Algorithm Theoretical Basis Document for the EUMETSAT ... - OSI SAF

Ocean & Sea Ice SAF

Algorithm Theoretical Basis Document for the EUMETSAT Ocean & Sea Ice Satellite Application Facility Regional Ice Edge Product

OSI-406

Version 1.1 — November 2011

Mari Anne Killie and Øystein Godøy Steinar Eastwood and Thomas Lavergne SAF/OSI/CDOP/met.no/SCI/MA/135

Documentation Change Record: Document version v0.1 v1.0 v1.1

Date 10.06.2009 10.07.2009 11.10.2011

Author MAK MAK MAK

Description Submitted to review Changes after PCR review Information on confidence levels added

SAF/OSI/CDOP/met.no/SCI/MA/135

Table of contents Table of contents List of acronyms

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1 Introduction 1.1 Scope of this document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 The polar orbiting satellites and the AVHRR instrument . . . . . . . . . . . .

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2 Classified sea ice passage product 2.1 AVHRR signatures used for classification . . . . . . . . . . . . . . . . . . 2.2 Channel 3B reflectance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Example scene illustrating the Channel 3B reflectance . . . . . . . 2.3 Bayes approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Training data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 The training data set . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Land mask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Confidence level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 The passage product algorithm summarized . . . . . . . . . . . . . . . . . 2.8 Case studies for classified images, examples of performance . . . . . . . 2.8.1 NOAA-17 scene, Svalbard and mainland Norway, May 25th 2009 . 2.8.2 MetOp-A scene, Greenland, May 24th 2009 . . . . . . . . . . . . . 2.8.3 NOAA-18 scene, Scandinavia, February 25th 2009 . . . . . . . . . 2.8.4 Sea ice in the Bothnian Bay, NOAA-18, February 25th 2009 . . . . 2.8.5 Sea ice on the east coast of Greenland, NOAA-18, May 27th 2009 3 Daily aggregated ice product 3.1 Algorithm description for the daily product . . . . . . . 3.1.1 Processing the classified passage files . . . . 3.1.2 Averaging the image and classifying the pixels 3.1.3 The cloudlim variable . . . . . . . . . . . . . . 3.2 Case studies for the daily product . . . . . . . . . . . . 3.2.1 Daily product, Scandinavia, May 25th 2009 . . 3.2.2 Sea ice near Svalbard, May 6th 2009 . . . . .

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3.2.3 The east coast of Greenland, February 11th 2009 . . . . . . . . . . . 3.2.4 The east coast of Greenland, May 27th 2009 . . . . . . . . . . . . . .

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A Appendix 39 A.1 From thermal channel equivalent blackbody temperature to measured radiance 39 A.2 Training data plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 References

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List of acronyms AVHRR EPS EUMETSAT HIRLAM IJPS NOAA NWP OSI SAF POES SAR TOA VIIRS

Advanced Very High Resolution Radiometer EUMETSAT Polar System European Organisation for the Exploitation of Meteorological Satellites High Resolution Limited Area Model Initial Joint Polar-Orbiting Operational Satellite System National Oceanic and Atmospheric Administration Numerical Weather Prediction Ocean and Sea Ice Satellite Application Facility Polar-Orbiting Environmental Satellites Synthetic Aperture Radar Top of the Atmosphere Visible/Infrared Imager Radiometer Suite

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1. Introduction The EUMETSAT Ocean & Sea Ice Satellite Application Facility (OSI SAF) is producing on an operational basis a range of air-sea interface products, namely: wind, sea ice characteristics, sea surface temperatures and radiative fluxes (surface solar irradiance and downward longwave irradiance). Updated information, products, and documents are available at http://www.osi-saf.org/. Visit http://www.eumetsat.int for more information about EUMETSAT.

1.1 Scope of this document This document describes the method related to the regional sea ice edge product. The current version includes data from AVHRR only. VIIRS data will be added in the future, and the product is to become a part of the EUMETSAT OSI SAF multi-source ice product. The sea ice edge product is currently based on satellite images from the AVHRR/3 instrument on-board the MetOp-A, NOAA-18, and NOAA-19 polar orbiting satellites. The NOAA-17 AVHRR instrument ceased to operate in October 2010. A general introduction regarding the AVHRR instrument follows. In Chapter 2 one finds a discussion on the satellite signatures, Bayesian method and training data used to transform the AVHRR images into classified images. Chapter 3 describes the accumulation of these individual, classified passage images into daily products. Case studies that demonstrate the performance of the products are attached at the end of both chapters. Comparisons with AVHRR RGB composites and with SAR data from Envisat and RADARSAT-1 are made. Finally, the Appendix contains some additional information, and references can be found at the very end of the document.

1.2 The polar orbiting satellites and the AVHRR instrument The Advanced Very High Resolution Radiometer (AVHRR) instrument has been in operation since 1978 on the NOAA Polar-Orbiting Environmental Satellites (POES), and also flies on the MetOp satellites of the EUMETSAT Polar System (EPS) programme. The first MetOp satellite, MetOp-A1 , was launched in October 2006. Through the Initial Joint Polar Satellite 1

Also called “MetOp-02”.

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Channel 1 2 3A (6) 3B 4 5

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Wavelength interval (µm) 0.58 – 0.68 0.725 – 1.00 1.58 – 1.64 3.55 – 3.93 10.3 – 11.3 11.5 – 12.5

Table 1: The AVHRR/3 channels

System (IJPS), a cooperation between EUMETSAT and NOAA to ensure complete global data coverage of intervals of no more than six hours, MetOp satellites will serve the midmorning (AM) orbit, while NOAA-satellites serve the afternoon (PM) orbit. At the point of writing MetOp-A is the AM primary satellite and the recently launched NOAA-19 is the PM primary satellite. NOAA-17 is the AM backup and NOAA-15 the AM secondary. NOAA-18 is the PM backup and NOAA-16 has operational status as the PM secondary. For updated information on spacecraft status visit http://www.oso.noaa.gov/poesstatus/. The current version of the AVHRR instrument, AVHRR/3, has six channels. There are three channels in the visual and near-infrared region, and three in the atmospheric window regions (infrared). The channel wavelengths are listed in Table 1. With the introduction of the current version of the AVHRR instrument the former Channel 3 was split into two channels: Channel 3A2 at 1.6 µm and (the original) Channel 3B at 3.7 µm. However, only five out of the six channels are transmitted to the ground at any given time. NOAA-17 and MetOp-A transmit in Channel 3A during daytime and switch to Channel 3B at night, while NOAA-15,-16,-18 and -19 transmit in Channel 3B throughout the day. The resolution of AVHRR is 1.1 km at nadir, and the swath width is approximately 2500 km. The group of satellites follow sun-synchronous orbits at heights of 800-900 km above the Earth. Each orbit takes about 100 minutes, leading to frequent satellite coverage of the polar areas. At latitudes of 80◦ N (Svalbard, Northern Greenland) each satellite pass overhead ∼10 times a day, while areas at 60◦ N (Southern Scandinavia, Iceland) are covered by ∼5 passes a day from each satellite. The AVHRR data are preprocessed and divided into 4 product tiles. The tiles are in a 1.5 km polar stereographic projection, and each tile covers 1200×1200 pixels, that is 1800×1800 km. The four tiles and the total area covered can be seen in Figure 1.

