Project overview

OPERA-S5 (OPEn platform for the Retrieval of Aerosol and CO2 from S5) includes two main objectives: the development of Open-Source Community Full-Physics XCO2 Retrieval Algorithm for Sentinel 5, and the demonstration of the developed algorithm by investigating the uncertainty characterization of CO2 and CH4 retrievals in the context of the challenging CO2M requirements using the proxy combination of Sentinel-5 data and 3MI/MetOp-SG. The developed platform will provide state-of-the-art XCO2, aerosol and surface properties products from the inversion of Sentinel-5 data as stand alone and in combination with 3MI/MetOp-SG measurements.

The platform will count with generalized interfaces to be configured and expanded in a “user friendly” straightforward manner with a totally modular architecture to boost the application and adaptation of the retrieval to any particular approach. 

OPERA-S5 will count with a open-source license allowing users complete free use of the tool for scientific purposes.

This project has been funded by the ESA call: “SENTINEL 5 CO2 OPEN-SOURCE COMMUNITY RETRIEVAL ALGORITHM EXPRO+”, motivated by the Sentinel User Preparation program to boost flexible multimission approaches.

Proposed Approach

The Sentinel-5 (S5) mission is part of the European space component of the Copernicus observation program that is developed by the European Space Agency (ESA). It consists of a high-resolution spectrometer operating in the spectral range from the ultraviolet to shortwave infrared with seven different spectral bands. The three bands listed in Tab. 1 are of relevance to this proposal. S5 will fly on the MetOp-SG A satellite together with the multi-spectral imaging radiometer METimage and the multiangle polarimeter 3MI. METimage has 20 spectral channels between 443 nm and 13.3 μm optimized to provide information on aerosols, cloud size, surface albedo, fire, land and ice surface temperature, snow cover and soil moisture. 3MI with 12 spectral channels, of which nine give polarized measurements (see Tab 2) and with its 14 different viewing angles, is a dedicated aerosol and cloud instrument.

Table 1. The spectral bands of Sentinel 5 that are relevant to this project:


spectral range

spectral resolution/sampling

molecular absorption


755-773 nm

0.4 / 0.1333



1590-1675 nm

0.25 / 0.1

CH4, CO2, H2O (HDO)


2305-2385 nm

0.25 / 0.1


Table 2. 3MI spectral bands:


wavelength [nm]

polarisation measurements (I,Q,U) [Y/N]





































The first part of the project is devoted to the development of the framework platform that will allow an agile and straight forward development of XCO2 and aerosol retrievals with a modularized and generalized structure. Consequently, the second phase of the project refers to the extension of the algorithm developed in the previous step to overcome the drawbacks, deficiencies and bottlenecks assessed during the first phase. This part of the project will also serve as a demonstration of the possibilities of the platform in terms of technical efforts, scientific development potential and validation.

GRASP modularity, i.e. the separation of independent pieces of code, is based on the physical processes that take part in the interaction of radiation with the Atmosphere-Surface system. Therefore, the utilization of this modularity will be highly intuitive for the users and developers in their efforts to configure, expand and tune the code. The main GRASP modules are the following: input, output, inversion, forward model interface, single scattering, radiative transfer, surface, gas absorption, vertical discretization, chemical components, lidar and aerosol models.

The figure below shows a visual summary of the new GRASP architecture were the were the forward model modules will be completely independent:

This modular structure of the platform will enable several fully transparent user-friendly configuration options for practically useful retrieval trade-offs for the generation of the XCO2 products and the development of new approaches that overcome these trade-offs. For example, such trade-offs are expected to optimize the retrieval performance by finding the best compromise between retrieval speed, solution accuracy, computer memory use, scope of the retrieved parameters, etc.

The following features is planned in the envisioned retrieval platform:

  • Modularity of the retrieval, where all modeling aspects, such as RT, single scattering calculations, etc., can be realized using different physical or technical approaches.
  • Flexibility in choosing specific modeling concepts inside of each module, for example, optimized vertical discretization of aerosol or gaseous profiles, optimized description of size distribution and composition, etc.
  • Measurement combination from different passive and active (e.g. lidar) satellite or ground based observations, as well as, diverse a priori or ancillary datasets based on reanalysis, climatologies, etc.

