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AATSR Data Formats Products
SST record 50 km cell MDS
BT/TOA Sea record 17 km cell MDS
Vegetation fraction for Land Surface Temperature Retrieval GADS
Topographic Variance data for Land Surface Temperature Retrieval GADS
Land Surface Temperature retrieval coefficients GADS
General Parameters for Land Surface Temperature Retrieval GADS
Climatology Variance Data for Land Surface Temperature Retrieval GADS
Level 0 SPH
Level 0 MDSR
Auxilliary Data SPH with N = 1
1.6 micron nadir view MDS
Summary Quality ADS
Scan pixel x and y ADS
Grid pixel latitude and longtitude topographic corrections ADS
Across-track Band Mapping Look-up Table
Configuration Data GADS
Processor configuration GADS
LST record 50 km cell MDS
Distributed product MDS
Level 2 SPH
10-arcminute mds
Limits GADS
Validation Parameters GADS
BT/TOA Land record 17 km cell MDS
General Parameters GADS
Temperature to Radiance LUT GADS
Radiance to Brightness Temperature LUT GADS
Medium/High Level Test LUT GADS
Infrared Histogram Test LUT GADS
11 Micron Spatial Coherence Test LUT GADS
11/3.7 Micron Nadir/Forward Test LUT GADS
11/12 Micron Nadir/Forward Test LUT GADS
Characterisation GADS
Browse Day_Time Colour LUT GADS
Browse SPH
Grid pixel latitude and longtitude topographic correction ADS
Level 2 SPH
Auxilliary Products
ATS_VC1_AX: Visible Calibration data
ATS_SST_AX: SST Retrieval Coeficients data
ATS_PC1_AX: Level-1B Processing configuration data
ATS_INS_AX: AATSR Instrument data
ATS_GC1_AX: General Calibration data
ATS_CH1_AX: Level-1B Characterization data
ATS_BRW_AX: Browse Product LUT data
Level 0 Products
ATS_NL__0P: AATSR Level 0 product
Browse Products
ATS_AST_BP: AATSR browse image
Level 1 Products
ATS_TOA_1P: AATSR Gridded brightness temperature and reflectance
Level 2 Products
ATS_NR__2P: AATSR geophysical product (full resolution)
ATS_MET_2P: AATSR Spatially Averaged Sea Surface Temperature for Meteo Users
ATS_AR__2P: AATSR averaged geophysical product
Frequently Asked Questions
The AATSR Instrument
Instrument Characteristics and Performance
In-flight performance verification
Instrument Description
Internal Data Flow
Instrument Functionality
AATSR Products and Algorithms
Common Auxiliary data sets
Auxiliary Data Sets for Level 2 processing
Instrument Specific Topics
Level 2 Products
Level 1B Products and Algorithms
Level 1B Products
Instrument Pixel Geolocation
The Level 0 Product
Differences Between ATSR-2 and AATSR Source Packets
Definitions and Conventions
Organisation of Products
Relationship Between AATSR and ATSR Products
AATSR Product Organisation
Data Handling Cookbook
Characterisation and Calibration
Monitoring of AATSR VISCAL Parameters
Latency, Throughput and Data Volume
Data Processing Software
Data Processing Centres
The AATSR Products User Guide
Image Gallery
Breakup of the Ross Ice Shelf
Land cover in the Middle East
Typhoon Saomai
Mutsu Bay, Japan
Deforestation in Brazil
Spatially Averaged Global SST, September 1993
Further Reading
How to use AATSR data
Why Choose AATSR Data?
Why Choose AATSR Data?
Special Features of AATSR
Principles of Measurement
Scientific Background
The AATSR Handbook
SST record 17 km cell MDS
Surface Vegetation class for Land Surface Temperature Retrieval GADS
1.6 micron forward view MDS
12 micron nadir view MDS
12 micron forward view MDS
Summary Quality ADS
Surveillance Limits GADS
Master Unpacking Definition Table GADS
1.6 micron Non-Linearity Correction LUT GADS
General Parameters GADS
Thin Cirrus Test LUT GADS
Fog/low Stratus Test LUT GADS
1.6 Micron Histogram
Browse MDS
ATS_CL1_AX: Cloud LUT data
Pre-flight characteristics and expected performance
Payload description, position on the platform
Auxiliary products
Auxiliary Data Sets for Level 1B processing
Summary of auxiliary data sets
Calculate Solar Angles
Image Pixel Geolocation
Level 0 Products
Acquisition and On-Board Data Processing
Product Evolution History
Hints and Algorithms for Higher Level Processing
Data Volume
Software tools
Summary of Applications vs Products
Geophysical Coverage
Geophysical Measurements
Visible calibration coefficients GADS
Level 1B SPH
LST record 17 km cell MDS
Conversion Parameters GADS
12 Micron Gross Cloud Test LUT GADS
ATS_PC2_AX: Level-2 Processor Configuration data
Level 2 Products
Hints and Algorithms for Data Use
BT/TOA Sea record 50 km cell MDS
BT/TOA Land record 50 km cell MDS
Level 2 Algorithms
Signal Calibration
Site Map
Frequently asked questions
Terms of use
Contact us