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also called Channel 6

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Figure 1: The area covered by the four product tiles.

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2. Classified sea ice passage product This chapter presents the algorithm for transforming individual AVHRR satellite passages into classified images. The classified images contain information for the probabilities of sea ice/snow, cloud and open water/land for each pixel. To give an indication on the quality of the classification, confidence level information is also included.

2.1 AVHRR signatures used for classification Four AVHRR spectral features are used in the pixel classification. Three of these are common for all the satellites: the bi-directional reflectance of Channel 1 (hereafter denoted “A1”), the ratio of the bi-directional reflectance of Channel 2 to the bi-directional reflectance of Channel 1 (hereafter “r21”), and the temperature difference between the AVHRR Channel 4 brightness temperature and the surface model temperature taken from HIRLAM12 models1 (hereafter “dT”). In addition satellites transmitting in Channel 3A during daytime use the ratio of the Channel 3A bi-directional reflectance to the Channel 1 bi-directional reflectance (hereafter “r3a1”), while satellites observing in Channel 3B use the ratio of the bi-directional reflectance of Channel 3B2 to the bi-directional reflectance of Channel 1 (hereafter named “r3b1”). The signatures are listed in Table 2 for reference. The reflectances measured in the visible channels 1 and 2 are useful for discriminating between cloud-free surfaces. Figure 2 shows typical reflectance curves for land and water, while Figure 3 shows typical spectra for snow and clouds. In general clouds and 1 2

Visit http://hirlam.org/ for information The thermal contribution is estimated and removed, conf. Section 2.2

AVHRR Signature Ch. 1 bi-directional reflectance Ratio of Ch. 2 bi-dir. reflectance to Ch. 1 bi-dir. reflectance Ratio of Ch. 3A bi-dir. reflectance to Ch. 1 bi-dir. reflectance Ratio of Ch. 3B bi-dir. reflectance to Ch. 1 bi-dir. reflectance Difference between Ch. 4 brightness temperature and HIRLAM12 surface temperature

Symbol A1 r21 r3a1 r3b1 dT

Table 2: The AVHRR signatures used for classification of satellite images.

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Figure 2: Typical spectral reflectance curves for vegetation, bare soil and water in the visible and infrared. The figure is taken from http://www.cpsamu.org/sf/notes/m1r-1-8.htm. sea ice/snow reflect well in the visible part of the spectrum, and it is difficult to discriminate between them using the visible channels only. Channel 3A is located at 1.6 µm, a region of the spectrum where clouds in general continue to reflect well, while snow and ice reflect very little. The good separability between snow and clouds in this wavelength region for satellites transmitting in Channel 3A is illustrated by Figure 3. Channel 3B is located further towards the infrared and is potentially also a good discriminator for snow-covered surfaces and clouds. Channel 3B, however, measures a larger portion of emitted terrestrial radiation and a smaller portion of reflected solar radiation than Channel 3A. A method to estimate and remove the thermal contribution, leaving a Channel 3B reflectance, is summarized in the following section.

2.2 Channel 3B reflectance AVHRR/3 Channel 3B is located around 3.7 µm. While the solar irradiance peaks at visible wavelengths and decreases towards the infrared, the thermal emission from Earth increases in the near-infrared area towards a peak around ∼10 µm. In the Channel 3B spectral band thermal emission is becoming comparable in amount to the solar reflection. This is illustrated in Figure 4, which shows the thermal emission from a black-body object with temperature 260 K together with the reflected solar radiance from objects (clouds) of 5% and 30% reflectance. The daytime measured radiance in Channel 3B contains contributions both from reflected sunlight and from thermal emission from the viewed surface. In order to fully use satellites transmitting in Channel 3B, the thermal contribution should be estimated and subtracted EUMETSAT OSI SAF



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Figure 3: Satellite channel wavelengths in microns (µm) for the visible AVHRR channels and typical reflectance spectra for snow and clouds. Channel 3A is centered around 1.6 µm. The figure is taken from http://nsidc.org/data/docs/daac/nsidc0066_avhrr_5km.gd.html.

Figure 4: The figure shows the thermal emission from a black-body object of temperature 260 K (dashed line) together with the reflected solar radiance from surfaces of 5% (lower solid line) and 30% (upper solid line) reflectance. The vertical lines indicate the Channel 3B band width (for NOAA-9). The figure is taken from [1].

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from the total measured radiance. A general method has been described in [1], [2], and others, and is summarized here. The daytime measured radiance in Channel 3B, L3B , can be expressed as the sum of a thermal and a reflected term: (2.1)

L3B = ǫ3B BT (T ) + ρ3B L⊙3B cosθ

Here, ǫ3B is the emittance in Channel 3B, the thermal radiance is expressed by the Planck function BT (T ), ρ3B is the reflectance of the viewed surface in Channel 3B, L⊙3B is the incident solar spectral radiance, and θ is the solar zenith angle. The intervening atmosphere is assumed to have a transmittance of 1, and the viewed object is considered to be optically thick. One can then write ǫ3B = 1 − ρ3B . The measured Channel 3B radiance, L3B , is given in the form of the equivalent black-body temperature T3B . This temperature can be converted back to a radiance value using the equations quoted in Appendix A.1. The measured Channel 4 brightness temperature, T4 , is assumed to be close to the true temperature of the observed surface and is used to estimate the thermal contribution (through Equations A.1 and A.2). With these simplifications in mind Equation (2.1) can be written BT (T3B ) − BT (T4 ) (2.2) ρ3B = L⊙ cosθ − BT (T4 ) and the reflectance component ρ3B of the measured radiance can be estimated. The solar radiance L⊙3B varies across the Channel 3B band width, and the various platforms have different Channel 3B response functions3 . This leads to platform-dependent values for L⊙3B . The values used in the algorithm are listed in Table 3. These are based on tabulated values for solar irradiance taken from [3] (also cited in Table 3), which are found by weighing the solar spectral irradiance by the spectral response functions. The tabulated values are valid for the mean Earth-Sun distance of 1 AU, and corrections are made to compensate for variations in the Earth-Sun distance through the year.

