near 760 nm (incorporating two flanking smaller water absorption features) and a spectrally broad (extending from about 930 nm to 960 nm) water vapour absorption peak (figure 8). Lower performances surrounding atmospheric absorption bands and somewhat within the blue wavelength region have been noted in other atmospheric adjustment studies (e.g. Smith and Milton 1999, Miller 2002, Staenz et al. 2002). Most often, high correspondence in shape and magnitude was depicted between field and modelled canopy reflectance spectra derived from the Hyperion reflected radiance (e.g.
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Figure 7. The mean Hyperion canopy radiance output during the model simulation (dashed line) compared to the field canopy radiance mean (solid line with error bars) (n534 sites). The variance about the mean prediction calculated within the model simulation is also shown. Note, the only observable difference is near 940 nm, and both spectra depicted at the Hyperion sensor.
Figure 8. The goodness-of-fit statistics between the modeled and field canopy reflectances (solid line) and the variance about the mean (standard error) output during the atmospheric model simulation and correction (dashed line). A total of 34 sites (observations) were entered into the model simulation.
figure 9, a selected pine canopy). However, examination of the modelled canopy reflectance also showed that residual artifacts of the normalization technique were present at the oxygen and water absorption peaks. The small artifact at the oxygen absorption peak is carried forward from the original field canopy reflectance spectra (figure 3). While slight misalignment in peak position between the downwelling
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Figure 9. A comparison of field (dashed line) and Hyperion modelled canopy reflectance (solid line) at a selected pine site. The reflectance spectra are shown before correction for model artifacts. The Hyperion target radiance of the same pine site input into the atmospheric model simulation (circles).
irradiance and upwelling radiance produced the artifact (e.g. figure 4), differences in the absorption peak width seemed to exaggerate the artifact (e.g. figure 9). A more obvious artifact resulted from a shift in the water vapour absorption peak alignment and difference in peak shape between the downwelling irradiance and Hyperion reflected radiance (figure 4). This non-correspondence and consequential artifact in the normalization may have resulted from issues associated with the crossover region between the VNIR and SWIR detectors (table 1, EO1/Hyperion Science Data User’s Guide 2001). Both artifacts were minimized by bridging the affected absorption regions with a straight line, and subsequently applying a 363 running average over the entire spectra (figure 10). After accounting for residual artifacts, the observed and predicted reflectance spectra means and the associated standard deviations (¡95%) were compared graphically (figures 11 (a) and (b)). The comparison illustrates the comparability of the means and variance, the high ranges in magnitude and shape of the sampled canopy reflectance, and especially the spectral variability encompassing the critical red wavelength regions (about 600–700 nm). In addition to graphic comparisons, an indication of model performance per field site was gained by comparison of observed and modelled spectra associated with each site. Goodness-of-fit and variance statistics were generated by regressing modelled-versus-observed reflectances over the entire spectral range (425–1094 nm). Correspondence between observed and modelled reflectances was higher than 99% at all sites (n534). Separate examination of the visible range (,700 nm, n527 wavelengths) indicated that correspondence remained high but was more variable, averaging 88%¡9%. The regression slope coefficients ranged from about 2.1 to 0.57, averaging 1.03¡0.27, and the regression intercept coefficients averaged 0.00¡0.01. Although some of the slope coefficients much different than one (1.1,slope,0.9, n56) were associated with relatively low goodness-of-fit statistics (,85%), 11 of the slope outliers were associated with high-to-moderately high
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Figure 10. A comparison of field (dashed line) and Hyperion modelled canopy reflectance spectra (dashed line with circle symbol) at a selected cypress-tupelo site before (dashed lines) and after (solid lines) correction for model artifacts.
