Special section for second-generation global imager (SGLI)

Ishizaka, Joji; Hirawake, Toru; Toratani, Mitsuhiro; Frouin, Robert

doi:10.1007/s10872-022-00651-8

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…The SIRAW method was jointly constructed by Shi et al [9,20,29]. It has been applied to the Japan Aerospace Exploration Agency (JAXA) scientific research platform, Greenhouse gases Observing SATellite-2 (GOSAT-2), and Global Change Observation Mission-Climate (GCOM-C) and proved to have a high ability to retrieve the aerosols and WLR [9,29,37,38].…”

Section: Methodsmentioning

confidence: 99%

“…The scalar 𝜔𝜔 represen single scattering albedo, which is defined as the ratio of the scattering coefficient extinction coefficient. Additionally, P is the phase matrix, and S is the source functi In the inhomogeneous atmospheric and oceanic layers, the solutions of RTE are computed based on the discrete ordinate and matrix operator methods [38], as well as a highly accurate and efficient truncation method [41]. Pstar has been shown to have good accuracy in simulating radiation processes in both the atmosphere and the ocean [20].…”

Section: Methodsmentioning

confidence: 99%

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Remote Sensing of Aerosols and Water-Leaving Radiance from Chinese FY-3/MERSI Based on a Simultaneous Method

Zhang,

Shi,

et al. 2023

Remote Sensing

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In this paper, a new simultaneous retrieval method of the SIRAW algorithm is introduced and carried out on FY3D/MERSI-II satellite images to obtain the aerosol optical thickness (AOT) and normalized water-leaving radiance (WLR) over the ocean. In order to improve the operation efficiency of SIRAW, a machine learning solver is developed to improve the speed of forward radiative transfer computation during retrieval. Ground-based measurement data from AERONET-OC and satellite products from VIIRS are used for comparative verification. The results show that the retrieved AOT and WLR from SIRAW are both in good agreement with those of AERONET-OC and VIIRS. Further, considering the degradation of the MERSI sensor, a new calibration scheme on 412 nm and 443 nm is adopted and an evaluation is carried out. Inter-comparison of derived WLR between MERSI and VIIRS indicates that the new calibration scheme could effectively improve the WLR retrieval accuracy of MERSI with better consistency to the official data of VIIRS. Therefore, this paper confirms that a simultaneous retrieval scheme combined with effective calibration coefficients can be used for high-precision retrieval of real aerosol and water-leaving radiation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Remote Sensing of Aerosols and Water-Leaving Radiance from Chinese FY-3/MERSI Based on a Simultaneous Method

Zhang,

Shi,

et al. 2023

Remote Sensing

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“…One such example is the Second-generation GLobal Imager (SGLI) aboard the Global Change Observation Mission -Climate (GCOM-C, Shikisai) satellite of the Japan Aerospace eXploration Agency (JAXA) launched in December 2017 (https://shikisai.jaxa.jp/index_en.html). The Shikisai, with its high spatial resolution of 250 m, is expected to enhance the study of coastal processes [20]. The other example is the first Geostationary Ocean Color Imager (GOCI) instrument launched in June 2010.…”

Section: Satellite Datamentioning

confidence: 99%

A Simple Procedure to Preprocess and Ingest High-Resolution Ocean Color Data into Google Earth Engine

Maúre¹,

Ilyushchenko²,

Terauchi³

2022

Preprint

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Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an Essential Climate Variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at high (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making high resolution OC data readily available in GEE.

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A Simple Procedure to Preprocess and Ingest Level-2 Ocean Color Data into Google Earth Engine

2022

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Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an essential climate variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at higher (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE at their native resolution. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making OC data at native resolution readily available in GEE.

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Special section for second-generation global imager (SGLI)

Cited by 3 publications

References 8 publications

Remote Sensing of Aerosols and Water-Leaving Radiance from Chinese FY-3/MERSI Based on a Simultaneous Method

Remote Sensing of Aerosols and Water-Leaving Radiance from Chinese FY-3/MERSI Based on a Simultaneous Method

A Simple Procedure to Preprocess and Ingest High-Resolution Ocean Color Data into Google Earth Engine

A Simple Procedure to Preprocess and Ingest Level-2 Ocean Color Data into Google Earth Engine

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