scholarly journals Improving the Remote Sensing Retrieval of Phytoplankton Functional Types (PFT) Using Empirical Orthogonal Functions: A Case Study in a Coastal Upwelling Region

2018 ◽  
Vol 10 (4) ◽  
pp. 498 ◽  
Author(s):  
Marco Correa-Ramirez ◽  
Carmen Morales ◽  
Ricardo Letelier ◽  
Valeria Anabalón ◽  
Samuel Hormazabal
Keyword(s):  
Remote Sensing ◽  
Coastal Upwelling ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Functional Types

Multi-scale ocean colour synergy products for coastal water quality monitoring

2020 ◽  
Author(s):  
Dimitry Van der Zande ◽  
Aida Alvera-Azcárate ◽  
Charles Troupin ◽  
João Cardoso Dos Santos ◽  
Dries Van den Eynde
Keyword(s):  
Spatial Resolution ◽  
Spectral Characteristics ◽  
Empirical Orthogonal Functions ◽  
High Temporal Resolution ◽  
Ocean Colour ◽  
Orthogonal Functions ◽  
Multi Scale ◽  
Medium Resolution ◽  
Sentinel 2

<p>High-quality satellite-based ocean colour products can provide valuable support and insights in the management and monitoring of coastal ecosystems. Today’s availability of Earth Observation (EO) data is unprecedented including medium resolution ocean colour systems (e.g. Sentinel-3/OLCI), high resolution land sensors (e.g. Sentinel-2/MSI) and geostationary satellites (e.g. MSG/SEVIRI). Each of these sensors offers specific advantages in terms of spatial, temporal or radiometric characteristics. In the Multi-Sync project, we developed advanced ocean colour products (i.e. remote sensing reflectance, turbidity, and chlorophyll a concentration) through the synergetic use of these multi-scale EO data taking advantage of spectral characteristics of traditional medium resolution sensors, the high spatial resolution of some land sensors and the high temporal resolution of geostationary sensors.</p><p>To achieve this goal a multi-scale DINEOF (Data Interpolating Empirical Orthogonal Functions) approach was developed to reconstruct missing data using empirical orthogonal functions (EOF), reduce noise and exploit spatio-temporal coherency by joining several spatial and temporal resolutions. Here we present the capacity of DINEOF to extract multi-scale information through the integration of Sentinel-3, Sentinel-2 and SEVIRI datasets.</p><p>The functionality of the advanced multi-scale products will be demonstrated in a case study for the Belgian Coastal Zone (BCZ) highly relevant to the user community: sediment transport modelling near the harbour of Zeebrugge in support of dredging operations. As stated in the OSPAR treaty (1992), Belgium is obliged to monitor and evaluate the effects of all human activities on the marine ecosystem. Dredging activities in and near Belgian harbors fall under this treaty and are performed daily to ensure accessibility of the port by ships. Optimization of these dredging activities requires monitoring data which is typically acquired through in situ observations or modelling data. In this case study we take advantage of Sentinel-3, Sentinel-2 and SEVIRI data characteristics to provide a satellite product that meets the end user requirements in terms of product quality and temporal/spatial resolution.</p><p> </p>

scholarly journals Ozone profile climatology for remote sensing retrieval algorithms

2019 ◽  
Vol 12 (9) ◽  
pp. 4745-4778 ◽  
Author(s):  
Kai Yang ◽  
Xiong Liu
Keyword(s):  
Remote Sensing ◽  
A Priori ◽  
Uv Spectra ◽  
Empirical Orthogonal Functions ◽  
Ozone Monitoring Instrument ◽  
Orthogonal Functions ◽  
Monitoring Instrument ◽  
Retrieval Algorithms ◽  
Ozone Monitoring ◽  
Priori Information

Abstract. New ozone (O3) profile climatologies are created from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) O3 record between 2005 and 2016, within the period of Aura Microwave Limb Sounder (MLS) and Aura Ozone Monitoring Instrument (OMI) assimilation. These two climatologies consist of monthly mean O3 profiles and the corresponding covariances dependent on the local solar time, longitude (15∘), and latitude (10∘), which are parameterized by tropopause pressure and total O3 column. They are validated through comparisons, which show good agreements with previous O3 profile climatologies. Compared to a monthly zonal mean climatology, both tropopause- and column-dependent climatologies provide improved a priori information for profile and total O3 retrievals from remote sensing measurements. Furthermore, parameterization of the O3 profile with total column O3 usually reduces the natural variability of the resulting climatological profile in the upper stratosphere further than the tropopause parameterization, which usually performs better in the upper troposphere and lower stratosphere (UTLS). Therefore tropopause-dependent climatology is more appropriate for profile O3 retrieval for complementing the vertical resolution of backscattered ultraviolet (UV) spectra, while the column-dependent climatology is more suited for use in total O3 retrieval algorithms, with an advantage of complete profile specification without requiring ancillary information. Compared to previous column-dependent climatologies, the new MERRA-2 column-dependent climatology better captures the diurnal, seasonal, and spatial variations and dynamical changes in O3 profiles with higher resolutions in O3, latitude, longitude, and season. The new MERRA-2 climatologies contain the first quantitative characterization of O3 profile covariances, which facilitate a new approach to improve O3 profiles using the most probable patterns of profile adjustments represented by the empirical orthogonal functions (EOFs) of the covariance matrices. The MERRA-2 daytime column-dependent climatology is used in the combo O3 and SO2 algorithm for retrieval from the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite, the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) on the Suomi National Polar Partnership (SNPP), and the Ozone Monitoring Instrument (OMI) on the Aura spacecraft.

