Automated atmospheric correction of AVHRR channel 1 and 2 data using dark surface targets F. Fell, J. Fischer, R. Preusker, and T. Schröder Freie Universität Berlin Institut für Weltraumwissenschaften Carl-Heinrich-Becker-Weg 6-10 D-12165 Berlin, Germany
[email protected]
Abstract – A method is presented to allow for operational atmospheric correction of AVHRR channels 1 and 2 data. The method is based on radiative transfer calculations and subsequent application of Artificial Neural Network techniques. The method is applied to derive atmospherically corrected NDVI maps of the river Elbe catchment area in Central Europe.
I. INTRODUCTION The Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA satellites provides one of the longest existing time series of space-borne Earth observation data. For several regions of the Earth surface, time series of AVHRR data covering nearly continuously twenty years or more are meanwhile available. These data sets are extremely valuable to study long-term trends of environmental parameters. However, there are two main difficulties when trying to derive surface-related information from AVHRR data: 1) The information content of AVHRR data is limited as compared to modern instruments due to its low spectral resolution of only two channels in the visible and near infrared. One of the most important parameters that can be derived from AVHRR data is the Normalised Difference Vegetation Index (NDVI), which may act as indicator of the effects of climate change. 2) The first two AVHRR channels are both considerably influenced by atmospheric effects. The upward radiance at the Top of Atmosphere (TOA) in the spectral range of AVHRR channel 1 (ca. 570 nm – 700 nm) is considerably influenced by Rayleigh and aerosol scattering; the spectral range of AVHRR channel 2 (ca. 690 nm – 1020 nm) is less influenced by Rayleigh scattering, still considerably influenced by aerosol scattering and additionally influenced by absorption through atmospheric gases, namely water vapour and oxygen. Atmospheric absorption and scattering may have different effects on the ratio between TOA reflectance and surface
reflectance: atmospheric absorption always leads to an apparent darkening of the Earth surface, atmospheric scattering leads to an apparent brightening over dark surfaces, but has the opposite effect over bright surfaces. In order to remove the atmospheric influence, it would be beneficial to dispose of a method for the operational atmospheric correction of AVHRR data in the visible and near infrared. We have derived a method with this regard, based on spectrally highly resolved radiative transfer simulations, taking atmospheric scattering and absorption by atmospheric gases into account. Artificial Neural Network (ANN) techniques are then used to approximate the relationship between the atmospheric contribution to the upward TOA light field over dark surfaces of known surface reflectance (reference targets) and neighbouring surfaces of arbitrary reflectance. The atmospheric correction scheme is applied to produce atmospherically corrected NDVI maps of the river Elbe catchment area in Central Europe.1
II. STRATEGY An optimal atmospheric correction of space-borne observations of the Earth surface would require detailed knowledge on the absolute amount and optical properties of the relevant atmospheric constituents. This knowledge is often not available for constituents showing a high spatial or temporal variability. This is especially true for atmospheric aerosol: only since very recently, a global network (AERONET) of standardised instruments for measuring aerosol optical depth is being established. A frequently applied approach for atmospheric correction over land surfaces is therefore based on methods which use the signal at the TOA over dark surface targets to estimate the atmospheric contribution to the TOA signal over the surrounding brighter surfaces. The surface types usually used for this purpose are 1
This work was supported by the German Ministry for Education and Research (BMBF) through the contract 01LA9827/8 (Europäische Ökosysteme 1989-1998: Quantitative Analyse unter Verwendung von Satellitenfernerkundungsdaten).
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areas covered by dense dark vegetation (DDV) [1]. The strategy followed in this work is based on a combination of the DDV approach, radiative transfer modelling and ANN techniques. It consists of four separate tasks: 1) use radiative transfer simulations to calculate the relationship between surface reflectance and TOA reflectance for different atmospheric compositions, surface albedo values and observation geometries, 2) identify dark reference areas within the area of interest and assess their respective surface reflectance values, 3) use ANN techniques to approximate the relationship between TOA reflectance and surface reflectance, taking the brightening of a neighbouring dark surface through atmospheric path radiance into account, 4) apply the trained ANN operationally to pixels identified as cloud free.
