A comparison of methods for the retrieval of surface ... - CiteSeerX

Can. J. Remote Sensing, Vol. 36, No. 4, pp. 397–411, 2010

A comparison of methods for the retrieval of surface reflectance factor from multitemporal SPOT HRV, HRVIR, and HRG multispectral satellite imagery Barnaby Clark, Juha Suomalainen, and Petri Pellikka Abstract. Absolute atmospheric correction for retrieval of the surface reflectance factor (rs) is a prerequisite for quantitative remote sensing utilizing multispectral optical satellite data. Areas where changes in land cover are occurring with greatest significance also often lack detailed meteorological data that allow full use of radiative transfer models (RTMs) for reflectance factor retrieval (RFR). This study assessed RFR accuracy of techniques applicable in such circumstances. The historical empirical line method (HELM), four image-based dark-object subtraction (DOS) methods, and the 6S RTM were applied to SPOT imagery datasets covering both the Taita Hills application site in southeast Kenya and the Helsinki metropolitan region control site in Finland. HELM was the only approach that achieved RFR in the visible and near-infrared bands with a RMSE of ,0.02rs and overall relative accuracy of ,10%. Further, HELM shortwave infrared (SWIR) performance was significantly better than that of the other techniques and the partially corrected at-satellite reflectance (rSAT). Although better than the DOS methods, application of 6S using standard atmosphere and aerosol models did not meet the desired RFR accuracy, even with the utilization of horizontal visibility meteorological data. Re´sume´. La correction atmosphe´rique absolue est primordiale pour l’extraction du facteur de re´flectance de surface (rs) en te´le´de´tection quantitative utilisant les donne´es satellitaires optiques multispectrales. Les zones ou` des changements dans le couvert se manifestent avec une plus grande intensite´ sont e´galement souvent caracte´rise´es par un manque de donne´es me´te´orologiques de´taille´es qui permettent l’utilisation des mode`les de transfert radiatif (RTM) pour l’extraction du facteur de re´flectance. Dans cette e´tude, on e´value la pre´cision de l’extraction du facteur de re´flectance des techniques applicables dans de telles circonstances. La me´thode HELM (« historical empirical line method »), les me´thodes DOS (« dark-object subtraction ») base´es sur quatre images et le mode`le 6S RTM ont e´te´ applique´s a` des ensembles d’images de SPOT couvrant le site d’application de Taita Hills, dans le sud-est du Kenya, et le site de controˆle de la re´gion me´tropolitaine d’Helsinki, en Finlande. La me´thode HELM a e´te´ la seule approche qui a permis d’extraire le facteur de re´flectance dans les bandes du visible et du proche infrarouge avec une erreur RMSE ,0,02rs et une pre´cision relative globale ,10%. De plus, la performance de la me´thode HELM dans la bande SWIR (infrarouge a` ondes courtes) e´tait significativement meilleure que les autres techniques de meˆme que la re´flectance partiellement corrige´e au niveau du satellite (rSAT). L’application du mode`le 6S utilisant les mode`les standard d’atmosphe`re et d’ae´rosols, meˆme avec des donne´es me´te´orologiques de visibilite´ horizontale, bien que meilleure que les me´thodes DOS, n’a pas atteint la pre´cision de´sire´e au plan de l’extraction du facteur de re´flectance. [Traduit par la Re´daction]

Introduction The Satellite pour l’Observation de la Terre (SPOT) series of satellites have been providing high-quality consistent optical imaging of the Earth since SPOT-1 was placed into orbit in 1986. To date, five satellites have been launched, although currently only SPOT-4 and SPOT-5 are still in orbit and fully operational. The high-resolution visible (HRV) sensors on SPOT-1, SPOT-2, and SPOT-3 collected data at 20 m spatial resolution in the green, red, and near-infrared (NIR)

wavelengths, and the SPOT-4 high resolution visible infrared (HRVIR) and the 10 m resolution SPOT-5 high resolution geometric (HRG) sensors additionally image in the shortwave infrared (SWIR). To increase revisit time, all SPOT sensors can view off-nadir cross track through ¡27u, which due to the Earth’s curvature relates to a sensor view incidence angle (hV) range of ¡31u. The digital numbers (DNs) recorded in a raw image are not an accurate measure of change over time, however, because they are a function not only of surface conditions

Received 30 March 2010. Accepted 30 September 2010. Published on the Web at http://pubservices.nrc-cnrc.ca/cjrs on 23 December 2010. B. Clark1 and P. Pellikka. Department of Geosciences and Geography, University of Helsinki, PO Box 64, FIN-00014 Helsinki, Finland. J. Suomalainen. Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, Geodeetinrinne 2, PO Box 15, FIN-02431 Masala, Finland. 1

Corresponding author (e-mail: [email protected]).

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but also of the diurnally variable atmospheric conditions, the seasonally variable Earth–Sun distance, the solar zenith angle (hZ), hV, and the sensor calibration (Moran et al., 2001). Sensor calibration can be achieved utilizing the supplied gain and offset coefficients to convert DN to top-ofatmosphere at-satellite radiance (LSAT, in W? m22? sr21? mm21). It is then possible to convert these radiances to at-satellite reflectance (rSAT), which normalizes for variations due to the Earth–Sun distance and hZ. This then leaves the effect of the atmosphere and off-nadir hV to be accounted for. The surface reflectance factor (rs) is the ratio of the radiant flux reflected by a surface to that reflected into the same reflected-beam geometry and wavelength interval by an ideal (i.e., nonabsorbing and nontransmitting) Lambertian standard surface under identical conditions of illumination (Schaepman-Strub et al., 2006). As noted by Moran et al. (2001), rs has become the basic measurement required for most remote sensing applications and models. If rs is to be accurately determined from LSAT, then all the effects of the intervening atmosphere need to be removed (Martonchik et al., 2000), i.e., an absolute atmospheric correction is necessary. Many approaches to absolute atmospheric correction have been developed, but fundamentally they all consist of two major steps (Liang et al., 2001): (i) determination of the various required atmospheric parameters; and (ii) prediction of rs, or the process of reflectance factor retrieval (RFR), as it is also known. The choices of techniques applied will depend mostly on the availability and quality of atmospheric or meteorological data coinciding with the image acquisition dates and geographic areas. Where detailed overpass concurrent measurements of atmospheric properties are available, notably atmospheric optical depth (AOD), it is possible to make full use of radiative transfer models (RTMs), such as the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) (Vermote et al., 1997a; 1997b). However, it is logistically difficult and costly to obtain detailed atmospheric measurements concurrent with image acquisition, and it may be impossible to obtain such data for archived imagery. Furthermore, in many areas of the world there is a lack of meteorological data available that are detailed enough, and have an appropriate spatial and temporal frequency, to allow for the full application of RTMs to multitemporal imagery datasets. Often these areas are also places where the most rapid and significant changes in land use and land cover (LULC) are occurring and where the need for environmental monitoring is greatest. Nevertheless, it is still possible to utilize an RTM for RFR using inputs derived from model atmospheres and estimates of AOD. Several image-based methods have been suggested for predicting AOD based on the expected relationships between reflectance in the mid-infrared region around 2.2 mm and the blue and red spectrums over areas of dark dense vegetation (the DDV approach) (Kaufman et al., 1997). Although application of similar AOD retrieval techniques to SPOT data is possible (Lin et al., 2002), matters are severely 398

