AVHRR Bidirectional Reflectance

NOAA/AVHRR Bidirectional Reflectance: Modeling and Application for the Monitoring of a Temperate Forest B. Duchemin*

I

n this article, bidirectional effects on NOAA/AVHRR short wavelength data were analyzed for the regional monitoring of the temperate pine (Pinus pinaster) Landes forest. Because of differences in soil hydrologic characteristics, the forest is described by five ecotypes, which determine the understorey type and the potential for pine growth. Bidirectional visible and near-infrared reflectances were analyzed from a daily NOAA-11 archive through years 1990–1994 using the bidirectional reflectance function model of Rahman. Rahman’s model fits 72% of AVHRR visible reflectance, and 93% of AVHRR near-infrared reflectance. A typical example shows that 1) in the principal plane, the ratio of hot spot to forward scatter reflectance is 2.9 and 2.1 for visible and near-infrared, respectively, 2) bidirectional effects become negligible across the principal plane, and 3) NDVI shows a shape opposite to that of reflectances. After normalization, the seasonal dynamics of AVHRR reflectances were related to the phenological cycle of pine trees and understorey vegetation. Visible reflectance drops earlier on coastal than on inland sites, due to an advance in the onset of pine growth. Near-infrared reflectance increases in summer for annual understorey, and remains steady for evergreen understorey. Differences in the seasonal dynamics of NDVI consequently characterize each Landes ecotype. From the modeled reflectances, we estimated the seasonal dynamics of visible and near-infrared albedo. We calculated the solar albedo by a combination of the last two. The visible albedo displays a seasonal dynamic similar to that of normalized visible reflectance. The solar al* Institut National de la Recherche Agronomique, Laboratoire de Bioclimatologie, Villenave D’Ornon Cedex, France Address correspondence to B. Duchemin, Inst. National de la Recherche Agronomique, Laboratoire de Bioclimatologie, BP 81, 33883 Villenave D’Ornon Cedex, France. E-mail: [email protected] Received 30 May 1998; revised 3 August 1998 REMOTE SENS. ENVIRON. 67:51–67 (1999) Elsevier Science Inc., 1998 655 Avenue of the Americas, New York, NY 10010

bedo was found constant and around 12.7% on each ecotype. In both cases, albedo strongly increases with solar zenith angle, consistent with ground measurements. These results clearly show the usefulness of coarse spatial resolution satellite data for the regional monitoring and modeling of temperate coniferous ecosystems. Elsevier Science Inc., 1998

INTRODUCTION Many studies have been carried out on regional and global land monitoring from coarse resolution satellite data, such as NOAA/AVHRR imagery (Tucker, 1996). Short wavelength reflectances and vegetation index have been used to estimate several biophysical variables of vegetation (Hall. et al., 1995; Sellers et al., 1994), including vegetation amount (Price, 1992), leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (Baret and Guyot, 1991), photosynthetic and stomatal conductance efficiencies (Myneni et al., 1992). These variables are required to assess energy, water, or carbon budgets of ecosystems and to model soil/vegetation/atmosphere transfer (SVAT). For temperate ecosystems, AVHRR imagery has been applied mainly to North America coniferous forests: The normalized difference vegetation index (NDVI) was related to LAI by Curran et al. (1992), Spanner et al. (1990a), and Peterson et al. (1987), and to photosynthesis and transpiration by Running and Nemani (1988). The processing of AVHRR data to retrieve the surface reflectance generally consists of sensor calibration, atmospheric correction, and cloud identification (Gutman and Ignatov, 1995). Several studies have focussed on the correction of AVHRR measurements from these three confounding sources of variation: 0034-4257/99/$–see front matter PII S0034-4257(98)00080-7

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• Reflectances time series of stable targets, such as desert area or clouds, permits postlaunch updated calibration algorithms to be developed (Vermote and Kaufman, 1995; Che and Price, 1992). • Atmospheric correction models, such as 6S (Vermote et al., 1997) or SMAC (Rahman and Dedieu, 1994), account for gaseous and aerosols effects on satellite short-wave signals. Climatological records provide atmospheric gaseous contents; the knowledge of the spatio-temporal variability of aerosol content is believed to be improved by the analysis of nearly simultaneous bidirectional measurements, such as those provided by ADEOS-POLDER. • Cloud identification can be performed by applying thresholds on reflectances and on brightness temperature (Derrien et al., 1993; Saunders and Kriebel, 1988), by filtering the NDVI time series (Viovy et al., 1992), or by compositing (Qi and Kerr, 1994; Holben, 1986). Recent studies have considered the bidirectional properties of surface reflectance, which is one of the major sources of variation in AVHRR short wavelength data (Burgess and Pairman, 1997; Li et al., 1996; Cilhar et al., 1994). Using airborne POLDER data, Bre´on et al. (1997) showed that the ratio of hot spot to forward scatter reflectance can be as large as six for the visible and three for the near-infrared. AVHRR measurements display three temporal scale of variation in the illumination and viewing geometry, that is, the relative location of the Sun, the target, and the sensor. First, due to the orbital cycle of the satellite, the sensor view zenith angle varies from about 2508 to 1508 within a 9-day period. Second, Sun elevation varies seasonally. Third, drifts occur in the overpass time of NOAA satellites (Privette et al., 1995). Because of these variations and the anisotropy of surface reflectance, bidirectional effects have to be taken into account. Bidirectional reflectance distribution functions (BRDFs) describing this anisotropy have been modeled (Rahman et al., 1993a,b; Roujean et al., 1992) to try to normalize in a given viewing and illumination geometry AVHRR short wavelength data. We recently analyzed the spatio-temporal variability of 1 km2 AVHRR-NVDI within the large temperate coniferous Landes forest (Duchemin et al., 1998). The forest is classically divided into five ecological classes (Lemoine 1981; 1969), the so-called Landes ecotypes, characterized by a specific understorey vegetation. For coniferous stands, the understorey is believed to be a large source of variation in short wavelength data, because canopies are poorly reflective (Spanner et al., 1990b) and are not totally closed. For instance, Curran et al. (1992) and Spanner et al. (1990b) observed a seasonal dependence and scatter in the relationships between LAI and NDVI due to variation in the understorey type and phenological status.