2.2.1 Example scene illustrating the Channel 3B reflectance Figure 5 shows an RGB composite of AVHRR channels 1, 2 and 4 in panel (a), the original Channel 3B in panel (b) and the reflectance part of Channel 3B in panel (c). The original Channel 3B image is visualized in the inverted form. The example is taken from a NOAA-18 passage at 11:54 UTC on March 28th 2007. 3 conf. Appendix D of the NOAA KLM User’s Guide for the response functions for the various platforms: http://www.ncdc.noaa.gov/oa/pod-guide/ncdc/docs/klm/html/d/app-d.htm

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Platform NOAA-15 NOAA-16 NOAA-17 NOAA-18

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TOA solar irradiance (10−2 W/(m2 cm−1 )) 1.6189 1.6019 1.5929 1.5842

TOA solar radiance (mW/(m2 sr cm−1 )) 5.153 5.099 5.070 5.043

Table 3: Constants for the TOA solar irradiance in Channel 3B for various satellites, taken from Table 3 of [3] (note that there is a factor of 10−2 missing from the referred table in [3]), and the corresponding values for TOA incident solar radiance used in the algorithm. All are valid for the mean Earth-Sun distance of 1 AU. NOAA-19 and MetOp-A should be included later on for completeness.

(a)

(b)

(c)

Figure 5: NOAA-18 passage at 11:54 UTC on 28 March 2007. Panel (a) shows an RGB composite of AVHRR channels 1, 2, and 4, panel (b) shows the original Channel 3B (inverted), and panel (c) shows the reflectance of AVHRR Channel 3B after the removal of the thermal contribution. The RGB composite shows cloud-free conditions for Scandinavia and the northern parts of Continental Europe. Snow covers parts of Norway and Sweden, and sea ice is visible in the Bothnian Bay. Some clouds can be seen in the lower, right corner of the image, and there is also a large cloud system covering parts of Great Britain and the ocean further north. In the original Channel 3B image (panel (b) of Figure 5) snow-covered surfaces appear as light grey to white due to their low temperature and also very low reflectance in this spectral region. Oceans have similar properties and appear as light grey. Land surfaces, also low in reflectance, are higher in temperature and therefore darker. Clouds are generally high in reflectance and can have a range of temperatures, leading to a variety of grey colors in the image. The Chanel 3B reflectance is shown in panel (c) of Figure 5. Light areas now correspond to high reflectance, while dark areas represent surfaces of low reflectance. The snow-covered surfaces, as well as areas of clear land, sea ice and open water, are very dark. Clouds appear in a wide range of grey tones, depending on water content. Wet clouds

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reflect highly in the 3.7 µm area while cold, icy clouds reflect less. Notice now the visibility of the clouds in the lower left corner, and the less visible higher, cooler clouds further north. In figure 6 the same scene is presented through three AVHRR RGB composites: the previously seen RGB 124, together with the original RGB 234 where Channel 3B is plotted as a thermal channel, and the new RGB 234 where Channel 3B contributes with reflectance only.

(a)

(b)

(c)

Figure 6: NOAA-17 passage at 11:54 UTC on 28 March 2007. Panel (a) shows the RGB composite of AVHRR channels 1, 2, and 4, panel (b) shows an RGB composite of AVHRR channels 2, the original 3B, and 4 (note that Channel 3B is inverted), and panel (c) shows an RGB composite of channels 2, 3B (the reflective part) and 4. This set of figures illustrates the appearance of snow before and after removing the thermal contribution. When the thermal contribution is removed snow-covered surfaces get a higher contrast to land and – to some extent – to clouds. Notice, however, the similarity in color between the snow-covered mountain regions of Scandinavia and some parts of the cloud system in the upper left corner of the image.

2.3 Bayes approach A general tool for combining various data sources containing probabilistic information is given by the Bayesian (inverse method) or Maximum Likelihood approach. Using this approach several measured features can be combined to yield an optimal estimate of a geophysical parameter (e.g. sea ice cover). The approach is based on pre-knowledge of the averaged relationship between each ice class and the satellite-measured features. In addition knowledge of the scatter of the expected measurement value for each ice class is needed. This knowledge can be expressed as a probability distribution for the measurement variable given the ice class. As an example, allowing two classes: ice and water, a simple algorithm for ice edge detection given a measured feature A can be derived. Setting the EUMETSAT OSI SAF



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a priori probabilities for both ice and water, p(ice) and p(water), equal to 50%, the probability of the class ice given the AVHRR feature A is represented by Equation (2.3): p(ice|A) =

p(A|ice) p(A|ice) + p(A|water)

(2.3)

Extending now to j independent classes, the probability for the class Ik given the measured feature A becomes p(A|Ik )p(Ik ) (2.4) p(Ik |A) = P j p(A|Ij )p(Ij ) This method can be further generalised for combining several satellite measured features to an optimal ice class estimate analysing several mutually independent classes. Assume that we have a set of n measured features A = A1 , A2 , . . . , An , which are independent given a certain ice class. The general expression for the probability of ice class Ik given the features A1 , A2 , . . . is represented by Equation (2.5). p(A1 |Ik )p(A2 |Ik ) . . . p(An |Ik )p(Ik ) p(Ik |A1 , A2 , . . . , An ) = P j p(A1 |Ij )p(A2 |Ij ) . . . p(An |Ij )p(Ij )

(2.5)

Here, p(Ai |Ik ) is the class-dependent probability density function for class Ik given the measured feature Ai , and p(Ij ) is the a priori probability that the pixel under investigation belongs to the class Ij . The method works in such a way that the measured feature, which the statistics shows to be most secure in distinguishing between ice classes, is the one that alters the probability. Further we do not only obtain an estimate of the most probable ice class, but also of the uncertainty of this estimate. Each pixel is classified to the class Ik with the highest a posteriori probability p(Ik |A1 , A2 , . . . , An ). The class density probabilities p(A1 |Ik ), p(A2 |Ik ),. . . can be estimated through various methods fitted to describe the dataset (such as normal distribution functions etc.). For the sea ice products described in this chapter there are three allowed classes: ice, cloud and water. Notice that in addition to processing pixels over water, the algorithm also processes pixels over land. For land pixels the allowed classes are snow, cloud and clear land. This leads to a total of five different classes, of which three are in use at any given time. The common notation is • ice (sea ice or snow) • cloud • clear (open water or clear land) The set of measured features A = A1 , A2 , . . . consists of four of the five AVHRR signa-

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ture combinations listed in Table 2, and the general Equation (2.5) is extended to p(ice|A1, r21, r31, dT) =

p(clear|A1, r21, r31, dT) =

p(A1|ice)p(r21|ice)p(r31|ice)p(dT|ice)p(ice) P j p(A1|Ij )p(r21|Ij )p(r31|Ij )p(dT|Ij )p(Ij )

