Remote Sensing of Environment
Since the outbreak of a large-scaleUlva proliferabloom in the Yellow Sea during the Qingdao Olympic SailingCompetition in summer 2008,Ulvablooms have been a marine hazard every summer. Accurate and timelyinformation onUlvaareal coverage and biomass is of critical importance for governmental responses, decisionmaking, andfield studies. Previous studies have shown that satellite remote sensing is the most effective methodfor this purpose, yetUlvaareal coverage has been estimated in different ways with significantly different results.The objective of this paper is to determine the lower and upper bounds (T0and T1) of algae-containing pixels inFloating Algae Index images with an objective method that accurately estimates theUlvaareal coverage inindividual images, and then converts coverage to biomass using a previously established conversion equation.First, a seawater background image, FAIsw, is constructed to determine T0, which varies for different algaepatches. Then, T1is determined from water tank and in situ measurements as well as radiative transfer simu-lations to account for different sensor configurations, solar/viewing geometry, and atmospheric conditions. Suchdetermined T1for MODIS 250-m resolution data is validated using concurrent and collocated 2-m resolutionWorldView-2 data. Finally,Ulvaareal coverage derived from MODIS data using this method are compared withthose from the high-resolution data (OLI/Landsat, WFV/GaoFen-1), with a mean relative difference of 9.6%.Furthermore, an analysis of 17 same-day MODIS/Terra and MODIS/Aqua image pairs shows that large viewingangles, atmospheric turbidity, and sunglint can lead to an underestimation ofUlvacoverage of up to 45% underextreme conditions.
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Hu, Lianbo; Zeng, Kan; Hu, Chuanmin; and He, Ming-Xia, "On the Remote Estimation of Ulva Prolifera Areal Coverage and Biomass" (2019). Earth System Science and Policy Faculty Publications. 9.