Evaluation of Earth Systems Models and Atmospheric Inversions using Total Column CO2 Observations from GOSAT and OCO-2

2020 ◽  
Author(s):  
Prabir K Patra ◽  
Tomohiro Hajima ◽  
Ryu Saito ◽  
Naveen Chandra ◽  
Yukio Yoshida ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Greenhouse Gases ◽  
Atmospheric Chemistry ◽  
Satellite Remote Sensing ◽  
Seasonal Cycle ◽  
Inverse Model ◽  
Earth System ◽  
Area Index ◽  
In Situ Data

Abstract The measurements of one of the major greenhouse gases, carbon dioxide (CO 2 ), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by JAXA in 2009 and NASA’s Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO 2 fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO 2 data have also made significant progress. In this article, we attempt, for the first time, to evaluate the CO 2 fluxes simulated by an earth system model (MIROC-ES2L) using GOSAT observations and the fluxes estimated by an inverse model (MIROC4-Inv) for the period 2009-2014. Further, we use the OCO-2 measurements for testing the consistency of inversion results for the period 2014-2018, along with the GOSAT data. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating CO 2 concentrations that are sampled at the time and location of the satellite measurements. Our results suggest the inverse model using in situ data are more consistent with the OCO-2 retrievals, compared to those of the GOSAT XCO 2 data, suggesting possible improvements in the present GOSAT retrieval system by better accounting for the degradation correction of the TANSO-FTS. The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO 2 fluxes, compared to that from the ACTM_InvF. This difference is revealed by greater ACTM_ES2LF vs GOSAT differences, compared to those of ACTM_InvF vs GOSAT. We also find that the simulated seasonal cycle amplitude of XCO 2 by ACTM_ES2LF are slightly weaker compared to those observed by GOSAT or ACTM_InvF. Using remote sensing based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO 2 measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data.

Evaluation of Earth System Model and Atmospheric Inversion using Total Column CO2 Observations from GOSAT and OCO-2

2020 ◽  
Author(s):  
Prabir K Patra ◽  
Tomohiro Hajima ◽  
Ryu Saito ◽  
Naveen Chandra ◽  
Yukio Yoshida ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Greenhouse Gases ◽  
Satellite Remote Sensing ◽  
Inverse Model ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Area Index ◽  
In Situ Data

Abstract The measurements of one of the major greenhouse gases, carbon dioxide (CO2), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by Japan Aerospace Exploration Agency (JAXA) in 2009 and National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO2 fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO2 data have also made significant progress. In this article, we attempt, for the first time, to evaluate the CO2 fluxes simulated by an earth system model (MIROC-ES2L) using GOSAT observations and the fluxes estimated by an inverse model (MIROC4-Inv) for the period 2009-2014. Further, we use the OCO-2 measurements for testing the consistency of inversion results for the period 2014-2018, along with the GOSAT data. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating CO2 concentrations that are sampled at the time and location of the satellite measurements. Our results suggest the inverse model using in situ data are more consistent with the OCO-2 retrievals, compared to those of the GOSAT XCO2 data, suggesting possible improvements in the present GOSAT retrieval system by better accounting for the degradation correction of the Thermal And Near infrared Sensor for carbon Observations - Fourier Transform Spectrometer (TANSO-FTS). The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO2 fluxes, compared to that from the ACTM_InvF. This difference is revealed by greater XCO2 differences for ACTM_ES2LF vs GOSAT, compared to those of ACTM_InvF vs GOSAT. Using remote sensing based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO2 measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data.

scholarly journals Evaluation of earth system model and atmospheric inversion using total column CO2 observations from GOSAT and OCO-2

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Prabir K. Patra ◽  
Tomohiro Hajima ◽  
Ryu Saito ◽  
Naveen Chandra ◽  
Yukio Yoshida ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Greenhouse Gases ◽  
Satellite Remote Sensing ◽  
Inverse Model ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
In Situ Data ◽  
Total Column

AbstractThe measurements of one of the major greenhouse gases, carbon dioxide (CO2), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by a three-way partnership between the Japan Aerospace Exploration Agency (JAXA), the Ministry of Environment (MoE) and the National Institute for Environmental Studies (NIES), and the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO2 fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO2 data have also made significant progress. We attempt, for the first time, to evaluate the CO2 fluxes simulated by an earth system model (MIROC-ES2L) and the fluxes estimated by an inverse model (MIROC4-Inv) using in situ data by comparing with GOSAT and OCO-2 observations. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating total column CO2 mole fraction (XCO2) that are sampled at the time and location of the satellite measurements. Both the ACTM simulations agreed well with the GOSAT and OCO-2 satellite observations, within 2 ppm for the spatial maps and time evolutions of the zonal mean distributions. Our results suggest that the inverse model using in situ data is more consistent with the OCO-2 retrievals, compared with those of the GOSAT XCO2 data due to the higher accuracy of the former. This suggests that the MIROC4-Inv fluxes are of sufficient quality to evaluate MIROC-ES2L simulated fluxes. The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO2 fluxes, compared with that from the ACTM_InvF. This difference is revealed by greater XCO2 differences for ACTM_ES2LF vs GOSAT, compared with those of ACTM_InvF vs GOSAT. Using remote sensing–based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO2 measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data.