The annual range of AVHRR-NDVI was found to be large (0.25) for our coniferous sites (Duchemin et al., 1998). Because visible and near-infrared reflectances were not normalized, it was nevertheless difficult to link this range with the seasonal dynamics of the vegetation. In fact, the use of 5-day composite AVHRR images built using the maximum value composite technique (Holben, 1986) without ancillary data makes the correction of bidirectional effects on reflectances impossible. The availability at our laboratory of a daily NOAA/AVHRR archive now allows us to normalize reflectances, and to confirm, specify, and extend our previous results. In this article, we focus on the angular and spatiotemporal variability of NOAA/AVHRR short wavelength data within the temperate coniferous Landes forest. First, BRDFs are retrieved by fitting the model of Rahman et al. (1993a,b) to AVHRR measurements. We then analyze bidirectional effects on the visible and near-infrared reflectances and on NDVI. Normalization in a given viewing and illumination geometry and temporal smoothing provide average seasonal dynamics of reflectances and NDVI characterizing each Landes ecotype. Finally, we discussed the capacity of AVHRR data to provide some state variables of the forest: the vegetation cycle of the Landes ecotypes, and estimates of visible, near-infrared, and solar albedo. TEST SITES The Landes forest is a large production forest (about 1.53106 ha) where maritime pine (Pinus pinaster) is dominant (90%). Details concerning the climate, silvicultural practices, and ecological classes can be found in Lemoine (1981; 1969) and Mauge´ (1987). The climate is oceanic. Mean monthly minimum and maximum air temperature are respectively 28C and 128C in January and 158C and 288C in July. Average annual rainfall is about 900 mm. The soil is generally sandy. The topography is flat except on coastal areas. Clear-cutting, which generally occurs after 50 years, has resulted in stands of various age and size from 1 ha to 60 ha. However, management and silvicultural practices have resulted in a high homogeneity within the stands, with pine trees of similar age, density, and height. Between a clear-cut stand and a 50-year-old pine stand, the LAI varies from 0 to 3.5 and the tree height from 0 m to 25 m. The forest is classically divided into five ecological classes, the so-called Landes ecotypes (Table 1), which reflect different soil hydrologic characteristics, and each corresponding to a specific understorey and potential for pine growth (Lemoine 1981; 1969). Coastal areas—which include two classes, the old- and recent-coastal Landes (OCL and RCL)—display higher sunshine, lower rainfall, and a strong soil drainage capacity. Within the inland forest, differences in soil type are described by three

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Table 1. Ecotypes and Test Sites within the Coniferous Landes Forest Understorey Vegetation

Landes Ecotype

Test Site

Old coastal

OCL

Recent coastal Humid

RCL

Various Erica, Pteridium aquilinum Various Erica

HL

Molinia caerula

Mesophile

ML

Pteridium aquilinum

Dry

DL

Calluna vulgaris

Dominant Species

other classes: the dry, humid, and mesophile Landes (DL, HL, and ML). On AVHRR imagery, we located five test sites (Fig. 1), referred to as DL, HL, ML, OCL, and RCL, which are representative of the five Landes ecotypes (Table 1). These sites correspond to pure pine pixels according to the National Forest Inventory geographic database, except in the case of DL because of the low spatial extent of the dry Landes ecotype. The fragmentation on this

Minor Species Arbutus unedo, Quercus ilex Arbutus unedo, Quercus ilex, Sarothamnus scoparius Erica tetralix, Erica ciliaris, Rhamnus frangula, Ulex nanus Ulex europaeus, Erica scoparia, Erica cinerea, Arrhenaterum thoreı¨ Erica species Helianthenum alyssoides

site is lower than 25% and due mainly to deciduous trees along the river system. THE NOAA/AVHRR ARCHIVE We used the NOAA-11/AVHRR archive at 1.1 km2 spatial scale developed for years 1989–1994 at the “Joint Research Centre” (JRC, Ispra, Italy) within the framework of the “Monitoring Agriculture with Remote Sensing”

Figure 1. Location of test sites.

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(MARS) project. The preprocessing scheme at JRC (Kerdiles, 1996) included geometric registration, calibration, and atmospheric correction for AVHRR Channels 1 and 2, and cloud detection. Visible (VIS) and near-infrared (NIR) reflectances (wavelengths 0.55–0.68 lm and 0.72–1.1 lm, respectively) were calibrated with postlaunch coefficients calculated on day 180 after the launch of NOAA-11 and kept constant onwards (H. Kerdiles, JRC, personal communication). The atmospheric correction algorithm was based on the 5S atmospheric radiative transfer model (Tanre´ et al., 1990). Climatological records of Tuller (1968) and London et al. (1976) provided the water vapor and ozone content, but aerosols effects were not accounted for in the atmospheric correction (Kerdiles, 1996). The cloud detection scheme was adapted from Saunders and Kriebel (1988). PROCESSING OF AVHRR DATA BRDF modeling requires the extraction of a maximum number of “clear-sky” reflectances on the same spatial area. However, working at the single pixel scale is unrealistic because of inaccuracy in geometric registration. Therefore, we divided the test site areas into subregions, each consisting of 333 AVHRR pixels (about 10 km2). The spatial extent of each test sites (about 20 km2) permitted two such subregions per Landes ecotype. We developed the processing scheme first on each test site, and then on each subregion. On each test site, we first preselected the data by restricting cloudiness (cloud cover below 50% according to the cloud mask elaborated at JRC), by confining the mean reflectances to within 1–20% for VIS and 10–50% for NIR, and by limiting the solar zenith angle to 858. We then built the spatial mean NDVI time-series and applied the best index slope extraction algorithm. This algorithm (Viovy et al., 1992) is a temporal filter based on the assumptions that cloudy and hazy atmospheres tends to decrease sharply the NDVI values. On each subregion of 333 pixels, we extracted the mean reflectances (qVIS and qNIR) and their standard deviation (rVIS and rNIR). To obtain the most representative reflectances, we limited the average to those pixels for which VIS reflectance varied between qVIS6rVIS and NIR reflectance varied between qNIR6rNIR. We then performed a post-selection by eliminating the data for which mean NDVI remained below 0.3, corresponding to unrealistic values on evergreen canopies, or for which there existed one cloudy pixel in a 737 window centered on the subregion (according to the cloud mask elaborated at JRC). Finally, we excluded year 1989 because of a calibration problem (Fig. 2), NIR reflectance being on average lower by 0.06 this year than in subsequent years. Table 2 shows the number of available data during successive stages of the processing scheme. An average of 138 AVHRR data was selected for each subregion,

