基于Sentinel-2遥感影像的巢湖蓝藻水华提取方法研究

Sentinel-2卫星兼具了空间分辨率高、重放周期短、谱段丰富三方面特点,为蓝藻水华爆发阶段及时准确的蓝藻水华提取提供了影像基础,但目前在大型湖泊蓝藻水华提取中的应用报道较少.为此,文章以2018—2020年巢湖的Sentinel-2遥感影像为例,开展包括浮游藻类指数(FAI)在内的多指标蓝藻水华提取方法研究,针对F...

Extraction of Cyanobacteria Bloom in Chaohu Lake Based on Sentinel-2 remote Sensing Images

Sentinel-2 satellite is characterized by its high resolution,rich spectrum and short revisit period,providing abundant images and allowing to extract cyanobacteria blooms timely and accurately.However,few researches on applications of the satellites images’for cyanobacteria blooms extraction have been published recently.In this study,Sentinel-2 remote sensing images of Chaohu Lake during the period of 2018–2020 is adopted to detect the cyanobacteria blooms in Chaohu Lake using the several indexes including Phytoplankton Alga Index(FAI).A method is proposed to determine the threshold of FAI by regression analysis.Experiments on Sentinel-2 images are performed and results show that(1)Compared with the results of low-resolution satellites MODIS and GF-1,Sentienl-2 allows to extract cyanobacteria blooms with an area as small as 100 m2,and the key different between Sentinel-2 and other satellites mainly concentrated in the edges of cyanobacteria bloom area and those scattered small bloom areas.It is proved that Sentinel-2 remote sensing images could estimate cyanobacterial bloom area more accurately.(2)Taking the NDVI threshold 0 as the benchmark,the FAI threshold is calculated by linear fitting to be?1.152(the coefficient of determination r2 reaches 0.9823,and the significance test P<0.001).For the Sentinel without cloud and fog occlusion from June to November 2019 Sentinel-2 remote sensing images use NDVI and FAI indicators to extract the area of cyanobacteria blooms.The results show that the distribution of cyanobacteria bloom area is less than 5%,which proves the effectiveness of FAI threshold determination method.And(3)the Sentinel-2 images with cloudcovered areas where there are no large-scale cyanobacteria blooms are adopted to calculated cyanobacteria bloom’areas by using NDVI and FAI.In areas without cloud and fog,the distribution of cyanobacteria blooms in the two indicators is the same,while in areas with cloud and fog,the extraction area of FAI indicators is only NDVI 53.89%of the total,proving that FAI indicators are less affected by clouds and fog.The high spatial resolution of Sentinel-2 remote sensing images makes the extraction of cyanobacteria blooms in Chaohu Lake more accurate,while will show greater value in the field for water quality monitoring in the future.