Zeng Qun, Zhao Yue, Tian Li-Qiao, Chen Xiao-Ling
Editorial Department of Journal, Central China Normal University, Wuhan 430079, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2013 May;33(5):1320-6.
HJ-1A/1B satellite CCD images have higher spatial and temporal resolution, making them of great potential in quantitatively monitoring the water quality of inland lakes. However, the atmospheric correction of the images restricts their application. Therefore, taking Poyang Lake, the biggest freshwater lake in China as study area , and using the in-situ data collected in 2009 and 2011, this paper compares the atmospheric correction results done by the four methods: FLAASH, 6S, COST and QUAC, and analyzes the influence of these atmospheric correction methods on the inversion accuracy of the total suspended sediments (TSS) concentration. The results indicate: (1) the band 1 (blue band) of HJ-1A/1B CCD satellite images should be recalibrated while being applied into water quality remote sensing. The accuracy of atmospheric correction done from band 2 (green band) and band 3 (red band) is higher than that of others , especially that of the correction done by FLAASH, 6S and COST is much higher while that of correction done by QUAC is lower. So the algorithms of QUAC should be pointedly improved. (2) The ratios done from band 2 and band 3 have a good match with in-situ data , with an average relative error of 8.2%, 9.5%, 7.6% and 11.6% respectively for FLAASH, 6S, COST and QUAC. Therefore, it would be better to use the ratio done from band 2 and band 3 as inversion factors in Poyang Lake. (3) It is found that the accuracy of directly building models by using the four atmospheric corrected results and the TSS concentration is higher than the models built by the in-situ remote sensing reflectance and the TSS concentration. The accuracy of the TSS concentration inverted by FLAASH, 6S and COST is much high with an average error of only 10.0%, 10.2% and 8.0% respectively, while the error inverted by QUAC is a little bit higher of being 18.6%. So it is suggested to build model with atmospheric correction results and the TSS concentration data, because it can avoid the cumulate error resulted from modeling by using the in-situ spectrum data. (4) Under a low requested situation, these four atmospheric correction algorithms can all be adopted; otherwise, the COST should be used in the case of lacking supplementary information.
HJ-1A/1B卫星电荷耦合器件（CCD）影像具有较高的空间和时间分辨率，使其在定量监测内陆湖泊水质方面具有巨大潜力。然而，影像的大气校正限制了它们的应用。因此，本文以中国最大的淡水湖鄱阳湖为研究区域，利用2009年和2011年收集的现场数据，比较了FLAASH、6S、COST和QUAC这四种方法的大气校正结果，并分析了这些大气校正方法对总悬浮沉积物（TSS）浓度反演精度的影响。结果表明：（1）HJ-1A/1B CCD卫星影像的波段1（蓝波段）在应用于水质遥感时应重新校准。波段2（绿波段）和波段3（红波段）的大气校正精度高于其他波段，尤其是FLAASH、6S和COST的校正精度高得多，而QUAC的校正精度较低。因此，应针对性地改进QUAC算法。（2）波段2和波段3的比值与现场数据匹配良好，FLAASH、6S、COST和QUAC的平均相对误差分别为8.2%、9.5%、7.6%和11.6%。因此，在鄱阳湖将波段2和波段3的比值作为反演因子效果较好。（3）发现直接利用四种大气校正结果和TSS浓度建立模型的精度高于利用现场遥感反射率和TSS浓度建立的模型。FLAASH、6S和COST反演的TSS浓度精度很高，平均误差分别仅为10.0%、10.2%和8.0%，而QUAC反演的误差稍高，为18.6%。因此，建议利用大气校正结果和TSS浓度数据建立模型，因为这样可以避免使用现场光谱数据建模产生的累积误差。（4）在要求较低的情况下，这四种大气校正算法均可采用；否则，在缺乏补充信息的情况下应使用COST算法。