(2.6)

p(A1|clear)p(r21|clear)p(r31|clear)p(dT|clear)p(clear) P j p(A1|Ij )p(r21|Ij )p(r31|Ij )p(dT|Ij )p(Ij ) (2.7)

p(cloud|A1, r21, r31, dT) =

p(A1|cloud)p(r21|cloud)p(r31|cloud)p(dT|cloud)p(cloud) P j p(A1|Ij )p(r21|Ij )p(r31|Ij )p(dT|Ij )p(Ij ) (2.8)

where j is the sum over all three classes. The a priori probabilities p(ice), p(clear) and p(cloud) are assumed to be equal. r31 represents either r3a1 or r3b1, depending on whether the satellite transmits in Channel 3A or Channel 3B. Statistical knowledge of the average, as well as of the scatter, of the expected measurement values is needed in order to compute the 15 class density probability terms used (p(A1|ice), p(r21|ice),. . . ). A large set of AVHRR observations (training data) has been collected for this purpose.

2.4 Training data Training data is needed to form the statistical basis for the Bayesian approach. Data was collected manually using Diana – a meteorological visualisation and production software developed at met.no – together with the UFFDA system – a tool for collecting radiometric signatures from geophysical surfaces. In Diana auxiliary information such as synop observations and images from other satellites act as helpful information to ensure good quality for the collected data. Going through one satellite scene (i.e. one tile) at the time, individual pixels are marked and labeled with a particular class. For each pixel a box of 13×13 pixels, centered on the chosen pixel, is collected and all the 169 pixels are labeled with the same class. It is therefore necessary to make sure that the surrounding pixels are indeed of the same class when choosing the “center pixel”. Both water and land surfaces are trained. This gives a data set that can be used for several purposes and products. The classes used for training data collection were: Ocean Land Cloud over water Cloud over land Cloud over seaice EUMETSAT OSI SAF



Snow Snow Snow Snow Snow

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in mountain above trees below trees in grassland on seaice

The UFFDA programme collects the AVHRR channel data, the time of the satellite passage, the satellite angle and the solar angle as well as data from corresponding numerical models (HIRLAM12) and cloud mask files. When a large amount of data has been collected the data is analysed.

2.4.1 The training data set The current training data set used in the Bayes approach is based on satellite images from the period February to May 2007, February 2008 and January to February 2009. Care was taken to collect pixels from all times of the day and from a large part of the AVHRR swath, so as to cover a wide range of solar zenith angles and scan angles. Efforts were made to find and mark pixels belonging to as many of the different classes listed above as possible for all images. Pixels near the edge of the 13×13 square may in some cases be influenced by a different surface type. A total of ∼400 tiles have been examined, and 5137 “center pixels” have been marked (corresponding to 868 153 collected pixels). ∼40% of the training data belongs to the classes for sea ice and snow, ∼55% are clouded pixels, ∼1.5% of the pixels are land pixels and the remaining ∼3.5% are collected over open water. The spectral properties of open water are quite different from those of sea ice and clouds, and the spectral properties of land differ in a similar fashion from those of snow and clouds (conf. Figures A.1 and A.2). The main challenge is to separate clouds from snow-covered surfaces, and the majority of the training data has therefore been collected over clouds of various types and over snow and sea ice. Less training data is needed for open water and land. The collection of training data is ongoing in order to further improve the algorithm. The current training data set consists of daytime scenes only. For the 2007 and 2008 data focus was on NOAA-18 images in order to get a good data basis for the Channel 3B reflectance. For the 2009 data special care was taken to get training data from various platforms, and to expand the amount of training data for snow on sea ice at low solar heights (i.e. the Greenland ice edge and the sea ice around Svalbard). The spectral features examined during analysis were the bi-directional reflectances of AVHRR channels 1, 2, 3A and 3B (using the method described earlier to make an estimate of the reflectance), and the brightness temperature of AVHRR Channel 4. Training data for all three cloud classes were combined to one class, as was the training data for the four classes covering snow on land surfaces. The list above is then shortened to the five classes needed for the algorithm: cloud, snow, sea ice, land, and water. Density plots for the spectral features of interest are included in Appendix A.2 (Figures A.1 and A.2). During analysis normal distribution curves were fitted to the data. The resulting mean and standard EUMETSAT OSI SAF



A1 r21 r3a1 r3b1 dT

mean st.dev. mean st.dev. mean st.dev. mean st.dev. mean st.dev.

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Cloud

Snow

Sea Ice

Land

Water

55.8537 14.7856 0.912815 0.0867034 0.669006 0.221688 0.190137 0.133128 19.5630 15.3665

50.9518 19.5791 0.870467 0.147283 0.142553 0.0706126 0.0260488 0.0246002 4.32104 6.26195

56.2843 10.5925 0.849512 0.103510 0.118639 0.0584951 0.0181969 0.0158772 4.32104 6.26195

7.93347 2.12576 1.88911 0.393586 1.39203 0.238156 0.368992 0.117323 4.32104 6.26195

7.55298 2.47133 0.463990 0.0646445 0.116905 0.0906175 0.0635349 0.0676766 4.32104 6.26195

Table 4: Mean and standard deviation values for the signatures and classes used in the algorithm. These are based on the collected set of training data (except for dT).

deviation values are listed in Table 4. The normal distribution curves are also plotted in Figures A.1 and A.2. The coefficients representing the statistical scatter of the temperature difference between AVHRR Channel 4 and the HIRLAM12 model surface temperature (the signature dT) are not taken from this set of training data, but based on experience from previous studies. It should be noted that although seasonal variations in the signatures obviously occurs, the statistical coefficients are currently kept static through the year. A future broader set of training data could open for seasonal sets of coefficients, which would potentially improve the performance of the algorithm. It should also be kept in mind that the normal distribution curves not necessarily are optimal for describing the distribution of the data. Using for instance gamma distribution instead to represent some classes is straightforward to do within the code.

2.5 Land mask Spectral signatures from sea ice are not identical to signatures from snow in mountain or in forest. The algorithm is run using coefficients for snow, clouds and land to classify pixels over land, and coefficients for sea ice, clouds and water for pixels over water. The same set of coefficients describing the statistical scatter of clouds are used for both cases. To determine whether a pixel is over land or over water a land mask based on GTOPO 304 is used. The GTOPO data has been transformed to the projection and four area tiles needed. Each pixel of the land mask files contains a value for the fraction of land cover. If the fraction of land is higher than a preset value the pixel is considered to be over land, otherwise 4

See http://www.npagroup.com/catalogue/shop/gtopo30/index.htm for more information on GTOPO 30.