complicated by the lack of an appropriate middle-infrared band or a blue band on the HRV–HRVIR–HRG sensors and also by the off-nadir hV range (Lin et al., 2002). Alternatively, there are also simplified methodologies that do not utilize RTMs or require in situ atmospheric– meteorological data and can be used operationally for RFR. Image-based corrections utilize only information derived entirely from a scene, and estimates of the atmospheric path radiance (LP) (i.e., the sensor recorded radiance contributed by the atmosphere itself) and down-welling diffuse irradiance (EDOWN) can be made for each of the SPOT bands based on the recorded radiance for within-scene areas of assumed very low reflectance. Such approaches are generally termed dark-object subtraction (DOS) techniques (Chavez, 1988), as an additive scattering component is estimated for each band and subtracted from every pixel in the image. However, the atmospheric transmittance also has a multiplicative effect, and further procedures, such as the COST method (Chavez, 1996), have been developed to improve the accuracy of DOS corrections. Furthermore, where the opportunity exists to visit the study area, which is the case in most local- or regional-scale remote sensing projects (for example, to collect LULC training and ground reference data), and a spectrometer or goniometer is available, then it is possible to measure rs in the field. Empirical line (EL) methods align LSAT data to rs field measurements of spectrally stable within-scene calibration sites (Smith and Milton, 1999). Research efforts into EL methods have enabled the minimization of required field measurements. For example, in outlining their refined empirical line (REL) method for Landsat data, Moran et al. (2001) showed that, because of the near-linear relationship between LSAT and rs, an accurate estimation of the correction lines could be obtained using detailed rs measurements for only one appropriate within-scene bright calibration target and ‘‘reasonable’’ estimates of LSAT for rs 5 0 derived using an RTM. Based on the limited data availability of working in the Taita Hills, Kenya, Clark (2010) developed the historical empirical line method (HELM) for atmospheric correction of multitemporal SPOT data. HELM is similar to REL but derives LP estimates from identified within-scene dark objects directly from the imagery, by making assumptions about their intrinsic reflectance (as with DOS methods), rather than using an RTM. Atmospheric correction assessments are often based on improvements in the accuracy of derived parameters such as classifications and landscape metrics (Mahiny and Turner, 2007), Jeffries–Matusita distances (Song et al., 2001), or the ability of the approaches to normalize the reflectance throughout multitemporal images (Schroeder et al., 2006). Arguably, though, the best method is to compare RFR with rs of targets as measured in the field. For SPOT data, however, this necessitates detailed multiangular measurements of validation sites, which is not always possible. Therefore, whilst the Taita Hills provided an application site representing the circumstances typical of a region of the developing world

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with limited ancillary data availability, it was also necessary to have a control site, where full meteorological data records were available and where the multiangular reflectance behaviour of the ground targets could be measured. For this study it was possible to utilize the Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO; Suomalainen et al., 2009), and the Helsinki metropolitan region was taken as the control site. Schroeder et al. (2006) state that a benchmark for establishing successful absolute atmospheric correction of optical satellite imagery in the visible and NIR (VIS–NIR) bands is an absolute accuracy of ¡0.02 reflectance units. Based on a comparison with spectrometer field measurements, Liang et al. (2002) were able to atmospherically correct Landsat enhanced thematic mapper plus (ETM+) data with a relative error of ,10% throughout the VIS–NIR bands. Further, using the REL approach, Moran et al. (2001) were able to retrieve rs with a mean absolute difference (MAD) of ƒ0.01 for the thematic mapper (TM) – ETM+ VIS–NIR bands. Similarly, ¡0.01–0.02 absolute rs accuracy was stated by Hall et al. (1992) as being achievable in the VIS–NIR bands of Landsat and SPOT data using RTMs and overpass concurrent radiosonde profiles, although the TM SWIR bands were found to be problematic, with ¡0.06 absolute accuracy (Hall et al., 1991). Given these studies, therefore, an absolute atmospheric correction methodology should be expected to achieve VIS–NIR RFR within ¡0.02 absolute accuracy, derive SWIR absolute accuracy better than that of the rSAT estimates, and achieve 10% overall relative accuracy in all spectral bands to be considered effective. The objective of this study is to provide an accuracy assessment of the HELM, DOS, COST, and 6S absolute atmospheric correction methodologies that are applicable in local- and regional-scale remote sensing studies utilizing multitemporal SPOT HRV–HRVIR– HRG data in circumstances where no detailed overpass concurrent atmospheric measurements or meteorological data are available.

Study areas and data Study areas The Helsinki metropolitan region is situated at 60u129N, 24u569E along the Baltic coastline of southern Finland (Figure 1). It was chosen as the control site for assessing the applied atmospheric correction methods because detailed meteorological data records were available and field measurements of the multiangular rs of validation targets could be made with the FIGIFIGO goniometer. The Taita Hills form the application site for this study and are located in southeast Kenya at 3u259S, 38u209E (Figure 1). The hills cover an area of ,1000 km2 and are ecologically important, with a small number of remnant patches of species-rich indigenous forest (Clark and Pellikka, 2009).

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Figure 1. Location of the Helsinki metropolitan region study area in Finland and the Taita Hills study area in Kenya.

SPOT data Because of the requirement for snow-free data, SPOT scene selection for the Helsinki control site, referred to as dataset 1 in this study, was limited to images acquired during the northern hemisphere late spring and summer period. The selection objective was to provide a range of scene sensor and solar geometries. Therefore, as detailed in Table 1, three scenes dating from 2002, 2003, and 2005 were chosen, providing a range of hV from near-nadir L2.5u to R28.7u, representing near-maximum off-nadir viewing. The objective of image selection for the Taita Hills application site, which is referred to as dataset 2 in this study (Table 1), was to choose a cloud-free scene most suitable for LULC mapping. The rural agricultural and savannah makeup of Taita Hills offers a very limited number of vegetationfree–manmade areas that can be taken as HELM calibration targets. There is also an absence of any significant areas of clear standing water, which may present difficulties for the identification of dark objects for the HELM and DOS approaches. All SPOT images were supplied as level 2A scenes, with a stated rectification accuracy of 350 m for SPOT-4 data and 30 m for SPOT-5 data. Consequently, the imagery required further geometric processing before it was useable. Dataset 1 scenes were rectified to a 1 : 20 000 scale 2 m resolution topographic ‘‘scanmap’’, with an accuracy of ƒ0.50 pixels. Because of rugged terrain in Taita Hills, the dataset 2 image was orthorectified utilizing a 20 m planimetric resolution 399

Vol. 36, No. 4, August/aouˆt 2010 Table 1. Details of the SPOT satellite imagery. Image date

Local time

Path

Row

SPOT sensor

hV (u)

Dataset 1: Helsinki metropolitan region control site, Finland (lat. 60u129N, long. 24u569E) 12:53:58 073 226 4 HRVIR 1 L2.5 11 June 2002c 10 May 2003 12:26:06 073 226 5 HRG 1 R28.7 13 July 2005 12:34:18 073 226 5 HRG 2 R16.1 Dataset 2: Taita Hills application site, Kenya (lat. 3u259S, long. 38u209E) 15 October 2003 10:49:36 143 357 4 HRVIR 1

R10.4

QV (u)a

hZ (u)

QZ (u)

Qr (u)b

288.3 103.4 106.2

37.5 43.8 39.2

170.1 161.8 161.1

298.2 121.6 125.2

98.8

21.0

104.3

174.5

Note: hV, sensor incidence angle; hZ, solar zenith angle; Qr, relative azimuth between sensor azimuth and solar principal plane; QV, sensor azimuth angle; QZ, solar azimuth angle. a Sensor azimuth is calculated as the orientation angle + 90 for the right (negative, east) off-nadir view and the orientation angle + 270 for the left (positive, west) off-nadir view. b Relative azimuth between sensor azimuth and solar principal plane follows the convention of Sandmeier et al. (1998); Sun is taken as 180u azimuth. Therefore, 0u relates to a sensor view of the forward scattering, and 180u to backscattering; 0u to 360u is clockwise. c The 2002 scene suffers sensor saturation in the green, red, and SWIR bands and problematic radiometric calibration in the red band using the provided gain coefficient. This is likely to negatively impact the atmospheric correction of this image.