from a total of 1700. Thus the number of clear-sky reflectances is on average 276 per Landes ecotype. Their directional dependency is clearly illustrated in Figure 2; when the reflectances were fitted by a simple secondorder polynomial, view zenith angle explained 41% and 77% of the variability in VIS and NIR, respectively. THE BRDF MODEL OF RAHMAN ET AL. (1993a,b) BRDF models aim to estimate the variation of reflectance across the viewing and illumination geometry shown in Figure 3. A review of BRDF models can be found in Cabot and Dedieu (1997). We chose the semi-empirical model developed by Rahman et al. (1993a,b) because it is believed to provide the BRDF for a large range of surfaces, from bare soils to full cover canopies. This model requires three parameters (q0, k, Q) to estimate the reflectance as a function of the viewing and illumination geometry according to the following equation: qMODEL(hs,hv,u)5q0 3

(cos hs cos hv)k21 (cos hs1coshv)12k

(12Q2) [11Q222Q cos(p2n)]3 ⁄ 2

1

3 11

2

12q0 , 11√tan hs1tan hv22 tan hs tan hv cos u 2

2

(1)

where qMODEL(hs, hv, u) is the VIS or the NIR reflectance for a solar zenith angle hs, a view zenith angle hv, and a relative azimuth between the sensor and the Sun, u (Fig. 3). n is the phase angle between the sensor and the Sun (Fig. 3). We used several methodologies (Table 3) to retrieve the BRDF, as determined by the test sites and the period used to fit the Rahman’s model. In all cases, the BRDF was retrieved by fitting the model to AVHRR data

Figure 2. NOAA/AVHRR visible (1) and near-infrared (3) reflectances in relation to view zenith angle (1989–1994 clearsky data on ML test site). The inverted symbols highlight the 1989 data.

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Table 2. Number of Data per Test Site during the Processing of AVHRR Data

Test Site Dry Landes (DL) Humid Landes (HL) Mesophile Landes (ML) Old Coastal Landes (OCL) Recent Coastal Landes (RCL) Average of the five test sites a

Subregion 1 2 1 2 1 2 1 2 1 2

All Available Data

After Preselection and BISEa

1690

226

1698

217

1691

269

1685

256

1683

252

1689

244

After Postselection

After 1989 Elimination

137 149 137 148 183 195 173 153 191 189 166

116 122 116 127 155 159 158 157 126 144 138

All “Clear-Sky” Data 238 243 314 315 270 276

BISE refers as the Best Index Slope Extraction technique (Viovy et al., 1992).

using the simplex method of Nelder and Mead (1965). The fit is based on the minimum root mean square error (RMSE) between the modeled (by the Rahman’s model) and measured (by AVHRR) reflectances. The results are often illustrated on the ML test site for which AVHRR data are both the most numerous and the most regularly time-distributed. They are similar in the other sites. BRDF SEASONAL DYNAMICS Methodology of Retrieval Our main assumption is that the seasonal dynamics of VIS and NIR surface reflectances are the same through years 1990–1994. This permits us to focus on the average seasonal evolution and on the intersite variability of the BRDF. This is a not a strong assumption at the surface, which is little varying because 1) we chose pure pine pixFigure 3. Viewing and illumination geometry of NOAA/ AVHRR measurements: hs5solar zenith angle; hv5view zenith angle; us5solar azimuth angle; uv5view azimuth angle; u5uv2us5relative azimuth (between satellite and Sun); n is the phase angle, such as cos n5cos hs cos hv1sin hs sin hv cos u.

els according to the IFN database, 2) clear-cutting of pine stands affects less than 2% of the surface, 3) change in land use after clear-cutting (for instance, from pine to maize) is negligible, and 4) the method we used to average the AVHRR reflectances on the test sites ensured that we used the most representative pixels. This might be a rough approximation at the satellite level because of degradations in the sensor gain values. However, Vermote and Kaufman (1995) showed that no degradation occurred on NOAA-11 AVHRR sensor during the 1989–mid-1991 period. In our data, no drifts in mean reflectances were detectable from 1990 to 1994; it therefore seems reasonable to use the constant calibration performed during the processing of the data at JRC. We also analyzed long-term changes in atmospheric aerosol contents due to exceptional events, which can introduce error in the estimation of surface reflectance. In our data, VIS reflectance is on average 0.055 for 1990, 1991, and 1994, but is 0.072 for 1992 and 0.062 for 1993; such a clear effect does not appear for NIR. This is probably due to persistent enhancement of atmospheric aerosol content resulting from the Mount Pinatubo eruption of June 1991, already noticed by many authors (Molineaux et al., 1998; Sellers et al., 1994). Although it is impossible to deal with this problem, we kept years 1992 and 1993 to have a sufficient number of data to retrieve the BRDF. Using the assumption of inter-annual stability, we combined the AVHRR measurements to construct an average year that is (at least) representative of years 1990– 1994. This allows the Rahman model to be fitted to a sufficient number of measurements, which display a large variability in their viewing and illumination geometry. In Figure 4, we plot the time course of the local time, solar zenith, and relative azimuth angles (hs and u, Fig. 3) corresponding to the clear-sky reflectances selected by the processing scheme on the ML test site. The delay in the overpass time of NOAA-11, on average 3 h between the beginning of 1990 and summer 1994, results in a large variability in the illumination geometry: solar zenith angle

0.94

b

0.85 20.09 0.67 0.031 101 HL 3

Displayed values are minimum–mean–maximum calculated on all the test sites and the entire 36-day running window. Absolute RMSE are per thousand.

