Search results for "CDOM"
showing 6 items of 6 documents
Influence of Riverine Input on Norwegian Coastal Systems
2020
Coastal ecosystems are of high ecological and socioeconomic importance and are strongly influenced by processes from land, sea, and human activities. In this study, we present physical, chemical, and biological observations over two consecutive years from three study regions along the Norwegian coast that represent a broad latitudinal gradient in catchment and oceanographic conditions (∼59–69°N): outer Oslofjord/southern Norway, Runde/western Norway, and Malangen/northern Norway. The observations included river monitoring, coastal monitoring, and sensor-equipped ships of opportunity (“FerryBox”). The riverine discharge and transports were an order of magnitude higher, and the spatiotemporal…
Photochemical mineralization of terrigenous DOC to dissolved inorganic carbon in ocean
2018
When terrigenous dissolved organic carbon (tDOC) rich in chromophoric dissolved organic matter (tCDOM) enters the ocean, solar radiation mineralizes it partially into dissolved inorganic carbon (DIC). This study addresses the amount and the rates of DIC photoproduction from tDOC and the area of ocean required to photomineralize tDOC. We collected water samples from 10 major rivers, mixed them with artificial seawater, and irradiated them with simulated solar radiation to measure DIC photoproduction and the photobleaching of tCDOM. The linear relationship between DIC photoproduction and tCDOM photobleaching was used to estimate the amount of photoproduced DIC from the tCDOM fluxes of the stu…
Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data
2018
The colored dissolved organic matter (CDOM) variable is the standard measure of humic substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient, a C D O M at at reference wavelength (e.g., ≈ 440 nm). Retrieval of CDOM is traditionally done using bio-optical models. As an alternative, this paper presents a comparison of five machine learning methods applied to Sentinel-2 and Sentinel-3 simulated reflectance ( R r s ) data for the retrieval of CDOM: regularized linear regression (RLR), random forest regression (RFR), kernel ridge regression (KRR), Gaussian process regression (GPR) and support vector machines (SVR). Two different datasets of radiative t…
Data from: Primary production calculations for sea ice from bio-optical observations in the Baltic Sea
2017
Bio-optics is a powerful approach for estimating photosynthesis rates, but has seldom been applied to sea ice, where measuring photosynthesis is a challenge. We measured absorption coefficients of chromophoric dissolved organic matter (CDOM), algae, and non-algal particles along with solar radiation, albedo and transmittance at four sea-ice stations in the Gulf of Finland, Baltic Sea. This unique compilation of optical and biological data for Baltic Sea ice was used to build a radiative transfer model describing the light field and the light absorption by algae in 1-cm increments. The maximum quantum yields and photoadaptation of photosynthesis were determined from 14C-incorporation in phot…
KOSMOS 2017 Peru Side Experiment: nutrients, phytoplankton abundances, enzyme rates, photophysiology
2022
This data was collected during an short-term incubation experiment in March 2017 that investigated the response of a surface plankton community to upwelling. This experiment was carried in the framework of the SFB754-funded KOSMOS mesocosm study that took place in La Punta, Callao, Peru between February-April 2017. A total of six different treatments were used to disentangle chemical and biological characteristics of deep water that influence surface plankton blooms: 2 different deep water sources with different nutrient concentrations; 3 treatments to distinguish the effects of inorganic nutrients, organic nutrients and deep water microbial populations. Measured variables include inorganic…
Assessment of Sentinel-2-MSI Atmospheric Correction Processors and In Situ Spectrometry Waters Quality Algorithms
2022
The validation of algorithms developed from in situ reflectance to estimate water quality variables has the challenge of atmospheric correction (AC) when applied to satellite images. Estimating water quality variables from satellite images requires an accurate estimation of remote sensing reflectances (Rrs) which vary according to the AC applied. Validation processes for both Rrs and water quality algorithms were carried out, relating the in situ Rrs (convoluted to Sentinel-2-MSI spectral response function) with the satellite Rrs coming from different ACs (C2RCC, C2X, C2XC, and Polymer), and also relating the in situ water quality variable data with estimated water quality variable values, …