digital elevation map (DEM), interpolated from 50 ft (1 ft 5 0.3048 m) interval contours captured from 1 : 50 000 scale scanmaps, and the SPOT geometric correction model in the ERDAS IMAGINE software. Orthorectification RMSE was 0.45 pixels, and nearest-neighbour resampling was employed to preserve the original DNs. The first stage in atmospherically correcting the SPOT imagery was to convert the DN to LSAT. The conversion was implemented using band-specific absolute calibration gain (G) and offset (B) coefficients supplied in the SPOT image metadata, and the simple equation as follows: LSAT ~ðDN=G ÞzB

ð1Þ

Field measurements of surface reflectance factor Multiangular rs measurements within the Helsinki control site area were collected using FIGIFIGO (Suomalainen et al., 2009), which utilizes an ASD FieldSpec Pro FR spectrometer with a spectral range from 350 to 2500 nm and 3u field-of-view (FOV) foreoptics. This gives a groundinstantaneous FOV (GIFOV) with a diameter of 10 cm at nadir. To allow for the compensation for any variation in the amount of illumination occurring during measurement, incident irradiance was continuously monitored with a pyranometer. The rs values acquired by FIGIFIGO have an

estimated relative accuracy of 1%–5% depending on wavelength, sample properties, and measurement conditions (Suomalainen et al., 2009). Validation target surface types included sand, gravel, asphalt, artificial turf, and managed turf (Table 2), offering a range of rs values. The most important selection criteria were that the sites were spatially extensive enough to counter the radiometric ‘‘contamination’’ from adjacency effects and from the point spread function (PSF) of the GIFOV of the SPOT sensors (Clark, 2010). Based on the 2005 image, the site areas of interest (AOI) were formed of a substantial number of pixels (except site 5, formed of six pixels) and were spectrally homogeneous, given the small standard deviations (SDs) and coefficients of variation (CVs) in the recorded LSAT values (Table 3). For each target a multiangular rs model based on the Lommel–Seeliger law (Suomalainen, 2006; Hapke, 1993) was fitted to allow extrapolation of the data. The rs values of the sites were then synthesized to simulate the response of the spectral bands of the various SPOT sensors that captured the dataset 1 imagery. This was done based on the specific wavelength intervals and relative sensitivities of the sensors and the geometrical circumstances in each scene (Table 1). This, therefore, gave the best possible rs estimates for utilization in the accuracy assessment. In the Taita Hills application site, nadir rs validation target measurements were made of a roadside quarry, a sandy school playground, a compacted red soil road area, and

Table 2. Details of the field data collected in the Helsinki metropolitan region control site study area. Site No.

Location Site name

Lat. N

Long. E

Surface type a

1

Hietsu beach

60u109280

24u549280

Medium sand

2 3 4 5 6

Vermo car park Pasila sports ground Pasila Velodrome Malmi airfield runway To¨o¨lo¨ soccer pitch turf

60u129520 60u129290 60u129100 60u159000 60u119110

24u509220 24u569390 24u569350 25u029410 24u559260

Asphalt Granular gravela Artificial turf Asphalt Managed grass

Measurement dates

Site usage

13 September 2005; 17 July 2006; 8 June 2007 20 and 25 May and 5 July 2005 7 June and 13 September 2005 4 and 7 June 2007 4 June 2007 19 July 2006; 5 June 2007

Calibration; validation Validation Validation Validation Validation Validation

a

Aggregate classes as described by the Wentworth scale.

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Canadian Journal of Remote Sensing / Journal canadien de te´le´de´tection Table 3. Spatial radiometric stability of the validation targets in the Helsinki metropolitan region control site as depicted in the 13 July 2005 SPOT-5 satellite image. Spectral band

Mean

SD

CV

Hietsu beach HELM calibration–validation target (n 5 50) 1. Green 71.1 1.0 2. Red 64.0 1.4 3. NIR 52.0 2.0 4. SWIR 13.6 0.5

1.4 2.2 3.9 3.6

Vermo asphalt car park validation target (n 5 69) 1. Green 74.1 2.4 2. Red 62.0 2.5 3. NIR 50.2 1.8 4. SWIR 12.0 0.6

3.2 4.1 3.7 5.2

Pasila gravel sports ground validation target (n 5 16) 1. Green 68.7 2.3 2. Red 59.3 2.3 3. NIR 48.1 1.7 4. SWIR 13.4 0.6

3.4 3.9 3.6 4.5

Pasila Velodrome artificial turf validation target (n 5 48) 1. Green 55.3 1.6 2. Red 34.9 1.4 3. NIR 35.7 1.4 4. SWIR 8.4 0.3

3.0 4.1 4.1 3.3

Malmi airfield runway asphalt validation target (n 5 6) 1. Green 49.8 0.3 2. Red 36.1 0.3 3. NIR 30.1 0.6 4. SWIR 7.5 0.2

0.5 0.9 2.1 2.0

To¨o¨lo¨ grass soccer pitch validation target (n 5 38) 1. Green 45.1 0.7 2. Red 24.6 0.4 3. NIR 105.6 1.0 4. SWIR 11.5 0.3

1.5 1.7 0.9 2.8

optic giving a GIFOV of 53 cm in diameter. No pyranometer was available for use to allow corrections for variation in illumination conditions during measurement sets. These nadir rs field measurements were then processed to simulate the response of the spectral bands of the SPOT-4 sensor that captured the 2003 image. The derivation of nadir-only rs introduced greater uncertainty into this accuracy assessment because of the 10.4u off-nadir hV (Table 1).

Methods

Note: All values are at-satellite radiance (LSAT, in W? m22? sr21? mm21) rounded to one decimal place. n, number of pixels covering the site area of interest (AOI).

asphalt hard standing (Table 4). A global positioning system (GPS) was used at each site to record the target centre location, and the nadir rs was measured during mid-morning utilizing an ASD FieldSpec hand-held visible near-infrared (VNIR; 325–1075 nm, 3.5 nm spectral resolution) spectrometer. The spectrometer was hand-held in a nadir view position at ,1.2 m height facing the sun, with a 25u bare-head

Within-scene dark-object radiance values Usually within a SPOT scene, some pixels will either be formed from a surface material with very low reflectance, such as clear water, or will be in complete topographic shadow. Such pixels are known as dark objects, and the LSAT recorded in the VIS–NIR bands can be considered to be composed primarily of atmospheric upwelling path radiance (LP), assuming the areas are large enough to counter adjacency effects of surrounding land cover (Chavez, 1996). A dark-object reflectance of 0.01 (1%) (Moran et al., 1992; Chavez, 1996) was considered appropriate for the green and red bands. In this study, however, based on reflectance measurements of clear lake water, a nominal value of 0.001 (0.1%) was assumed for the NIR, as the reflectance from water surfaces is more or less zero at wavelengths beyond the red (Tso and Mather, 2001). Furthermore, because the amount of scatter is negligible in the SWIR (Moran et al., 2003), it was considered that scattering could be ignored for SPOT band 4, which is primarily attenuated by absorption by atmospheric water vapour (Moran et al., 2003). Historical empirical line method (HELM) for atmospheric correction HELM allows for RFR based on (re)constructing the historical relationship between LSAT, as recorded in multitemporal SPOT imagery, and field-measured rs (for details, see Clark, 2010). The main assumptions are that the atmosphere is approximately homogeneous throughout the scene area and that there is a linear relationship between LSAT and rs. As Moran et al. (1990) note, although this relationship is quadratic for the full range of reflectance, it is sufficiently linear over 0–0.7rs to allow linear interpolation with negligible error. Consequently, an accurate estimation of the

Table 4. Details of the field data collected in the Taita Hills application site study area. Location

Site No.

