Agricultural and Biosystems Engineering Publications
Campus Units
Agricultural and Biosystems Engineering
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
12-1-2018
Journal or Book Title
Remote Sensing of Environment
Volume
218
First Page
55
Last Page
68
Research Focus Area(s)
Land and Water Resources Engineering
DOI
10.1016/j.rse.2018.09.017
Abstract
A practical atmospheric correction algorithm, called Coupled Moderate Products for Atmospheric Correction (CMPAC), was developed and implemented for the Multispectral Camera (MUX) on-board the China-Brazil Earth Resources Satellite (CBERS-4). This algorithm uses a scene-based processing and sliding window technique to derive MUX surface reflectance (SR) at continental scale. Unlike other optical sensors, MUX instrument imposes constraints for atmospheric correction due to the absence of spectral bands for aerosol estimation from imagery itself. To overcome this limitation, the proposed algorithm performs a further processing of atmospheric products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as input parameters for radiative transfer calculations. The success of CMPAC algorithm was fully assessed and confirmed by comparison of MUX SR data with the Landsat-8 OLI Level-2 and Aerosol Robotic Network (AERONET)-derived SR products. The spectral adjustment was performed to compensate for the differences of relative spectral response between MUX and OLI sensors. The results show that MUX SR values are fairly similar to operational Landsat-8 SR products (mean difference < 0.0062, expressed in reflectance). There is a slight underestimation of MUX SR compared to OLI product (except the NIR band), but the error metrics are typically low and scattered points are around the line 1:1. These results suggest the potential of combining these datasets (MUX and OLI) for quantitative studies. Further, the robust agreement of MUX and AERONET-derived SR values emphasizes the quality of moderate atmospheric products as input parameters in this application, with root-mean-square deviation lower than 0.0047. These findings confirm that (i) CMPAC is a suitable tool for estimating surface reflectance of CBERS MUX data, and (ii) ancillary products support the application of atmospheric correction by filling the gap of atmospheric information. The uncertainties of atmospheric products, negligence of the bidirectional effects, and two aerosol models were also identified as a limitation. Finally, this study presents a framework basis for atmospheric correction of CBERS-4 MUX images. The utility of CBERS data comes from its use, and this new product enables the quantitative remote sensing for land monitoring and environmental assessment at 20 m spatial resolution.
Access
Embargoed
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Copyright Owner
Elsevier Inc.
Copyright Date
2018
Language
en
File Format
application/pdf
Recommended Citation
Martins, Vitor S.; Soares, João V.; Novo, Evlyn M.L.M.; Barbosa, Claudio C.F.; Pinto, Cibele T.; Arcanjo, Jeferson S.; and Kaleita, Amy, "Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations" (2018). Agricultural and Biosystems Engineering Publications. 1029.
https://lib.dr.iastate.edu/abe_eng_pubs/1029
Included in
Agriculture Commons, Atmospheric Sciences Commons, Bioresource and Agricultural Engineering Commons, Environmental Monitoring Commons
Comments
This is a manuscript of an article published as Martins, Vitor S., João V. Soares, Evlyn MLM Novo, Claudio CF Barbosa, Cibele T. Pinto, Jeferson S. Arcanjo, and Amy Kaleita. "Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations." Remote Sensing of Environment 218 (2018): 55-68. DOI: 10.1016/j.rse.2018.09.017. Posted with permission.