scholarly journals Evaluation of Earth System Model and Atmospheric Inversion using Total Column CO2 Observations from GOSAT and OCO-2

2021 ◽  
Author(s):  
Prabir K Patra ◽  
Tomohiro Hajima ◽  
Ryu Saito ◽  
Naveen Chandra ◽  
Yukio Yoshida ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Greenhouse Gases ◽  
Satellite Remote Sensing ◽  
Earth System Model ◽  
System Model ◽  
Co2 Fluxes ◽  
Earth System ◽  
In Situ Data ◽  
Total Column

Abstract The measurements of one of the major greenhouse gases, carbon dioxide (CO2), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by Japan Aerospace Exploration Agency (JAXA) in 2009 and National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO2 fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO2 data have also made significant progress. We attempt, for the first time, to evaluate the CO2 fluxes simulated by an earth system model (MIROC-ES2L) and the fluxes estimated by an inverse model (MIROC4-Inv) using in situ data by comparing with GOSAT and OCO-2 observations. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating total column CO2 mole fraction (XCO2) that are sampled at the time and location of the satellite measurements. Both the ACTM simulations agreed well with the GOSAT and OCO-2 satellite observations, within 2 ppm for the spatial maps and time evolutions of the zonal mean distributions. Our results suggest that the inverse model using in situ data are more consistent with the OCO-2 retrievals, compared to those of the GOSAT XCO2 data due to the higher accuracy of the former. This suggests that the MIROC4-Inv fluxes are of sufficient quality to evaluate MIROC-ES2L simulated fluxes. The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO2 fluxes, compared to that from the ACTM_InvF. This difference is revealed by greater XCO2 differences for ACTM_ES2LF vs GOSAT, compared to those of ACTM_InvF vs GOSAT. Using remote sensing based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO2 measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data.

scholarly journals Utility of Satellite Remote Sensing for Land–Atmosphere Coupling and Drought Metrics

2017 ◽  
Vol 18 (3) ◽  
pp. 863-877 ◽  
Author(s):  
Joshua K. Roundy ◽  
Joseph A. Santanello
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Prediction Models ◽  
Water Cycle ◽  
Spatial Scales ◽  
Complex Nature ◽  
Sufficient Information ◽  
Tropospheric Temperature ◽  
In Situ Data

Abstract Feedbacks between the land and the atmosphere can play an important role in the water cycle, and a number of studies have quantified land–atmosphere (LA) interactions and feedbacks through observations and prediction models. Because of the complex nature of LA interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in situ data and reanalysis products. NASA’s Aqua satellite and retrievals of soil moisture and lower-tropospheric temperature and humidity properties are used as input. Overall, the Aqua-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in situ and reanalysis products. In addition, this work represents the first time that in situ observations were utilized for the coupling classification and CDI. The combination of in situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics.

scholarly journals Identifying Flood Events over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations, Hydrological Models and In Situ Data

2018 ◽  
Vol 10 (5) ◽  
pp. 713 ◽  
Author(s):  
Hao Zhou ◽  
Zhicai Luo ◽  
Natthachet Tangdamrongsub ◽  
Zebing Zhou ◽  
Lijie He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Poyang Lake ◽  
Lake Basin ◽  
Hydrological Models ◽  
Flood Events ◽  
In Situ Data ◽  
Poyang Lake Basin

scholarly journals In situ data supporting remote sensing estimation of spruce forest parameters at the ecosystem station Bílý Kříž

Beskydy ◽  
2017 ◽  
Vol 10 (1-2) ◽  
pp. 75-86 ◽  
Author(s):  
Lucie Homolová ◽  
Růžena Janoutová ◽  
Petr Lukeš ◽  
Jan Hanuš ◽  
Jan Novotný ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Scales ◽  
Carotenoid Content ◽  
Area Index ◽  
Needle Age ◽  
Quantitative Parameters ◽  
In Situ Data

Remote sensing offers an effective way of mapping vegetation parameters in a spatially continuous manner, at larger spatial scales and repeatedly in time compared to traditional in situ mapping approaches that are typically accurate, but limited to a few distributed location and few repetitions. In case of forest ecosystems, remote sensing allow to assess quantitative parameters or indicators related to forest health status such as leaf area index, leaf pigment content, chlorophyll fluorescence, etc. Development, calibration and validation of remote sensing-based methods, however, still rely on supportive in situ data. The aim of this contribution is to introduce the individual in situ components in the framework for the retrieval of forest quantitative parameters from airborne imaging spectroscopy data. All measurements were acquired during an extensive in situ/flight campaign that took place at the Norway spruce dominated study site Bílý Kříž (Moravian-Silesian Beskydy Mts., Czech Republic) during August 2016. In addition to airborne remote sensing data acquisition, the in situ activities included terrestrial laser scanning for tree 3D modelling, measurements of needle biochemical and optical properties, leaf area index measurements and spectral measurements of various natural and artificial surfaces. Leaf pigments varied between 25.2 and 49.1 µg cm-2 for chlorophyll a+b content, 4.9 – 10.6 µg cm-2 for carotenoid content depending on needle age and its adaptation to sun illumination, whereas ratio between the two pigments was stable around 4.6 – 5. 3. Specific leaf area of spruce needles varied between 49.3 and 105.8 cm2 g-1, being the highest for the shade adapted needles of the current year. Leaf area index of spruce stands of various age and densities varied between 5.3 and 9. 3.