Site name

Lat. S

Long. E

Surface type

Measurement date

Site usage

1

Roadside quarry

03u309190

38u159460

25 January2005

2 3 4

Roadside asphalt hard standing Bare red soil School playground sand

03u239520 03u299500 03u219010

38u329560 38u199250 38u209290

Calcareous bare rock; pebbles; sand Asphalt Bare lateritic soil Medium sand

Calibration; validation Validation Validation Validation

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27 January 2005 27 January 2005 26 January 2005

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HELM correction lines for each SPOT band can be obtained using only two within-scene reflectance targets: (i) a fieldmeasured spectrally bright and pseudo-invariant-in-time calibration site; and (ii) an estimate of LP derived directly from the imagery through identified within-scene dark objects. Consequently, HELM negates any requirement to utilize an RTM or to estimate atmospheric parameters, and it is designed for use in circumstances where no detailed meteorological data are available. For practical reasons, however, HELM is limited to regional-scale and local landscape-level studies because of the requirement for a calibration target to be found within each scene area and measured in the field. If the calibration targets are spectrally pseudo-invariant over time, then rs measurement need not coincide with image acquisition. However, one of the main potential problems with applying HELM to SPOT data is to identify spectrally homogeneous calibration targets that are of sufficient spatial extent to counter radiometric contamination from adjacency effects and from the PSF of the GIFOV of the sensors. Further, to properly account for the ¡31u variation in the SPOT data off-nadir hV, ideally the bidirectional reflectance distribution function (BRDF) of the calibration target should be modelled based on multiangular rs goniometer measurements. This then allows for the derivation of modelled rs relating directly to the illumination and view geometries of the imagery. However, it may not always be possible to collect multiangular measurements reliably, and Clark (2010) showed that in the majority of view geometries, away from azimuth angles near to the solar principal plane, the variation between nadir and view angle rs is minimal for appropriate targets. Consequently, LSAT can often be calibrated to nadir rs measurements without introducing significant error (i.e., error ƒ0.02rs) into VIS–NIR RFR. Because the emphasis in this study was on atmospheric correction in data-limited circumstances, HELM was applied calibrated to nadir rs. Site 1 (Table 2), Hietsu beach, was chosen as the dataset 1 HELM calibration target because of its temporal persistence, large size, relative brightness, spectral homogeneity, and limited multiangular reflectance properties (Clark, 2010). Site 1 derived an AOI of 50 pixels (5000 m2) for the 2005 SPOT-5 scene (Table 3). Based on the multiangular reflectance characteristics of the sand, calibration error to nadir rs for the illumination and view geometries of the dataset 1 imagery was estimated and induced an average RMSE for all years and all bands of 0.014rs. Calibration error increased with greater zenith angles away from nadir and was larger in the backscattering view direction than in the forward-scattering direction. Also, error increased with an increase in wavelength, as longer wavelengths displayed greater rs anisotropy with viewing zenith angle. Error was highest for the 2003 scene, which was viewing the backscattering at a near-maximum hV, and slightly exceeded the desired VIS–NIR error limit of 0.02rs in the red and NIR bands. A 60 m wide and 200 m long quarry was taken as the Taita Hills application site HELM calibration target (Table 4). 402

The HELM processing step for both datasets was to simulate the calibration site nadir rs for the VIS–NIR and SWIR (dataset 1 only) bands based on the wavelength intervals and spectral sensitivities of the sensors. The average LSAT value for each spectral band of the pixels in the calibration site AOIs was collected from every image. The utilization of spectrally homogeneous calibration sites, covering a number of uncontaminated pixels, meant that the spatially averaged site mean reflectance spectrum could be equated with the support of the SPOT sensor response. Next, VIS–NIR within-scene dark-object LSAT values were identified, and then a separate HELM correction line was calculated for each spectral band in each image utilizing a standard linear regression equation of the form y 5 ax + b, where the SPOT LSAT was taken as the independent variable and nadir rs as the dependent variable; a is the slope of the regression line, representing atmospheric attenuation; and b is the intercept with the x axis, representing LP. The calculated regression coefficients for each band and image were then used to compute an output rs file from each input LSAT SPOT image. Image-based atmospheric correction methods DOS methodologies have the significant benefit that they are totally image based and can consequently be implemented easily on historical multitemporal satellite imagery with no need for additional information. Ignoring atmospheric polarization and refraction and adjacency effects, and assuming isotropic sky irradiance in a homogeneous cloudfree atmosphere and Lambertian reflectance from a flat, uniform ground surface, rs can be derived from LSAT as follows (Chavez, 1996; Song et al., 2001): rs ~

pðLSAT {LP Þ TV ðEO cos hZ TZ zEDOWN Þ

ð2Þ

where LP is the path radiance (in W? m22? sr21? mm21); TV is the atmospheric transmittance from the ground target to the sensor; TZ is the atmospheric transmittance from the Sun to the ground target; EO is the exoatmospheric solar constant (in W? m22? mm21); EDOWN is the down-welling diffuse irradiance (in W? m22? mm21); and cos hZ is the cosine of the solar zenith angle. The solar constant EO 5 E/d, where E is the SPOT sensor- and band-specific equivalent solar irradiance (in W? m22? mm21), and d is the date-corrected Earth– Sun distance in astronomical units (d 5 au2). Equation (2) forms the basis for the implementation of the image-based atmospheric correction approaches applied in this study, and it can be seen that there are four unknown atmospheric correction variables to be estimated, namely LP, TV, TZ, and EDOWN. Omitting these completely derives the at-satellite reflectance (rSAT), which only corrects for variation in the Earth–Sun distance and hZ: rSAT ~

pLSAT EO cos hZ

ð3Þ

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Similar to the work of Song et al. (2001), four DOS approaches were implemented, each of which estimates rs based on different simplifying assumptions for the calculation of LP, TV, TZ, and EDOWN, as summarized in Table 5. Assuming a dark-object surface reflectance of 1% for the SPOT green and red bands, LP can be calculated as follows: ð4Þ

LP ~LDOS {0:01f½EO cosðhZ ÞTZ zEDOWN TV =pg

where LDOS is the radiance value of the identified darkest within-scene object. The difference for the NIR is an assumption of a 0.1% dark-object reflectance. It was considered unnecessary to apply a correction to remove LP for the SWIR band. DOS1 is the basic haze-removal approach where TV and TZ for all spectral bands are assumed to be 100% and EDOWN is ignored (Chavez, 1989). SWIR rs prediction is identical to rSAT, as the assumption is no SWIR scattering. DOS1 does not correct for the multiplicative effect of transmittance. Chavez (1996) argued that for Landsat images with an atmospheric optical thickness between 0.08 and 0.30 and a solar zenith angle of 30u–50u (considered as usual viewing conditions) TZ can be approximated to a first order by cos hZ. Thus, a simple multiplicative DOS correction can be derived by equating TZ to cos hZ for the VIS–NIR bands, which Chavez termed the COST approach. COST forms the basis of the DOS2 approach in this study, although, as a further step, variation in TV due to differences in the SPOT imagery off-nadir hV was accounted for by equating TV to cos hV for all bands. In DOS2, SWIR TZ is assumed to be 100%, and therefore the only difference in rs prediction compared with rSAT is the correction for hV. Note that both DOS1 and DOS2 ignore EDOWN.

