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Selection of atmospheric correction method and estimation of Chlorophyll-a (Chl-a) in coastal waters of Hong Kong

机译:香港沿海水域大气校正方法的选择和叶绿素a(Chl-a)的估算

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Precise atmospheric correction is important for applications where small differences in Surface Reflectance (SR) are significant, such as biomass estimation, crop phenology and retrieval of water quality parameters. As a precursor to monitor water quality parameter Chlorophyll-a (Chl-a), around the coastal waters of Hong Kong using medium resolution sensor, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), this study evaluated the performance of five atmospheric correction methods. The estimated SR, using the five methods including, 6S (Second Simulation of the Satellite Signal in the Solar Spectrum), FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), ATCOR (ATmospheric CORection), DOS (Dark Object Subtraction) and ELM (Empirical Line Method), was validated with in situ Multispectral Radiometer (MSR) SR measurements over sand, artificial turf, grass and water surfaces for the first four reflective bands of Landsat 7 ETM+ and HJ-1 A/B satellites. Among the five methods 6S was observed to be consistently more precise for SR estimation, with significantly less difference from the in situ measured SR, especially over lower reflective water surfaces. Of the two image-based methods, DOS performed well over the darker surfaces of water and artificial turf, although still inferior to 6S, while ELM worked well for grass sites and equaled the good performance of 6S over the high reflective sand surfaces. Therefore, the Landsat TM/ETM+ atmospherically corrected images along with the in situ Chl-a data from 2000 to 2012 were used to develop and validate the regression models for Chl-a concentration of 0.3 to 13.0 ug/l. The validation results showed that the most accurate Chl-a was estimated using the ratio of Band 3 (0.63–0.69 µm) and (Band 1) (0.45–0.52 µm) with correlation coefficient (R) 0.86, Root Mean Square Error (RMSE) of 2.70 ug/l and Mean Absolute Error (MAE) of 1.13 ug/l for coasta- waters of Hong Kong.
机译:精确的大气校正对于表面反射率(SR)差异很小的应用非常重要,例如生物量估计,作物物候和水质参数的检索。作为使用中分辨率传感器Landsat Thematic Mapper(TM)和Enhanced Thematic Mapper Plus(ETM +)监测香港沿岸水域水质参数叶绿素a(Chl-a)的先驱,本研究评估了水质参数叶绿素a(Chl-a)的性能。五种大气校正方法。使用以下5种方法估算出SR:6S(太阳光谱中的卫星信号的第二次模拟),FLAASH(光谱超立方体的快速视线大气分析),ATCOR(大气共校正),DOS(暗物扣除) )和ELM(经验线法)已通过Landsat 7 ETM +和HJ-1 A / B卫星的前四个反射带在沙子,人造草皮,草和水表面上的原位多光谱辐射计(MSR)SR测量进行了验证。在这五种方法中,观察到6S可以更精确地估计SR,与现场测量的SR的差异要小得多,尤其是在较低反射水面上。在这两种基于图像的方法中,DOS在水和人造草皮的较暗表面上表现良好,尽管仍不及6S,而ELM在草地上的效果很好,在高反射性沙地表面上具有6S的良好性能。因此,使用Landsat TM / ETM +大气校正图像以及2000年至2012年的原位Chl-a数据来开发和验证0.3-13.0 ug / l的Chl-a浓度的回归模型。验证结果表明,最准确的Chl-a估计使用频带3(0.63-0.69 µm)和(频带1)(0.45-0.52 µm)的比率,相关系数(R)为0.86,均方根误差(RMSE) )为2.70 ug / l),香港沿海地区的平均绝对误差(MAE)为1.13 ug / l。

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