基于Sentinel-2数据的立陶宛富营养化湖泊叶绿素-a浓度反演

Dalia Grendaitė, E. Stonevičius, J. Karosienė, Ksenija Savadova, J. Kasperovičienė
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引用次数: 29

摘要

内陆水域是动植物的重要栖息地,也用于审美、娱乐和工业需求;因此,监测淡水现状并采取措施改善水质具有重要意义。为了建立一个能够覆盖大面积的有效监测系统,遥感数据的使用至关重要。本研究测试了Sentinel-2多光谱成像仪数据在富营养化湖泊蓝藻爆发事件观测和浮游植物生物量指标叶绿素-a浓度反演中的适用性。分析使用立陶宛四个湖泊的数据进行-两个富营养化开花湖泊和两个低-中营养化非开花湖泊。结果表明,富营养化湖泊的反射率高于中营养化湖泊,这是由于水体中存在一种光学活性成分,即叶绿素-a色素。我们测试了从其他研究中导出的富营养化湖泊叶绿素-a浓度反演的经验方程,以检验它们是否可以在不适应当地条件的情况下使用。大多数方程表现良好(R2 = 0.5 ~ 0.8);但均有较高的rmse = 17-53 μg L-1。与大气底部数据CHL8_L2A (R2 = 0.76)对应的方程RMSE = 9 μg L-1最低。此外,我们还推导了立陶宛富营养化湖泊的经验方程。基于Sentinel-2波段比B5/B4和3个波段(B4、B5和B8A)表达的方程表现最佳(R2 = 0.77 ~ 0.79), RMSE = 7 μg L-1低于其他研究的经验方程。更大的原位数据集可以提高算法检索叶绿素- A浓度的性能。利用Sentinel-2 MSI传感器接收的数据,首次尝试绘制立陶宛富营养化湖泊的水质参数图,结果很好,因为可以从卫星图像中看到由叶绿素-a浓度变化引起的反射率变化。
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Chlorophyll-a concentration retrieval in eutrophic lakes in Lithuania from Sentinel-2 data
Inland waters are an important habitat for flora and fauna and are also used for aesthetic, recreational, and industrial needs; therefore, monitoring the current state of freshwaters and applying measures to improve water quality are of high importance. To have an efficient monitoring system that could cover large areas, the use of remote sensing data is crucial. In this study the suitability of the Sentinel-2 Multispectral Imager data is tested for observing cyanobacteria bloom events in the eutrophic lakes and retrieving the chlorophyll-a concentration – an indicator of phytoplankton biomass. The analysis is carried out using data from four lakes in Lithuania – two eutrophic blooming lakes and two oligo-mesotrophic non-blooming lakes. The results showed that reflectances are higher in the eutrophic lakes than in the oligo-mesotrophic lakes due to the presence of an optically active constituent, namely, chlorophyll-a pigment. We tested empirical equations for chlorophyll-a concentration retrieval in eutrophic lakes derived in other studies to check whether they could be used without adaptation to local conditions. Most of the equations performed well (R2 = 0.5–0.8); however, they had high RMSEs = 17–53 μg L–1. The equation used with the bottom of atmosphere data CHL8_L2A (R2 = 0.76) had the lowest RMSE = 9 μg L–1. In addition, we derived empirical equations for eutrophic lakes in Lithuania. The equations that were based on the Sentinel-2 band ratio B5/B4 and the three band (B4, B5, and B8A) expression performed the best (R2 = 0.77–0.79) and had lower RMSE = 7 μg L–1 than empirical equations from other studies. A larger in situ dataset could improve the algorithm performance in retrieving the chlorophyll-a concentration. The first attempts to map water quality parameters in eutrophic lakes in Lithuania using the data received from the Sentinel-2 MSI sensor show good results, as the changes in reflectance, caused by the changes in chlorophyll-a concentration, can be seen from satellite images.
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