We compared 10 established and 2 new satellite reflectance algorithms 36 for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio 37 using coincident hyperspectral aircraft imagery and dense coincident surface 38 observations collected within one hour of image acquisition to develop simple 39 proxies for algal blooms in water bodies sensitive to algal blooms (especially toxic 40 or harmful algal blooms (HABs)) and to facilitate portability between multispectral 41 satellite imagers for regional algal bloom monitoring. All algorithms were 42 compared with narrow band hyperspectral aircraft images. These images were 43 subsequently upscaled spectrally and spatially to simulate 5 current and near future 44 satellite imaging systems. Established and new Chl-a algorithms were then applied 45 to the synthetic satellite images and compared to coincident surface observations of 46Chl-a collected from 44 sites within one hour of aircraft acquisition of the imagery. 47We found several promising algorithm/satellite imager combinations for routine 48Chl-a estimation in smaller inland water bodies with operational and near-future 49 satellite systems. The CI, MCI, FLH, NDCI, 2BDA and 3 BDA Chl-a algorithms 50 worked well with CASI imagery. The NDCI, 2BDA, and 3BDA Chl-a algorithms 51 worked well with simulated WorldView-2 and 3, Sentinel-2, and MERIS-like 52 imagery. NDCI was the most widely applicable Chl-a algorithm with good 53 performance for CASI, WorldView 2 and 3, Sentinel-2 and MERIS-like imagery 54 and limited performance with MODIS imagery. A new fluorescence line height 55 "greenness" algorithm yielded the best Chl-a estimates with simulated Landsat-8 56 imagery. 57 ARTICLE INFO 58 Article history: 59 Received ….. 60 Submission to Remote Sensing of Environment 3 Keywords: chorophyll-a, algal bloom, harmful algal bloom, algorithm, satellite, 61 hyperspectral, multispectral 62 63 64 65

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Quantifying the distribution and abundance of ice algae is fundamental for understanding the evolving processes of algal blooms in supraglacial environments, particularly over the Greenland ice sheet, given the role of algal impurities in modulating surface albedo and meltwater production. Field observations of ice algae in Greenland are very limited over space and time. Here we show for the first time the regional variability in algal abundance across the dark zone in southwest Greenland, derived from Sentinel‐3 images acquired during the summertime in 2016 and 2017. We demonstrate the capacity of Sentinel‐3 imagery to characterize the spatial pattern of algal abundance using the reflectance ratios between 709‐ and 673‐nm bands, highly consistent with field measurements. The estimated algal abundance reveals a significant linear growth pattern of algal population with time after the peak of dark ice presence, shown to be tightly linked to surface runoff and meltwater production.

Abstract:We analyzed 27 established and new simple and therefore perhaps portable satellite phycocyanin pigment reflectance algorithms for estimating cyanobacterial values in a temperate 8.9 km 2 reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident water surface observations collected from 44 sites within 1 h of image acquisition. The algorithms were adapted to real Compact Airborne Spectrographic Imager (CASI), synthetic WorldView-2, Sentinel-2, Landsat-8, MODIS and Sentinel-3/MERIS/OLCI imagery resulting in 184 variants and corresponding image products. Image products were compared to the cyanobacterial coincident surface observation measurements to identify groups of promising algorithms for operational algal bloom monitoring. Several of the algorithms were found useful for estimating phycocyanin values with each sensor type except MODIS in this small lake. In situ phycocyanin measurements correlated strongly (r 2 = 0.757) with cyanobacterial sum of total biovolume (CSTB) allowing us to estimate both phycocyanin values and CSTB for all of the satellites considered except MODIS in this situation.

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