The potential of Sentinel-5P’s high spectral resolution for ocean applications

2021 ◽  
Author(s):  
Astrid Bracher ◽  
Julia Oelker ◽  
Svetlana Losa ◽  
Mariana Altenburg Soppa ◽  
Andreas Richter ◽  
...  
Keyword(s):  
Attenuation Coefficient ◽  
Spectral Range ◽  
Short Wave ◽  
High Spectral Resolution ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Phytoplankton Diversity ◽  
Diffuse Attenuation Coefficient ◽  
In Situ Data ◽  
Spatial Coverage

<p>Hyperspectral satellite data are a source of the top of the atmosphere radiance signal which can be used for novel algorithms aimed for observations of marine ecosystems and the light-lit ocean. Atmospheric sensors such as SCIAMACHY, GOME-2 and OMI have proven in the past to yield valuable information on phytoplankton diversity, sun-induced marine fluorescence, and the underwater light field, however at low coverage and spatial resolution. Within the ESA Sentinel-5p+ Innovation themes, we explore TROPOMI's potential for deriving the diffuse attenuation coefficient and the quantification of different phytoplankton groups. As commonly used for the retrieval of atmospheric trace gases, we apply the differential optical absorption spectroscopy combined with radiative transfer modeling (RTM) to infer these oceanic parameters. We present results on a measure describing the diminishing of incoming radiation in the ocean with depth, the diffuse attenuation coefficient KD. KD is derived by the retrieval of the vibrational Raman scattering signal in backscattered radiances measured by TROPOMI in the UV and spectral range which then is further converted to the associated KD using RTM. The final TROMPOMI KD data sets resolved for three spectral regions (UV-B+short wave UV-A, UV-A and short blue) agree well with in situ data sampled during an expedition with RV Polarstern in 2018 in the Atlantic Ocean.  Further, KD-blue compared to wavelength-converted KD(490nm) products (OLCI-A and the merged OC-CCI) from common, multispectral, ocean color sensors, show that differences between the three data sets are within uncertainties given for the OC-CCI product. Our study shows for the first time KD products for the UV spectral range retrieved from space based data. TROPOMI KD-blue results have higher quality and much higher spatial coverage and resolution than previous ones from SCIAMACHY, GOME-2 and OMI.  Additionally, first results on TROPOMI’s potential for retrieving three phytoplankton groups will be shown and compared to similar multispectral phytoplankton group data for the same time period and ocean region as shown for TROPOMI KD.</p>

TROPOMI's potential for ocean applications

2020 ◽  
Author(s):  
Julia Oelker ◽  
Svetlana Losa ◽  
Mariana Altenburg Soppa ◽  
Andreas Richter ◽  
Astrid Bracher
Keyword(s):  
Attenuation Coefficient ◽  
Heat Budget ◽  
Solar Spectrum ◽  
Optical Absorption Spectroscopy ◽  
Fluorescence Signal ◽  
Data Sets ◽  
Phytoplankton Diversity ◽  
Diffuse Attenuation Coefficient ◽  
Underwater Light Field ◽  
Surface Ocean

<p>The backscattered light from within the ocean carries information about surface ocean optical constituents, e.g., phytoplankton and the amount of light in the ocean. Global quantified insight in these parameters is important for estimating primary productivity and heat budget, and for a better understanding and modeling of biogeochemical cycles. Atmospheric sensors such as SCIAMACHY and GOME-2 have proven to yield valuable information on phytoplankton diversity, sun-induced marine fluorescence, and the underwater light field. As commonly used for the retrieval of atmospheric trace gases, the oceanic parameters are inferred from differential optical absorption spectroscopy combined with radiative transfer modeling. Within the ESA Sentinel-5p+ Innovation themes, we explore TROPOMI's potential for deriving the diffuse attenuation coefficient, quantification of different phytoplankton groups and the fluorescence signal of phytoplankton. Here we present results on deriving the diffuse attenuation coefficient from the vibrational Raman scattering signal in backscattered radiances measured by TROPOMI. The diffuse attenuation coefficient describes how fast the incoming radiation in the ocean is diminished with depth. Retrieval results in three spectral regions covering the ultraviolet and blue region of the solar spectrum are presented and intercompared. In future, we will explore if information on sources of colored dissolved organic matter and ultraviolet-absorbing phytoplankton pigments can be inferred from these data sets.</p>

scholarly journals Total water vapour columns derived from Sentinel 5p using the AMC-DOAS method