Following Song et al. (2001), DOS3 was a simplified Rayleigh scattering only atmosphere model approach whereby the effect of aerosols is ignored. The optical thickness for Rayleigh scattering (tr) was estimated on a per-band basis using the following equation (Kaufman, 1989) and taking the central wavelength in each SPOT band:
ð5Þ tr ~0:008569l{4 1z0:0113l{2 z0:00013l{4 where l is the wavelength (in mm). Following the standard approximation for atmospheric transmittance (Moran et. al., 1992), TV and TZ for the Rayleigh scattering only atmosphere model were estimated as exp(–tr/cos hv) and exp(–tr/cos hz). Further, EDOWN for a Rayleigh atmosphere was estimated from the 6S RTM defined as zero aerosol optical depth at 0.55 mm, based on the midlatitude summer standard atmosphere model for dataset 1 and the tropical standard atmosphere model for dataset 2. EDOWN was taken as scene-specific atmospheric diffuse irradiance at ground level reported by 6S. Calculation of LP iterated on the 6S estimates of EDOWN. In DOS4, an attempt was made to account for the additional effects of atmospheric aerosols, and TV and TZ were calculated as exp(–t/cos hV) and exp(–t/cos hz). Given the assumption of isotropic sky radiance, 4pLP was considered as an estimate of exoatmospheric irradiance loss, and the optical thickness of the atmosphere can therefore be estimated by the following (Song et al., 2001): TZ ~ expð{t=cos hZ Þ~1{

Table 5. Parameterizations utilized in the four implemented DOS approaches. Method

TV

TZ

EDOWN

DOS1 DOS2 (COST) DOS3 DOS4

1 cos hV exp(–tr/cos hv) exp(–t/cos hv)

1 cos hZa exp(–tr/cos hZ) exp(–t/cos hZ)

0 0 6S pLP

a

For the green, red, and NIR bands; unity for SWIR.

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ð6Þ

Solving for t and substituting with Equation (4) for estimating LP for a 1% dark reflector in the visible wavelengths gives (Song et al., 2001)

  4pfLDOS {0:01½EO cosðhZ ÞTZ zEDOWN TV =pg t~{cos hZ ln 1{ EO cos hZ

The difference for the NIR is the assumption of a 0.1% dark-object reflectance. In DOS4, all four atmospheric correction variables (LP, TV, TZ, and EDOWN) are initially unknowns before t is estimated. Consequently, it was necessary to derive a solution iteratively. Initial calculations were made based on defining LP as the uncorrected radiance

4pLP EO cos hZ

ð7Þ

values of the identified within-scene dark objects, deriving EDOWN directly from these approximations of LP, and setting TV 5 TZ 5 1. After the initial calculation of t, new values of TV and TZ were obtained and fed back into the calculations again until the estimate of t stabilized. Second simulation of the satellite signal in the solar spectrum (6S) RTM The 6S RTM (Vermote et al., 1997a; 1997b) was used in this study because it is robust, universally applicable, freely available, and widely utilized and offers a viable alternative to HELM and DOS correction methods if used with atmospheric models and reasonable aerosol estimates. 6S requires inputs for the geometrical conditions of the scene acquisition, a model of the gaseous composition of the atmosphere, 403

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a description of the concentration and type of aerosols, details of the spectral sensitivity of the satellite band, and the type and spectral variation of the ground reflectance (Vermote et al., 1997b). Various typical atmospheric scenarios are already implemented and available for use. Definition of the geometrical conditions at the time of scene capture was straightforward, based on the supplied SPOT metadata. However, 6S does not account for SPOT hV. For the 2003 Taita Hills image, the 6S tropical standard atmosphere model and the continental aerosol model were selected as the closest approximation. To determine the concentration of aerosols, users can specify either the AOD at 0.55 mm or the horizontal visibility in kilometres at the time of scene acquisition (which 6S uses to estimate AOD based on the standard aerosol profile). For dataset 2, because of the lack of horizontal visibility measures, the AOD at 0.55 mm was approximated as the average of the AERONET measurements for southern Africa, namely 0.19, as reported by Remer et al. (2005, their Table 8, p. 965). This compares with a global average AOD at 0.55 mm for land of 0.18 (Remer et al., 2005). The midlatitude summer standard atmosphere model and the continental aerosol model were selected for the Helsinki metropolitan region control site dataset 1. To facilitate a comparison of the possible differences in the output rs, 6S corrections were applied to dataset 1 using both overpass concurrent measurements of horizontal visibility, taken as the average Coordinated Universal Time (UTC) 09:00 (12:00 local time) visibility of the five weather stations falling in the Helsinki area (World Meteorological Organization (WMO) stations 02795, 02974, 02978, 05105, and 05194; obtained from the Finnish Meteorological Institute), and also a general estimate of AOD at 0.55 mm of 0.17 derived as the average of the AERONET measurements for western Europe, as reported by Remer et al. (2005, their Table 8, p. 965). Input values to be atmospherically corrected were given as LSAT. For both dataset 1 and dataset 2, the same Hietsu beach and roadside quarry HELM calibration sites were also utilized for 6S calibration. Within 6S, the surface type was defined as sand in all cases, this being correct for Hietsu beach and the nearest approximation for the roadside quarry surface. As the assumption is being made that detailed knowledge of the BRDF of the calibration sites is unknown at the time of atmospheric correction, the sand surfaces were defined as homogeneous with no directional effects (i.e., Lambertian). The elevation of the calibration sites was also required for 6S, this being sea level for Hietsu beach and 960 m above sea level for the roadside quarry. The a, b, and c correction coefficients output from 6S can be used in conjunction with the following two equations to apply the correction to the whole SPOT image, given an assumption of a homogeneous atmosphere throughout the scene area. For a specified spectral band, rs ~ 404

y 1zcy

ð8Þ

and y~aLSAT {b

ð9Þ

Accuracy assessment methodology The performance and accuracy of the applied atmospheric correction methodologies were assessed considering the difference between predicted and field-measured rs for the verification sites, the precision of both of which was derived to 0.001rs. The HELM calibration sites for datasets 1 and 2 were included in the accuracy assessments as validation targets, as in this way the HELM-induced error in calibration to nadir rs was included in the assessment. Further, for control dataset 1, the HELM results excluding the calibration sites were also reported to enable a completely independent accuracy assessment. For control dataset 1, the field estimates of rs were defined as those relating to the specific illumination and hV geometry of the 2005, 2003, and 2002 SPOT scenes, as modelled from goniometer measurements. For dataset 2, only nadir rs values were available. Both the absolute and relative root mean square error (RMSE and RMSEr, respectively) and the bias of the different atmospheric correction approaches in predicting rs in each spectral band for each site, compared to that derived from field measurements, were calculated.

Results and discussion Prediction of atmospheric correction variables for the Helsinki control site HELM, as an empirical–statistical method, gives no estimation of atmospheric parameters; rather, direct prediction of rs is derived for given input LSAT values. For the DOS and 6S approaches, however, a comparison of the estimated atmospheric correction variables was made for the 13 July 2005 SPOT-5 scene covering the Helsinki control site. Outputs from 6S, based on the average horizontal visibility of the scene at the time of image capture (29.25 km), were considered as the most accurate and were therefore utilized as reference values. The close similarity of LP estimates from the DOS and 6S approaches (Table 6) indicates both that suitable dark targets were identified and that the assumptions made about their reflectance were appropriate (this also applies to HELM). Furthermore, this close similarity suggests that all the applied techniques were adequate predictors of LP. Utilizing 6S with the meteorological data (6S Met Data), rather than a general estimate of AOD at 0.55 mm of 0.17 (6S AOD 0.17), increased LP only slightly. As would be expected, all approaches predicted lower LP with increasing wavelength and negligible LP in the SWIR, justifying the applied assumption of zero LP for SPOT band 4. EDOWN estimations showed significant differences between the DOS approaches and 6S. Both DOS1 and DOS2

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Canadian Journal of Remote Sensing / Journal canadien de te´le´de´tection Table 6. Predictions of atmospheric correction variables for the 13 July 2005 SPOT-5 image covering the Helsinki metropolitan region control site study area. Spectral band

