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Satellite estimates of the long-term trend in phytoplankton size classes in the coastal waters of north-western Bay of Bengal

机译:孟加拉北部海湾沿海水域浮游植物大小课程的卫星估计

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The study presents long-term variability in satellite retrieved phytoplankton size classes (PSC) at two coastal sites, off Gopalpur and Visakhapatnam, in the north-western Bay of Bengal. The abundance-based models by Brewin et?al. (2010) (B10) and Sahay et?al. (2017) (S17), for retrieval of PSC (micro, nano, and picophytoplankton), from satellite data, were validated. Both the models performed well in the retrieval of nano and microphytoplankton. However, B10 performed poorly in retrieving picophytoplankton. The statistical analysis indicated better performance of the S17 model and hence was applied to Moderate Resolution Imaging Spectroradiometer onboard Aqua satellite (MODISA) data to understand the temporal (at monthly climatology) and spatial variability (from nearshore to offshore). The spatial distribution indicated nearshore dominance of micro and offshore dominance of picophytoplankton. In nearshore waters off Gopalpur, microphytoplankton dominated throughout the year except for months of south-west monsoon (June and July) where the dominance of picophytoplankton was observed. All PSC exhibited similar distribution at an annual scale with a primary peak during pre-monsoon (March and April) and a secondary peak during post-monsoon (September–November). However, microphytoplankton concentration during post-monsoon was higher off Gopalpur in comparison to Visakhapatnam. The higher microphytoplankton concentration during pre-monsoon was attributed to recurrent phytoplankton blooms. Whereas, post-monsoon increment could be attributed to enhanced phytoplankton growth by availing nutrients sourced from monsoonal precipitation induced terrigenous influx. The outcome of the present study recommends the use of the S17 model for satellite retrieval of PSC from the north-western Bay of Bengal.
机译:该研究介绍了卫星的长期变异,在两个沿海地点,距离孟加拉西北湾的沿海网站,野蛮地区的沿海网站上的Phytoplankton大小课程(PSC)。 Brewin等的基于丰富的模型。 (2010)(B10)和Sahay等。 (2017)(S17)(S17)从卫星数据中检索PSC(Micro,Nano和Picophytoplankton)进行验证。在纳米和微晶型的检索中,这两个模型都表现良好。然而,B10在检索皮比尔多尔库尔顿时表现不佳。统计学分析表明S17模型的更好性能,因此应用于中等分辨率的成像光谱辐射器(MODISA)数据,以了解时间(以每月气候学)和空间变异性(从近岸到海上)。空间分布表明了皮比尔多尔库尔顿的微型和海上统治的近岸优势。在近岸水偏离Gopalpur,麦克斯库尔顿在全年占主导地位,除了观察到皮比尔多尔顿的蒙芦(6月和7月)的数月。所有PSC在季风前(3月和4月)期间,每年展示与季风(3月和4月)的主要峰值相似的分布,并在季风(9月至11月)期间的二级峰值。然而,与Visakhapatnam相比,季风后季后翁在季隆期间的微粒浓度较高。在季风前较高的脑脊液浓度归因于复发性浮游植物绽放。然而,季风增量可归因于通过利用来自季风降水的营养素来增强浮游植物的增长诱导诱导的植牛流入。本研究的结果建议使用S17模型从孟加拉北部的西北湾卫星检索卫星检索。

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