III.
MATERIALS AND METHODS
AVHRR data archive The AVHRR data used in the frame of this study stem from the archive of the Institut für Meteorologie of the Freie Universität Berlin, where AVHRR-LAC data are operationally received since 1981. The data are radiometrically post-calibrated using surface targets in the Libyan desert [2]. The region investigated in this study covers the spherical rectangle defined by the co-ordinates 49.88° N, 7.92° E and 55.00° N, 14.96° E. The covered area measures approx. 352000 km2. Pixels are resampled such that one pixel represents an area of 0.01°×0.01°. Geolocation is improved by using coastlines or lakes. The position of a specific feature varies by less than one pixel between different images, except for viewing angles larger than ca. 70° at the surface. Dark surface identification A robust method was chosen to identify the darkest surfaces within the considered area. For a period of a year (1995), the number of cases were counted for which the TOA reflectance was above 10% for AVHRR channel 1 resp. above 5% for AVHRR channel 2 (Fig. 1). As expected, the lowest number of TOA reflectance values above the threshold values were observed above coniferous forests in AVHRR channel 1 and above lakes or the open sea in AVHRR channel 2. DDV are geographically rather well distributed, while there are no sufficiently large water bodies in the southern parts of the considered area. Nine resp. six dark surface targets were defined for AVHRR channels 1 and 2. For AVHRR channel 1, the dark targets show minimal values of the TOA nadir reflectance of 4%-5% (except during snow cover). The minimal aerosol optical depth observed in the area is on the order of 0.05 at 550 nm. From radiative transfer calculations,
Figure 1: Identification of dark surface areas for AVHRR channel 1 in the river Elbe catchment area. The selected areas cover 5×5 or 11×11 pixels. Additionally shown are three control areas (Butjadingen, Berlin Stadt, Schwedt), representing different land cover types (pasture, urban, arable land).
the corresponding surface reflectance was estimated at 2.5%. For AVHRR channel 2, a similar reasoning results in estimation of the surface reflectance of 1.5% for the inland water bodies and of 0.3% for the open sea. Cloud masking Cloud masking is done on a pixel by pixel basis through a suite of four tests [3, 4]. The thresholds are chosen such that as many cloud influenced pixels as possible are identified. If a pixel is classified as cloud covered, the surrounding pixels are equally classified as cloud covered. These measures lead to rejection of cloud free pixels, but help to avoid erroneous atmospheric correction of cloud influenced pixels. Due to the first test applied (is NDVI negative ?), water surfaces are classified as cloud covered. A separate scheme is applied to identify cloud free water surfaces, subsequently used as reference targets for AVHRR channel 2. Radiative transfer modelling The radiative transfer simulations used for this study have been performed with the computer code MOMO. This code is based on the Matrix-Operator method and allows the simulation of the ambient light field in a combined atmosphere-ocean or atmosphere-land surface system [5]. The code calculates the azimuthally resolved radiance at a discrete number of solar incident and observation zenith angles. Vertical profiles of the atmospheric constituents are introduced through an appropriate number of homogeneous plane parallel layers. Absorption by atmospheric gases is modelled using a modified k-distribution approach [6]. The land surface is modelled as isotropic reflector.
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For each pixel: • AVHRR channels 1, 2, 4, 5 • observation zenith
n=0
n=n+1
yes
NDVI negative ? no
n=n+1
yes
ρTOA(1) > 40% ? no
n=n+1
yes
ρTOA(2) / ρTOA(1) ε [0.8, 1.25] ? no
n=n+1
yes
T(4) - T(5) > f(T(4), θOBS) ? no
n>0?