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it is considered to be over water. Currently a land cover fraction of 80% is used as the limit. The system implemented in the OSI SAF will always have a valid land mask. If necessary, however, the system can run without a land mask. All pixels are then assumed to be over water, and the possible classes are sea ice, water and cloud. This will cause a less accurate classification over land.

2.6 Confidence level To give an indication of the quality of the calculated probabilities a confidence level value is calculated for each pixel. Each pixel is originally assumed to be of “excellent” quality, and the possible confidence levels are: • 5 excellent • 4 good • 3 acceptable • 2 unreliable • 1 erroneous • 0 unprocessed Experience shows that the quality of the pixel classification tends to be reduced for the following situations: • Large solar zenith angles • Large satellite zenith angles (i.e., edge of swath) • Proximity to clouds For occurences of any of these three, the confidence level is reduced by one.

2.7 The passage product algorithm summarized The satellite passage product ice classification algorithm goes through the following steps: • The AVHRR image is read. • HIRLAM12 NWP data is read. • A land mask is read. • Statistical coefficients for the Bayesian approach are read. EUMETSAT OSI SAF



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• A routine to process the individual pixels is called. • The product is written to file: ⇒ an hdf5-file containing probabilities for ice/snow, cloud and clear land/water and the confidence level information (4 fields) ⇒ an mitiff-file containing the probability for ice/snow (each pixel is categorized in one out of 20 classes covering the range from 0 to 100% probability in 5% steps) In the routine that performs individual pixel processing the following is done for each pixel within the swath: • The satellite zenith angle is calculated (to reduce processing time this is done for every 9th pixel in every 9th row only) • The solar zenith angle is estimated. ⇒ If the solar zenith angle is larger than 85◦ the pixel is not processed. • It is checked whether Channel 3A or 3B is in use. This determines which signatures to use. • It is checked whether the pixel is dominated by land or by water. This determines which classes that are allowed and which statistical coefficients to use. ⇒ if there is an error with the land mask file the pixel is assumed to be over water. • The geophysical parameters reflectance and temperature are estimated • The temperature difference dT = T0m − T4 is calculated • If Channel 3B is active: the Channel 3B reflectance is estimated • The routine to estimate the class probabilities is called: the expressions (2.6)–(2.8) are evaluated, giving the probabilities for ice, water and cloud – or snow, land and cloud. • The confidence level value, initially set to 5 excellent, is found: ⇒ if the solar zenith angle is larger than 80◦ , the confidence level value is reduced by one ⇒ if the satellite zenith angle is larger than 50◦ , the confidence level value is reduced by one ⇒ if the probability for cloud is larger than 50% within a box of 5 by 5 pixels - centered on the pixel in question, the confidence level is reduced by one In the following examples the classified images show the probability for snow/sea ice (i.e. the mitiff product file). EUMETSAT OSI SAF



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2.8 Case studies for classified images, examples of performance Included in this section are case studies giving an indication of the performance of the algorithm. For all cases the classified image shown gives the probability of the class sea ice (or snow). Similar images giving the probabilities for the classes cloud and clear (water or land) are not shown.

2.8.1 NOAA-17 scene, Svalbard and mainland Norway, May 25th 2009 Figure 7 shows in the left panel a NOAA-17 image from May 25th 2009 at 09:29 UTC covering mainland Norway and Svalbard, while the right panel shows the probability of sea ice from the classified passage product. In the classified image red colors indicate high probability of sea ice/snow, green correspond to ∼30-∼60% probability, while blue colors translate to a low probability. The sea ice east of Svalbard is correctly identified by the algorithm, as is the Svalbard snow cover and snow in the mountain regions of Scandinavia. The sea ice or snow probabilities for these areas are generally above ∼90%. Notice that high, cold clouds in some cases are misclassified and given a ∼50% probability of sea ice (conf. the ocean area outside of Lofoten).

2.8.2 MetOp-A scene, Greenland, May 24th 2009 Figure 8 shows parts of a MetOp-A swath from May 24th 2009, 13:04 UTC. Greenland and the Baffin Bay dominates the image. Parts of the Baffin Island coast line is seen in the upper left corner. The left panel shows the AVHRR RGB 124 composite, the middle panel shows the RGB 264 composite, and the right panel shows the classified image (probability of sea ice). The Greenland ice cap is correctly classified with a high probability of ice. The RGB 264 composite reveals clouded areas that are not easily seen in the RGB 124 composite. Especially the clouds covering much of the Baffin Bay sea ice – almost invisible in the RGB 124 – becomes very visible in RGB 264. The algorithm correctly labels this area with a low probability of sea ice (and with a high probability of cloud, not shown here). Clouded areas over Greenland are also recognized in the classified image, as are the snowfree land areas on the west coast of Greenland.

2.8.3 NOAA-18 scene, Scandinavia, February 25th 2009 Figure 9 is taken from a NOAA-18 swath on February 25th, 11:03 UTC, covering Scandinavia. This example demonstrates the performance of the algorithm over land. In spite of the rather different signatures from the snow-covered mountains of Central and Northern Norway and the forest areas in Northern Sweden, clearly seen by the north-south borderline in the RGB 124 composite in panel (a), both areas are classified with a high probability of snow (panel (b) shows the classified image for sea ice/snow). Snow-covered areas further north in Norway and in Finland and Russia are also correctly identified. The clouds covering

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Figure 7: OSI SAF AVHRR Sea Ice processing result for NOAA-17 at 09:29 UTC on 25 May 2009. The left panel shows the RGB composite of AVHRR channels 1, 2, and 4, while the right panel shows the corresponding classed image for ice probability.

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Figure 8: OSI SAF AVHRR Sea Ice processing result for MetOp-A at 13:04 UTC on 24 May 2009. The left panel shows the RGB composite of AVHRR channels 1, 2, and 4, while the middle panel shows the RGB composite of channels 2, 6 (3A) and 4. The right panel shows the classified image in which red pixels indicate high probability of ice while blue pixels translate to a low probability. southern parts of Finland are somewhat difficult to see from this RBG 124 image but clearly show in the classified image. The sea ice in the northern end of the Bothnian Bay is correctly classified, as is the ice further south along the Swedish and Finish coasts. This is also the case for the sea ice covering the Kandalaksha Gulf south of the Kola Peninsula. Note again in this example that for the case of cool clouds over ocean some cloudcovered pixels can be wrongfully classified with a high (∼50%) probability of ice. A second issue is the misclassification of cloud shadows into ice. In this example the southern parts of Scandinavia are covered by layers of clouds. Notice that shadows from high clouds falling on the lower clouds in some cases are misclassified as ice (conf. the area east of the great Swedish lakes).

