Effects of ocean products variability from PSF blurring in NIR band

Abstract

Development and operational planning for ocean color satellite requires lots of careful consideration of the spatial and radiometric performance, which are represented by modulation transfer function (MTF) and signal-to-noise ratio (SNR) respectively. Those representative values are crucial indicator of sensor performance so that small changes of ocean properties (e.g., remote sensed reflectance (Rrs), surface chlorophyll-a concentrations (Chl-a), and so on) can be quantified and directly related with those values. MTF is affected from a performance of instrument itself and environmental conditions, and its variation leads to change the final products. The goal of this study is to simulate and to analyze the relationship between MTF parameter and ocean product variations, and then to provide a reference for the design of future ocean color sensors. In this study, we used the Geostationary Ocean Color Imager (GOCI) data to generate the simulated atmospheric correction band image. And then Rrs data and ocean products were generated with imagery from two different locations and acquisition times, and we analyzed and compared the statistical results with study area having different characteristics. For ocean products relationships, we notify the linear variation of the absolute percentage difference (APD) according to the changeable MTF value. Especially, Case-II water (turbid water) area shows more sensitive variation tspectively. Those representative values are crucial indicator of sensor performance so that small changes of ocean properties (e.g., remote sensed reflectance (Rrs), surface chlorophyll-a concentrations (Chl-a), and so on) can be quantified and directly related with those values. MTF is affected from a performance of instrument itself and environmental conditions, and its variation leads to change the final products. The goal of this study is to simulate and to analyze the relationship between MTF parameter and ocean product variations, and then to provide a reference for the design of future ocean color sensors. In this study, we used the Geostationary Ocean Color Imager (GOCI) data to generate the simulated atmospheric correction band image. And then Rrs data and ocean products were generated with imagery from two different locations and acquisition times, and we analyzed and compared the statistical results with study area having different characteristics. For ocean products relationships, we notify the linear variation of the absolute percentage difference (APD) according to the changeable MTF value. Especially, Case-II water (turbid water) area shows more sensitive variation t