Atmospheric correction in high solar zenith angles - Advanced Technology

Home > Research > Advanced Technology > Atmospheric correction in high solar zenith angles

Research Background

Ocean color remote sensing involves the utilization of spaceborne visible infrared radiometers to capture the upward spectral radiation emitted by the sea surface. By applying atmospheric correction techniques, this approach enables the retrieval of marine environmental elements such as chlorophyll-a concentration and suspended solids content, based on their bio-optical properties. These remote sensing methods play a crucial role in monitoring and assessing the ecological state of marine environments.

In recent years, geostationary satellites have emerged as a primary tool for high-frequency Earth observation, owing to their capability to provide continuous monitoring of specific geographic areas. However, observations conducted by hydrochrome satellites in geostationary orbit encounter challenges associated with large solar zenith angles during morning and evening time periods. The current hydrochrome satellite remote sensing algorithms exhibit significant errors when processing observation data acquired under solar zenith angles exceeding 70°. Similarly, polar-orbiting satellites conducting observations in high-latitude regions, such as the polar regions, also face similar issues pertaining to large solar zenith angles. Uncertainty persists regarding the applicability of existing atmospheric correction algorithms under conditions involving substantial solar zenith angles. Thus, the development of a suitable water color remote sensing model that accounts for the solar zenith angle is imperative.

Within this study, a comprehensive dataset derived from satellite observations was employed as the training dataset and distinct neural network atmospheric correction models were developed to cater specifically to geostationary satellites and polar orbiting satellites. Notably, a novel approach was introduced for the first time, involving the alignment of remote sensing albedo products captured at low solar zenith angles during midday with the Rayleigh-corrected radiance acquired during the dawn and dusk periods characterized by large solar zenith angles. By establishing a neural network atmospheric correction model based on this approach, the study successfully generated water color remote sensing products for morning and evening hours when solar zenith angles are significantly varied.

unfold

Figures

Fig2 Monthly Average Chlorophyll-a Product in the Chukchi Sea from MODIS-Aqua in October 2019.
Fig1 : Rrs Product of GOCI in the 443 nm Band during the Twilight Period in the Northern Japan Sea on March 16, 2018. (a)(b) Inversion results of the Neural Network Atmospheric Correction Model; (c)(d) Inversion results of the KOSC Atmospheric Correction Model; (e)(f) Inversion results of the NIR Atmospheric Correction Model;(g)(h) Solar Zenith Angle during the Twilight Period.

Scientific Progress

The remote sensing reflectance (Rrs) dataset of the Antarctic waters comprises remote sensing reflectance at 412nm, 443nm, 469nm, 488nm, 531nm, 547nm, 555nm, 645nm, 667nm, and 678nm bands. This dataset features a high spatial resolution of 4 km, covering the region south of 50°S from January 2003 to December 2020. Validation using in-situ data indicates an overall root mean square error of approximately 0.234 mg/m³ for chlorophyll concentration estimates derived from this dataset.

The dataset employs the total radiance, Rayleigh-scattering radiance, solar zenith angle, relative azimuth angle, and observation zenith angle of MODIS Level 1 products as input. Through a neural network model, global monthly remote sensing reflectance products were generated for the period 2003-2020. In comparison to NASA's products, this dataset includes observational data from regions with high solar zenith angles, such as high-latitude oceanic areas. As a result, it can be utilized to study water properties of polar regions, particularly during autumn and winter seasons. This endeavor provides a robust data foundation for further research into the global marine carbon cycle.

The HY1C water color remote sensing dataset includes remote sensing reflectance (Rrs) at 412nm, 443nm, 469nm, 488nm, 531nm, 547nm, 555nm, 645nm, 667nm, and 678nm bands. This dataset boasts a high spatial resolution of 4 km, encompassing the region north of 50°N latitude from January 2018 to December 2020. Cross-validation using satellite data reveals an overall correlation coefficient of around 0.65 for chlorophyll concentration estimates derived from this dataset.

The dataset utilizes the total radiance, Rayleigh-scattering radiance, solar zenith angle, relative azimuth angle, and observation zenith angle of HY1C Level 1 products as input. Employing a neural network model, global monthly remote sensing reflectance products were generated for the period 2018-2020. In comparison to NASA's products, this dataset encompasses observational data from regions with high solar zenith angles, such as high-latitude oceanic areas. As such, it can be employed to investigate water properties of polar regions, particularly during autumn and winter seasons. This effort lays a robust data foundation for further research into the global marine carbon cycle.

unfold

References

Li, Hao, Xianqiang He, Yan Bai, Palanisamy Shanmugam, Young-Je Park, Jia Liu, Qiankun Zhu, Fang Gong, Difeng Wang, and Haiqing Huang. 2020. “Atmospheric Correction of Geostationary Satellite Ocean Color Data under High Solar Zenith Angles in Open Oceans.” Remote Sensing of Environment 249: 112022. https://doi.org/10.1016/j.rse.2020.112022.

unfold

Research Background

Figures

Scientific Progress

References

Links