Using MODTRAN4 to build up a general look-up-table database for the atmospheric correction of hyperspectral imagery

Hu, Shunshi; Zhang, Lifu; Baig, Muhammad Hasan Ali; Tong, Qingxi

doi:10.1109/igarss.2012.6350806

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Because the pre-constructed 6SV LUT used in this study has six dimensions (SZA, VZA, RAA, TPW, TCO, and AOD), a six-dimensional (6D) interpolation method involving repeated bilinear interpolations was applied using a python function. This method can estimate atmospheric parameters with high accuracy and calculation efficiency [67,68].…”

Section: Lsr Retrievalmentioning

confidence: 99%

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

et al. 2022

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Land surface reflectance (LSR) is well known as an essential variable to understand land surface properties. The Geostationary Ocean Color Imager (GOCI) be able to observe not only the ocean but also the land with the high temporal and spatial resolution thanks to its channel specification. In this study, we describe the land atmospheric correction algorithm and present the quality of results through comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data for GOCI-II. The GOCI LSR shows similar spatial distribution and quantity with MODIS LSR for both healthy and unhealthy vegetation cover. Our results agreed well with in-situ-based reference LSR with a high correlation coefficient (>0.9) and low root mean square error (<0.02) in all 8 GOCI channels. In addition, seasonal variation according to the solar zenith angle and phenological dynamics in time-series was well presented in both reference and GOCI LSR. As the results of uncertainty analysis, the estimated uncertainty in GOCI LSR shows a reasonable range (<0.04) even under a high solar zenith angle over 70°. The proposed method in this study can be applied to GOCI-II and can provide continuous satellite-based LSR products having a high temporal and spatial resolution for analyzing land surface properties.

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Section: Lsr Retrievalmentioning

confidence: 99%