yes no
Table 1: Parameters used for radiative transfer calculations. Model atmosphere AFGL Midlatitude Summer Considered gas absorption H2O, O2, O3 Aerosol model continental aerosol, 70% relative humidity Aerosol optical depth 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.70 1.00 Rayleigh scattering according to 1013 hPa surface pressure Spectral resolution 51 spectral channels between 550 nm and 1050 nm, halfwidth of each channel 10 nm Surface albedo [%] 0 1 2 4 7 10 15 20 30 50 70 100 Solar zenith [deg] 0.0 7.4 13.6 19.8 25.9 32.0 38.1 42.1 44.2 50.3 56.4 60.0 62.5 68.6 74.7 80.0 87.0 Observation zenith [deg] 0.0 7.4 13.6 19.8 25.9 32.0 38.1 42.1 44.2 50.3 56.4 60.0 62.5 68.6 74.7 80.0 87.0 Azimuth difference [deg] 0 18 36 54 72 90 108 126 144 162 180
pixel cloud covered pixel cloud free
Figure 2: Strategy for cloud detection on a pixel by pixel basis. After the four tests have been applied to a full scene, all pixels neighbouring a cloud covered pixel are equally classified as cloud covered.
The MOMO code has been thoroughly tested and validated through a number of measures [5]: it has been compared to analytical solutions of the radiative transfer equation, it has been successfully applied to canonical problems of oceanic radiative transfer [7], and it has been compared to other codes on occasion of a model comparison exercise executed in the frame of the MERIS algorithm development activities. The observed deviations between the MOMO output and the analytical solutions are mostly below 0.1%, the differences between the compared codes range between < 0.1% up to about 5%, depending on the difficulty of the treated problem. These results confirm the suitability of MOMO for the execution of the presented study. The parameters used for the radiative transfer calculations are shown in Tab. 1. In total, 7956 cases were computed (12 surface albedo values × 13 aerosol optical depth × 51 wavelengths), each of which consists of 3179 observation geometries (17 solar zenith angles × 17 observation zenith angles × 11 azimuth differences). About 550h CPU time (Pentium III, 550 MHz, 1 GB RAM) were required for the calculations.
Approximation through Artificial Neural Network techniques Artificial Neural Network (ANN) techniques are well suited for the approximation of complex non-linear functional relationships [8], such as the relation between surface reflectance and TOA reflectance. In the present study, we use a particular class of ANNs, the so-called Multi-Layer Perceptrons (MLPs). The MLP used within this study consists of three layers: a 10-neuron input layer, a 30-neuron hidden layer and a 2-neuron output layer. A bias parameter is added to input and hidden layer. The neurons in the hidden layer are fully linked to the neurons of the input and output layers through weighted connections. At each neuron of hidden and output layers, transition of information is obtained trough a sigmoidal function. Weight estimation is done trough the back-propagation method, which is a supervised learning technique that compares the responses of the output neurons with the known true responses (training data), traces the errors back trough the MLP and readjusts the randomly initialised weights. The training data were created from the radiative transfer calculations. They consist of 10000 combinations of the TOA reflectance over a dark surface and a surface of arbitrary reflectance for randomly selected observation geometries. Separate training data were established for AVHRR channels 1 and 2. One single data record consists of the parameters shown in Tab. 2. Table 2: Parameters and corresponding Artificial Neural Network. Surface reflectance of dark pixel Solar zenith for dark pixel Observation zenith for dark pixel Azimuth difference for dark pixel TOA reflectance for dark pixel Solar zenith for arbitrary pixel Observation zenith for arbitrary pixel Azimuth difference for arbitrary pixel TOA reflectance for arbitrary pixel Surface reflectance of arbitrary pixel Aerosol optical depth
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ranges as used for the training of the [0%, 7%] [19.8°, 80°] [0°, 60°] [-180°, 180°] MOMO output [19.8°, 80°] [0°, 60°] [-180°, 180°] MOMO output [0%, 100%] [0.0, 1.0]
Input Input Input Input Input Input Input Input Input Output Output
IV. RESULTS The presented method for atmospheric correction was applied to an AVHRR image acquired on the 18th of May, 1997 (Fig.3). The North-Western and South-Eastern parts of this scene are mostly cloud free, while different cloud types (Cu, Cb, Ci, Sc) are observed in the South-West and NorthEast. The performance of the cloud detection scheme is depicted in Fig. 4.
The performance of the atmospheric correction scheme is shown in Fig. 5: due to atmospheric path radiance, dark surfaces appear brighter at the TOA for AVHRR channel 1, whereas bright surfaces appear darker. The hinge point is situated around 20% to 25%. For AVHRR channel 2, most surfaces appear darker at TOA due to stronger atmospheric absorption and weaker atmospheric scattering as compared to AVHRR channel 1.
