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Normalization of time-series satellite reflectance data to a standard sun-target-sensor geometry using a semi-empirical model
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Paper Abstract

Time series of satellite reflectance data have been widely used to characterize environmental phenomena, describe trends in vegetation dynamics and study climate change. However, several sensors with wide spatial coverage and high observation frequency are usually designed to have large field of view (FOV), which cause variations in the sun-targetsensor geometry in time-series reflectance data. In this study, on the basis of semiempirical kernel-driven BRDF model, a new semi-empirical model was proposed to normalize the sun-target-sensor geometry of remote sensing image. To evaluate the proposed model, bidirectional reflectance under different canopy growth conditions simulated by Discrete Anisotropic Radiative Transfer (DART) model were used. The semi-empirical model was first fitted by using all simulated bidirectional reflectance. Experimental result showed a good fit between the bidirectional reflectance estimated by the proposed model and the simulated value. Then, MODIS time-series reflectance data was normalized to a common sun-target-sensor geometry by the proposed model. The experimental results showed the proposed model yielded good fits between the observed and estimated values. The noise-like fluctuations in time-series reflectance data was also reduced after the sun-target-sensor normalization process.

Paper Details

Date Published: 5 October 2017
PDF: 8 pages
Proc. SPIE 10428, Earth Resources and Environmental Remote Sensing/GIS Applications VIII, 104280B (5 October 2017); doi: 10.1117/12.2277307
Show Author Affiliations
Yongguang Zhao, Key Lab. of Quantitative Remote Sensing Information Technology, CAS (China)
Academy of Opto-Electronics, CAS (China)
Chuanrong Li, Key Lab. of Quantitative Remote Sensing Information Technology, CAS (China)
Academy of Opto-Electronics, CAS (China)
Lingling Ma, Key Lab. of Quantitative Remote Sensing Information Technology, CAS (China)
Academy of Opto-Electronics, CAS (China)
Lingli Tang, Key Lab. of Quantitative Remote Sensing Information Technology, CAS (China)
Academy of Opto-Electronics, CAS (China)
Ning Wang, Key Lab. of Quantitative Remote Sensing Information Technology, CAS (China)
Academy of Opto-Electronics, CAS (China)
Chuncheng Zhou, Key Lab. of Quantitative Remote Sensing Information Technology, CAS (China)
Academy of Opto-Electronics, CAS (China)
Yonggang Qian, Key Lab. of Quantitative Remote Sensing Information Technology, CAS (China)
Academy of Opto-Electronics, CAS (China)


Published in SPIE Proceedings Vol. 10428:
Earth Resources and Environmental Remote Sensing/GIS Applications VIII
Ulrich Michel; Karsten Schulz, Editor(s)

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