scholarly journals Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data

2020 ◽  
Vol 240 ◽  
pp. 111704 ◽  
Author(s):  
Hongyan Xi ◽  
Svetlana N. Losa ◽  
Antoine Mangin ◽  
Mariana A. Soppa ◽  
Philippe Garnesson ◽  
...  
Keyword(s):  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Functional Types

scholarly journals Using empirical orthogonal functions derived from remote-sensing reflectance for the prediction of phytoplankton pigment concentrations

Ocean Science ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 139-158 ◽  
Author(s):  
A. Bracher ◽  
M. H. Taylor ◽  
B. Taylor ◽  
T. Dinter ◽  
R. Röttgers ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Empirical Orthogonal Functions ◽  
Optical Measurements ◽  
Small Data ◽  
Linear Regression Models ◽  
Orthogonal Functions ◽  
Phytoplankton Pigment ◽  
Remote Sensing Reflectance ◽  
Pigment Concentrations ◽  
Reflectance Data

Abstract. The composition and abundance of algal pigments provide information on phytoplankton community characteristics such as photoacclimation, overall biomass and taxonomic composition. In particular, pigments play a major role in photoprotection and in the light-driven part of photosynthesis. Most phytoplankton pigments can be measured by high-performance liquid chromatography (HPLC) techniques applied to filtered water samples. This method, as well as other laboratory analyses, is time consuming and therefore limits the number of samples that can be processed in a given time. In order to receive information on phytoplankton pigment composition with a higher temporal and spatial resolution, we have developed a method to assess pigment concentrations from continuous optical measurements. The method applies an empirical orthogonal function (EOF) analysis to remote-sensing reflectance data derived from ship-based hyperspectral underwater radiometry and from multispectral satellite data (using the Medium Resolution Imaging Spectrometer – MERIS – Polymer product developed by Steinmetz et al., 2011) measured in the Atlantic Ocean. Subsequently we developed multiple linear regression models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables. The model results show that surface concentrations of a suite of pigments and pigment groups can be well predicted from the ship-based reflectance measurements, even when only a multispectral resolution is chosen (i.e., eight bands, similar to those used by MERIS). Based on the MERIS reflectance data, concentrations of total and monovinyl chlorophyll a and the groups of photoprotective and photosynthetic carotenoids can be predicted with high quality. As a demonstration of the utility of the approach, the fitted model based on satellite reflectance data as input was applied to 1 month of MERIS Polymer data to predict the concentration of those pigment groups for the whole eastern tropical Atlantic area. Bootstrapping explorations of cross-validation error indicate that the method can produce reliable predictions with relatively small data sets (e.g., < 50 collocated values of reflectance and pigment concentration). The method allows for the derivation of time series from continuous reflectance data of various pigment groups at various regions, which can be used to study variability and change of phytoplankton composition and photophysiology.

scholarly journals Ozone Profile Climatology for Remote Sensing Retrieval Algorithms

2019 ◽  
Author(s):  
Kai Yang ◽  
Xiong Liu
Keyword(s):  
Remote Sensing ◽  
A Priori ◽  
Uv Spectra ◽  
Empirical Orthogonal Functions ◽  
Ozone Monitoring Instrument ◽  
Orthogonal Functions ◽  
Monitoring Instrument ◽  
Retrieval Algorithms ◽  
Ozone Monitoring ◽  
Priori Information

Abstract. New ozone (O3) profile climatologies are created from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) O3 record between 2005 and 2016, within the period of Aura Microwave Limb Sounder (MLS) and Aura Ozone Monitoring Instrument (OMI) assimilation. These two climatologies consist of local solar time, longitudinal (15°), and latitudinal (10°) dependent monthly mean O3 profiles and the corresponding covariances, which are parameterized respectively by tropopause pressure and total O3 column. They are validated through comparisons, which show good agreements with previous O3 profile climatologies. Compared to a monthly zonal mean climatology, both tropopause- and column-dependent climatologies provide improved a priori information for profile and total O3 retrievals from remote sensing measurements. Furthermore, parameterization of O3 profile with total column usually reduces the natural variability of the resulting climatological profile in the upper stratosphere further than the tropopause parameterization, which usually performs better in the upper troposphere and lower stratosphere (UTLS). Therefore tropopause-dependent climatology is more appropriate for profile O3 retrieval for complementing the vertical resolution of backscattered ultraviolet (UV) spectra, while the column-dependent climatology is more suited for use in total O3 retrieval algorithms, with an advantage of complete profile specification without requiring ancillary information. Compared to previous column-dependent climatologies, the new MERRA-2 column-dependent climatology better capture the diurnal, seasonal, and spatial variations and dynamical changes of O3 profiles with higher resolutions in O3, latitude, longitude, and season. The new MERRA-2 climatologies contain first quantitative characterization of O3 profile covariances, which facilitate a new approach to improve O3 profiles using the most probable patterns of profile adjustments represented by the empirical orthogonal functions (EOFs) of the covraniance matrices. The MERRA-2 daytime column-dependent climatology is used in the combo O3 and SO2 algorithm for retrieval from the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite, the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) on the Suomi National Polar Partnership (SNPP), and the Ozone Monitoring Instrument (OMI) on the Aura spacecraft.

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