ρ TOA (AVHRR1)
0.6
0.4
0.2
0.0 0.0 Figure 3: AVHRR channel 2 image of the Elbe catchment area from May 18, 1997.
0.2 0.4 ρ Surface (AVHRR1)
0.6
1.0
ρ TOA (AVHRR2)
0.8 0.6 0.4 0.2 0.0 0.0
0
1 2 3 4 Number of tests per pixel
5
Figure 4: Cloud masking scheme applied to the AVHRR image shown in Fig. 3. Black aeras are cloud free. The different colours indicate how many of the cloud detection tests were fulfilled.
0.2
0.4 0.6 ρ Surface (AVHRR2)
0.8
1.0
Figure 5: Atmospheric correction scheme applied to AVHRR channel 1 (top) and AVHRR channel 2 (bottom).
The effect of the atmospheric correction on the NDVI is shown in Fig. 6: As compared to the uncorrected NDVI at TOA, atmospheric correction of the considered satellite image results in higher values of the NDVI above densely vegetated areas and rather unchanged values of the NDVI above bare soil. The upper envelope observed in Fig. 6 corresponds to those AVHRR channel 1 values that are least influenced by the atmospheric correction.
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1.0
NDVI (TOA)
0.8 0.6 0.4 0.2 0.0 0.0
0.2
0.4 0.6 NDVI (CORR)
0.8
1.0
Figure 6: Impact on the atmospheric correction on the NDVI.
V. CONCLUSION A method for the atmospheric correction of AVHRR images was presented. The method is fast: it takes less than one minute to correct a 704×512 image of both AVHRR channels 1 and 2 (Pentium III, 550 MHz, 1 GB RAM). It is therefore well suited for the operational atmospheric correction of time series of AVHRR data. Possible improvements concern the consideration of the seasonal course of the dark surface targets and the treatment of water surfaces in the radiative transfer calculations.
REFERENCES [1] Y. J. Kaufman and C. Sendra, “Algorithm for automatic atmospheric corrections to visible and near-IR imagery,” Int. J. Remote Sensing, vol. 9 (8), pp. 1357-1381, 1988.
[2] D. Koslowsky, “Mehrjährige validierte und homogenisierte Reihen des Reflexionsgrades und des Vegetationsindexes aus täglichen AVHRRDaten hoher Auflösung,” Meteorologische Abhandlungen, Freie Universität Berlin, 238 pp., 1996. [3]B. Hu, W. Lucht, A. H. Strahler, C. B. Schaaf, and M. Smith, “Surface albedos and angle-corrected NDVI from AVHRR observations of South America,” Remote Sens. Environ., vol. 71, pp. 119-132, 2000. [4] R. W. Saunders and K. T. Kriebel, “An improved method for detecting clear sky and cloudy radiances from AVHRR data,” Int. J. Remote Sensing, vol. 9 (1), pp. 123150, 1988. [5] F. Fell and J. Fischer, “Numerical simulation of the light field in the atmosphere-ocean system using the matrixoperator method,” J. Quant. Spectrosc. Radiat. Transfer, vol. 69, pp. 351-388, 2001. [6] R. Bennartz and J. Fischer, “A modified k-distribution approach applied to narrow band water vapor and oxygen absorption estimates in the near infrared,” J. Quant. Spectrosc. Radiat. Transfer, vol. 66, pp. 539-555, 2000. [7] C. Mobley, B. Gentili, H. Gordon, Z. Jin, G. Kattawar, A. Morel, P. Reinersman, K. Stamnes, and R. Stavn, “Comparison of numerical models for computing underwater light fields,” Appl. Opt., vol. 32, pp. 74847504, 1993. [8] C. M. Bishop, Neural networks for pattern recognition, Oxford: Clarendon, 1995.
ACKNOLEDGMENTS The authors would like to thank Dirk Koslowsky (Freie Universität Berlin, Institut für Meteorologie) and Alberte Bondeau (Potsdamer Institut für Klimafolgenforschung - PIK) for pre-processing the AVHRR satellite data.
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