The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters
The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters
复制标题
Landsat-8 OLI 内陆水域大气改正算法评估
DOI：
10.3390/rs11020169
复制
发表时间：
2019
期刊：
影响因子：
5
通讯作者：
Loiselle Steven Arthur
中科院分区：
文献类型：
--
作者：
Wang Dian;Ma Ronghua;Xue Kun;Loiselle Steven Arthur
Landsat-8卫星上的OLI（业务陆地成像仪）传感器具有满足水色遥感要求的潜力。然而，内陆沃茨的光学特性比海洋沃茨的光学特性更复杂，内陆大气校正提出了额外的挑战。我们研究了大气校正（AC）方法在中国三个高度浑浊或富营养化的内陆沃茨：洪泽湖，巢湖，太湖遥感的性能。四种水-AC算法（短波红外），EXP（指数外推法），DSF（暗光谱拟合）和MUMM（管理单元数学模型））和三种陆地-AC算法（FLAASH（光谱超立方体的快速视线大气分析），6SV（太阳光谱中卫星信号的第二次模拟版本），和QUAC（快速大气校正））进行了评估，使用Landsat-8 OLI数据和同期原位数据。结果表明，EXP（和DSF）和6SV算法提供了最好的遥感反射率（Rrs）和波段比分别在水AC算法和土地AC算法，。AC算法显示了不同的水类型（混浊的沃茨，在水中的藻类沃茨，和浮动水华沃茨）的判别精度。对于浑浊的沃茨，EXP在可见波段给出了最好的Rrs。然而，对于水中藻类和漂浮水华沃茨，由于不适当的气溶胶模型和1609 nm处的非零反射率，所有水算法都失败了。研究结果表明，可以实现的改进，考虑短波红外波段和使用波段比，并需要进一步发展的AC算法复杂的水生和大气条件下，典型的内陆沃茨。
The OLI (Operational Land Imager) sensor on Landsat-8 has the potential to meet the requirements of remote sensing of water color. However, the optical properties of inland waters are more complex than those of oceanic waters, and inland atmospheric correction presents additional challenges. We examined the performance of atmospheric correction (AC) methods for remote sensing over three highly turbid or hypereutrophic inland waters in China: Lake Hongze, Lake Chaohu, and Lake Taihu. Four water-AC algorithms (SWIR (Short Wave Infrared), EXP (Exponential Extrapolation), DSF (Dark Spectrum Fitting), and MUMM (Management Unit Mathematics Models)) and three land-AC algorithms (FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), 6SV (a version of Second Simulation of the Satellite Signal in the Solar Spectrum), and QUAC (Quick Atmospheric Correction)) were assessed using Landsat-8 OLI data and concurrent in situ data. The results showed that the EXP (and DSF) together with 6SV algorithms provided the best estimates of the remote sensing reflectance (Rrs) and band ratios in water-AC algorithms and land-AC algorithms, respectively. AC algorithms showed a discriminating accuracy for different water types (turbid waters, in-water algae waters, and floating bloom waters). For turbid waters, EXP gave the best Rrs in visible bands. For the in-water algae and floating bloom waters, however, all water-algorithms failed due to an inappropriate aerosol model and non-zero reflectance at 1609 nm. The results of the study show the improvements that can be achieved considering SWIR bands and using band ratios, and the need for further development of AC algorithms for complex aquatic and atmospheric conditions, typical of inland waters.