0.124

0.70

20.09

8.4b [4.5] 16.7b [7.2] 0.98

9.9b [18.6] 7.8b [15.1b] 0.87 20.20 0.57 RCL 2

Day 136– day 235 June– August

110

0.022

(Fig. 6) .20 on each 36-day window required 36-day running window One test site

a

4.1–9.9–19 [7.4–16.7–28.2] 0.28–0.77–0.97

b

1

Temporal Spatial No.

Domain of Validity

20.12 0.094

(Fig. 6)

k q0 k q0

Parameters

H

RMSE:a Absolute (‰) [Relative (%)]

0.64

7.2–12.1–22.4b [3.4–6.1–11.5] 0.56–0.98–0.99

Correlationa Coefficient R Parameters

H

Near-Infrared Visible

Correlationa Coefficient R Number of Data

Methodology

Spatio-temporal Variability of BRDF Statistics on the result of the fit are given in Table 3 (methodology 1). Large correlation coefficients between the modeled and measured reflectances confirm the importance of bidirectional effects, and the ability of the Rahman BRDF model to account for them. The fit is significantly better for NIR than for VIS (Table 3 methodology 1). In both cases the absolute RMSE is on average about 0.01, but the relative RMSE (absolute RMSE divided by the mean value of the measured reflectance) is 16.7% and 6.1% for VIS and NIR, respectively. This is expected because of the lower level of VIS compared to NIR reflectance on coniferous forests. Furthermore, it is well known that, for vegetated surfaces, atmospheric correction is more inaccurate for VIS than for NIR reflectance (Vermote et al., 1997; Rahman, 1996). The lowest correlation coefficient (0.28 for VIS and 0.56 for NIR, see Table 3, methodology 1) occurred at the beginning of the year when both the number of data and their angular sampling in the lowest. For example, on the ML site (Fig. 4B), the 36-day window centered on day

Result

(hs) varies between 218 and 84.58, and relative azimuth angle (u) between 251.58 and 13.58 in backscattering, and between 1208 and 1928 in forward scattering (Fig. 4A). Combining the 1990–1994 AVHRR data into one year, the number of measurements and variability in solar incidence sharply increase, especially in summer (Fig. 4B). Moreover the orbital drift of NOAA-11 satellite results in some measurements being acquired near the so-called principal plane (the vertical plane including the target and the Sun, relative azimuth u of 08 or 1808 in Figs. 3 and 4). It can be seen in Figure 4A that these measurements occur mainly in summer 1993 and in spring and autumn 1994. They are particularly interesting when dealing with the retrieval of BRDF because directional effects on reflectances are greatest in the principal plane. This is due to measurements acquired in the hot spot geometry, that is, when illumination and viewing geometry are equal (hv5hs and u50, Fig. 3), which result in peaks of reflectances because of minimum fraction of shadow viewed. The seasonal dynamics of the VIS and NIR BRDF were retrieved by fitting the Rahman model to AVHRR reflectance for the average year, using a running window technique (Table 3, methodology 1). We moved a 36-day window through this year by 9-day steps, keeping only the dates when the window includes more than 20 measurements to fit the model. The size (36 days) and the step (9 days) of the running window ensure a consistency with the 9-day NOAA orbital cycle. The size of the window is a compromise between the limited phenological change of the vegetation at the surface and a sufficient number of data to fit the model.

RMSE:a Absolute (‰) [Relative (%)]

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Table 3. Methodologies and Results of the Fit of BRDF Model on AVHRR Data

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Figure 4. Temporal dynamics of solar zenith angle hs (j), relative azimuth angle u (s) and local solar time (-) of clear-sky NOAA/ AVHRR measurements on ML test site: A) through 1990–1994, B) through one year, the 1990–1994 data combined. Local solar time is not shown in B.

40 contains 21 measurements for which hs varies between 558 and 768, whereas the window centered on day 220 contains 65 measurements for which hs varies between 268 and 668. In winter, larger inaccuracy in the atmospheric correction due to increasing atmospheric path (increasing hs) can also play a role. Figure 5 shows the seasonal dynamics of q0, which determines the mean level of reflectances across the viewing geometry, and of k and Q, the directional parameters of the BRDF model, which characterize the anisotropy of surface reflectance. The growth of the vegetation is revealed by the decrease of q0 for VIS (Fig. 5A). For NIR, differences in q0 seasonal dynamic appear between test sites with evergreen or annual understorey (Fig. 5B). For NIR, the spatio-temporal variability of the directional parameters (k and Q) is remarkably low (Figs. 5E

and 5F). This appears to be consistent with the fact that the seasonal dynamic of the vegetation of the observed surface is limited (evergreen canopy with LAI seasonal variation of about 25% for pine trees, according to Hassika and Berbigier, 1997). For VIS, the spatio-temporal variability of directional parameters is rather large, but neither a seasonal pattern nor an inter-site hierarchy clearly appears (Figs. 5C and 5E). Noncoherent fluctuations of k and Q are observed at the beginning of the season. These fluctuations correspond to the largest inaccuracy of the fit: Minimum and mean correlation coefficient are respectively 0.28 and 0.64 before day 120, respectively 0.64 and 0.84 after day 120. Several explanations—low number of data and low angular sampling in the illumination geometry (see Fig. 4B), inaccuracy due to atmospheric correction—have already been given.

