A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications

Abstract. Light emerging from natural water bodies and measured by remote sensing radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities to characterize aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission; the NASA Surface Biology and Geology observable mission; and NASA Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP/AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries and oceans. Specific in situ measurements include coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering, as well as remote-sensing reflectance, and irradiance reflectance. The dataset can be downloaded from https://doi.pangaea.de/10.1594/PANGAEA.902230 (Casey et al., 2019).

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A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications

Earth System Science Data ◽

10.5194/essd-12-1123-2020 ◽

2020 ◽

Vol 12 (2) ◽

pp. 1123-1139

Author(s):

Kimberly A. Casey ◽

Cecile S. Rousseaux ◽

Watson W. Gregg ◽

Emmanuel Boss ◽

Alison P. Chase ◽

...

Keyword(s):

Remote Sensing ◽

Organic Matter ◽

Optical Property ◽

Dissolved Organic Matter ◽

Spectral Resolution ◽

Infrared Imaging ◽

High Spectral Resolution ◽

Colored Dissolved Organic Matter ◽

Sensing Applications

Abstract. Light emerging from natural water bodies and measured by radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities for characterizing aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; the NASA Surface Biology and Geology designated observable mission; and NASA Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP–AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries, and oceans. Specific in situ measurements include remote-sensing reflectance, irradiance reflectance, and coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.902230 (Casey et al., 2019).

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Use of optical absorption indices to assess seasonal variability of dissolved organic matter in Amazon floodplain lakes

Biogeosciences ◽

10.5194/bg-17-5355-2020 ◽

2020 ◽

Vol 17 (21) ◽

pp. 5355-5364

Author(s):

Maria Paula da Silva ◽

Lino A. Sander de Carvalho ◽

Evlyn Novo ◽

Daniel S. F. Jorge ◽

Claudio C. F. Barbosa

Keyword(s):

Remote Sensing ◽

Organic Matter ◽

Optical Absorption ◽

Dissolved Organic Matter ◽

Seasonal Variability ◽

Large Scale ◽

Remote Sensing Data ◽

Floodplain Lakes ◽

Sentinel 2

Abstract. Given the importance of dissolved organic matter (DOM) in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. In this study we assess the use of optical absorption indices available in the literature based on in situ data to both characterize the seasonal variability of DOM in a highly complex environment and for application in large-scale studies using remote sensing data. The study area comprises four lakes located in the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of colored dissolved organic matter (CDOM) and measurements of remote sensing reflectance (Rrs) were acquired in situ. The Rrs was used to simulate the response of the visible bands of the Sentinel-2 MultiSpectral Instrument (MSI), which was used in the proposed models. Differences between lakes were tested using the CDOM indices. The results highlight the role of the flood pulse in the DOM dynamics at the floodplain lakes. The validation results show that the use of the absorption coefficient of CDOM (aCDOM) as a proxy of the spectral slope between 275 and 295 nm (S275–295) during rising water is worthwhile, demonstrating its potential application to Sentinel-2 MSI imagery data for studying DOM dynamics on the large scale.

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Use of absorption optical indices to assess seasonal variability of dissolved organic matter in amazon floodplain lakes

10.5194/bg-2019-324 ◽

2019 ◽

Author(s):

Maria Paula da Silva ◽

Lino A. Sander de Carvalho ◽

Evlyn Novo ◽

Daniel S. F. Jorge ◽

Claudio C. F. Barbosa

Keyword(s):

Remote Sensing ◽

Organic Matter ◽

Dissolved Organic Matter ◽

Seasonal Variability ◽

Large Scale ◽

River Bank ◽

In Situ Data ◽

Amazon Floodplain ◽

Significant Difference

Abstract. Given the importance of DOM in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. This study assesses the use of available absorption optical indices based on in situ data to both characterize the seasonal variability of the DOM dynamics in a highly complex environment and their viability of being used for satellite remote sensing on large scale studies. The study area comprises four lakes located at the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of coloured dissolved organic matter (CDOM) and remote sensing reflectance (Rrs) were acquired in situ. The Rrs was applied to simulate MSI visible bands and used in the proposed models. Differences between lakes were tested regarding CDOM indices. Significant difference in the average of aCDOM (440), aCDOM spectra and S275–295 were found between lakes located inside the flood forest and those near the river bank. The proposed model showed that aCDOM can be used as proxy of S275–295 during rising water with good validation results, demonstrating the potential of Sentinel/MSI imagery data in large scale studies on the dynamics of DOM.