2021 ◽  
Author(s):  
Tobias Küchler ◽  
Stefan Noël ◽  
Heinrich Bovensmann ◽  
John Philip Burrows ◽  
Thomas Wagner ◽  
...  
Keyword(s):  
Water Vapour ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Independent Data ◽  
Total Water ◽  
Max Planck ◽  
Ozone Monitoring Instrument ◽  
Data Set ◽  
Medium Range Weather Forecast ◽  
Spatial Coverage

Abstract. Water vapour is the most abundant natural greenhouse gas in the Earth's atmosphere and global data sets are required for meteorological applications and climate research. The Tropospheric Ozone Monitoring Instrument (TROPOMI) onboard Sentinel 5 Precursor (S5P) launched on 13 October 2017 has a very high spatial resolution of around 5 km and a daily global coverage. Currently, there is no operational total water vapour product for S5P measurements. Here, we present first results of a new scientific total column water vapour (TCWV) product for S5P using the so-called Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) scheme. This method analyses spectral data between 688 and 700 nm and has already been successfully applied to measurements from the Global Monitoring Experiment (GOME) on ERS-2, the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) on Envisat and GOME-2 on MetOp. The adaptation of the AMC-DOAS method to S5P data especially includes an additional post-processing procedure to correct the influences of surface albedo, cloud height and cloud fraction. The quality of the new S5P AMC-DOAS water vapour product is assessed by comparisons with data from GOME-2 on MetOp-B retrieved also with the AMC-DOAS algorithm and with four completely independent data sets, namely re-analysis data from the European Centre for Medium range Weather Forecast (ECMWF ERA5), data obtained by the Special Sensor Microwave Imager and Sounder (SSMIS) flown on the Defense Meteorological Satellite Program (DMSP) platform 16 and two scientific S5P TCWV products derived from TROPOMI measurements. Both are recently published TCWV products for S5P provided by the Max Planck Institute for Chemistry (MPIC) in Mainz and the Netherlands Institute for Space Research (SRON), Utrecht. The SRON TCWV is limited to clear sky scenes over land. These comparisons reveal a good agreement between the various data sets but also some systematic deviations between all of them. On average, the derived offset between AMC-DOAS S5P TCWV and AMC-DOAS GOME-2B TCWV is negative (around −1.5 kg m−2) over land and positive over ocean surfaces (more than 1.5 kg m−2). In contrast, SSMIS TCWV is on average lower than AMC-DOAS S5P TCWV by about 3 kg m−2. TCWV from ERA5 and S5P AMC-DOAS TCWV comparison shows spatial differences over both land and water surface. Over land there are systematical spatial structures with enhanced discrepancies between S5P AMC-DOAS TCWV and ERA5 TCWV in tropical regions. Over sea, S5P AMC-DOAS TCWV is slightly lower than ERA5 TCWV by around 2 kg m−2. The S5P AMC-DOAS TCWV and S5P TCWV from MPIC agree on average within 1 kg m−2 over both land and ocean. TCWV from SRON shows differences to AMC-DOAS S5P TCWV of around 1.2 kg m−2. All of these deviations are in line with the accuracy of these products and with the typical range of deviations of 5 kg m−2 obtained when comparing different TCWV data sets. The AMC-DOAS TCWV product for S5P provides therefore a valuable new and independent data set for atmospheric applications which also shows a better spatial coverage than the other S5P TCWV products.