Figure 11. (a) Mean field and (b) Hyperion modelled target reflectance spectra and their associated two standard deviations about the mean.
regression goodness-of-fits. In these cases, the relative differences changed nearly linearly with increase in wavelength throughout the visible range. No clear explanation for these inferred linear dependencies was apparent. Regressions over the near-infrared range (.700 nm, n522 wavelengths) generated goodness-of-fits averaging 98%¡1% and a regression slope and intercept of 0.99¡0.10 and 0.002¡0.014, respectively. In another examination of model performance, statistics associated with modelled-minus-observed reflectance differences were calculated (figure 12). Considering all sites, a small bias (mean offset) of 20.0004 (¡0.001, ¡1 std about
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Figure 12. Mean difference in the field and Hyperion modelled target reflectance spectra (solid line) (n534 sites). Associated variance about the mean (1 standard deviation) showing the typical differences expected from the atmospheric model simulation (dashed line).
the mean) existed in the visible and 20.0016 (¡0.0035, ¡1 std) in the near-infrared wavelengths. Compared per site in the visible, the maximum differences ranged from ¡0.010 to ¡0.023 (¡1.0% to ¡2.3%), with a standard deviation overall differences (¡1 std, 68%) of 0.0046 (about ¡0.5%). Per visible wavelength, maximum differences within ¡1 std increased from ¡0.5% at about 425 nm to ¡0.8% at about 690 nm (68% of the sites), while 95% of the differences were within ¡2 std (¡1.0% to ¡1.7%). Graphic and tabular inspections of the difference spectra in the visible region showed that at 28 of the 34 field sites differences were within 1%, and except for somewhat higher differences at two to three wavelengths; three of the remaining six sites exhibited differences near to or less than 1%, while the remaining three sites were associated with maximum differences at more than one to three wavelengths higher than 1.6% (,¡2.3%). Again, compared per site, maximum differences in the nearinfrared wavelengths ranged from 0.020 to 0.060 (2% to 6%), with a standard deviation of ¡0.028 (about ¡3% reflectance, ¡1 std, 68%) and ¡0.056 (¡2 std, 95%). At 24 of the 34 sites, differences in the near-infrared region were lower than about ¡1 std (¡3%). Within the remaining 10 sites, differences at seven sites were ,5% while three had differences between 5% and 6%. Of these 10 sites, the highest differences were located in landscapes that were spatially highly variable, including three that were located in cypress-tupelo forests that tend to have open and highly variable canopies. 5.
Conclusion
Hyperion sensor data from the EO1 satellite were collected to test whether high spectral data could detect the subtle spectral differences necessary for identifying low occurrences of Chinese tallow in the matrix of native vegetation and at the same time provide regional coverage necessary for operational resource management. Our challenge was to extract a highly accurate estimate of the canopy reflectance from the sensor signal, thus maximizing the potential spectral distinctiveness (information) of the canopy reflectance spectra. To provide the necessary rendering precision of canopy reflectance, we used an estimate of optical depth derived from a
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measurement of downwelling irradiance to drive a radiative transfer simulation of atmospheric scattering and attenuation. As a required input into the model simulation, an apparent whole-terrain background reflectance was estimated from the calibrated image data; however, the estimate contained atmospheric influences reducing the appropriateness of this estimate in the model simulation. Our solution was to enter the whole-terrain background reflectance as a variable within the atmospheric correction model. The background reflectance was changed successively to produce a minimum difference in the observed (field) and predicted (Hyperion image derived) canopy reflectance spectra. While producing a better estimate of the whole terrain background reflectance, the atmospheric adjustment strategy also minimized possible inappropriateness of the atmospheric model simulation of the atmospheric condition existent during the collection. Reflectance images were produced at each of the Hyperion wavelengths from about 400 nm to 1000 nm. Artifacts in the modelled target reflectance spectra, primarily associated with prominent absorption features in the downwelling and upwelling spectra, were minimized by linear extrapolation and smoothing. Subsequently, validity of the model-predicted canopy reflectance images was accomplished by statistical comparison to the field canopy reflectance measurements. Regression between the observed and modelled reflectance per site and over the entire spectrum showed that the atmospheric adjustment and normalization strategy explained over 99% of the predicted reflectance spectra variances (n534). Examined separately, the observed and modelled reflectance spectra correspondence in the visible wavelength region averaged 88%¡9%, while in the near-infrared region the correspondence averaged 99%¡1%. Although regression statistics showed that an average one-to-one correspondence (and nearly zero intercept) existed between observed and modelled reflectance spectra in both the visible and near-infrared wavelengths, about half of the 34 site regression comparisons in