Watanabe Fernanda, Alcântara Enner, Rodrigues Thanan, Rotta Luiz, Bernardo Nariane, Imai Nilton
Department of Cartography, São Paulo State University, Rua Roberto Simonsen, 305, 19060-900 Presidente Prudente, SP, Brazil.
Department of Environmental Engineering, São Paulo State University, Rodovia Presidente Dutra, Km 137,8, 12247-004 São José dos Campos, SP, Brazil.
An Acad Bras Cienc. 2018 Aug;90(2 suppl 1):1987-2000. doi: 10.1590/0001-3765201720170125. Epub 2017 Aug 31.
In this present research, we assessed the performance of band algorithms in estimating chlorophyll-a (Chl-a) concentration based on bands of two new sensors: Operational Land Imager onboard Landsat-8 satellite (OLI/Landsat-8), and MultiSpectral Instrument onboard Sentinel-2A (MSI/Sentinel-2A). Band combinations designed for Thematic Mapper onboard Landsat-5 satellite (TM/Landsat-5) and MEdium Resolution Imaging Spectrometer onboard Envisat platform (MERIS/Envisat) were adapted for OLI/Landsat-8 and MSI/Sentinel-2A bands. Algorithms were calibrated using in situ measurements collected in three field campaigns carried out in different seasons. The study area was the Barra Bonita hydroelectric reservoir (BBHR), a highly productive aquatic system. With exception of the three-band algorithm, the algorithms were spectrally few affected by sensors changes. On the other hands, algorithm performance has been hampered by the bio-optical difference in the reservoir sections, drought in 2014 and pigment packaging.
在本研究中，我们基于两颗新卫星搭载的传感器波段评估了波段算法估算叶绿素a（Chl-a）浓度的性能：陆地卫星8号（Landsat-8）上的业务陆地成像仪（OLI/Landsat-8）以及哨兵2A号（Sentinel-2A）上的多光谱仪器（MSI/Sentinel-2A）。为陆地卫星5号（Landsat-5）上的专题制图仪（TM/Landsat-5）和环境卫星（Envisat）平台上的中分辨率成像光谱仪（MERIS/Envisat）设计的波段组合被应用于OLI/Landsat-8和MSI/Sentinel-2A波段。利用在不同季节开展的三次野外考察中收集的现场测量数据对算法进行了校准。研究区域是巴拉博尼塔水电站水库（BBHR），这是一个高产的水生系统。除了三波段算法外，其他算法在光谱上受传感器变化的影响较小。另一方面，水库不同区域的生物光学差异、2014年的干旱以及色素包裹效应阻碍了算法性能。