Diffuse Attenuation Coefficient (Kd) from ICESat-2 ATLAS Spaceborne Lidar Using Random-Forest Regression

2021 ◽  
Vol 87 (11) ◽  
pp. 831-840
Author(s):  
Forrest Corcoran ◽  
Christopher E. Parrish
Keyword(s):  
Attenuation Coefficient ◽  
Infrared Imaging ◽  
Impulse Response Function ◽  
Geographic Location ◽  
Data Sets ◽  
Random Forest Regression ◽  
Diffuse Attenuation Coefficient ◽  
Multispectral Satellite Imagery ◽  
Kd Values ◽  
Using Data

This study investigates a new method for measuring water turbidity—specifically, the diffuse attenuation coefficient of downwelling irradiance Kd —using data from a spaceborne, green-wavelength lidar aboard the National Aeronautics and Space Administration's ICESat-2 satellite. The method enables us to fill nearshore data voids in existing Kd data sets and provides a more direct measurement approach than methods based on passive multispectral satellite imagery. Furthermore, in contrast to other lidar-based methods, it does not rely on extensive signal processing or the availability of the system impulse response function, and it is designed to be applied globally rather than at a specific geographic location. The model was tested using Kd measurements from the National Oceanic and Atmospheric Administration's Visible Infrared Imaging Radiometer Suite sensor at 94 coastal sites spanning the globe, with Kd values ranging from 0.05 to 3.6 m –1 . The results demonstrate the efficacy of the approach and serve as a benchmark for future machine-learning regression studies of turbidity using ICESat-2.

scholarly journals Phytoplankton Genera Structure Revealed from the Multispectral Vertical Diffuse Attenuation Coefficient

2021 ◽  
Vol 13 (20) ◽  
pp. 4114
Author(s):  
Cleber Nunes Kraus ◽  
Daniel Andrade Maciel ◽  
Marie Paule Bonnet ◽  
Evlyn Márcia Leão de Moraes Novo
Keyword(s):  
Light Intensity ◽  
Attenuation Coefficient ◽  
Light Availability ◽  
Green Light ◽  
Phytoplankton Diversity ◽  
Diffuse Attenuation Coefficient ◽  
Amazon Floodplain ◽  
Phytoplankton Communities ◽  
Spectral Ranges

The composition of phytoplankton and the concentration of pigments in their cells make their absorption and specific absorption coefficients key parameters for bio-optical modeling. This study investigated whether the multispectral vertical diffuse attenuation coefficient of downward irradiance (Kd) gradients could be a good framework for accessing phytoplankton genera. In situ measurements of remote sensing reflectance (Rrs), obtained in an Amazon Floodplain Lake (Lago Grande do Curuai), were used to invert Kd, focusing on Sentinel-3/Ocean and Land Color Instrument (OLCI) sensor bands. After that, an analysis based on the organization of three-way tables (STATICO) was applied to evaluate the relationships between phytoplankton genera and Kd at different OLCI bands. Our results indicate that phytoplankton genera are organized according to their ability to use light intensity and different spectral ranges of visible light (400 to 700 nm). As the light availability changes seasonally, the structure of phytoplankton changes as well. Some genera, such as Microcystis, are adapted to low light intensity at 550–650 nm, therefore high values of Kd in this range would indicate the dominance of Microcysts. Other genera, such as Aulacoseira, are highly adapted to harvesting blue-green light with higher intensity and probably grow in lakes with lower concentrations of colored dissolved organic matter that highly absorbs blue light (405–498). These findings are an important step to describing phytoplankton communities using orbital data in tropical freshwater floodplains. Furthermore, this approach can be used with biodiversity indexes to access phytoplankton diversity in these environments.

scholarly journals Global carbon monoxide products from combined AIRS, TES and MLS measurements on A-train satellites

2014 ◽  
Vol 14 (1) ◽  
pp. 103-114 ◽  
Author(s):  
J. X. Warner ◽  
R. Yang ◽  
Z. Wei ◽  
F. Carminati ◽  
A. Tangborn ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Fusion ◽  
Satellite Data ◽  
Lower Troposphere ◽  
Data Sets ◽  
Data Set ◽  
Upper Troposphere ◽  
In Situ Data ◽  
Spatial Coverage

