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 Water Mixing Conditions Influence Sentinel-2 Monitoring of Chlorophyll Content in Monomictic Lakes

2021 ◽  
Vol 13 (14) ◽  
pp. 2699
Author(s):  
Michela Perrone ◽  
Massimiliano Scalici ◽  
Luisa Conti ◽  
David Moravec ◽  
Jan Kropáček ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Phytoplankton Biomass ◽  
Circulation Pattern ◽  
Water Circulation ◽  
Water Bodies ◽  
Mixture Density ◽  
In Situ Data ◽  
Sentinel 2

Prompt estimation of phytoplankton biomass is critical in determining the ecological quality of freshwaters. Remote Sensing (RS) may provide new opportunities to integrate with situ traditional monitoring techniques. Nonetheless, wide regional and temporal variability in freshwater optical constituents makes it difficult to design universally applicable RS protocols. Here, we assessed the potential of two neural networks-based models, namely the Case 2 Regional CoastColour (C2RCC) processor and the Mixture Density Network (MDN), applied to MSI Sentinel-2 data for monitoring Chlorophyll (Chl) content in three monomictic volcanic lakes while accounting for the effect of their specific water circulation pattern on the remotely-sensed and in situ data relation. Linear mixed models were used to test the relationship between the remote sensing indices calculated through C2RCC (INN) and MDN (IMDN), and in situ Chl concentration. Both indices proved to explain a large portion of the variability in the field data and exhibited a positive and significant relationship between Chl concentration and satellite data, but only during the mixing phase. The significant effect of the water circulation period can be explained by the low responsiveness of the RS approaches applied here to the low phytoplankton biomass, typical of the stratification phase. Sentinel-2 data proved their valuable potential for the remote sensing of phytoplankton in small inland water bodies, otherwise challenging with previous sensors. However, caution should be taken, since the applicability of such an approach on certain water bodies may depend on hydrological and ecological parameters (e.g., thermal stratification and seasonal nutrient availability) potentially altering RS chlorophyll detection by neural networks-based models, despite their alleged global validity.

scholarly journals Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 742 ◽  
Author(s):  
Tuuli Soomets ◽  
Kristi Uudeberg ◽  
Dainis Jakovels ◽  
Agris Brauns ◽  
Matiss Zagars ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Inland Waters ◽  
Water Type ◽  
Water Types ◽  
In Situ Data ◽  
Spatial Coverage ◽  
Level 2 ◽  
Sentinel 2

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84–0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.

scholarly journals Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3004
Author(s):  
Antonia Ivanda ◽  
Ljiljana Šerić ◽  
Marin Bugarić ◽  
Maja Braović
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Coastal Waters ◽  
Optical Parameter ◽  
Data Set ◽  
Chl A ◽  
Sentinel 2

In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the parameters that indicates water quality and that can be measured by in situ measurements or approximated as an optical parameter with remote sensing. Remote sensing products for monitoring Chl-a are mostly based on the ocean and open sea monitoring and are not accurate for coastal waters. In this paper, we propose a method for remote sensing monitoring that is locally tailored to suit the focused area. This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements. We augmented the data set horizontally by transforming the original feature set, and vertically by adding synthesized zero measurements for locations without Chl-a. By transforming features, we were able to achieve a sophisticated model that predicts Chl-a from combinations of features representing transformed bands. Multiple Linear Regression equation was derived to calculate Chl-a concentration and evaluated quantitatively and qualitatively. Quantitative evaluation resulted in R2 scores 0.685 and 0.659 for train and test part of data set, respectively. A map of Chl-a of the case study area was generated with our model for the dates of the known incidents of algae blooms. The results that we obtained are discussed in this paper.

scholarly journals Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3634 ◽  
Author(s):  
Angel Fernandez-Carrillo ◽  
Zdeněk Patočka ◽  
Lumír Dobrovolný ◽  
Antonio Franco-Nieto ◽  
Beatriz Revilla-Romero
Keyword(s):  
Remote Sensing ◽  
Central Europe ◽  
Bark Beetle ◽  
Forest Damage ◽  
Economic Losses ◽  
In Situ Data ◽  
Damage Mapping ◽  
Beetle Damage ◽  
Forest Vitality ◽  
Sentinel 2

Over the last decades, climate change has triggered an increase in the frequency of spruce bark beetle (Ips typographus L.) in Central Europe. More than 50% of forests in the Czech Republic are seriously threatened by this pest, leading to high ecological and economic losses. The exponential increase of bark beetle infestation hinders the implementation of costly field campaigns to prevent and mitigate its effects. Remote sensing may help to overcome such limitations as it provides frequent and spatially continuous data on vegetation condition. Using Sentinel-2 images as main input, two models have been developed to test the ability of this data source to map bark beetle damage and severity. All models were based on a change detection approach, and required the generation of previous forest mask and dominant species maps. The first damage mapping model was developed for 2019 and 2020, and it was based on bi-temporal regressions in spruce areas to estimate forest vitality and bark beetle damage. A second model was developed for 2020 considering all forest area, but excluding clear-cuts and completely dead areas, in order to map only changes in stands dominated by alive trees. The three products were validated with in situ data. All the maps showed high accuracies (acc > 0.80). Accuracy was higher than 0.95 and F1-score was higher than 0.88 for areas with high severity, with omission errors under 0.09 in all cases. This confirmed the ability of all the models to detect bark beetle attack at the last phases. Areas with no damage or low severity showed more complex results. The no damage category yielded greater commission errors and relative bias (CEs = 0.30–0.42, relB = 0.42–0.51). The similar results obtained for 2020 leaving out clear-cuts and dead trees proved that the proposed methods could be used to help forest managers fight bark beetle pests. These biotic damage products based on Sentinel-2 can be set up for any location to derive regular forest vitality maps and inform of early damage.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