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Figure 5. Seasonal dynamic of the Rahman model parameters (using methodology 1 in Table 3) for visible (left) and near-infrared (right) BRDF: A–B) q0, C–D) k, E–F) Q.

Modeled and Measured Reflectances The close agreement between the modeled and measured reflectances confirms the importance of bidirectional effects on AVHRR reflectances, and supports the Rahman model (Figs. 6 and 7). In Figure 6, we plot the time courses of reflectances measured from AVHRR and modeled in the viewing and illumination geometry of these measurements. It can been seen that the model simulates most of the variation, especially for NIR. In particular, the model is able to reproduce the peaks due to backscatter measurements near the principal plane. The model nevertheless overestimates VIS reflectances, especially for year 1994. Years 1993 and 1994 were chosen to provide an example when the variability of reflec-

tances is the largest, due to several AVHRR measurements in the principal plane (see Fig. 4A). Further analysis shows that for 1992 the model clearly underestimates VIS reflectances. This result is comparable to that of Molineaux et al. (1998) and suggests that Pinatubo aerosol effects have play a role here. When considering all the test sites and all the years (Fig. 7), it can been seen that the result of the simulation is excellent: The correlation coefficient between measured and modeled reflectances is 0.96 for NIR; it is lower for VIS but it is still high (R50.85). In both cases, the absolute RMSE is about 0.01, corresponding to relative RMSE (absolute RMSE divided by the mean value of the measured reflectance) of about 18% and 6% for

AVHRR-BRDF for Monitoring a Coniferous Temperate Forest

Figure 6. Measured (dotted lines) and modeled (solid lines) reflectances on ML test site, years 1993 and 1994 (methodology 1 in Table 3): A) visible, B) near-infrared.

VIS and NIR, respectively. This is comparable to the results of Rahman et al. (1993b) and Cilhar et al. (1994) provided by AVHRR on various vegetated surfaces, including coniferous forests. The trend of the model to reproduce inaccurately the lower reflectances has already been noticed by Cabot and Dedieu (1997), who suggest this is due to measurements acquired in forward scatter geometry when the fraction of shadow viewed is maximum. EXAMPLE OF BIDIRECTIONAL EFFECTS ON REFLECTANCES A typical example of BRDF was retrieved by fitting the Rahman model to the 110 clear-sky measurements acquired on RCL from day 136 to day 235 (methodology 2 in Table 3). The low seasonal variability of the parameter of the Rahman model (see Fig. 5) ensures here that the mean level and the angular variation of VIS and NIR surface reflectances are constant, and that phenological changes of the surface vegetation are weak. Assuming the inter-annual stability of seasonal dynamics of VIS and NIR surface reflectances through years 1990–1994, this

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Figure 7. Measured reflectances as a function of modeled reflectances (using methodology 1 in Table 3) on all the test sites and the entire 36-day running window: A) visible, B) near-infrared.

permits a rough simulation of a data set of reflectances “simultaneously” acquired on RCL. We first illustrated bidirectional effects in the principal plane (relative azimuth u of 0 or 1808, see Fig. 3) because they are the largest. Figure 8 shows the angular sampling of AVHRR measurements used for the fit. Because we mixed the 1990–1994 AVHRR measurements to fit the model and because of the orbital drift of NOAA-11 (see Fig. 4), the variability in the illumination geometry is rather large even if the period (from day 136 to 235) is short: In Figure 8A, hs is on average 428, with minimum and maximum of 24.58 and 708, respectively. In Figure 8B, it can be seen that a significant number of measurements—18 between day 136 to 235 for RCL with a tolerance of 58 on the relative azimuth angle u (see Fig. 4B for ML)—are acquired near the principal plane. This angular sampling ensures that in the principal plane, the model is used in its domain of validity. Figure 9 shows the viewing effect on the reflectances modeled for a solar incidence of 428 and measured in the principal plane. The reflectances were modeled through the viewing geometry using Eq. (1) with the parameters (q0, k, Q) in Table 3, methodology 2,

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from hv 5 608 in backscattering (u508) to hv5608 in forward scattering (u51808). The trends in the variation of reflectances with view zenith angle are well defined by the model (Fig. 9), and comparable to those provided by simultaneous measurements acquired from aircraft sensors on coniferous species (Bre´on et al. 1997; Abuel-

Figure 8. Illumination and viewing angular sampling of NOAA/AVHRR reflectances on RCL test site, days 136–235, years 1990–1994: A) solar zenith angle hs, and B) view zenith angle hv, versus relative azimuth angle u. Circles in 8B highlight the measurements in the principal plane, for which u is 08 or 1808 with a tolerance of 58.

gasim and Strahler, 1994). The ratio of hot spot to forward scatter reflectance is about 2.9 and 2.1 for VIS and NIR, respectively. The comparison of the 18 AVHRR measurements acquired in the principal plane (circles in Fig. 9) with modeled reflectances is remarkably good. Despite the fact that solar incidence varies (hs from 43.58 to 708) during these measurements, there is no bias and the correlation coefficient with the modeled reflectances is 0.92 and 0.98 for VIS and NIR, respectively. The rela-

Figure 9. Reflectances modeled (lines) in the principal plane for a solar incidence of 428, as a function of view zenith angle hv (using methodology 2 in Table 3): A) visible, B) near-infrared. AVHRR data (circles) correspond to the measurements in the principal plane shown in Fig. 8 (relative azimuth angle of 0658 or 180658).