Abstract. This study tests a novel methodology to add value to satellite data sets. This methodology, data fusion, is similar to data assimilation, except that the background model-based field is replaced by a satellite data set, in this case AIRS (Atmospheric Infrared Sounder) carbon monoxide (CO) measurements. The observational information comes from CO measurements with lower spatial coverage than AIRS, namely, from TES (Tropospheric Emission Spectrometer) and MLS (Microwave Limb Sounder). We show that combining these data sets with data fusion uses the higher spectral resolution of TES to extend AIRS CO observational sensitivity to the lower troposphere, a region especially important for air quality studies. We also show that combined CO measurements from AIRS and MLS provide enhanced information in the UTLS (upper troposphere/lower stratosphere) region compared to each product individually. The combined AIRS–TES and AIRS–MLS CO products are validated against DACOM (differential absorption mid-IR diode laser spectrometer) in situ CO measurements from the INTEX-B (Intercontinental Chemical Transport Experiment: MILAGRO and Pacific phases) field campaign and in situ data from HIPPO (HIAPER Pole-to-Pole Observations) flights. The data fusion results show improved sensitivities in the lower and upper troposphere (20–30% and above 20%, respectively) as compared with AIRS-only version 5 CO retrievals, and improved daily coverage compared with TES and MLS CO data.

scholarly journals Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Random Noise ◽  
Synthetic Data ◽  
Regression Function ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Great Opportunity ◽  
Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

scholarly journals Satellite monitoring of different vegetation types by differential optical absorption spectroscopy (DOAS) in the red spectral range

2007 ◽  
Vol 7 (1) ◽  
pp. 69-79 ◽  
Author(s):  
T. Wagner ◽  
S. Beirle ◽  
T. Deutschmann ◽  
M. Grzegorski ◽  
U. Platt
Keyword(s):  
Optical Absorption ◽  
Absorption Spectroscopy ◽  
Spectral Range ◽  
Near Infrared ◽  
Vegetation Type ◽  
Differential Optical Absorption Spectroscopy ◽  
Trace Gas ◽  
Satellite Monitoring ◽  
Optical Absorption Spectroscopy ◽  
Vegetation Types

Abstract. A new method for the satellite remote sensing of different types of vegetation and ocean colour is presented. In contrast to existing algorithms relying on the strong change of the reflectivity in the red and near infrared spectral region, our method analyses weak narrow-band (few nm) reflectance structures (i.e. "fingerprint" structures) of vegetation in the red spectral range. It is based on differential optical absorption spectroscopy (DOAS), which is usually applied for the analysis of atmospheric trace gas absorptions. Since the spectra of atmospheric absorption and vegetation reflectance are simultaneously included in the analysis, the effects of atmospheric absorptions are automatically corrected (in contrast to other algorithms). The inclusion of the vegetation spectra also significantly improves the results of the trace gas retrieval. The global maps of the results illustrate the seasonal cycles of different vegetation types. In addition to the vegetation distribution on land, they also show patterns of biological activity in the oceans. Our results indicate that improved sets of vegetation spectra might lead to more accurate and more specific identification of vegetation type in the future.

scholarly journals Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B

2014 ◽  
Vol 7 (3) ◽  
pp. 3021-3073 ◽  
Author(s):  
M. Grossi ◽  
P. Valks ◽  
D. Loyola ◽  
B. Aberle ◽  
S. Slijkhuis ◽  
...  
Keyword(s):  
Water Vapour ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Vertical Column ◽  
Data Set ◽  
Vertical Column Density ◽  
Data Processor ◽  
Climate Analysis ◽  
Total Column

Abstract. The knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors and perform an extensive inter-comparison and validation in order to estimate their absolute accuracy and long-term stability. We use the recently reprocessed data sets retrieved by the GOME-2 instruments aboard EUMETSAT's MetOp-A and MetOp-B satellites and generated by DLR in the framework of the O3M-SAF using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines H2O/O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. We evaluate the overall consistency between about 8 months measurements from the newer GOME-2 instrument on the MetOp-B platform with the GOME-2/MetOp-A data in the overlap period. Furthermore, we compare GOME-2 results with independent TCWV data from ECMWF and with SSMIS satellite measurements during the full period January 2007–August 2013 and we perform a validation against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project. We find global mean biases as small as ± 0.03 g cm−2 between GOME-2A and all other data sets. The combined SSM/I-MERIS sample is typically drier than the GOME-2 retrievals (−0.005 g cm−2), while on average GOME-2 data overestimate the SSMIS measurements by only 0.028 g cm−2. However, the size of some of these biases are seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, but are not as evident in the validation with the ECMWF and the SSM/I + MERIS data. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three independent data sets, especially for land areas, although some discrepancies over ocean and over land areas with high humidity and a relatively large surface albedo are also present.

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