Conversion study on multi-spectral information of remote sensing images GF-1 WFV, Landsat-8 OLI and Sentinel-2A MSI
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摘要: 地表环境的宏观动态监测研究中，受卫星回归周期及天气的影响，单颗卫星难以获取长期、连续的光学遥感数据，因此，定量分析多平台遥感数据的光谱信息关系是十分必要的.本研究基于两组同日过境的无云卫星影像（GF-1与Landsat-8和Sentinel-2A），结合地面调查数据，进行了不同传感器影像间对应波段的光谱信息对比，并通过统计回归分析获得了两组卫星对应波段（蓝、绿、红和近红外）的反射率转换方程.研究结果显示，GF-1与Landsat-8和GF-1与Sentinel-2A对应波段的反射率都具有很强的相关性，得到的转换方程能够对上述三种卫星数据间对应波段的光谱信息实现高精度的转换.本研究能够实现同日多光谱遥感数据的光谱信息转换及协同应用，并为区域资源环境的长期定量遥感监测提供技术支持.Abstract: Owing to the restrictions in weather conditions and revisit cycle of satellites, single satellite can't successively acquire effective optical RS data for long-term monitoring the terrestrial environments. Therefore, it is crucial important to analyze the multi-spectral information of multi-source RS data. In this study, two groups of clear RS images (GF-1 and Landsat-8, GF-1 and Sentinel-2A), in along with ground survey data, were respectively acquired on identical date. Four bands, blue, green, red and near infrared (NIR), were selected to compare their spectral characteristics. At the same time, conversion equation for reflectance of four corresponding spectral bands was derived respectively via statistical regression method. The result shows that each pair of bands in the two comparing groups has a strong correlation. And, these conversion equations can effectively converse spectral information in between each band of two comparing groups with a better precision. This study provides a useful technological approach for the identical date information integration and syngeneic application of multi-spectral RS data from the same day, as well as quantitatively monitoring the long-term dynamics in environments and resources at regional scale.
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Key words:
- GF-1 /
- Landsat-8 /
- Sentinel-2A /
- remote sensing /
- sensor /
- reflectance /
- conversion equation
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图 2 用于对比的遥感图像以及研究区典型地物类型
Fig. 2 Remote sensing images for comparison and typical land covers in study areas
图 3 TOA反射率和BOA反射率对比散点图
注: a、b、c、d分别指代蓝、绿、红、近红外波段
Fig. 3 Scatter plots of TOA and BOA reflectance comparison
图 5 Landsat-8 OLI和Sentinel-2A MSI的转换值与GF-1的观测值对比验证结果图
Fig. 5 The results of Landsat-8 OLI and Sentinel-2A MSI conversion value contrast with the observed values of GF-1
表 1 GF-1、Landsat-8及Sentinel-2A卫星参数表
Tab. 1 System parameters of GF-1, Landsat-8 and Sentinel-2A
卫星 分辨率/m 重访周期/d 幅宽/km 波段数 传感器 GF-1 16 4(有侧摆) 800(4台相机) 4 WFV2; WFV3 Landsat-8 30 16 185 9 OLI Sentinel-2A 10 10(双星5) 290 13 MSI 对应波段及波长范围/${\mu}$m} 地方时间 轨道号 B1(Blue): B2(Green): B3(Red): B4(NIR): WFV2: 2014/09/08 WFV3: 2016/08/22 WFV2: WFV: 0.450$\sim$0.520 0.520$\sim$0.590 0.630$\sim$0.690 0.770$\sim$0.890 11:01 11:30 P594R87 P595R87 B2(Blue): B3(Green): B4(Red): B5(NIR): 2014/09/08 10:28 P119R31 0.450$\sim$0.520 0.520$\sim$0.590 0.630$\sim$0.690 0.770$\sim$0.890 B2(Blue): B3(Green): B4(Red): B8(NIR): 2016/08/22 10:38 相对轨道: 绝对轨道: 0.430$\sim$0.550 0.515$\sim$0.605 0.623$\sim$0.702 0.765$\sim$0.920 R089 R00609 
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表 2 GF-1 WFV与Landsat-8 OLI和Sentinel-2A MSI的光谱转换方程
Tab. 2 Equations for spectrally conversing Landsat-8 OLI to GF-1 WFV2 and Sentinel-2 MSI to GF-1 WFV3
传感器 波段 TOA反射率转换方程 BOA反射率转换方程 WFV2与OLI 蓝 $y=1.141\;0x+0.021\;7$ $y=1.219\;1x-0.013\;6$ 绿 $y=1.125\;4x+0.023\;2$ $y=1.086\;9x-0.009\;3$ 红 $y=1.054\;9x+0.019\;9$ $y=0.953\;9x-0.001\;3 $ 近红外 $y=1.025\;3x+0.058\;2$ $y=0.947\;7x+0.029\;6 $ WFV3与MSI 蓝 $y=0.374\;3x+0.017\;9$ $y=0.709\;5x-0.007\;5$ 绿 $y=0.396\;9x+0.010\;1$ $y=0.760\;7x+0.001\;3$ 红 $y=0.414\;4x+0.013\;4$ $y=0.827\;7x+0.010\;3$ 近红外 $y=0.360\;0x+0.040\;2$ $y=0.722\;6x+0.044\;8$
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