Next Article in Journal
Doppler Spectrum-Based NRCS Estimation Method for Low-Scattering Areas in Ocean SAR Images
Next Article in Special Issue
A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping
Previous Article in Journal
Burst Misalignment Evaluation for ALOS-2 PALSAR-2 ScanSAR-ScanSAR Interferometry
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(3), 217; doi:10.3390/rs9030217

Improved Aerosol Optical Thickness, Columnar Water Vapor, and Surface Reflectance Retrieval from Combined CASI and SASI Airborne Hyperspectral Sensors

1
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (CAS), Beijing 100101, China
2
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Earth Science and Resource Engineering, Australian Resources Research Centre (ARRC), 26 Dick Perry Avenue, Kensington, WA 6151, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Alexander A. Kokhanovsky, Thomas Ruhtz and Prasad S. Thenkabail
Received: 24 November 2016 / Revised: 2 February 2017 / Accepted: 18 February 2017 / Published: 28 February 2017
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
View Full-Text   |   Download PDF [3619 KB, uploaded 28 February 2017]   |  

Abstract

An increasingly common requirement in remote sensing is the integration of hyperspectral data collected simultaneously from different sensors (and fore-optics) operating across different wavelength ranges. Data from one module are often relied on to correct information in the other, such as aerosol optical thickness (AOT) and columnar water vapor (CWV). This paper describes problems associated with this process and recommends an improved strategy for processing remote sensing data, collected from both visible to near-infrared and shortwave infrared modules, to retrieve accurate AOT, CWV, and surface reflectance values. This strategy includes a workflow for radiometric and spatial cross-calibration and a method to retrieve atmospheric parameters and surface reflectance based on a radiative transfer function. This method was tested using data collected with the Compact Airborne Spectrographic Imager (CASI) and SWIR Airborne Spectrographic Imager (SASI) from a site in Huailai County, Hebei Province, China. Various methods for retrieving AOT and CWV specific to this region were assessed. The results showed that retrieving AOT from the remote sensing data required establishing empirical relationships between 465.6 nm/659 nm and 2105 nm, augmented by ground-based reflectance validation data, and minimizing the merit function based on AOT@550 nm optimization. The paper also extends the second-order difference algorithm (SODA) method using Powell’s methods to optimize CWV retrieval. The resulting CWV image has fewer residual surface features compared with the standard methods. The derived remote sensing surface reflectance correlated significantly with the ground spectra of comparable vegetation, cement road and soil targets. Therefore, the method proposed in this paper is reliable enough for integrated atmospheric correction and surface reflectance retrieval from hyperspectral remote sensing data. This study provides a good reference for surface reflectance inversion that lacks synchronized atmospheric parameters. View Full-Text
Keywords: atmospheric correction; columnar water vapor (CWV); aerosol optical thickness (AOT); hyperspectral remote sensing; look-up table; CASI; SASI atmospheric correction; columnar water vapor (CWV); aerosol optical thickness (AOT); hyperspectral remote sensing; look-up table; CASI; SASI
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yang, H.; Zhang, L.; Ong, C.; Rodger, A.; Liu, J.; Sun, X.; Zhang, H.; Jian, X.; Tong, Q. Improved Aerosol Optical Thickness, Columnar Water Vapor, and Surface Reflectance Retrieval from Combined CASI and SASI Airborne Hyperspectral Sensors. Remote Sens. 2017, 9, 217.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top