1. 中国科学院水利部成都山地灾害与环境研究所，四川 成都 610041 2. 中国科学院大学，北京 100049 3. Department of Geography, University of Maryland, College Park 20741, USA
An Improved DDV Method to Retrieve AOT for HJ CCD Image in Typical Mountainous Areas
ZHAO Zhi-qiang1, 2, LI Ai-nong1*, BIAN Jin-hu1, 2, HUANG Cheng-quan3
1. Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China 2. University of Chinese Academy of Sciences, Beijing 100049, China 3. Department of Geography, University of Maryland, College Park 20741, USA
Abstract：Domestic HJ CCD imaging applications in environment and disaster monitoring and prediction has great potential. But, HJ CCD image lack of Mid-Nir band can not directly retrieve Aerosol Optical Thickness (AOT) by the traditional Dark Dense Vegetation (DDV) method, and the mountain AOT changes in space-time dramatically affected by the mountain environment, which reduces the accuracy of atmospheric correction. Based on wide distribution of mountainous dark dense forest, the red band histogram threshold method was introduced to identify the mountainous DDV pixels. Subsequently, the AOT of DDV pixels were retrieved by lookup table constructed by 6S radiative transfer model with assumption of constant ratio between surface reflectance in red and blue bands, and then were interpolated to whole image. MODIS aerosol product and the retrieved AOT by the proposed algorithm had very good consistency in spatial distribution, and HJ CCD image was more suitable for the remote sensing monitoring of aerosol in mountain areas, which had higher spatial resolution. Their fitting curve of scatterplot was y=0.828 6x-0.01 and R2 was 0.984 3 respectively. Which indicate the improved DDV method can effectively retrieve AOT, and its precision can satisfy the atmospheric correction and terrain radiation correction for HJ CCD image in mountainous areas. The improvement of traditional DDV method can effectively solve the insufficient information problem of the HJ CCD image which have only visible light and near infrared band, when solving radiative transfer equation. Meanwhile, the improved method fully considered the influence of mountainous terrain environment. It lays a solid foundation for the HJ CCD image atmospheric correction in the mountainous areas, and offers the possibility for its automated processing. In addition, the red band histogram threshold method was better than NDVI method to identify mountain DDV pixels. And, the lookup table and ratio between surface reflectance between red and blue bands were the important influence factor for AOT retrieval. These will be the important research directions to further improve algorithm and improve the retrieve accuracy.
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