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Monitoring seasonal variations of colored dissolved organic matter for the Saginaw River based on Landsat-8 data

Water Science & Technology Water Supply ◽

10.2166/ws.2018.077 ◽

2018 ◽

Vol 19 (1) ◽

pp. 274-281 ◽

Cited By ~ 6

Author(s):

Jiang Chen ◽

Weining Zhu ◽

Yuhan Zheng ◽

Yong Q. Tian ◽

Qian Yu

Keyword(s):

Remote Sensing ◽

Organic Matter ◽

Environmental Factors ◽

Dissolved Organic Matter ◽

Spatial Resolution ◽

Seasonal Variations ◽

Pearson Correlation ◽

Landsat 8 ◽

Colored Dissolved Organic Matter ◽

Positive Role

Abstract Remote sensing is an effective tool for studying CDOM (colored dissolved organic matter) variations and its relevant environmental factors. Monitoring CDOM distribution and dynamics in small water is often limited by the coarse spatial resolution of traditional ocean color sensors. In this study, because of its high spatial resolution of 30 m, Landsat-8 data were used to assess seasonal variations of CDOM in the Saginaw River, by using an empirical statistic model. Pearson correlation analysis between CDOM variations and other environmental factors, such as temperature, discharge, and dissolved oxygen, shows that temperature was negatively correlated to CDOM variations and discharge played a positive role. We also calculated the monthly mean aCDOM(440) (the absorption coefficient of CDOM at 440 nm) for the Saginaw River between April and November from 2013 to 2016. This study demonstrates a good example for future applications in small waters: observing CDOM variations and other relevant environmental factors change by using Landsat remote sensing, so that we can know more about water quality and ecosystem health of small waters as well as the climate change impact on regional watersheds.

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Lidar Innovations for Technologies and Environmental Sciences (LITES) – An Remote Sensing Infrastructure Facility: Setup and Measurements Examples

EPJ Web of Conferences ◽

10.1051/epjconf/202023707017 ◽

2020 ◽

Vol 237 ◽

pp. 07017

Author(s):

Boyan Tatarov ◽

Detlef Müller ◽

Matthias Tesche ◽

Sung-Kyun Shin

Keyword(s):

Remote Sensing ◽

Spectral Resolution ◽

High Energy ◽

High Spectral Resolution ◽

Ultra High Energy ◽

Lidar Signal ◽

Environmental Sciences ◽

Time Resolved ◽

The University

At the University of Hertfordshire, we have been developing a new remote sensing facility (LITES) to explore the feasibility of using Raman and/or fluorescence backscattering for chemical aerosol profiling. This paper provides an overview of the instruments of the facility and measurement examples. LITES includes a ultra-high-energy Nd:YAG/OPO setup, spectroscopic equipment with high spectral resolution, several imaging and single detectors that allow for time-resolved (lidar) signal detection, a Raman/fluorescence microscope, and a suite of gas and aerosol chambers. We present examples of elastic, rotational and vibrational spectroscopic lidar signals, as well as in-situ microscopic spectrums of dust and bio-aerosol compounds.

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Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties

Remote Sensing ◽

10.3390/rs10101655 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1655 ◽

Cited By ~ 4

Author(s):

Nariane Bernardo ◽

Enner Alcântara ◽

Fernanda Watanabe ◽

Thanan Rodrigues ◽

Alisson Carmo ◽

...

Keyword(s):

Remote Sensing ◽

Optical Properties ◽

Wind Speed ◽

In Situ Measurements ◽

Colored Dissolved Organic Matter ◽

The Third ◽

Sensing Applications ◽

Remote Sensing Reflectance ◽

Remote Sensing Applications

The quality control of remote sensing reflectance (Rrs) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to glint effect into the in situ measurements. Our study evaluates four different methods to reduce the glint effect from the Rrs spectra collected in cascade reservoirs with widely differing optical properties. The first (i) method adopts a constant coefficient for skylight correction (ρ) for any geometry viewing of in situ measurements and wind speed lower than 5 m·s−1; (ii) the second uses a look-up-table with variable ρ values accordingly to viewing geometry acquisition and wind speed; (iii) the third method is based on hyperspectral optimization to produce a spectral glint correction, and (iv) computes ρ as a function of wind speed. The glint effect corrected Rrs spectra were assessed using HydroLight simulations. The results showed that using the glint correction with spectral ρ achieved the lowest errors, however, in a Colored Dissolved Organic Matter (CDOM) dominated environment with no remarkable chlorophyll-a concentrations, the best method was the second. Besides, the results with spectral glint correction reduced almost 30% of errors.

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