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引用本文:

DOI:

10.11834/jrs.20211244

收稿日期:

2021-04-25

修改日期:

2021-06-26

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基于新一代国产高光谱卫星ZY1E的典型内陆水体叶绿素a浓度反演
刘瑶1, 李俊生2, 肖晨超1, 张方方2, 王胜蕾2
1.自然资源部国土卫星遥感应用中心;2.中国科学院空天信息创新研究院
摘要:

2019年9月12日成功发射的资源一号02D卫星(ZY1-02D)搭载了新一代可见短波红外高光谱相机(Advanced Hyperspectral Imager,AHSI),其丰富的细分波段和较高的空间分辨率在内陆湖库水质监测方面具有较大潜力,但数据可用性有待分析和验证。本研究以我国华东和华北平原的典型富营养湖库(太湖、于桥水库)和中营养湖库(小浪底水库)为研究区,开展基于ZY1-02D高光谱影像的叶绿素a浓度反演研究。以3个研究区在46个采样点地面测量的叶绿素a浓度和同步获取影像的遥感反射率作为数据源,基于5种典型的叶绿素a半经验模型进行了模型参数优化和叶绿素a反演精度验证。结果表明,基于中心波长为705nm和671nm的波段比值模型叶绿素a反演精度最高,模型R2为0.78,平均无偏相对误差(AURE)和均方根误差(RMSE)分别为13.5%和4.5mg/m3。研究表明,ZY1-02D高光谱数据在内陆水体叶绿素a浓度高精度反演方面具有重要潜力,但未来需要通过多星组网提升监测能力,以及发展针对于ZY1-02D水体图像的降噪和大气校正方法。

Inland water Chlorophyll a retrieval based on the China’s new generation hyperspectral ZY1-02D satellite observations
Abstract:

China’s ZY1-02D satellite was successfully launched on September 12, 2019. It carries the new generation Advanced Hyperspectral Imager (AHSI), which has 166 bands in the visible to short-wave infrared bands. AHSI can acquire images at 30 m spatial resolution with a 60 km swath. Owing to its abundant narrow bands and relatively high spatial resolution, ZY1-02D satellite shows great potential to be applied for inland water quality monitoring. However, this satellite has been launched for a short period, and the applicability of this data needs to be further analyzed and tested. In this paper, Taihu Lake (eutrophic), Yuqiao reservoir (eutrophic) and Xiaolangdi Reservoir (mesotrophic) in China were used as study areas for the Chlorophyll-a (Chla) retrieval based on the ZY1-02D hyperspectral images. Within 1 day of the ZY1-02D satellite overpass, in situ spectra and Chla concentrations were collected at sampling sites in these study areas. We selected 5 typical chlorophyll-a semi-empirical models based on spectral indices, which were the band ratio (BR), normalized difference chlorophyll index (NDCI), the three-band index (TBI), enhanced three-band index (ETBI), and the baseline height (BH). We used in situ measured chlorophyll-a concentration at 46 sampling sites in the 3 study areas and the simultaneously acquired ZY1-02D images to optimize parameters in these models. We first evaluated the accuracies of image-derived Rrs at sampling sites, and then conducted accuracy analysis for estimated chla concentrations using optimized empirical models. ZY1-02D image-derived Rrs were consistent with in situ measured Rrs in the 671 nm and 705 nm, whereas the 731nm and 748 nm band Rrs had greater uncertainties since they were more likely to be affected by the image noise. In addition, the accuracy analysis for the estimated Chla concentrations show that the model based on the 705nm to 671nm band ratio achieves the highest accuracy, with an R2 of 0.78, and the mean unbiased relative error (AURE) and root mean square error (RMSE) are 13.5% and 4.5mg/m3, respectively. In contrast, models based on the ETBI and BH yield Chla concentration estimates with low accuracies. In conclusion, ZY1-02D hyperspectral data shows good potential in terms of accurate retrieval of chlorophyll-a concentration for inland waters. We plan to conduct more in situ experiment when the ZY1-02D satellite overpasses, in order to improve the Chla concentration retrieval model applied on the ZY1-02D data. In the future, it is necessary to improve the monitoring capacity through establishing a hyperspectral satellite constellation, and developing noise reduction and atmospheric correction methods for ZY1-02D’s inland water application.

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