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predicts larger reflectances in the backscatter (hv,08) than in the forward scatter direction (hv.08), especially near the principal plane (u508). Bidirectional effects are negligible across the principal plane: When u is 908, modeled reflectances range within 0.044–0.46 and within 0.164–0.179 for VIS and NIR, respectively. However, here the model is used out of its domain of validity because only a limited angular domain is sampled with no measurements across the principal plane (see Fig. 8). Sensitivity analysis (results not showed) showed nevertheless the same trends in BRDF shape, regardless of the test site, period, and angular sampling used to fit the model. EXAMPLE OF BIDIRECTIONAL EFFECTS ON NDVI

Figure 10. Bidirectional reflectances modeled for a solar incidence of 428 (using methodology 2 in Table 3) as a function of relative azimuth angle u and view zenith angle hv: A) visible, B) near-infrared.

tive RMSE is 16% and 6% for VIS and NIR, respectively. The model nevertheless slightly overestimates the AVHRR visible reflectance near nadir. In Figure 10, we extrapolated the VIS and NIR reflectance previously modeled (methodology 2 in Table 3) to the whole upper hemisphere viewing for a solar incidence (hs) of 428. The angular increment is 58 for both the relative azimuth angle and the view zenith angle (u and hv, see Fig. 3). We limited u within the range 0–908 because the model is symmetric with regard to the principal plane [qMODEL(hs, hv, u)5qMODEL(hs, hv, 2u) in Eq. (1)]. Peaks of reflectance in hot spot geometry (hv5hs5428 and u508) can be easily located. The model

Because anisotropy of the surface reflectance is due to shadow-hiding in most vegetation canopies (Hapke et al., 1996), angular variations of VIS and NIR reflectances are similar, although not identical, because atmospheric effects and surface optical properties depend on the wave nature of light (Rahman et al., 1993a; Holben et al., 1986). This point can be seen in Figure 10, where the VIS and NIR BRDF slightly differ (see the parameters in Table 3, methodology 2). Bidirectional effects are consequently believed to be weaker on NDVI than on reflectances, but are not completely removed (Li et al., 1996; Cilhar et al., 1994; Burgess and Pairman, 1994; Leroy and Roujean, 1994). Figure 11 shows the sensitivity of NDVI to the view zenith angle hv. NDVI is here derived from the reflectances previously modeled for a solar incidence (hs) of 428 (see the previous section, methodology 2 in Table 3, and Figs. 9 and 10). In the principal plane (Fig. 11A), NDVI shows a trend opposite to that of reflectances (Fig. 9). NDVI is 0.51 in the maximum backward scatter direction (hv52608) and up to 0.63 in the maximum forward scatter directions (hv5608). The trend is consistent with those found on various coniferous species by Li et al. (1996), Cilhar et al. (1994), and Burgess and Pairman (1994). The trend agrees with the 18 measurements acquired near the principal plane (circles in Fig. 11A), even if the above mentioned overestimation of VIS reflectances (see Fig. 9) leads to the underestimation of NDVI near nadir. The low dynamic range of the modeled NDVI, the low number—absence in extreme forward scatter direction (hv.308 in Fig. 11A)—and fluctuations of measured NDVI made nevertheless any conclusion impossible. Figure 11B shows the NDVI shape over the whole upper hemisphere, as Figure 10 for reflectances. Bidirectional effects are the largest in the principal plane (u508). They become negligible across the principal plane: when u5908, NDVI varies between 0.57 and 0.59.

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we normalized the VIS and NIR reflectances in the viewing and illumination geometry defined by hs5458 and hv508, using the following equation: qNORM5qAVHRR(hs, hv, u) 3qMODEL(458, 08[,u0])/qMODEL(hs, hv , u),

Figure 11. A) Same as Figure 9 for NDVI. B) Same as Figure 10 for NDVI.

(2)

where qNORM, qAVHRR, and qMODEL are the normalized, measured, and modeled reflectances, (hs, hv, u) is the actual geometry of AVHRR measurements, (458, 08, u0) is the geometry used for the normalization. It can be noticed that hMODEL(458, 08[,u0]) is not dependent of u0, as in Eq. (1), cos(p2n)5cos(p2hs) and tan hv50 when hv508. In the calculation of qNORM, the BRDF seasonal dynamic, which provides qMODEL, was retrieved as previously using methodology 1 (see Table 3, Fig. 5 and the section Methodology of Retrieval). The geometry used for the normalization—viewing and solar zenith angles of 458 and 08, respectively—corresponds to the seasonal mean viewing and illumination geometry of AVHRR measurements at the latitude of the test sites (see Fig. 4 for hs on ML test site, hv varies from about 2508 to 1508 within a 9-day period). This ensures that the model is used in its domain of validity, and consequently that the qMODEL(458, 08[,u0]) term in Eq. (2) is the best estimation through the season. Figure 12 shows the result of the normalization on the ML test site. As expected, the fluctuations in the seasonal dynamics from non-normalized (Fig. 12A) to normalized reflectances (Fig. 12B) are considerably reduced. To analyze the effects of the normalization of AVHRR reflectances, we smoothed their time courses using a 36-day running average (Fig. 12). After smoothing, the seasonal dynamics of non-normalized (Fig. 12C) and normalized reflectances (Fig. 12D) are comparable for VIS, but differences can be noticed for NIR. Without normalization, NIR displays a pronounced peak in summer (Fig. 12C), due to the maximum illumination (highest Sun elevation) of the understorey, which exhibits in summer large NIR reflectance values (maximum greenness and LAI in summer). After normalization, NIR slowly increases until midsummer, then flattens out (Fig. 12D), consistent with the continuous growth of vegetation. APPLICATION IN LANDES MONITORING

NORMALIZATION OF NOAA/AVHRR REFLECTANCES Figures 9 and 10 clearly demonstrate that AVHRR reflectances are anisotropic on coniferous areas and must be normalized. The normalization, that is, the correction of AVHRR reflectances from the actual to a given viewing and illumination geometry, is particularly relevant to eliminate the seasonal effect of changing solar incidence. After normalization, the seasonal dynamics of reflectances corresponds to phenological changes of the surface. Here

