Radiative Transfer in the Atmosphere for Correction of Ocean Color Remote Sensors

1993 ◽  
pp. 33-77 ◽  
Author(s):  
H. R. Gordon
Keyword(s):  
Radiative Transfer ◽  
Ocean Color ◽  
Remote Sensors

Radiometric considerations for ocean color remote sensors

1990 ◽  
Vol 29 (22) ◽  
pp. 3228 ◽  
Author(s):  
Howard R. Gordon
Keyword(s):  
Ocean Color ◽  
Remote Sensors

scholarly journals Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model

2021 ◽  
Vol 14 (6) ◽  
pp. 4083-4110
Author(s):  
Meng Gao ◽  
Bryan A. Franz ◽  
Kirk Knobelspiesse ◽  
Peng-Wang Zhai ◽  
Vanderlei Martins ◽  
...  
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Deep Neural Network ◽  
Ocean Color ◽  
Synthetic Data ◽  
Forward Model ◽  
High Spectral Resolution ◽  
Acceptable Error ◽  
Microphysical Properties ◽  
Aerosol Properties

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the Ocean Color Instrument (OCI) and two multi-angle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems for applications to the HARP2 instrument and its predecessors. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in a GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water-leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water-leaving signals can be retrieved efficiently and within acceptable error. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is 103 times faster with CPU or 104 times with GPU processors. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth-observing satellite missions with similar capabilities.

scholarly journals Physically Based Approach for Combined Atmospheric and Topographic Corrections

2019 ◽  
Vol 11 (10) ◽  
pp. 1218 ◽  
Author(s):  
Federico Santini ◽  
Angelo Palombo
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Test Cases ◽  
Remote Sensors ◽  
Sensing Applications ◽  
Physically Based ◽  
Remote Sensing Applications ◽  
The Impact

The enhanced spectral and spatial resolutions of the remote sensors have increased the need for highly performing preprocessing procedures. In this paper, a comprehensive approach, which simultaneously performs atmospheric and topographic corrections and includes second order corrections such as adjacency effects, was presented. The method, developed under the assumption of Lambertian surfaces, is physically based and uses MODTRAN 4 radiative transfer model. The use of MODTRAN 4 for the estimates of the radiative quantities was widely discussed in the paper and the impact on remote sensing applications was shown through a series of test cases.

Uncertainties in radiative transfer computations: consequences on the ocean color products

2003 ◽  
Author(s):  
Eric Dilligeard ◽  
Francis Zagolski ◽  
Juergen Fischer ◽  
Richard P. Santer
Keyword(s):  
Radiative Transfer ◽  
Ocean Color

scholarly journals Comparison of Aerosol Reflectance Correction Schemes Using Two Near-Infrared Wavelengths for Ocean Color Data Processing

2018 ◽  
Vol 10 (11) ◽  
pp. 1791 ◽  
Author(s):  
Jae-Hyun Ahn ◽  
Young-Je Park ◽  
Hajime Fukushima
Keyword(s):  
Radiative Transfer ◽  
Near Infrared ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Single Scattering ◽  
Weighting Factor ◽  
Simulation Code ◽  
Aerosol Model ◽  
Median Error ◽  
Visible Wavelengths

This paper reanalyzes the aerosol reflectance correction schemes employed by major ocean color missions. The utilization of two near-infrared (NIR) bands to estimate aerosol reflectance in visible wavelengths has been widely adopted, for example by SeaWiFS/MODIS/VIIRS (GW1994), OCTS/GLI/SGLI (F1998), MERIS/OLCI (AM1999), and GOCI/GOCI-II (A2016). The F1998, AM1999, and A2016 schemes were developed based on GW1994; however, they are implemented differently in terms of aerosol model selection and weighting factor computation. The F1998 scheme determines the contribution of the most appropriate aerosol models in the aerosol optical thickness domain, whereas the GW1994 scheme focuses on single-scattering reflectance. The AM1999 and A2016 schemes both directly resolve the multiple scattering domain contribution. However, A2016 also considers the spectrally dependent weighting factor, whereas AM1999 calculates the spectrally invariant weighting factor. Additionally, ocean color measurements on a geostationary platform, such as GOCI, require more accurate aerosol correction schemes because the measurements are made over a large range of solar zenith angles which causes diurnal instabilities in the atmospheric correction. Herein, the four correction schemes were tested with simulated top-of-atmosphere radiances generated by radiative transfer simulations for three aerosol models. For comparison, look-up tables and test data were generated using the same radiative transfer simulation code. All schemes showed acceptable accuracy, with less than 10% median error in water reflectance retrieval at 443 nm. Notably, the accuracy of the A2016 scheme was similar among different aerosol models, whereas the other schemes tended to provide better accuracy with coarse aerosol models than the fine aerosol models.

scholarly journals Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model

2021 ◽  
Author(s):  
Meng Gao ◽  
Bryan A. Franz ◽  
Kirk Knobelspiesse ◽  
Peng-Wang Zhai ◽  
Vanderlei Martins ◽  
...  
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Deep Neural Network ◽  
Ocean Color ◽  
Synthetic Data ◽  
Forward Model ◽  
High Spectral Resolution ◽  
Acceptable Error ◽  
Microphysical Properties ◽  
Aerosol Properties

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral Ocean Color Instrument (OCI) and two Multi-Angle Polarimeters (MAP): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols, and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) model to represent the radiative transfer simulation of coupled atmosphere and ocean systems, for applications to the HARP instrument. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water leaving signals can be retrieved efficiently and within acceptable error. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth observing satellite missions with similar capabilities.

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