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Remote Sens. 2016, 8(9), 727; doi:10.3390/rs8090727

Reconstruction of MODIS Spectral Reflectance under Cloudy-Sky Condition

1,2,3,4
,
1,2,3,4,* , 5
and
5
1
Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
2
Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China
3
Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
4
College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
5
State Key Laboratory of Remote Sensing Science, The Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Academic Editors: Dongdong Wang, Richard Müller and Prasad S. Thenkabail
Received: 27 June 2016 / Revised: 20 July 2016 / Accepted: 29 August 2016 / Published: 9 September 2016
View Full-Text   |   Download PDF [5986 KB, uploaded 9 September 2016]   |  

Abstract

Clouds usually cause invalid observations for sensors aboard satellites, which corrupts the spatio-temporal continuity of land surface parameters retrieved from remote sensing data (e.g., MODerate Resolution Imaging Spectroradiometer (MODIS) data) and prevents the fusing of multi-source remote sensing data in the field of quantitative remote sensing. Based on the requirements of spatio-temporal continuity and the necessity of methods to restore bad pixels, primarily resulting from image processing, this study developed a novel method to derive the spectral reflectance for MODIS band of cloudy pixels in the visual–near infrared (VIS–NIR) spectral channel based on the Bidirectional Reflectance Distribution Function (BRDF) and multi-spatio-temporal observations. The proposed method first constructs the spatial distribution of land surface reflectance based on the corresponding BRDF and the solar-viewing geometry; then, a geographically weighted regression (GWR) is introduced to individually derive the spectral surface reflectance for MODIS band of cloudy pixels. A validation of the proposed method shows that a total root-mean-square error (RMSE) of less than 6% and a total R2 of more than 90% are detected, which indicates considerably better precision than those exhibited by other existing methods. Further validation of the retrieved white-sky albedo based on the spectral reflectance for MODIS band of cloudy pixels confirms an RMSE of 3.6% and a bias of 2.2%, demonstrating very high accuracy of the proposed method. View Full-Text
Keywords: MODIS; reflectance; BRDF; cloudy pixel; GWR MODIS; reflectance; BRDF; cloudy pixel; GWR
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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).

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Gao, B.; Gong, H.; Wang, T.; Jia, L. Reconstruction of MODIS Spectral Reflectance under Cloudy-Sky Condition. Remote Sens. 2016, 8, 727.

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