Vegetation Cycle of Landes Ecotypes After having normalized and smoothed the reflectances, non-synchronous and distinct effects of pine trees and understorey vegetation are observed in the seasonal dynamic of VIS and NIR reflectances (Fig. 13). The timing (about 45 days) and the high homogeneity of VIS decrease within the coastal sites and within the inland sites suggest a main effect of pine trees (Fig. 13A). The dates of the decrease, around day 120 in the coastal sites, and around day 155 in the inland sites (Fig. 13A), are in close

AVHRR-BRDF for Monitoring a Coniferous Temperate Forest

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Figure 12. Average seasonal dynamics of AVHRR visible (dotted lines) and near-infrared (solid line) reflectances on ML test site: A) “raw” clear-sky reflectances; B) after normalization; C) after smoothing; D) after normalization and smoothing. BRDF was retrieved using methodology 1 in Table 3. Normalization [see Eq. (2) in text] is done in the viewing and illumination geometry defined by hv508 and hs5458. The smoothing consists in 36-day running average.

agreement with the ground observation of the pine phenology of Desprez-Loustau and Dupuis (1994). According to these authors, a warmer climate near the coast induces an advance in the onset of pine growth in spring. The NIR seasonal dynamic depends on the type and phenological status of the understorey (Fig. 13B), being flat when the understorey is evergreen (mainly heather on RCL, Table 1), and increases by about 3–4% in the others sites with the growth of annual plants (fern in OCL and ML or molinia for HL, Table 1). Although the understorey is mainly heather, NIR reflectances displays an increase of about 1.5% on DL, but we have to keep in mind that this site is fragmented, as a consequence of the low spatial extent of the dry Landes ecotype. These results illustrate the potential of AVHRR short wavelength data to study the spatial variability of the phenological cycle of both the pine trees and the understorey within the Landes forest. Differences in the NDVI seasonal dynamic between the test sites, which are obviously the direct consequences of the distinct VIS and NIR seasonal dynamics, characterize each Landes ecotype (Fig. 14). The coastal test sites are special cases (Fig. 14A): NDVI is greatest on OCL, and remains steady on RCL. Within the inland forest (Fig. 14B), NDVI is nearly similar and displays the largest seasonal dynamic on ML and HL; NDVI is least on DL. Because DL is fragmented, the presence of deciduous trees, which exhibit a large NDVI annual range

(Duchemin et al., 1998), suggests that the NDVI seasonal dynamic of the coniferous forested area is actually lower than it appeared in Figure 14B. Because bidirectional effects on reflectances were accounted for, the spatio-temporal variability of NDVI can be attributed to changes in the phenological status of the surface. NDVI could therefore be used to classify the Landes ecotypes, as already suggested in Duchemin et al. (1998), AVHRR data being there non-normalized. Albedo Surface albedo is the ratio of reflected to incident solar radiation in a given spectral band. It can be estimated by integrating over the upper hemisphere the BRDF retrieved from satellite short-wave data (Cabot and Dedieu, 1997), according to Eq. (3), following. The procedure provides the satellite-band albedo (visible and nearinfrared for NOAA/AVHRR) in clear-sky conditions and for a given solar zenith angle (hs): hv5848 u53598

Ai(hs)5

o uo0 h 0 v5 8

5 8

qMODEL (hs, qv, u) , 360*85

i5VIS or NIR.

(3)

In the estimation of albedo, the angular increment of both hv and u is 18. We limited hv to values lower than 858 because the model is used out of its domain of validity for larger hv [hs was limited to 858 when we selected

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Figure 13. Average seasonal dynamics of AVHRR reflectances on the five test sites, after normalization and smoothing (as Fig. 12D for ML test site). For visible (Fig. 13A), differences appear between the coastal (dotted lines) and the inland (solid lines) sites. For near-infrared (Fig. 13B), differences are related to the nature of the understorey vegetation: mainly annual (solid lines) or evergreen (dotted lines).

the data and the model is symmetric in hs and hv, see Eq. (1)]. The albedo derived from the NOAA/AVHRR visible channel (AVIS, in the 0.55–0.68 lm channel) is closely related to the fraction of photosynthetically active radiation absorbed by the surface (in the 0.4–0.7 lm spectral band). The solar albedo (ASOL) determines the amount of solar energy absorbed by the surface, and strongly controls the energy exchanges with the atmosphere. ASOL can be calculated from NOAA/AVHRR by combining the visible (AVIS) and near-infrared (ANIR) albedo (Gutman, 1988). We used here the formula of Weiss et al. (1997): ASOL(hs)50.57 AVIS(hs)10.46 ANIR(hs) .

(4)

Figure 15 shows the seasonal dynamics of the nadir visible, near-infrared, and solar albedo (AVIS, ANIR, and ASOL) on ML and on RCL, estimated for hs5458 in Eqs. (3) and (4). The BRDF seasonal dynamic provides qMODEL and was retrieved using methodology 1 (see Table 3, Fig. 5, and the section Methodology of Retrieval). The AVIS and ANIR seasonal dynamics are comparable to those of

Figure 14. Same as Fig. 13 for NDVI: A) coastal test sites; B) inland test sites.

the normalized and smoothed reflectances (compare Figs. 12 and 15A for ML site). ASOL is about 12.7% and nearly constant, as a consequence of AVIS and ANIR either displaying inverse patterns or remaining steady, as illustrated in Figure 15A on ML (annual understorey) and in Figure 15B on RCL (evergreen understorey). Results are similar on the other test sites. Figure 16 shows AVIS and ASOL as a function of the solar zenith angle, calculated from the BRDF retrieved on HL in summer (months of June to August, see methodology 3 in Table 3), together with grounds measurements obtained on a typical humid Landes stand about 22 years old (INRA experimental test site of Le Bray; see, for instance, Hassika and Berbigier, 1997). The measurements were performed by P. Berbigier (INRA, Bordeaux, France) in several clear days in 1989 for solar albedo, and by P. Hassika (INRA, Bordeaux, France) in 1995 (day 193) for visible albedo. AVIS and ASOL increase by about 5% and 10%, respectively, from the nadir to the lowest solar elevation (Fig. 16). In both cases the trend is consistent with measurements, even if a little bias (about 1%) appears. The bias could result from differences in the observed surface: a mature stand of pine