DOS1 22

Path radiance, LP (W? m ? sr 1. Green 24.7 2. Red 9.7 3. NIR 4.3 4. SWIRb 0.2

DOS2 (COST) 21

? mm

DOS3

DOS4

25.2 9.9 2.2 0.0

25.8 9.9 4.4 0.2

6S Met Data

6S AOD 0.17

21 a

) 25.7 10.5 4.4 0.2

25.4 11.8 3.8 0.2

24.4 11.1 3.5 0.1

Atmospheric transmittance ground target 1. Green 1.0 2. Red 1.0 3. NIR 1.0 4. SWIR 1.0

to sensor, TVc 0.961 0.961 0.961 0.961

0.900 0.948 0.982 0.999

0.831 0.944 0.946 0.990

0.902 0.936 0.958 0.981

0.910 0.943 0.963 0.984

Atmospheric transmittance sun to ground 1. Green 1.0 2. Red 1.0 3. NIR 1.0 4. SWIR 1.0

target, TZd 0.774 0.774 0.774 1.000

0.878 0.936 0.978 0.999

0.796 0.931 0.934 0.988

0.876 0.917 0.944 0.976

0.885 0.925 0.951 0.979

Down-welling diffuse irradiance, EDOWN 1. Green 0.0 2. Red 0.0 3. NIR 0.0 4. SWIRb 0.0

(W? m22? mm21)e 0.0 0.0 0.0 0.0

84.2 34.3 8.1 0.1

81.1 31.2 13.7 0.7

276.1 179.9 80.2 6.3

250.3 159.6 69.8 5.4

a

Values for 6S Met Data and 6S AOD 0.17 taken as reported atmospheric intrinsic radiance at satellite level. Calculated LP and EDOWN for SWIR given, but assumed as zero for DOS1, DOS2 (COST), DOS3, and DOS4 implementations. Values for 6S Met Data and 6S AOD 0.17 taken as reported total upward atmospheric transmittance. d Values for 6S Met Data and 6S AOD 0.17 taken as reported total downward atmospheric transmittance. e Values for 6S Met Data and 6S AOD 0.17 taken as reported atmospheric diffuse irradiance at ground level. b c

ignore EDOWN, which is clearly erroneous given the significant amount of energy expected and predicted by 6S for SPOT bands 1 and 2. According to Moran et al. (1992), EDOWN can contribute as much as 25% of the spectral radiance received at the surface, even for relatively clear atmospheres. EDOWN estimates for the DOS3 Rayleigh scattering only atmosphere were similar to DOS4 estimates, but were actually slightly higher in the green and red bands, despite that DOS4 attempted to account for aerosols by predicting EDOWN as pLP. As noted by Song et al. (2001), who obtained a similar result, this clearly shows that DOS4 underestimates EDOWN because a real atmosphere should not lead to lower EDOWN values than those for a Rayleigh scattering only atmosphere. Furthermore, given that 6S and DOS4 derived

very similar estimates of LP, this suggests pLP is not an adequate predictor for EDOWN. Both DOS3 and DOS4 significantly underestimated EDOWN compared with 6S Met Data. Utilizing 6S Met Data rather than 6S AOD 0.17 increased EDOWN, as the atmosphere was hazier at the time of overpass than that assumed by the generalized estimate. Rayleigh scattering only AOD values were identical between DOS3, 6S Met Data, and 6S AOD 0.17 (Table 7), as all three were determined by the midlatitude summer standard atmosphere model. The 6S integrated value of total AOD for Rayleigh and aerosol scattering shows the significant contribution aerosols make to haziness. For example, when accounting for aerosols, SPOT band 2 for 6S Met Data showed an increase in AOD from 0.049 to 0.214. As might be

Table 7. Atmospheric optical depth (AOD) predictions for the 13 July 2005 SPOT-5 image covering the Helsinki metropolitan region control site study area. Spectral band

DOS4

6S–DOS3 (Rayleigh)a

6S AOD 0.17 (aerosol)b

6S Met Data (aerosol)b

6S Met Datac

6S AOD 0.17c

1. Green 2. Red 3. NIR 4. SWIR

0.177 0.056 0.053 0.009

0.105 0.049 0.018 0.001

0.172 0.139 0.102 0.042

0.205 0.165 0.121 0.050

0.310 0.214 0.139 0.051

0.278 0.188 0.121 0.043

a

Reported integrated value of optical depth total for Rayleigh scattering only. Reported integrated value of optical depth total for aerosol scattering only. Reported integrated value of optical depth total (Rayleigh and aerosol scattering).

b c

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Vol. 36, No. 4, August/aouˆt 2010 Table 8. Overall rs prediction accuracy of the different correction methods for the Helsinki metropolitan region control site study area based on the 2002, 2003, and 2005 SPOT images and six validation targets. Spectral band

Overall RMSE

Overall RMSEr (%)

Overall bias

rSAT 1. Green 2. Red 3. NIR 4. SWIR Avg. VIS–NIR Avg. all bands

0.027 0.047 0.052 0.064 0.042 0.047

20.1 31.0 20.2 23.0

0.007 20.002 20.039 20.048 20.011 20.021

DOS1 1. Green 2. Red 3. NIR 4. SWIR Avg. VIS–NIR Avg. all bands

0.052 0.048 0.070 0.064 0.056 0.058

DOS2 (COST) 1. Green 2. Red 3. NIR 4. SWIR Avg. VIS–NIR Avg. all bands

0.024 0.033 0.026 0.052 0.027 0.034

DOS3 1. Green 2. Red 3. NIR 4. SWIR Avg. VIS–NIR Avg. all bands

0.037 0.042 0.056 0.062 0.045 0.049

DOS4 1. Green 2. Red 3. NIR 4. SWIR Avg. VIS–NIR Avg. all bands

0.024 0.050 0.048 0.062 0.041 0.046

6S Met Data 1. Green 2. Red 3. NIR 4. SWIR Avg. VIS–NIR Avg. all bands

0.031 0.049 0.026 0.039 0.035 0.036

6S AOD 0.17 1. Green 2. Red 3. NIR 4. SWIR Avg. VIS–NIR Avg. all bands

0.030 0.048 0.027 0.040 0.035 0.036

406

23.6 39.6 31.3 27.1 23.4 30.3 18.5 22.3 10.0 19.1 17.5 27.9 28.1 21.8 22.7 25.1 17.9 33.7 18.5 23.0 23.3 23.3 32.7 10.1 14.4 20.1 22.4 32.5 10.6 14.8 20.1

20.045 20.030 20.056 20.048 20.044 20.045 20.014 0.012 0.021 20.035 0.006 20.004 20.029 20.020 20.040 20.045 20.030 20.034 20.016 20.012 20.036 20.038 20.021 20.026 20.026 20.009 20.013 20.025 20.016 20.018 20.024 20.009 20.014 20.026 20.016 20.018

Table 8 (concluded). Spectral band

Overall RMSE

HELM nadir calibrationa 1. Green 0.010 (0.010)

Overall RMSEr (%)

7.8 (8.4)

2. Red

0.017 (0.018)

11.6 (13.5)

3. NIR

0.014 (0.014)

5.5 (5.5)

4. SWIR

0.025 (0.026)

9.3 (10.4)

Avg. VIS–NIR

0.014 (0.014)

Avg. all bands

0.017 (0.017)

8.5 (10.5)

Overall bias

20.001 (0.001) 0.007 (0.011) 0.000 (0.002) 0.014 (0.019) 0.002 (0.005) 0.005 (0.008)

a Results shown in parentheses are those derived from five validation sites, excluding the site 1 HELM calibration target.