1.1.2 Scientific Background

This section provides a summary of the scientific requirements upon which the AATSR mission is based. The primary driver has been the retrieval of high quality SST measurements, however, a secondary objective is to provide scientifically useful products over land. Although the AATSR mission is not specifically designed for applications in the atmosphere and cryosphere, consideration was also given to the high level requirements of such research, to ensure maximum flexibility in extending the applications of AATSR wherever possible. SST

The scientific principles behind the design of the (A)ATSR instruments are dominated by the need for the high accuracy SSTs required for global climate monitoring and research. Accurate SST measurement is of great importance for climate research; for example, in modelling climatic phenomena such as the El Niño Southern Oscillation, in the monitoring of global warming due to the enhanced greenhouse effect, and in the investigation of ocean-atmosphere heat transfer. Consequently, the AATSR instrument and ground processing system are required to produce SST retrievals routinely with an absolute accuracy of better than 0.3K, globally, both for a single sample and when averaged over areas of 0.5° longitude by 0.5° latitude, under certain cloud free conditions (i.e. >20% cloud free samples within each area).

The global coverage offered by AATSR is also very important. The response of weather and climate to SST is the subject of growing scientific interest, due to the large quantities of energy stored in the ocean, the energy exchanges possible with the atmosphere, and the effects of any carbon dioxide-induced climate change on the oceans. Predictions of the effects of anthropogenic doubling of atmospheric carbon dioxide show rises in the ocean mixed-layer temperature of up to a few degrees when globally averaged. Models also show that ocean warming is not spatially uniform, with variations in the magnitude of warming around the globe.

Surface observations of SST from ships of opportunity and buoys are too sparse to provide adequate analyses on this scale, except in the areas of heavily travelled shipping lanes.  Ships of opportunity are also prone to measurement inconsistencies, due to the different ways in which SST data are collected (e.g. hull thermistors, cooling water intakes).  In addition, measurements from buoys and from ships of opportunity are of bulk temperature, which can differ by several tenths of a degree from the true 'skin' temperature of the sea surface.

Data from AATSR will add to data received from predecessor instruments, ATSR-1 and ATSR-2, and lead to a 15+ year record of precise and accurate global sea surface temperature. Global historical SST fields are not only an important boundary condition on Atmospheric Global Circulation Models (GCM), but they can also be used for validation of model outputs (for example, SSTs are a predicted variable for ocean-atmosphere coupled General Circulation Models).

These high level objectives give rise to a number of more detailed requirements dictating instrument design.

Primary Sensing Wavelengths

The ocean surface is considered to be an emitter of radiation, with a peak in emission occurring at around 10 µm. Assessment of atmospheric absorption in this area shows that the region between 10 and 13 µm is a suitable window with both low atmospheric absorption and good radiance sensitivity to small changes in SST. The AATSR channels at 11 and 12 µm were selected on this basis. The 3.7 µm channel was selected to provide an additional channel at night, because although measurements in the 3-5 µm window are affected by reflected solar irradiance during the day, they show very high radiometric sensitivity at night.

Atmospheric Correction

Given the overall requirement for accurate SST measurements, it is important to precisely account for the contribution of atmospheric absorption and emission to the upwelling radiances observed by the instrument. Early work with Advanced Very High Resolution Radiometer (AVHRR) data demonstrated that differential measurements of upwelling radiances in two closely spaced thermal channels allow an accurate assessment of atmospheric effects, if the atmospheric effects differ between the two channels. Consequently, AATSR provides corrections for the effects of the atmosphere on the SST retrievals through the use of multiple thermal channels in the retrieval.

