A coupled atmospheric and topographic correction algorithm for remotely sensed satellite imagery over mountainous terrain. Issue 3 (4th May 2018)

• Record Type: Journal Article
• Authors: Zhang, Zhaoming; He, Guojin; Zhang, Xiaomei; Long, Tengfei; Wang, Guizhou; Wang, Mengmeng
• Is Part Of: GIScience & remote sensing. Volume 55:Issue 3(2018)
• Page Start: 400
• Page End: 416
• Publication Date: 2018-05-04
• Subjects: remote sensing -- rugged terrain -- land surface reflectance -- Yong'an County; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2017.1382066
• Languages: English
• ISSNs: 1548-1603

Abstract

Abstract : Radiometric correction is an important issue in the quantitative remote-sensing community. By integrating dark object subtraction (DOS)-based atmospheric correction with physics-based topographic correction, a coupled land surface reflectance retrieval algorithm (coupled atmospheric and topographic correction algorithm, named the CAT algorithm) for rugged mountainous regions is proposed. Terra MODIS-derived atmospheric characterization data (including aerosol optical depth, integrated precipitable water, surface pressure, and ozone concentration) are employed as inputs for the proposed algorithm. A physics-based path radiance estimation model is proposed and embedded in the CAT algorithm, and band-specific per-pixel path radiance values are calculated. After the CAT algorithm was performed, the correlation between reflectance and terrain was dramatically reduced, with correlation coefficients nearly equal zero, especially for the near infrared and short-wave infrared bands, meanwhile the image information content increased over 20%. To provide a comparison with previous studies, two commonly used methods in the literature (DOS + Cosine and DOS + C) were employed. The results of the comparison show that the proposed algorithm performed better in both atmospheric and topographic corrections without empirical regression.