2.8.4 Sea ice in the Bothnian Bay, NOAA-18, February 25th 2009 Figure 10 gives a closer look on the Bothnian Bay area for the satellite passage presented in the previous example. The sea ice in the Bothnian Bay is clearly identified, as are clouds covering the northernmost part of the bay (shown indirectly through a low probability of ice). The tongue of water that intrudes the sea ice on the Swedish side matches well with the feature in the classified image. Snow-covered forest areas on both sides of the Bay are also identified, and they appear with very similar probabilities in the classified image although

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(a)

(b)

Figure 9: OSI SAF AVHRR Sea Ice processing result for NOAA-18 at 11:03 UTC 25 February 2009. Panel (a) shows the RGB composite of AVHRR channels 1, 2, and 4, and panel (b) shows the probability of ice in the corresponding classified image.


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Figure 10: OSI SAF AVHRR Sea Ice processing results for NOAA-18 at 11:03 UTC on 25 February 2009. The left panel shows the RGB composite of AVHRR channels 1, 2, and 4, while the right panel shows the corresponding classified image (probability of ice). Conference Figure 9 for the color table. their signatures differ somewhat in the RGB 124 composite. The clouds in the lower right corner of the image are also clearly and correctly identified by the algorithm.

2.8.5 Sea ice on the east coast of Greenland, NOAA-18, May 27th 2009 The final example of this chapter is taken from a NOAA-18 passage on May 27th 2009, 06:47 UTC. Figure 11 shows the east coast of Greenland, north of the large Scoresby Sund fjord. The left panel shows the AVHRR RGB 124 composite, the middle panel shows the RGB 234 composite (where the reflective part of Channel 3B is used), and the right panel shows the classified image in which red corresponds to a high probability for ice, while blue refers to a low probability of ice. The RGB 124 composite clearly shows the ice edge crossing the image from lower left to upper right. Outside of the ice edge ice sheets of various sizes are seen. Clouds are formed over water in the lower parts of the image, and there are also some higher clouds scattered over Greenland. The RGB 234 composite reveals that the low clouds actually cover much more of the open sea ice area than what is readily seen from the RGB 124 composite. In the middle panel the reflectance of Channel 3B contributes with green to the RGB composite. Clouds are generally very reflective in this channel5, while ice and water are not (Figure 3). This leads to the dominant green color of 5

Water clouds are very reflective, cool ice clouds are less reflective.

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Figure 11: OSI SAF AVHRR Sea Ice processing result for NOAA-18 passage at 06:47 UTC on 28 May 2009. The left panel shows the RGB composite of AVHRR channels 1, 2, and 4, the middle panel shows the RGB composite of AVHRR channels 2, 3 (reflective part only) and 4, and the right panel shows the probability of snow/sea ice (conf. Figure 9 for the color table).

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the (water) clouds in RGB 234. The algorithm estimates the probabilities for three mutually exclusive classes (ice, cloud, clear) given the measured radiometric signatures. Even though large amounts of sea ice can be easily seen through the cloud cover in the RGB 124 and RGB 234 composites these pixels are classified with a high probability of cloud, and correspondingly a low (∼zero) probability of sea ice. Cloud shadows can for some Sun-Earth-satellite geometries become a challenge for the algorithm. Figure 12 shows a closer look on some of the clouds in the center right area of Figure 11. Encircled in the RGB 234 composite of Figure 12 is an area where higher clouds cast shadows on the lower cloud layer. The shadow pixels are misclassified with a high probability of ice in the classified image (also encircled).

Figure 12: OSI SAF AVHRR Sea Ice processing result for NOAA-18 passage at 06:47 UTC on 28 May 2009. The left panel shows the RGB composite of AVHRR channels 2, 3 (reflective part only) and 4 and the right panel shows the probability of ice (conf. Figure 9 for the color table).

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3. Daily aggregated ice product The individual satellite passages are classified as described in Chapter 2, and the classified passage products then enter into the composition of a daily averaged ice product. The aim is to compile a cloud-free image showing the probability of sea ice (or snow) given a cloud-free situation. In reality, however, an image based on 24 hours of data can seldom become cloud-free as there will most likely always be some pixels that are covered by clouds for all passages during the 24-hour period. Pixels with a solar zenith angle greater than 85◦ are not processed, and there are large seasonal variations in the number of individual satellite passes with sufficient daylight. At latitudes of 80◦ N the Sun is above the horizon for four months during the summer and the majority of the satellite passes will then be able to contribute to the aggregated product. During wintertime the Sun is below the horizon for four months, and there are no daylight scenes on which a daily product can be based. At vernal and autumnal equinoxes typically 3-4 images from each satellite can contribute to the daily product.

3.1 Algorithm description for the daily product The algorithm for the daily product compiles a list of the individual passage product files that fulfill a set of requirements for the satellite passage time, satellite sensor and geographical area (tile). This list is then sorted according to the tile covered by the file, and a daily product is made for one tile at the time. The overall recipe for each tile is as follows: 1. The headers of all files on the tile list are read and compared to ensure that the geographical location is identical for all. 2. The confidence level value for each pixel of the daily product is initalized to 0. 3. One by one the passage files are read. For each file the pixels are processed individually (see 3.1.1), preparing the basis for computing a daily product. 4. The daily product is made (see 3.1.2). This includes a classified image where the allowed classes are ice cover and no ice or very open ice. Pixels that for various reasons lack ice cover information (land pixels, pixels without satellite coverage etc.) are given fillvalue. 5. The product is written to files: EUMETSAT OSI SAF



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⇒ a netCDF-file containing 4 fields: the classified image, a status flag field explaining the fillvalue from the classified image, the confidence level field and a field containing the number of observations on which the sea ice information is based. ⇒ an mitiff-file showing the classified image where the allowed classes are clear, ice/snow, land, undefined and no data.