However the strict demands placed on the accuracy of AATSR SST retrievals requires further improvements to atmospheric correction. This can be achieved by making two observations of the same ocean surface through different atmospheric path lengths. As a result, AATSR also employs a 'dual view' technique to achieve the best possible atmospheric correction.

Cloud Clearing

A key condition to meet the overall SST accuracy requirements is that of effective cloud clearing. AATSR employs a number of cloud tests using different combinations of thermal channel data. However, these techniques can be improved with reference to a visible channel during the daytime (when cloud will be bright compared to the sea surface), hence the inclusion of the additional channel at 1.6 µm, used primarily for cloud clearing.

Sampling Distance and Instantaneous Field of View (IFOV)

The overall SST accuracy requirement has been set on the basis that 20% of samples need to be cloud-free over a 0.5 degree by 0.5 degree cell. This means that for adequate noise reduction through averaging of individual sample values, a minimum of 500 cloud free samples are required. In the limiting scenario of 80% cloud cover, this requires a total of 2500 samples, which for a 50 by 50 km cell gives a sample size of 1 km. The AATSR sampling distance has therefore been set at 1 km. Research with previous sensors has also shown that 1 km is a reasonable compromise between data volume and spatial resolution for SST feature mapping.

In addition, for land applications a 1 km sampling distance is good for mapping and monitoring on large scales, whilst providing adequate discrimination of land surface types.

Co-alignment of the AATSR channel IFOVs is required to 0.1 of the sampling distance. This is to allow the cloud clearing algorithms to work successfully at the edge of cloud masses.

Calibration and Characterisation Requirements

To retrieve SST effectively from the AATSR detector signals, the spectral response and IFOV of the channels are measured prior to launch. In order to meet the strict accuracy requirements, a pre-launch end-to-end radiometric calibration of the instrument is also conducted.

In-orbit radiometric calibration will also play an important part in ensuring the long-term stability of AATSR SST measurements over the mission lifetime. To achieve this, the instrument carries two high precision black body targets. These are viewed during every scan to provide accurate in-orbit calibration of the thermal channels. Remote Sensing Over Land

Visible and near infrared wavebands were added to ATSR-2 and AATSR specifically to enable land remote sensing studies. However, they have been added in such a way as to avoid compromising the primary SST measurement requirements.

Work using Landsat and AVHRR data has shown that global monitoring of land, especially vegetation, at moderate resolution (i.e. ~1 to ~4 km) is of great value in studying a number of urgent global environmental problems. Parallel research has also demonstrated the benefit of using combinations of several visible bands for vegetation remote sensing. These are the principles behind the inclusion of AATSR visible bands, to combine the advantages of global coverage and improved spectral discrimination.

Band Selection

The reflectance spectrum of vegetation typically shows a low reflectance (~0.05) in the visible part of the spectrum coinciding with maximum solar irradiance, at which wavelengths light is absorbed by vegetation for photosynthesis. In the near infra-red (NIR), foliage has a high reflectance (~0.5), with a very rapid transition between the red and NIR regions at ~0.75 µm. Soil typically has a fairly flat spectrum (gradually rising at higher wavelengths) over the same region, though its absolute reflectance varies with soil-type and moisture (wet soil being darker than dry soil).

Either the ratio or difference between two spectral bands on either side of the ~0.75 µm transition will indicate the presence of photosynthetically active vegetation. Those bands usually chosen are centred in the red part of the spectrum at ~0.66 µm and in the NIR at ~0.87 µm. A number of 'vegetation indices' involving these two bands have been proposed. In order to provide assessment of vegetation quantity using these established vegetation indices, AATSR provides two reflection channels in the red and NIR spectral regions (at 0.67 and 0.87 µm).

Healthy growing vegetation has a reflectance peak in the green part of the spectrum, which is indicative of the amount of chlorophyll present. Thus, the reflectance in a green spectral band is reduced for senescent, diseased or damaged foliage. Studies using a green band in conjunction with the red and near-infrared bands show this effect, and important extra information is obtained on the growth stage (as well as indications of damage) by combining the three bands in this multi-spectral approach. AATSR includes a narrow band channel in the centred at 0.55 µm for this purpose.

