Smoke from chinmeys

SLSTR AOD Retrieval Performance (SARP)

Smoke from chinmeys
Smoke from chinmeys

This study focuses on the performance of the Sentinel-3 SLSTR sensor to provide a mature AOD (Aerosol Optical Depth) product.

Last Updated

03, November 2020

Aerosols consists of small solid, or liquid, particles, and the fluid in which these particles are suspended. In this study, we consider atmospheric aerosols, i.e. particles or droplets in the air, which, together with the atmospheric gases, attenuate the incoming solar radiation before it reaches the earth surface, as well as the outgoing reflected and terrestrial radiation which can be observed from space.

Atmospheric aerosols are important for climate because they partly reduce the warming effect of greenhouse gases, by reflection of incoming solar radiation (cooling). Absorbing aerosols, however, augment the greenhouse gas effect — although this warming effect is much smaller than the overall cooling effect.

Absorbing aerosols also influence local meteorology. Hygroscopic particles contribute to cloud formation and influence cloud albedo and cloud droplet radius. Therefore, aerosol particles indirectly affect climate due to their effect on the reflection of solar radiation by clouds, while the smaller cloud droplets may reduce precipitation and affect the hydrological cycle. In addition to climate effects, aerosols strongly reduce air quality, leading to millions of premature deaths every year.

To retrieve information on atmospheric composition from satellite-based sensors, the whole atmosphere-Earth system needs to be taken into account, i.e. upwelling radiation from the Earth surface due to (sub-) surface reflection and reflection by atmospheric constituents: gases, aerosols and clouds.

In this study we are interested in the retrieval of aerosol properties and, in particular, the aerosol optical depth (AOD). AOD retrieval requires clear sky conditions, i.e. a cloud-free atmosphere, and an effective separation of atmospheric and surface contributions to the reflection observed at the top of the atmosphere (TOA). The first step in aerosol retrieval is cloud screening, i.e. cloud detection and removal of any pixels containing clouds. The second step is the correction for surface contributions to the TOA reflectance.

In the SARP study this is achieved over the ocean by using an ocean surface reflectance model. Over land the dual view offered by the SLSTR (Sea and Land Surface Temperature Radiometer) is used. A dual view algorithm (ADV) was developed for the Along Track Scanning Radiometers (ATSR), the predecessor of SLSTR with some adjustments, i.e. SLSTR has an aft view versus the ATSR forward view, a wider swath and the addition of some spectral channels. ADV was adapted to SLSTR and is here called SDV. It is used in this study to evaluate the performance of SLSTR for AOD retrieval.

In previous studies, ADV has been proven to be sufficiently mature to provide operational and high quality aerosol products from dual-view space-borne sensors. SDV raises similar expectations, which will be evaluated versus reference AOD datasets from ground-based remote sensing, as well as by comparison with AOD datasets from other sensors.


The objective of the SARP study is to quantify the performance of the Sentinel-3 SLSTR sensor to provide a mature AOD product globally, compliant with near-real time requirements (NRT), by using a reference algorithm with demonstrative capabilities for aerosol dual-view radiometers and with a suitable retrieval configuration w.r.t. SLSTR. To this end, the mature SDV algorithm will be applied to SLSTR data and the resulting retrieval product, mainly AOD at 550 nm (AOD550), will be evaluated using reference measurements from the AERONET network, as well as by comparison with the most commonly used satellite AOD product, i.e. the MODIS-Terra C6.1 merged Dark Target Deep Blue AOD product (the MODIS-Terra observations are close in time to the SLSTR observations). Retrieval errors will be characterised to provide the per pixel uncertainty in the aerosol product.


The SARP project will review the SDV algorithm. Figure 1 shows examples of the AOD retrieved using the SDV algorithm. Geophysical and instrumental errors, as well as effects of major physical assumptions, will be used to characterise the per pixel uncertainty in the aerosol product.

Figure 1: Examples of the AOD retrieved from SLSTR using the SDV algorithm
Figure 1: Examples of the AOD retrieved from SLSTR using the SDV algorithm

A SLSTR input dataset will be specified over selected regions, together with reference datasets for further evaluation of the SDV retrieval performance. In this exercise MODIS C6.1 data will also be used for comparison, especially over areas where AERONET reference data are not available.

In addition, the LibradTran radiative transfer model will be used to generate a synthetic dataset for evaluation of SDV, with a focus on specific error sources (e.g. understanding of the impact of SLSTR calibrations on AOD retrieval, SLSTR geometry effects, etc.). Recommendations for further improvement of SDV resulting from the evaluation exercises will be provided.

Following the finalisation of the current main phase of the project, the option is going to be activated (around April 2020). The goal of this option is to investigate further the specific SLSTR configuration in view of enhancing AOD retrieval quality based on the SDV/SSv algorithm (see example in Figure 2). This notably includes 1) testing new spectral parameterization in the key element (so-called 'k-ratio') with respect to SLSTR geometry and spectral channels based on the state-of-the-art land BRDF simulations 2), further investigations of the SLSTR L1 impacts on the L2 AOD product, and 3) possible new analyses of retrieval sensitivities in the rough southern oceans.

Upon activation of Option 2, the SDV algorithm source code will be transferred to EUMETSAT and documentation will be prepared.

Figure 2: Example of the AOD retrieved from SLSTR using the SDV/SSv algorithm
Figure 2: Example of the AOD retrieved from SLSTR using the SDV/SSv algorithm

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