3.1.1 Processing the classified passage files The following is done for each pixel of the classified passage product: • If not processing land: the land mask is checked. ⇒ It the land mask indicates that the pixel is over land, the pixel is flagged “Land”, and the routine starts over with the next pixel. • The pixel probability values Pice, Pcloud, and Pclear are checked against the valid maximum and minimum values (100% and 0%, respectively). ⇒ If failing this test the pixel does not contain probability information. The undefined counter for this pixel element, numUndef[elem], is increased by one, and the routine starts over with the next pixel. • The pixel cloud probability value, Pcloud, is compared with a cloud threshold value cloudlim. ⇒ If Pcloud is larger than or equal to cloudlim, the cloud counter for this pixel element, numCloud[elem], is increased by one, and the routine starts over with the next pixel. • The passage product pixel confidence level (hereafter pppcl) is compared with the daily product pixel confidence level (hereafter dppcl). ⇒ If pppcl is smaller than dppcl, the pixel is skipped, and the routine starts over with the next pixel. ⇒ If pppcl is equal to dppcl, the contributions to the relative probabilities for clear and ice - given “cloud-free” - are computed based on the ratio between them, and the “cloud-free” counter is increased by one: sumCloudfree = Pice + Pcloud sumIce[elem] += Pice/sumCloudfree sumClear[elem] += Pclear/sumCloudfree numCloudfree[elem] ++ ⇒ If pppcl is larger than dppcl, the “cloud-free” counter numCloudfree[elem] and the relative probabilities for clear and ice - given “cloud-free” (sumIce[elem] and sumClear[elem]) are reset to zero, after which the calculations above are done. EUMETSAT OSI SAF



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In effect, the averaged probabilities are based on pixels from passages with the highest possible confidence level only.

3.1.2 Averaging the image and classifying the pixels When all the passage files have been treated as described in 3.1.1 the pixels of the tile are run through one last time: • If numCloudfree[elem] is larger than zero, the mean probability values for ice/snow (probice) and for open water/land (probclear) – assuming cloud-free – are calculated, and the pixel is classified: probice[elem] = sumIce[elem]/numCloudfree[elem]; probclear[elem] = sumClear[elem]/numCloudfree[elem]; ⇒ If probice[elem] is larger than or equal to probclear[elem] the pixel is classified as ice cover. ⇒ If probice[elem] is smaller than probclear[elem] the pixel is classified as no ice or very open ice. For both cases the pixel status flag is set to “0 nominal”. • If the pixel is flagged “land”, the daily product pixel is fillvalue, and the status flag is set to “100 land”. • If numCloudfree[elem] is zero and numCloud[elem] is larger than zero the pixel is given fillvalue, and the status flag is given value “103 cloud”. • If numUndef[elem] equals the number of files on the tile list, the pixel is undefined for all satellite passes of the period. The pixel value is then fillvalue, and the status flag becomes “101 missing”.

3.1.3 The cloudlim variable The cloudlim variable determines at which probability for cloud a pixel is considered to be cloud-covered. Passage product pixels with a probability of cloud lower than the cloudlim variable are considered “cloud-free” and form the basis on which the daily product probabilities are calculated. Investigations show that a value of 0.4 for cloudlim leads to good results.

3.2 Case studies for the daily product This chapter starts with a general example that illustrates the daily product and continues with three examples which are compared with AVHRR images and with SAR data. EUMETSAT OSI SAF



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3.2.1 Daily product, Scandinavia, May 25th 2009 Figure 13 shows a daily ice edge product for May 25th 2009 covering the four tiles. The pixels are classified in the categories ice, clear, cloud, or unclassified. This product is based on classified images from NOAA-17, NOAA-18 and MetOp-A. Large areas, both over land and over water, belong to the class clouded, meaning that these pixels had a probability for cloud larger than the cloudlim value for all the classified individual passages of that day. 15 passage files had sufficient daylight and contributed to the daily product for the northeastern tile, while 10 individual passage products contributed to the products for the southeastern tile and the northwestern tile. The southwestern tile is based on 5 individual passage files. Parts of the sea ice edge is visible in the Baffin Bay, along the coast of Greenland and east of Svalbard. Areas where the white sea ice pixels border to blue cloud pixels, without a belt of dark grey clear water pixels in between, indicate that clouds cover (parts of) the sea ice. Notice that the issue with misclassified ice – seen in some of the individual satellite passes of the previous chapter – is still present (conf. ocean areas north of Iceland and outside of the Norwegian coast). The corresponding ice probability image can be seen in Figure 14. Here, red pixels are areas with a high probability of sea ice (or snow) given a “cloud-free” situation, green pixels have probabilities in the ∼30-∼60% range, while blue colors correspond to a low probability of sea ice (i.e. they have a high probability for open water or clear land). Black pixels in Figure 14 represent pixels that are clouded or unclassified. The large green areas seen in the Baltic Sea and over Mainland Scandinavia claim a ∼50% probability of sea ice or snow. Closer investigations of the individual passage products of that day reveal that – for the case of the southeastern tile – these erroneous areas are due to three unfortunate passage products. The green area in the Baltic Sea is due to sunglint in two afternoon satellite passes (the MetOp-A pass at 17:37 UTC and the NOAA-17 pass at 17:42 UTC). Figure 15 shows the AVHRR RGB 124 composite for the NOAA-17 passage at 17:42 UTC together with the corresponding classified passage product for ice probability. A large region of the Baltic Sea has been classified with a high probability of sea ice. This region matches very well with the green area of Figure 14. Figure 15 also shows cases of cloud shadows over land being misclassified as ice (notice the line of “dots” following the edge of the cloud system in the center of the image). Both of these erroneous effects have a visible influence on the daily product due to the limited number of input files (13 for the southeastern tile of the original product), and they appear as areas of probability for sea ice/snow in the ∼30-∼50% range.

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Figure 13: OSI SAF AVHRR Daily classified product of May 25 2009. This product is based on NOAA-17, NOAA-18 and MetOp-A classified images.

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Figure 14: OSI SAF AVHRR Daily ice probability product of May 25 2009. The probability for sea ice (or snow) is shown assuming cloud-free.

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(a)

31

(b)

Figure 15: NOAA-17 passage 17:42 UTC on 25 May 2009. Panel (a) shows the RGB composite of AVHRR channels 1, 2, and 4, while panel (b) shows the corresponding classified ice probability product (conf. Figure14 for the ice probability color table). The green land area was found to come from an early morning passage by NOAA-18 at 03:57 UTC in which an unfortunate Sun-Earth-satellite viewing geometry lead to land being classified as ice. Removing the three passes improves the performance dramatically. This is demonstrated by Figure 16, which shows the original accumulated product in panel (a) and the new accumulated product in panel (b). The latter is based on the same input files as the original product shown in Figures 13 and 14, except that the early NOAA-18 pass and the two late passes by NOAA-17 and MetOp-A are excluded.

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(a)

(b)

Figure 16: Two OSI SAF daily ice edge products. Panel (a) shows the original product, based on 13 input files, while panel (b) shows the product based on 10 input files. Conf. Figure 14 for the ice probability color table. A large number of input passage images will allow erroneous effects like those described here to be “averaged” out in the daily product. Remains of misclassified ice due to sunglint, cloud shadows, etc. can reduce the quality of the classified ice probability product (Figure 14), but the classed daily product (Figure 13) is less troubled.

