Li Yuan, Li Yun-Mei, Guo Yu-Long, Zhang Yun-Lin, Zhang Yi-Bo, Hu Yao-Duo, Xia Zhong
School of Tourism and Urban & Rural Planning, Zhejiang Gongshang University, Hangzhou 310018, China.
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
Huan Jing Ke Xue. 2019 Jan 8;40(1):200-210. doi: 10.13227/j.hjkx.201804210.
Multispectral satellite sensors have several limitations with respect to capturing the target's spectral information due to their band setting and number of bands. The hyperspectral reconstruction technique is an effective method to obtain hyperspectral information from multispectral data. In this study, we propose a hyperspectral reconstruction algorithm based on the sparse representation of water remote sensing reflectance. The proposed algorithm was validated for five ocean color sensors (Sentinel-2A MSI, MERIS, MODIS Aqua, GOCI, and ⅦRS) using measured above-water remote sensing reflectance. The mean absolute percentage error (MAPE) and root mean square error (RMSE) of the reconstructed and measured spectra for five ocean color sensors were less than 10% and 0.005 sr, respectively. Compared with the spectra reconstruction algorithm based on multi-variable linear regression, the proposed algorithm can obtain the features of complex water remote sensing reflectance without using -measured reflectance for algorithm tuning. In addition, the accuracy of the proposed algorithm is better than the spectra reconstruction algorithm based on multi-variable linear regression. Two spectra reconstruction algorithms were applied to five ocean color sensors to test the applicability of the remotely estimated water constituent concentration. The statistical results for the reconstructed spectral factors and water constituent concentration suggest that the reconstructed reflectance derived by the proposed algorithm has a performance similar to that of -measured hyperspectral reflectance. The reconstructed reflectance derived by the proposed algorithm performs better than the spectra reconstruction algorithm based on multi-variable linear regression. Finally, the proposed algorithm was applied to GOCI data to remotely estimate the chlorophyll-a and total suspended matter concentrations. The accuracy of the water constituent concentration estimated from reconstructed images is better than that using original multispectral images. For the estimation of the chlorophyll-a concentration, the MAPE improved from 80.6% to 51.5% and the RMSE improved from 12.175 μg·L to 7.125 μg·L. For the estimation of total suspended matter, the MAPE improved from 19.1% to 18.8% and the RMSE improved from 29.048 mg·L to 28.596 mg·L.
由于多光谱卫星传感器的波段设置和波段数量，在捕获目标光谱信息方面存在若干限制。高光谱重建技术是从多光谱数据中获取高光谱信息的有效方法。在本研究中，我们提出了一种基于水体遥感反射率稀疏表示的高光谱重建算法。利用实测的水上遥感反射率，对五个海洋颜色传感器（哨兵 - 2A 多光谱成像仪、中分辨率成像光谱仪、中分辨率成像光谱仪 - 水色仪、静止海洋水色成像仪和可见光红外成像辐射仪组）验证了所提出的算法。五个海洋颜色传感器重建光谱与实测光谱的平均绝对百分比误差（MAPE）和均方根误差（RMSE）分别小于 10% 和 0.005 sr。与基于多元线性回归的光谱重建算法相比，所提出的算法无需使用实测反射率进行算法调优即可获取复杂水体遥感反射率的特征。此外，所提出算法的精度优于基于多元线性回归的光谱重建算法。将两种光谱重建算法应用于五个海洋颜色传感器，以测试远程估计水体成分浓度的适用性。重建光谱因子和水体成分浓度的统计结果表明，所提出算法导出的重建反射率与实测高光谱反射率具有相似的性能。所提出算法导出的重建反射率比基于多元线性回归的光谱重建算法表现更好。最后，将所提出的算法应用于静止海洋水色成像仪数据，以远程估计叶绿素 a 和总悬浮物浓度。从重建图像估计的水体成分浓度精度优于使用原始多光谱图像的情况。对于叶绿素 a 浓度的估计，MAPE 从 80.6% 提高到 51.5%，RMSE 从 12.175 μg·L 提高到 7.125 μg·L。对于总悬浮物的估计，MAPE 从 19.1% 提高到 18.8%，RMSE 从 29.048 mg·L 提高到 28.596 mg·L。