An important part of the efforts of the project will be based on the application of AI-based tools to develop the capabilities of the algorithm:

  1. We will analyze AI cloud clearing algorithms that are developed by SRON and are based on: Random forest classifier (RFC) for cloud clearing using METimage data and Additional AI cloud clearing using 3MI data. 
  2. We investigate the replacement of the forward model by machine learning based tools.
  3. We will investigate an AI solution to specify the level-2 data quality based on recent analysis of the RemoTeC CO2 and CH4 data product from GOSAT-2 using quality filtering by machine learning [RD20].

The development of the proposed approach relies on the following conceptual factors:

  1. The maturity of aerosol and surface properties retrieval by GRASP algorithm.
  2. High degree of generalization and modularity already realized in the GRASP algorithm software implementation.
  3. High potential of GRASP retrieval for realizing synergy retrieval using diverse combinations of remote sensing measurements.
  4. SRON’s profound experience in CO2 retrieval.
  5. RemoTAP algorithm as the reference to obtain CO2 products and expand GRASP capabilities.
Project consortium

GRASP shorten from ‘Generalized Retrieval of Atmosphere and Surface Properties’ is a company that was founded in February 2015 with the main goal of development of remote sensing algorithms and scientific methods for environment studies of atmosphere and surface of the Earth. The initial idea of GRASP has been developed by CNRS and University of Lille. Then this base scientific concept has been realized in open-source GRASP-OPEN software adapted to diverse remote sensing applications.

The main GRASP activities cover a wide range of remote sensing topics:

  • Developments of algorithms for advanced atmosphere and surface characterization from passive and active ground based and spaceborne remote sensing.
  • Scientific consulting in environmental studies.
  • Distribution and support of GRASP open source code.

Since its creation, GRASP has been involved in collaboration with world-wide environmental public organizations and private companies, universities and the largest space agencies (ESA, EUMETSAT, NASA, JAXA) with the goal to improve the scientific knowledge of the atmosphere and surface properties, which have an essential impact on Earth climate, and tightly interconnected with human activities.

The GRASP code was developed for advanced aerosol and surface retrieval from remote sensing measurements. GRASP-SAS is composed by a unique team with full understanding of all aspects of the code: physical and mathematical basis, software optimization etc. GRASP team has led several projects to retrieve atmosphere and surface parameters from different satellite sensors (PARASOL, MERIS, Sentinel 3 and 4, 3MI).

Once new products are validated, they are planned for exploitation in science studies to address the project objectives towards better understanding the aerosol effects on Earth climate, aerosol-cloud interactions, and physical processes in the atmosphere. These products will be examined on their impact on data assimilation and high-resolution modelling over different aerosol regimes (domains in the Atlantic Ocean and the Mediterranean, following the ESA field campaigns for Aeolus and EarthCARE Cal/Val), and observational aerosol-cloud relationships. This is considered as a crucial step towards science exploitation by bridging the AIRSENSE efforts with the EC HE ACI projects.


SRON, Netherlands Institute for Space Research, is the expert space research institute for the Netherlands. The Earth-group within SRON is an expert group in atmospheric satellite remote sensing focusing on long lived greenhouse gasses (CO2 and CH4), carbon monoxide (CO) and aerosols.

Target research areas are the carbon cycle, aerosol effects on climate including aerosol cloud interaction, and the water cycle. The group has strong expertise in hardware technology development, calibration of polarimetric and radiometric observations, radiation transfer modeling, retrieval techniques and data analysis and assimilation. SRON co-initiated the development of the TROPOMI instrument and is the co-PI institute for TROPOMI responsible for safeguarding the scientific performance of TROPOMI with respect to the SWIR channel measuring CH4, CO and water vapor and is the PI institute of the TANGO mission. The team is also responsible for the development of the algorithms and codes for the operational products CO and CH4. SRON also initiated the SPEXone instrument development for the NASA PACE mission focusing on the characterisation of aerosol, and is the PI institute for this instrument.

