An assessment of thin cloud detection by applying bidirectional reflectance distribution function model‐based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea

Abstract

In this study, a new assessment of thin cloud detection with the application of bidirectional reflectance distribution function (BRDF) model‐based background surface reflectance was undertaken by interpreting surface spectra characterized using the Geostationary Ocean Color Imager (GOCI) over a land surface area. Unlike cloud detection over the ocean, the detection of cloud over land surfaces is difficult due to the complicated surface scattering characteristics, which vary among land surface types. Furthermore, in the case of thin clouds, in which the surface and cloud radiation are mixed, it is difficult to detect the clouds in both land and atmospheric fields. Therefore, to interpret background surface reflectance, especially underneath cloud, the semiempirical BRDF model was used to simulate surface reflectance by reflecting solar angle‐dependent geostationary sensor geometry. For quantitative validation, Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data were used to make a comparison with the proposed cloud masking result. As a result, the new cloud masking scheme resulted in a high probability of detection (POD = 0.82) compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) (POD = 0.808) for all cloud cases. In particular, the agreement between the CALIPSO cloud product and new GOCI cloud mask was over 94% when detecting thin cloud (e.g., altostratus and cirrus) from January 2014 to June 2015. This result is relatively high in comparison with the result from the MODIS Collection 6 cloud mask product (MYD35).

Retrieval and Uncertainty Analysis of Land Surface Reflectance Using a Geostationary Ocean Color Imager

“…GOCI provides hourly observations from 00 UTC to 07 UTC, with a spatial resolution of 500 m, which exceeds those of other geostationary satellites observing Northeast Asia, such as the Advanced Meteorological Imager (AMI) and Advanced Himawari Imager [40]. Owing to its advantage of high spatial and temporal resolutions, GOCI has been widely used for land surface, atmosphere, and ocean monitoring [47,48]. In this study, we used GOCI Level 1B (L1B) and GOCI AOD products for atmospheric correction.…”

“…Son and Kim [46] conducted a feasibility study to produce a land cover map using principal component analysis, K-means clustering, and GOCI NDVI via BRDF modeling. Yeom et al [47] and Kim et al [48] have suggested novel cloud masking methods using BRDF-based background surface reflectance and Top-Of-Atmosphere (TOA) reflectance of GOCI. The abovementioned studies estimated LSR using absolute atmospheric correction.…”

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

“…Since the mapped location of the clouds detected depends, in part, on the VZA, parallax shifts can also complicate comparing the location of clouds between sensors with different VZA (Zakšek et al, 2013). However, it is possible to correct for parallax shifts with knowledge of VZA and feature (cloud or surface) altitude (Kim et al, 2017;Yeom et al, 2020).…”

Exploring Aerosols Near Clouds With High‐Spatial‐Resolution Aircraft Remote Sensing During SEAC4RS

“…Side scattering into the lidar's field of view from adjacent objects provides negligible enhancement as compared with the active return from the target. Therefore, a lidar can help to validate imager cloud masks, as demonstrated by the many studies that have used lidar data from CALIPSO to validate cloud identification schemes from different imagers (e.g., Hutchison et al, ; Kim et al, ; Kopp et al, ; Marquis et al, ; Wang et al, ).…”