3.2.2 Sea ice near Svalbard, May 6th 2009 Figure 17 shows Svalbard and the northern parts of Scandinavia on May 6th 2009. The AVHRR RGB 124 (upper left) and RGB 264 (upper right) composites, taken from a MetOpA passage at 10:55 UTC, are shown together with the daily classified product (lower left) and daily ice probability product (lower right). The accumulated products are based on the available daylight NOAA-17, NOAA-18 and MetOp-A passages of that day, seven in total. In this example one can see large portions of the sea ice edge on both sides of Svalbard. Notice that there are some small, scattered regions of wrongfully classified pixels, in particular near Bjørnøya and in connection with the cloud system southwest of Svalbard. The snow-covered areas in Mainland Norway are somewhat larger in the daily products than in the MetOp-A passage, showing that the cloud cover has shifted throughout the day and allowed more of the product to become cloud-free. Figure 18 takes a closer look on the area west of Svalbard. The daily product for probability of sea ice – given cloud-free – is shown in panel (a). Panel (b) shows a SAR image from RADARSAT 1 at 06:47 UTC on May 6 2009 for the same area. There is very good

EUMETSAT OSI SAF


Figure 17: AVHRR RGB 124 and RGB 264 for MetOp-A on May 6 2009 10:55 UTC on the top row, and OSI SAF AVHRR daily ice edge products on the lower row. The lower left panel shows the daily classified product, and the lower right panel shows the daily ice probability product. Seven AVHRR images contributed to the daily product, one of which being the MetOp-A passage shown on top.


34

(a)

(b)

Figure 18: OSI SAF AVHRR daily ice product from May 6 2009 is seen in panel (a). Panel (b) shows a RADARSAT-1 image from May 6 2009 06:47 UTC. correspondence between the two panels. The three peculiar ice edge features west of Svalbard in panel (a) map very well to the SAR image in panel (b). The open water area north of Spitzbergen also compares well, as does the “small”, half-circle shape of open water by the Spitzbergen coast (upper, right area of both panels).

3.2.3 The east coast of Greenland, February 11th 2009 An example from February 11th 2009 is shown in Figure 19. Due to the short daytime period only two individual passage products, both from NOAA-17, contribute to the aggregated, classified product seen in the lower image. The upper image shows the AVHRR RGB 124 from one of the passes of that day. The ice edge seen along the Greenland coast in the daily classified product (the borderline between white and dark grey pixels) seems to correspond well with the ice edge seen in the AVHRR image. Much of the ocean is covered by clouds leaving just av few areas of dark grey in the classified product. Figure 20 gives a comparison with SAR data from Envisat. The upper image shows a mosaic of SAR data, while the lower image is a combination of the SAR mosaic and the classified ice edge product shown in Figure 19. There is also for this case a good correspondence between the SAR ice edge and the AVHRR ice edge product.

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Figure 19: RGB composite of AVHRR channels 1, 2, and 4 for a NOAA-17 passage at 12:37 UTC on February 11 2009 (top) and the OSI SAF aggregated classified ice edge product (bottom).

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Figure 20: The upper image shows a mosaic of SAR data from Envisat on February 11 2009. The lower image shows the Envisat data together with the OSI SAF AVHRR ice edge product for the same day.

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3.2.4 The east coast of Greenland, May 27th 2009 The final example, seen in Figure 21, covers again the east coast of Greenland, south of the Scoresby Sund. The upper image is the AVHRR RGB 124 composite from the MetOp-A passage at 12:01 UTC on May 27th 2009. The middle panel shows the classified aggregated ice edge product of that day, and the lower image shows a comparison between the aggregated ice edge product and a mosaic of SAR data from Envisat. Several features along the ice edge are easily recognized in all three figures. The aggregated daily product is composed of nine individual satellite passes from NOAA-17, NOAA-18 and MetOp-A.

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Figure 21: The upper panel shows an AVHRR RBG 124 composite for a MetOp-A satellite passage at 12:01 UTC on May 27 2009, the middle panel shows the classed daily product for the same day, and the lower panel shows a comparison between the daily product and a mosaic of SAR data from Envisat.


39

A. Appendix A.1 From thermal channel equivalent blackbody temperature to measured radiance The measured radiances of the AVHRR thermal channels 3B, 4, and 5 are given in form of the equivalent blackbody temperatures. The relation between the equivalent blackbody temperature, Tch , and the Earth scene radiance of a channel, NE , is given by the two equations ∗ = A + BTch Tch c1 νc3 NE = exp Tc2c hνc∗

(A.1) (A.2)

∗ is an “effective” blackbody temperature, A and B are (platform dependent) conHere, Tch stants, νc is the centroid wave number (also platform dependent) and the first and second radiation constants c1 and c2 are:

c1 = 1.1910427 × 10−5 mW/(m2 sr cm−4 )

(A.3)

c2 = 1.4387752 cm K

(A.4)

The values for νc , A, and B are unique for each spacecraft and can be found in appendix D of the NOAA KLM User’s Guide1 .

A.2 Training data plots Figures A.1 and A.2 show density plots for the training data used to extract statistical coefficients for the probability distribution functions. The histograms outline the probability density functions for four2 of the five features used (conf. Table 2).

1 2

http://www.ncdc.noaa.gov/oa/pod-guide/ncdc/docs/klm/html/d/app-d.htm The fifth feature, “dt”, is not based on this set of training data and therefore not shown here.

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Figure A.1: AVHRR training data density plots. The upper row shows Channel 1 reflectance (“A1”) for cloud, snow, land (left) and cloud, sea ice, water (right). The lower row shows the ratio of Channel 2 to Channel 1 reflectance (“A2/A1”) for cloud, snow, land (left) and cloud, sea ice, water (right). Curves for normal distribution functions, fitted to the training data, are also plotted (thin lines).

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Figure A.2: AVHRR training data density plots. The upper row shows Channel 3A to Channel 1 reflectance (“r3a1”) for cloud, snow, land (left) and cloud, sea ice, water (right). The lower row shows the ratio of Channel 3B reflectance (after the thermal contribution has been removed) to Channel 1 reflectance (“r3b1”) for cloud, snow, land (left) and cloud, sea ice, water(right).

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References [1] R. C. Allen, P. A. Durkee, and C. H. Wash, “Snow/cloud discrimination with multispectral satellite measurements,” J. Appl. Meteor., vol. 29, pp. 994–1004, 1990. [2] G. Gesell, “An algorithm for snow and ice detetion using avhrr data: An extension to the apollo software package,” Int. J. Remote Sens., vol. 10, pp. 897–905, 1989. [3] S. Platnick and J. Fontenla, “Model calculations of solar spectral irradiance in the 3.7˘ 03bcm band for earth remote sensing applications,” J. Appl. Meteor. Climatol., vol. 47, p. 124, 2008.

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