气候变化领域集成服务门户(CCPortal): Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8

CCPortal

DOI	10.1016/j.rse.2020.112093
	Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8
	Su T.; Laszlo I.; Li Z.; Wei J.; Kalluri S.
发表日期	2020
ISSN	00344257
卷号	251
英文摘要	For the past two decades, quantitative retrievals of aerosol optical depth (AOD) have been made from both geostationary and polar-orbiting satellites, and the results have been widely used in numerous studies. Despite the progress made in improving the accuracy of AOD retrievals, there are still major challenges, especially over land. A notable one for the so-called Dark-Target (DT) algorithms is building the surface reflectance (SR) relationships (SRR) to derive SR in the visible channels from SR in the short-wave infrared (SWIR) channel, mainly because these relationships are strongly subjected to entangled factors (e.g., viewing geometry, surface type, and vegetation state). In this study, we examine the benefits of a new method for deriving the SRR using deep learning techniques. The SRR constructed by the deep neural network (DNN) considers multiple related inputs, such as the SWIR normalized difference vegetation index (NDVISWIR), viewing geometry, and seasonality, among others. We then incorporate the DNN-constrained SRR into a DT algorithm developed at NOAA/STAR to retrieve AOD from the Advanced Himawari Instrument (AHI) onboard the new generation of geostationary satellites, Himawari-8. The revised DT algorithm with the deep learning technique (DTDL) demonstrates improved performance over the study region (95–125°E, 18–30°N, a portion of the AHI full disk), as attested by significantly reduced random noise, especially for low NDVISWIR and high surface albedo cases. Robust independent tests indicate that this algorithm can be applied to untrained regions, not only to those used in training. The method directly benefits the algorithm development for Himawari-8 and can also be adopted for other geostationary or polar-orbiting satellites. Our study illustrates how artificial intelligence could significantly improve AOD retrievals from multi-spectral satellite observations following this new approach. © 2020 Elsevier Inc.
语种	英语
scopus关键词	Aerosols; Deep neural networks; Geostationary satellites; Infrared radiation; Learning systems; Optical properties; Orbits; Quantum entanglement; Reflection; Vegetation; Aerosol optical depths; Algorithm development; Learning techniques; Normalized difference vegetation index; Polar-orbiting satellites; Short wave infrared; Spectral surface reflectance; Surface reflectance; Deep learning; aerosol; algorithm; artificial intelligence; NDVI; NOAA satellite; optical depth; surface reflectance
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179119
作者单位	Department of Atmospheric and Oceanic Science and ESSIC, University of Maryland, College Park, MD 20740, United States; Center for Satellite Applications and Research, NOAA/NESDIS, College Park, MD 20740, United States
推荐引用方式 GB/T 7714	Su T.,Laszlo I.,Li Z.,et al. Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8[J],2020,251.
APA	Su T.,Laszlo I.,Li Z.,Wei J.,&Kalluri S..(2020).Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8.Remote Sensing of Environment,251.
MLA	Su T.,et al."Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8".Remote Sensing of Environment 251(2020).