the visible, generated slope coefficients were less than 0.9 and higher than 1.1. At a majority of sites, regression correspondence between modelled and observed canopy reflectance was high (.85%), indicating a consistent linear dependency. The reason for the apparent linear dependency at certain sites was not determined. Observed and predicted reflectance differences were ,¡1% in 28 of the 34 site comparisons, while (excluding three sites) typically the differences of the remaining sites were ,¡1% at all but a few of the 27 wavelengths defining the visible region. In the near-infrared wavelengths, differences less than ¡3% characterized 24 of the 34 site comparisons, while at the remaining 10 sites reflectance predictions were within ¡6% of the observed reflectance spectra. At least 6 of the latter 10 sites were located in landscapes with higher spatial variability as compared to all other sites observed in ground-based and air surveys. In effect, 28 in the visible and 24 in the near-infrared of the 34 comparisons showed little or no difference in the observedversus-predicted reflectance spectra. These variances typify the expected maximum differences between the observed and modelled canopy reflectance spectra. Although not directly comparable, maximum differences found in this study were within the range of best results produced when multiple radiative transfer methods were applied to higher fidelity (relative to Hyperion) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor image data collected from a playa lake; however, the wavelength region examined in that study extended further than the region in our study, reaching past 1000 nm to about 2500 nm (Staenz et al. 2002). In another study, where a vegetated landscape was imaged instead of a more nearly
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ideal reflectance target (playa lake), but where, conversely, a broadband sensor (Landsat Thematic Mapper) was used (versus a high spectral resolution (AVIRIS) sensor), differences between field helicopter and modelled reflectance values (retrieved with a radiative transfer model in combination with a dark target) were often higher than maximum differences found in this study (Quaidrari and Vermote 1999). Additionally, although limited to six field sites (see §1.1), maximum differences in the visible region found in this study were about half those produced by applying ATREM at the same sites, while differences were similar in the nearinfrared region. Finally, whether indicated by the mean¡standard deviation or maximum reflectance values (excluding maximum values .2% at eight wavelengths at one site), calculated differences between observed and predicted canopy reflectance spectra throughout the visible wavelengths were lower than the estimated 2% reflectance difference necessary for detecting a few mature tallow trees clumped or scattered throughout the Hyperion 30-m pixel. Overall, atmospheric variables and canopy spectra generated within the atmospheric adjustment and normalization model generally followed expected spectral trends and contained expected prominent features. Without spectrally invariant targets within the image, our simple approach to conversion of apparent reflectance data to canopy reflectance spectra took advantage of numerous field-reflected canopy radiance and surface downwelling irradiance measurements made concurrently with the EO1 Hyperion image collection. Differences in the observed and modelled canopy spectra were most often low or nearly nonexistent, and where relatively higher differences existed, these were most likely associated with problems in the comparison rather than in the model generation. Gaining accurate canopy spectral information provides the best possibility for successfully identifying subtle spectral features necessary for detecting the occurrence of senescing Chinese tallow. In following manuscripts (Ramsey et al. 2005), we will describe the application of a spectral analysis tool to the corrected canopy reflectance spectra to provide the percent occurrence of Chinese tallow within the Hyperion image. Acknowledgments We thank Mr Jimmy Wimberly for access to the Wimberly Properties, Mr David Richard for access to the Gray Estate landholdings in Calcasieu and Cameron Parishes, and Mr Sam Bellafo for access to the Neumin Production Co. landholdings in Vinton Parish. We are grateful to Benjamin C. Seal Jr of Southern Helicopters Inc., Sunshine, Louisiana for the helicopter flights. Also, we would like to thank Johnson Controls Inc. personnel Ms Kristine Martella and Ms Amina Rangoonwala for the many hours of work on planning field logistics and data collections; US Geological Survey personnel Ms Beth Vairin for editing this manuscript; NASA personnel Tom Brakke and Lawrence Ong for tireless efforts to provide us with usable Hyperion data, and their technical advice; and the anonymous reviewers who greatly improved this manuscript. Partial funding for this work was provided through NASA grant number EO-1-0100-0042. Mention of trade names or commercial products is not an endorsement or recommendation for use by the US Government. References AHERN, F., GOODENNOUGH, G., JAIN, S. and RAO, V., 1977, Use of clear lakes as standard reflectors for atmospheric measurements. In Proceedings of the Eleventh International Symposium of Remote Sensing of Environment, Environmental Research Institute of Michigan, Ann Arbor, Michigan, pp. 731–755.
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