expected, this invalidates the DOS3 assumption that aerosols can be ignored. Based on a midlatitude summer standard atmosphere model and a continental aerosol model in 6S, a horizontal visibility of 29.25 km equates to an AOD at 0.55 mm of 0.205 compared with the general estimate of 0.17. This shows, again, that the atmosphere was actually hazier than that implied by the general estimate. As a consequence, the AODs predicted by 6S Met Data were higher in all SPOT bands. Despite attempting to account for aerosols, DOS4 still significantly underestimated total AOD in all bands, although giving better AOD estimates than DOS3 (Table 7). DOS2 estimated the lowest TZ for the VIS–NIR bands (Table 6). TZ increases with wavelength as scattering is reduced, and comparison with the TZ estimates from 6S Met Data shows that DOS2 increasingly underestimated TZ for SPOT bands 1, 2, and 3. An assumption of TZ 5 1 for the SWIR was justifiable, as there is very high transmittance in this channel, as indicated by 6S Met Data. Based on the assumptions for the DOS1 and DOS3 techniques, both will overestimate TV and TZ. With the exception of DOS1, all approaches estimated higher TV than TZ. As would be expected, DOS3, DOS4, and 6S predicted increased transmittance with a longer wavelength. It can also be seen that differences between estimates for the different approaches decreased with increasing wavelength. Estimating DOS2 TV as cos hV was an improvement on the DOS1 TV 51 assumption, and therefore an improvement over the originally proposed COST model as well, but this was wavelength invariant and overestimated TV for the visible bands and underestimated TV in the SWIR compared with 6S. Since aerosol scattering is stronger in the forward direction than in the backward direction, DOS4 is theoretically likely to overestimate TV (Song et al., 2001). By accounting for aerosols, DOS4 estimated lower TV and TZ

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Canadian Journal of Remote Sensing / Journal canadien de te´le´de´tection

than DOS3 Rayleigh scattering only, although the DOS4 estimates were also lower than 6S Met Data for bands 1 and 3. The 6S Met Data predicted lower TV and TZ than 6S AOD 0.17. Surface reflectance factor retrieval accuracy assessment The overall average RFR accuracy assessment results for the dataset 1 Helsinki control site SPOT scenes are detailed in Table 8. The 2002 image suffered problems with sensor calibration in the red band, which affected LSAT values for all validation sites, and saturation in the green, red, and SWIR bands, which affected several of the spectrally brighter validation targets and the HELM calibration site. Nevertheless, the overall averaged results were coherent and consistent with the results obtained from the 2003 and 2005 scenes individually, giving the same ranked order of decreasing RFR accuracy performance of the applied techniques, namely HELM, DOS2 (COST), 6S Met Data, 6S AOD 0.17, DOS4, DOS3, and DOS1. Based on the control site scenes, HELM derived the least overall RMSE in predicting rs for the six validation targets,

and the HELM results based on five validation targets excluding the calibration site were very similar (Table 8). The VIS–NIR error value was within the desired 0.02rs benchmark, and the SWIR accuracy was significantly better than that of the rSAT estimates. The overall average RMSEr for all bands and for each band individually was ,10%, although the overall RMSEr for the red band was slightly above this level (Table 8). Most importantly, none of the other applied methodologies were within the desired VIS– NIR accuracy limits or equalled HELM SWIR performance. RFR for the DOS2 (COST), DOS4, and 6S approaches was more accurate than that for rSAT, but DOS3 and DOS1 were worse than applying no atmospheric correction (Table 8). Additionally, HELM derived the lowest average RMSE in every band, had a small overall average bias, and was the only method that did not underestimate rs overall. Considering RFR for each dataset 1 validation site from the 2005 scene, as can be seen in Figure 2, rSAT behaviour was as would be expected based on the effect of atmospheric scattering and absorption on an uncorrected image. Due to scattering in the shorter wavelength red and more so green

Figure 2. Plots of retrieved versus field-measured rs for all spectral bands for each atmospheric correction methodology applied to the 13 July 2005 SPOT-5 image covering the Helsinki metropolitan region control site. RMSE, average root mean square error for all spectral bands expressed in absolute values of rs; RMSEr, average relative root mean square error for all spectral bands expressed as a percentage.

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Vol. 36, No. 4, August/aouˆt 2010

SPOT bands, the rs of the darker targets was overestimated and the rs of the brighter targets was underestimated. Consequently, where rSAT green and red RFR cross the 1 : 1 line represents where the effect of scattering effectively cancels out the influence of the atmosphere and explains why it is possible to have accurate rSAT visible estimates of rs in certain circumstances. The overestimation of dark-target rs also explains the slight positive bias seen in the rSAT green band. Further, the lack of scattering in the longer wavelength NIR and SWIR SPOT bands meant that dark-target rs values were close to the surface values. However, the effect of atmospheric absorption led to the progressive underestimation of rs for brighter targets, which meant that the high NIR rs for the brightest target was significantly underestimated. This gave rise to the negative bias in the NIR and SWIR bands. The effect of VIS–NIR LP removal applied to all the DOS corrections on rs predictions for the darker targets, compared with that on rSAT, can been seen in Figure 2, as they are moved nearer to the 1 : 1 line. The expected DOS1 limitations in not correcting for the multiplicative effect of transmittance in the VIS–NIR bands can also be identified. For

the brighter targets where rs was progressively underestimated, LP removal actually further increased rs underestimation, giving higher RMSE and negative bias for the DOS1 VIS–NIR bands compared with those for rSAT. Considering the 2003 SPOT-4 scene for the Taita Hills application site, the results were similar to those for dataset 1 in that HELM gave the lowest average and band-specific RMSE of all the applied corrections, and DOS1 gave the highest. HELM was again the only method that had an average RMSE within the desired 0.02rs benchmark, although the RMSE for the NIR band was over this limit at 0.024. However, inspection of the data showed the NIR result was affected by the suboptimal asphalt hard standing validation target (Figure 3). Because of the low fieldmeasured NIR rs of the asphalt (0.10) and the small size of the site, it is believed that dense vegetation on two sides of the target significantly contributed to the recorded radiance because of the high NIR scattering of vegetation. Ignoring this site in the assessment, HELM NIR RMSE is 0.01. The biggest differences from the dataset 1 assessment results were that the next best method to HELM was DOS4, whilst the

Figure 3. Plots of retrieved versus field-measured rs for all spectral bands for each atmospheric correction methodology applied to the 15 October 2003 SPOT-4 image covering the Taita Hills application site. Problematic NIR ‘‘asphalt’’ validation site is denoted in each plot. See text for details.

408

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Canadian Journal of Remote Sensing / Journal canadien de te´le´de´tection