Radiometric Requirements

The AATSR visible channels need to encompass all possible normal variations in brightness over the whole of the Earth's surface, except sunglint, without saturation. The maximum signal is derived by assuming the value at the top of the atmosphere with 100% spectral albedo of ideal Lambertian reflection. The 100% spectral albedo value should encompass most natural levels of reflected sunlight except sunglint. It is however possible that spectral radiance values greater than this can be obtained by the addition of an atmospheric signal due to aerosol scatter, or due to non-Lambertian reflection. For example, the highest cloud target reflectances are predicted to correspond to an effective albedo of 110-120%. Conversely, it may be found during flight that 100% albedo is unrealistically high for most situations of scientific interest. To maximise the precision of the measurements whilst retaining flexibility in the range of the instrument, the gain and offset of the AATSR visible channels are therefore selectable in flight.

In order for the instrument to successfully support land applications, it was considered desirable that AATSR should measure differences in vegetation reflectance of the order of ~1% for ~10% vegetation cover. Experience with the AVHRR (with 10-bit digitisation) and Landsat TM (with 8-bit digitisation) instruments has shown that in some circumstances they are inadequate, and that a signal-to-noise ratio of at least 20 to 1 at 0.5% albedo is necessary for the red and NIR bands. Also, ignoring atmospheric contributions, and with 10% ground cover by typical leaves (reflectances red: 5%, NIR: 50%) over a typical soil (reflectances red: 20%, NIR: 30%), this noise level would, in principle, enable ~2% differences in leaf area, for constant soil reflectances, to be measured. The signal to noise ratio for the AATSR visible channels has been set accordingly.

Calibration Requirements

To retrieve land parameters from AATSR detector signals by modelling, the spectral response and IFOV of the visible channels need to be measured prior to launch. Consequently, AATSR has undergone a rigorous pre-launch calibration campaign. In addition, for long term monitoring of land parameters, it is important to have confidence in the stability of the sensing system. Thus, an in-orbit calibration system for the visible channels is also included. Cryosphere, Cloud and Atmospheric Measurements

Cloud and atmospheric measurements

The objective of research with (A)ATSR data in this field is to extend its usefulness as a research tool for cloud type identification and cloud top temperature measurement. The multi-channel and two-view features offered by the instruments are a powerful combination for cloud, aerosol and atmospheric investigations, in addition to the stereo viewing capability offered by the dual view.

Although the addition of the visible channels was primarily for land measurement, they also have a number of applications that will enhance cloud and atmospheric monitoring capabilities. In particular, the NIR band provides additional information, which is complementary to the 1.6 µm band, for ice/water cloud discrimination, and for sizing cloud droplets. Estimation of atmospheric aerosol loadings are of particular interest in quality assessing SST retrievals, which can be significantly affected during, for example, atmospheric contamination due to volcanic ash injections and schemes already exist to use the 1.6 µm channel to estimate aerosol optical depths over oceans.

Since a large part of the atmospheric attenuation/emission of thermal radiation in the atmospheric window regions used to remotely sense SST is caused by highly variable water vapour, it is possible to estimate the content of the atmospheric column from ATSR data. The accurate and precise measurement of atmospheric water vapour is potentially extremely important for climate research, as water vapour is the major greenhouse gas, and is also involved in atmospheric processes such as cloud formation, precipitation and evaporation.


Variations in the annual sea ice growth and decay cycles in polar regions are a strong indicator of climatic change, particularly as modelling shows anthropogenic climate change to be pronounced in polar regions. In addition, the sea ice extent itself affects the climate (due to the difference in albedo of the sea ice in comparison with the ocean surface, and the thermal insulation), and so is an important parameter for climate modellers and for studies of ice-ocean-atmosphere exchanges.

Work with ATSR data, together with data from other ERS sensors (e.g. the Radar Altimeter (RA) and the Synthetic Aperture Radar (SAR)) has already proved effective in the study of Antarctic and Arctic sea ice boundaries, and the investigation of ice surface temperatures. The discriminatory power of the 1.6 µm channel for cloud and ice/snow is of particular interest, and the enhancements of cloud/ice discrimination with the reflection channels is a particular goal.