scholarly journals SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP

Irriga ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 585-598
Author(s):  
Pedro Henrique Jandreice Magnoni ◽  
Cesar De Oliveira Ferreira Silva ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Remote Sensing ◽  
Water Budget ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
In Situ Data ◽  
Joint Application ◽  
E Mail ◽  
Sentinel 2

SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP*     PEDRO HENRIQUE JANDREICE MAGNONI1; CÉSAR DE OLIVEIRA FERREIRA SILVA1 E RODRIGO LILLA MANZIONE2   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista", Avenida Universitária, n° 3780, Altos do Paraíso, 18610-034, Botucatu, São Paulo, Brasil,  [email protected]; [email protected]. 2 Departamento de Engenharia de Biossistemas, Faculdade de Ciências e Engenharia, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Rua Domingos da Costa Lopes, 780, CEP 17602496, Tupã – SP, Brasil. E-mail: [email protected]. *Este artigo é proveniente das dissertações de mestrado dos dois primeiros autores.     1 RESUMO   Ferramentas baseadas em sensoriamento remoto possibilitam o monitoramento do balanço hídrico da água em diferentes resoluções espaciais e temporais. Ainda assim, modelos que exigem dados in-situ impossibilitam sua aplicação em áreas com escassez de dados. No sentido de lidar com esse desafio, o presente trabalho apresenta uma abordagem de escolha do momento de irrigar, pelo balanço hídrico da água no solo, baseada em estimativa da evapotranspiração real (ETA) obtida com o uso conjunto de imagens multiespectrais do sensor MSI/SENTINEL-2 e dados de uma estação meteorológica pública. A área de estudo foi um pivô central localizado no munícipio de Itatinga-SP. Para a tomada de decisão do momento de irrigar, com base em um manejo por lâmina de irrigação fixa, foi feita a interpolação da fração evapotranspirativa entre os dias com imagens disponíveis para obter a ETA nos dias sem imagens por meio do seu produto com a evapotranspiração de referência. Essa abordagem captou variações climáticas essenciais para a estimativa do balanço hídrico em dias sem imagem. Destaca-se nessa aplicação conjunta sua capacidade de ser realizada sem necessitar de parâmetros específicos da cultura, do microclima ou do relevo, tornando-se interessante para regiões com escassez de dados.   Palavras-chave:  evapotranspiração, momento de irrigar, agriwater.     MAGNONI, P. H. J.; SILVA, C. O. F.; MANZIONE, R. L. REMOTE SENSING APPLIED TO IRRIGATION MANAGEMENT IN AREAS WITH LACK OF DATA: A CASE STUDY IN A CENTRAL PIVOT IN ITATINGA-SP     2 ABSTRACT   Remote sensing-based tools allow the monitoring of water budgets over different spatial and temporal resolutions. Nevertheless, some models require in situ data, preventing their application in areas with a lack of data. To address this challenge, this work presents an approach for irrigation scheduling, based on soil water budget estimation using actual evapotranspiration (ETA) obtained using MSI/SENTINEL-2 multispectral images and data from a public meteorological station. The study area consisted of a central pivot located in the municipality of Itatinga-SP, Brazil. For decision-making of irrigation scheduling, considering a fixed irrigation rate, the evapotranspiration fraction was interpolated between the days with available images to obtain the ETA on the days without images using its product with the reference evapotranspiration. This approach captured essential climate variations for estimating the water budget on non-image days. Noteworthy in this joint application is its suitability to be performed not requiring crop-, microclimate- or relief-specific parameters, making it useful for regions with a lack of data.   Keywords: evapotranspiration, irrigation scheduling, agriwater.

scholarly journals Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

2019 ◽  
Vol 12 (1) ◽  
pp. 50 ◽  
Author(s):  
Mahyar Aboutalebi ◽  
Alfonso F. Torres-Rua ◽  
Mac McKee ◽  
William P. Kustas ◽  
Hector Nieto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Point Cloud ◽  
Spatial Information ◽  
Point Cloud Data ◽  
Area Index ◽  
Cloud Data ◽  
Fractional Cover ◽  
Spatially Distributed

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

scholarly journals Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3609 ◽  
Author(s):  
Kyryliuk ◽  
Kratzer
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Attenuation Coefficient ◽  
Secchi Depth ◽  
Local Algorithm ◽  
The Baltic Sea ◽  
In Situ Data ◽  
The Baltic ◽  
Level 2

In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient Kd(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (Rrs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data.

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