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Inversion of aerosol optical depth for Landsat 8 OLI data using deep belief network

  • role： First author第一作者
  • Affiliation：

    Geomatics College, Shandong University of Science and Technology, Shandong 266590, China

  • Email：jiachen_1991@163.com
  • Introduction：贾臣，1991年生,男,博士研究生,研究方向为气溶胶遥感、深度学习技术遥感应用。E-mail: jiachen_1991@163.com
JIA Chen,  
  • role： Corresponding author通信作者
  • Affiliation：

    Geomatics College, Shandong University of Science and Technology, Shandong 266590, China

  • Email：sunlin6@126.com
  • Introduction：孙林，1975年生,男,教授,研究方向为定量遥感、深度学习技术遥感应用。E-mail: sunlin6@126.com
SUN Lin*,  
  • Affiliation：

    Geomatics College, Shandong University of Science and Technology, Shandong 266590, China

CHEN Yunfang,  
  • Affiliation：

    Geomatics College, Shandong University of Science and Technology, Shandong 266590, China

ZHANG Xikong,  
  • Affiliation：

    Geomatics College, Shandong University of Science and Technology, Shandong 266590, China

WANG Weiyan,  
  • Affiliation：

    Geomatics College, Shandong University of Science and Technology, Shandong 266590, China

WANG Yongji

Resümee

Traditional methods of aerosol remote sensing inversion can achieve high accuracy in areas with low surface reflectance, homogeneous structures, and dense vegetation while facing serious challenges in highly bright and spatially heterogeneous areas, such as cities and mining areas. High land surface reflectance causes insufficient aerosol information acquired by sensors that can lead to difficulties in aerosol inversion. A remote sensing inversion method of Aerosol Optical Depth (AOD) using deep learning algorithm is proposed in this study to extract aerosol identification information from satellite signals further and apply it to Landsat 8 OLI data. Aerosol-measured data of Aerosol Robotic Network (AERONET) sites in different regions of the world and the corresponding geometric angle and apparent reflectance of the Landsat 8 OLI data are selected to construct sample data sets according to the reasonable space-time matching method. The Deep Belief Network (DBN) is applied to implement the proposed method. The network is trained and tested on the basis of the reasonable setting of training batches and times. An AOD fitted network model on satellite remote sensing information is then generated to achieve aerosol inversion. Inversions are verified using independent AERONET-measured data. The verification showed that the proposed method can realize AOD inversion with continuous spatial coverage over different surface types and high accuracy (R = 0.8745, RMSE = 0.0391, MAE = 0.0616, and EE = 87.94%). Compared with traditional approaches, the proposed method can achieve high-accuracy aerosol inversions using only single-phase satellite remote sensing data, simplify the steps of aerosol inversion, and improve the stability and time–space adaptability of the results.

Schlüsselwort

remote sensing;aerosol optical depth;remote sensing inversion;Landsat 8 OLI;deep learning;Deep Belief Network

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