As such, SRON is responsible for the operational algorithm development for the full data processing chain (L0-L2). SRON is responsible for the CH4 and CO operational processors of S5 (SICOR and RemoTeC) and develops one of the operational CO2 algorithms for CO2M (RemoTAP).


Landgraf, J., Butz, A., Hasekamp, O., Hu, H., aan de Brugh, J., Sentinel 5 L2 Prototype Processors, Algorithm Theoretical Baseline Document: Methane Retrieval, SRON-ESA-S5L2PP-ATBD-001-v3.1- 20190517-CH4, SRON Netherlands Institute for Space Research, Leiden, The Netherlands, 2023 Meijer, Y. et al., Copernicus co2 monitoring mission requirements document. techreport 3.0, European Space Agency, October 2020.

Rusli, S. P., Hasekamp, O., aan de Brugh, J., Fu, G., Meijer, Y., and Landgraf, J.: Anthropogenic CO monitoring satellite mission: the need for multi-angle polarimetric observations, Atmos. Meas. Tech., 14, 1167–1190,, 2021.

Lu, S., Landgraf J., Fu G., van Diedenhoven, B., Wu L., Rusli, S.P., Hasekamp O.P.: Simultaneous Retrieval of Trace Gases, Aerosols, and Cirrus Using RemoTAP—The Global Orbit Ensemble Study for the CO2M Mission, Front. Remote Sens., 18 July 2022

Borsdorff et al., A Novel AI Approach for the Cloud Clearing of the Operational TROPOMI CH4 data product, in preparation, 2023

Cheng Fan, Guangliang Fu, Antonio Di Noia, Martijn Smit, Jeroen H.H. Rietjens, Richard A. Ferrare, Sharon Burton, Zhengqiang Li, and Otto P. Hasekamp. Use of a neural network-based ocean body radiative transfer model for aerosol retrievals from multi-angle polarimetric measurements. Remote Sensing, 11(23), 2019.

Barr A., Landgraf, J., Borsdorff, B: CH4 and CO2 Retrievals of GOSAT-2 from RemoTeC with Quality Filtering by Machine Learning, in preparation 2023.

Herreras-Giralda, M. (2022). Development of an algorithm for retrievals of atmospheric aerosol properties using synergy of solar and thermal IR spectrum (Doctoral dissertation, Université de Lille).

Herreras-Giralda, M., Litvinov, P., Dubovik, O., Derimian, Y., Lapyonok, T., Fuertes, D., … & Fischer, J. (2022). Thermal emission in the successive orders of scattering (SOS) radiative transfer approach. Journal of Quantitative Spectroscopy and Radiative Transfer, 291, 108327.

Herrera, M. E., Dubovik, O., Torres, B., Lapyonok, T., Fuertes, D., Lopatin, A., … & Ristori, P. R. (2022). Estimates of remote sensing retrieval errors by the GRASP algorithm: application to ground-based observations, concept and validation. Atmospheric Measurement Techniques, 15(20), 6075-6126.

Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018,, 2011.

Dubovik, O., Fuertes, D., Lytvynov, P., Lopatin, A., Lapyonok, T., Doubovik, I., Xu, F., Ducos, F., Chen, C., Torres, B., Derimian, Y., Li, L., Herrera, M., Karol, Y., Matar, C., Schuster, G., Espinosa, R., Puthukkudy, A., Li, Z., Juergen, F., Preusker, R., Cuesta, J., Kreuter, A., Cede, A., Aspetsberger, M., Marth, D., Bindreiter, L., Hangler, A., Lanzinger, V., Holter, C., and Federspiel, C.: A Comprehensive Description of Multi-Term LSM for Applying Multiple a Priori Constraints in Problems of Atmospheric Remote Sensing: GRASP Algorithm, Concept, and Applications, Front. Remote Sens., doi:10.3389/frsen.2021.706851, 2021.



The data will be available soon.