DOS2 (COST) average RMSE was now worse than that for rSAT. In both the 2005 Helsinki and 2003 Taita Hills scenes, all methods except HELM generally underestimated brighter target rs in all SPOT spectral bands (Figures 2 and 3). This implies that there was actually higher humidity and heavier aerosol loading in the atmosphere at the time of both scene captures than was accounted for by the DOS and 6S methods. The one exception was the DOS2 (COST) NIR band for the Helsinki image, which had a slight overall positive bias in RFR. This was related to the wavelength-invariant TZ estimate of 0.774 (Table 6), which, although significantly lower than the 6S Met Data NIR estimate (0.944) derived more accurate RFR, with only a slight overcorrection (for the Helsinki image, DOS2 performance was better than that of 6S Met Data for all VIS–NIR bands). Therefore, the DOS2 NIR TZ was a slight underestimation, and the actual value must be higher, but it appears 6S and the other DOS approaches all overestimated TZ. However, DOS2 was ineffective in correcting the 2003 Taita Hills scene, deriving a VIS–NIR overall RMSE worse than that for rSAT. As was noted by Wu et al. (2005) in a study of COST RFR from QuickBird data, it is a known problem that the cosine function for TZ generally underestimates TZ for larger values of hZ and overestimates TZ for smaller values of hZ. The higher DOS2 RMSE for the 2003 image was due to the much smaller hZ (21u; Table 1) than in the 2005 scene (39.2u), which was also outside the 30u–50u hZ range originally tested by Chavez (1996). This led to an overestimation of TZ compared with that for the actual conditions for all bands and a consequent underestimation of rs (Figure 3). As with the 2005 scene, DOS2 NIR RMSE for the 2003 image was lower than that for the visible bands, the difference in this case being that in all bands TZ was overestimated, and increasingly so in the red and green bands. Furthermore, Figure 3 suggests LP removal was an overcorrection for DOS1, DOS2, and DOS3 green and red bands in the 2003 scene, as the asphalt dark-target rs values were underestimated. This would also contribute to the higher DOS2 RMSE for this scene. The possible selection of less appropriate dark objects in the 2003 Taita Hills scene, compared to the Helsinki 2005 image, reflected both the fact that large expanses of clear water and sea around Helsinki gave easily identifiable dark objects and also that there were no sizeable dark objects in the Taita Hills scene. Given that 6S and all the DOS approaches underestimated rs, especially for the brighter targets, it has been noted that the predicted TZ and TV are overestimated compared with the values based on the actual atmospheric conditions in the images. For the 2005 scene, because of the large hZ, DOS2 generated the lowest TZ estimates in all bands and thus gave the best DOS RFR performance, except in the green band. In this band, DOS2 overestimated TV, and DOS4 gave the lowest TV prediction and also derived the lowest DOS RMSE, which was better in fact than that from the 6S Met Data. This indicates that, in the circumstances of this study,

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the derivation of appropriate TZ and TV was key to better DOS RFR performance. In the 2003 Taita Hills scene, DOS4 derived the lowest TZ and TV for all bands, except for the NIR band, in which DOS2 derived the lowest TZ estimate. Correspondingly, DOS2 therefore had a low RMSE for the NIR band, but the lower DOS4 TV estimate meant the DOS4 RMSE was actually slightly lower. DOS4 thus gave the best DOS RFR for all bands in the 2003 scene. It can be stated, therefore, that DOS4 RMSE was lower than that for DOS2 for the 2003 scene, where the DOS2 performance was limited by the small hZ, because DOS4 gave the lowest estimates of TZ and TV. Furthermore, the DOS4 RMSE and bias were lower in every band in the 2003 scene than in the 2005 scene. A sensitivity analysis showed that dark-object selection has a strong influence on DOS4 predictions of AOD, TZ, TV, and EDOWN, especially for the SPOT green band where LP is higher. Consequently, given it has already been established that DOS4 significantly underestimates AOD, it is possible that an overestimation of LP for the 2003 scene actually led DOS4 to derive TZ, TV, and EDOWN estimates closer to those based on the real atmospheric conditions occurring at overpass time than was the case with the 2005 image. Thus, DOS4 rs underestimation RMSE was lower in 2003 than in 2005, and 2003 RFR prediction accuracy was better than that of 6S for the green and red bands. However, DOS2 and DOS4 RFR performance was inconsistent, considering all the tested imagery, unlike the performance of 6S that (although it also underestimated rs) was much more consistent and derived lower bias overall. Nonetheless, the application of 6S using standard atmosphere and aerosol models, even when horizontal visibility data were available, underestimated rs with statistically significant bias and did not meet the required accuracy levels. Furthermore, despite leading to changes in the estimation of atmospheric variables (Tables 6 and 7), the utilization of overpass concurrent horizontal visibility data did not improve the overall RMSE and bias (Table 8). This shows that the 6S atmosphere and aerosol models did not match the actual conditions at the time of scene acquisition and suggests that very detailed radiosonde-type atmospheric profile data are required for accurate RFR with 6S. Although many studies have compared atmospheric correction methods, a very limited number have made direct comparisons of RFR with field-measured multiangular rs specifically for SPOT satellites, whilst a few were undertaken when SPOT-1 was launched. For example, Moran et al. (1990; 1995) compared rs field measures taken with the same hV as that for the SPOT HRV data with rs retrievals from the imagery derived using on-site atmospheric measurements and the SMAC RTM and found RMS errors of 0.015, 0.031, and 0.057 in the green, red and NIR bands, respectively. The HELM results derived in this study were more accurate and indicate the benefit of calibrating SPOT imagery directly to field measurements rather than trying to estimate complex atmospheric parameters and utilize an RTM. 409

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Conclusions It is argued that the application of absolute atmospheric correction for retrieval of rs is a prerequisite for quantitative remote sensing utilizing multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors. HELM, DOS, and 6S atmospheric corrections were applied to SPOT imagery covering both the Taita Hills application site in southeast Kenya and the Helsinki metropolitan region control site in Finland. The results showed that all the applied methods, except HELM, consistently underestimated rs for both study areas. Most importantly, HELM derived the best and most consistent RFR performance and was the only approach that achieved ,10% or less relative RMSE and ,0.02rs absolute RMSE in the VIS– NIR bands. Further, HELM SWIR performance was significantly better than that of the other techniques, as well as rSAT, which was taken as the basis of successful SWIR correction. The DOS1 method does not account for the multiplicative effects of atmospheric attenuation, leading to less accurate RFR than rSAT. Because the applied DOS2 (COST) method derives estimates of atmospheric transmittances based on the cosine of hV and hZ, its RFR accuracy was found to be too dependent on the SPOT scene illumination and view geometries, which are not related to atmospheric conditions at the time of image acquisition. It is a wellknown problem with COST that the cosine function for TZ generally underestimates TZ for larger values of hZ and overestimates TZ for smaller values of hZ. The DOS3 method models a Rayleigh-scattering-only atmosphere and does not consider the role of aerosols, which were clearly demonstrated as fundamental in determining the radiometric attenuation properties of a real atmosphere. The DOS4 method derived inconsistent RFR results and was found to be sensitive to the estimates of LP derived from the identification of within-scene dark objects. Although better overall than the DOS methods, application of 6S using standard atmosphere and aerosol models was not accurate enough to meet the desired RFR accuracy requirements, even with the utilization of horizontal visibility meteorological data. For HELM to be effective requires careful selection of suitable dark objects and the identification of one appropriate pseudo-invariant spectrally bright ground calibration target per SPOT scene area. Use of appropriate HELM calibration targets is important to enable up-scaling of averaged rs field measurements to the SPOT sensor’s GIFOV and to counter adjacency effects. Given the results of this study, in circumstances where no suitable HELM targets can be identified or where there is no field access within a particular scene area, the application of 6S with standard atmosphere and aerosol models, and general estimates of AOD at 0.55 mm, would likely give the next best RFR performance. Furthermore, if the spectrometer used to measure rs for HELM does not cover the SWIR, 6S is again likely to offer the next best rs estimates. However, the 6S prediction error in the VIS–NIR bands is likely to exceed the desired 0.02rs 410

benchmark and, depending on the specifics of the SPOT scene illumination and viewing geometry, could be more than twice the expected HELM RFR RMSE.

Acknowledgements This study forms part of the Academy of Finland funded TAITA and TAITATOO projects, conducted at the Department of Geosciences and Geography of the University of Helsinki. The Taita Hills SPOT data were acquired through the TAITA project as part of the Centre National d’E´tudes Spatiales (CNES) Incentive for the Scientific Use of Images from the Spot System (ISIS) program. The Helsinki SPOT data were provided by Spot Image under OASIS research grant 51. The authors are grateful to Alemu Gonsamo Gosa and Janne Heiskanen for their comments and suggestions and also to Teemu Hakala, Eetu Puttonen, Mika Siljander, and Antero Keskinen for their assistance in the collection of field data. Constructive criticism from two anonymous reviewers was invaluable in revising this article.

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