Use of geographic information systems and remote sensing as automated tool for variable rate irrigation prescriptions

2019 ◽  
Author(s):  
◽  
Anh Thi Tuan Nguyen
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Regression Models ◽  
Field Data ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Crop Growth ◽  
Variable Rate ◽  
Water Application ◽  
Center Pivot

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Economic as well as water shortage pressure on agricultural use of water has placed added emphasis on efficient irrigation management. Center pivot technology has made great improvement with variable rate irrigation (VRI) technology to vary water application spatially and temporally to maximize the economic and environmental return. Proper management of VRI systems depends on correctly matching the pivot application to specific field temporal and areal conditions. There is need for a tool to accurately and inexpensively define dynamic management zones, to sense within-field variability in real time, and control variable rate water application so that producers are more willing to adopt and utilize the advantages of VRI systems. This study included tests of the center pivot system uniformity performance in 2014 at Delta Research Center in Portageville, MO. The goal of this research was to develop MOPivot software with an algorithm to determine unique management areas under center pivot systems based on system design and limitations. The MOPivot tool automates prescriptions for VRI center pivot based on non-uniform water needs while avoiding potential runoff and deep percolation. The software was validated for use in real-time irrigation management in 2018 for VRI control system of a Valley 8000 center pivot planted to corn. The water balance model was used to manage irrigation scheduling. Field data, together with soil moisture sensor measurement of soil water content, were used to develop the regression model of remote sensing-based crop coefficient (Kc). Remote sensing vegetation index in conjunction with GDD and crop growth stages in regression models showed high correlation with Kc. Validation of those regression models was done using Centralia, MO, field data in 2016. The MOPivot successfully created prescriptions to match system capacity of the management zone based on system limitations for center pivot management. Along with GIS data sources, MOPivot effectively provides readily available graphical prescription maps, which can be edited and directly uploaded to a center pivot control panel. The modeled Kc compared well with FAO Kc. By combining GDD and crop growth in the models, these models would account for local weather conditions and stage of crop during growing season as time index in estimating Kc. These models with Fraction of growth (FrG) and cumulative growing degree days (cGDD) had a higher coefficient of efficiency, higher Nash-Sutcliffe coefficient of efficiency and higher Willmott index of agreement. Future work should include improvement in the MOPivot software with different crops and aerial remote sensing imagery to generate dynamic prescriptions during the season to support irrigation scheduling for real-time monitoring of field conditions.

scholarly journals ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

2018 ◽  
Vol IV-5 ◽  
pp. 223-228 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Remote Sensing ◽  
Water Resource Management ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Physical Relationship ◽  
Sensing Data ◽  
Surface Snow ◽  
Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

scholarly journals Measurements of Aerosols and Trace Gases in Southern Romania

2017 ◽  
Vol 68 (4) ◽  
pp. 873-878
Author(s):  
Alexandru Dandocsi ◽  
Anca Nemuc ◽  
Cristina Marin ◽  
Simona Andrei
Keyword(s):  
Remote Sensing ◽  
Vertical Profiles ◽  
Aerosol Mass Spectrometer ◽  
Boundary Layer Height ◽  
Passive Remote Sensing ◽  
Aerosol Mass ◽  
Scanning Lidar ◽  
532 Nm

An intensive measurement campaign was performed during September 2014 in southern Romania in two different locations: Magurele, Ilfov County and Turceni, Gorj County. This paper presents one case study with analysis of the aerosol properties from in-situ, passive remote sensing and active remote sensing measurements. A Multiwavelength Raman Lidar (RALI) provided one hour averaged vertical profiles of extinction and backscatter from the 532 nm and 1064 nm channels in Magurele. The UV scanning Lidar (MILI) provided one hour averaged backscattered and extinction vertical profiles for Turceni. Planetary Boundary Layer Height (PBLH) was calculated using the altitude of the maximum negative gradient of the range corrected signal. Mass concentrations for different aerosol species (organics, nitrate, sulphate, ammonium and chloride) were obtained from in-situ measurements using Aerosol Mass Spectrometer located in M�gurele and Aerosol Chemical Speciation Monitor (ACSM) located in Turceni.

scholarly journals Remote Sensing Applications for Planning Irrigation Management. The Use of SEBAL Methodology for Estimating Crop Evapotranspiration in Cyprus

2012 ◽  
Vol 9 (1) ◽  
pp. 17-21
Author(s):  
George Papadavid ◽  
Skevi Perdikou ◽  
Michalakis Hadjimitsis ◽  
Diofantos Hadjimitsis
Keyword(s):  
Remote Sensing ◽  
Water Allocation ◽  
Surface Energy Balance ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Severe Drought ◽  
Remotely Sensed Data ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Remote Sensing Techniques

Abstract Water allocation to crops has always been of great importance in the agricultural process. In this context, and under the current conditions, where Cyprus is facing a severe drought the last five years, the purpose of this study is basically to estimate the needed crop water requirements for supporting irrigation management and monitoring irrigation on a systematic basis for Cyprus using remote sensing techniques. The use of satellite images supported by ground measurements has provided quite accurate results. Intended purpose of this paper is to estimate the Evapotranspiration (ET) of specific crops which is the basis for irrigation scheduling and establish a procedure for monitoring and managing irrigation water over Cyprus, using remotely sensed data from Landsat TM/ ETM+ and a sound methodology used worldwide, the Surface Energy Balance Algorithm for Land (SEBAL).

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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