Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment
Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment
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DOI：
10.1080/01431161.2014.951742
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发表时间：
2014-01-01
影响因子：
3.4
通讯作者：
Yung，Ying-Kit
中科院分区：
文献类型：
--
作者：
Nazeer，Majid;Nichol，Janet E.;Yung，Ying-Kit
精确的大气校正对于地表反射率(SR)差异很大的应用非常重要，例如生物量估计、作物物候和水质参数的反演。它还可以在不同的图像日期和不同的传感器之间进行直接比较。作为利用中分辨率传感器Landsat TM/ETM和HJ-1A/B监测香港沿岸不同水质参数的先兆，这项研究评估了五种大气校正方法的性能。对Landsat 7 ETM+的前四个反射波段和HJ-1A/B卫星相同波段的预估光谱比对了五种大气校正方法--太阳光谱中卫星信号的第二次模拟(6S)、光谱超立方体的快速视线大气分析(FLAASH)、大气校正(ATCor)、暗物体减去(DOS)和经验线方法(ELM)--在沙子、人造草坪、草地和水面上的多光谱辐射计(MSR)测量结果。在五种方法中，6S方法被观察到一致地更精确地估计SR，与现场测量的SR的差异明显小，特别是在低反射水面上。在两种基于图像的方法中，DOS在较暗的水面和人造草坪上表现良好，但仍逊于6S，而ELM在草地上的表现好于DOS，与6S在高反射沙地上的表现相当。研究还利用Thein现场测量的SR数据对新的标准Landsat SR产品Landsat生态系统干扰自适应处理系统(LEDAPS)进行了评估。对于高、中反射亮沙和人工草坪，LEDAPS的效果较差，而对于较暗的草坪，LEDAPS的效果较好，但仍逊于6S和ELM方法。这可能是由于香港的气溶胶类型和大气状况各不相同，因为LEDAPS主要是参考较大的大陆地区编制的。
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. It also enables direct comparison between different image dates and different sensors. As a precursor to monitoring different parameters of water quality around the coastline of Hong Kong using medium-resolution sensors Landsat TM/ETM, and HJ-1A/B, this study evaluated the performance of five atmospheric correction methods. The estimated SR of the first four reflective bands of Landsat 7 ETM+ and of the identical bands of the HJ-1A/B satellites was compared within situmultispectral radiometer (MSR) SR measurements over sand, artificial turf, grass, and water surfaces for the five atmospheric correction methods – second simulation of the satellite signal in the solar spectrum (6S), fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH), atmospheric correction (ATCOR), dark object subtraction (DOS), and the empirical line method (ELM). Among the five methods, 6S was observed to be consistently more precise for SR estimation, with significantly less difference from thein-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 as compared to the DOS and equalled the good performance of 6S over the high reflective sand surfaces.The study also evaluated the new standard Landsat SR product Landsat ecosystem disturbance adaptive processing system (LEDAPS) using thein situmeasured SR data for the three land surface types – sand, artificial turf, and grass. For the highly and moderately reflecting bright sand and artificial turf, LEDAPS performed poorly, while for the darker grass site it performed better, although still inferior to 6S and ELM methods. This is probably due to the variable aerosol types and atmospheric conditions of Hong Kong, as LEDAPS was mainly compiled with reference to larger continental landmass areas.