AVHRR-BRDF for Monitoring a Coniferous Temperate Forest

Figure 15. Average seasonal evolution of visible (1), nearinfrared (3), and solar (o) albedo: A) ML site with annual understorey; B) RCL site with evergreen understorey. BRDF was retrieved using the methodology 1 in Table 3. Albedo are calculated for hs5458 using Eqs. (3) and (4) in text.

with molinia for ground measurements, several km2 including stands from 0 to 50 years old for estimation from AVHRR. CONCLUSION In this article, bidirectional effects on NOAA/AVHRR reflectances were analyzed in the framework of the regional monitoring of a temperate coniferous ecosystem, the very large and nearly pure maritime pine (Pinus pinaster) Landes forest. The forest is classically divided into five ecological classes, the so-called Landes ecotypes (see Lemoine 1981; 1969). These ecotypes are induced by differences in soil hydrologic characteristics, characterized by the type of the understorey, and determine the potential for pine growth. We delimited five test sites corresponding to the five Landes ecotypes for modeling the bidirectional reflectance distribution function (BRDF). We used a 1.1 km2 spatial resolution daily NOAA-11 archive through years 1990–1994 (see Kerdiles, 1996). The processing scheme we developed to select clear-sky reflectances on the five test sites is based on various thresholds and on the best index slope extraction tech-

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Figure 16. Albedo as a function of solar zenith angle hs on HL test site: A) visible; B) solar. The BRDF was retrieved using methodology 3 in Table 3 to compare the results with ground measurements (3) on a mature pine stand in Humid Landes. Albedo are calculated using Eqs. (3) and (4) in text.

nique (Viovy et al., 1992). About 276 AVHRR clear-sky reflectances were selected per Landes ecotype to fit the Rahman BRDF model (Rahman et al. 1993a,b). The fit confirms the importance of bidirectional effects on AVHRR reflectances, and the ability of the Rahman BRDF model to account for them. When considering all the test sites, the anisotropy of the surface reflectance explains on average 72% of the variability for visible (VIS) and 93% for near-infrared (NIR). According to a typical example, bidirectional effects are large in the principal plane: the ratio of hot spot to forward scatter reflectance is 2.9 and 2.1 for VIS and NIR, respectively; NDVI shows a shape opposite to that of reflectances, with a dynamic range of up to 0.1 from back to forward scattering. Bidirectional effects become negligible across the principal plane. These results agrees with the ones found in numerous studies (Bre´on et al., 1997; Cilhar et al., 1997; Li et al., 1996; Abuelgasim and Strahler, 1994 ; Cilhar et al., 1994; Burgess and Pairman, 1994; Rahman et al., 1993b). After the normalization in a given viewing and illumination geometry, the seasonal dynamics of VIS and NIR reflectances were related to the phenological cycle

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of pine trees and understorey vegetation, respectively. Due to a warmer climate, VIS drops earlier on coastal than on inland sites, in agreement with the ground phenological observations of Desprez-Loustau and Dupuis (1994). NIR increases in summer for annual understorey, and remains steady for evergreen understorey. Differences in NDVI seasonal dynamics between the test sites consequently characterize each Landes ecotype. These results illustrate the potential of AVHRR short wavelength data to classify the Landes ecotypes within the Landes forest, and to study on each of them the spatiotemporal variability of the phenological cycle of both pine trees and the understorey. By integrating the reflectances through the upper hemisphere, we estimated the seasonal evolution of visible and near-infrared albedo. We calculated the solar albedo by a combination of the last two. The visible albedo, which is closely related to the fraction of photosynthetically active radiation absorbed, displays a seasonal dynamic similar to that of normalized visible reflectance. The solar albedo, which determines the amount of solar energy absorbed by the surface, was found constant and around 12.7% on each ecotype. Visible and solar albedo strongly increase with solar zenith angle, consistent with ground measurements performed at our laboratory. These procedures, based on the Rahman BRDF semi-empirical modeling of AVHRR reflectances, are helpful to monitor the vegetation phenological cycle across the whole Landes forest. A physical modeling is nevertheless advisable to unmix the combined effects of canopies and understorey vegetation. These procedures could provide dynamical input data—describing the phenological cycle of pine trees and the understorey—to the modeling of the energy, water, or carbon exchanges of the forest with the atmosphere. This is required to upscale ground models developed at our laboratory, which focus at the present time on a single humid Landes stand (the test site of Le Bray; see, for instance, Hassika and Berbigier, 1997). This clearly shows the usefulness of coarse spatial resolution satellite data—now NOAA/ AVHRR, ATSR, POLDER, and, in the near future, VEGETATION, MODIS, MISR—for the regional monitoring and modeling of temperate coniferous ecosystems. This work was supported by the CONSEIL RE´GIONAL D’AQUITAINE and the European VEGETATION PREPARATORY PROGRAMME. I wish to express my gratitude to the team of Monitoring Agriculture with Remote Sensing (MARS) project for providing me the NOAA-AVHRR archive. The author is grateful to Paul Berbigier and Paulin Hassika (INRA, Bordeaux, France) for providing the ground measurements of albedo. I thank Dominique Guyon, Yves Brunet, Jean-Pierre Lagouarde (from the same laboratory), and Fre´de´ric Baret (INRA, Avignon, France) for their useful comments on this article. I thank Roderick Dewar (INRA, Bordeaux, France) for reading the manuscript before submission.

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