作者:Zhong, B (Zhong, B.)[ 1 ] ; Yang, A (Yang, A.)[ 1 ] ; Wu, S (Wu, S.)[ 1 ] ; Li, J (Li, J.)[ 1 ] ; Liu, S (Liu, S.)[ 2 ] ; Liu, Q (Liu, Q.)[ 1 ]

REMOTE SENSING OF ENVIRONMENT

卷: 204  页: 412-423

DOI: 10.1016/j.rse.2017.10.014

出版年: JAN 2018

摘要

The major global biogeophysical products obtained from remote sensing are usually composites of 8 or 16 days, and are almost always retrieved from a single sensor. Subsequently, the applications of these products are limited in cases requiring higher temporal frequency. With the increasing number of freely available moderate resolution remote sensing datasets, multi-sensor synergy to increase temporal sampling is warranted. However, radiometric consistency of multi-sensor data is not as good as expected; therefore, first the constituency should be evaluated. A new cross-calibration approach of reflective bands for moderate resolution remotely sensed data is proposed in this paper. The new approach uses a time series of MODIS data from both Terra and Aqua to retrieve both the bidirectional reflectance distribution function (BRDF) and aerosol optical depth (AOD) simultaneously over an invariant desert target. The MODIS retrieved BRDF and AOD are then used to cross calibrate the medium resolution spectral imager (MERSI) and visible and infra-red radiometer (VIRR) onboard FengYun3-A and B satellites (FY3A/B) and advanced very high resolution radiometer (AVHRR) onboard NOAA-16, 17, and 18. The cross-calibration method is validated in two ways: 1) by comparing with the vicarious calibration; and 2) the top of atmosphere (TOA) reflectance before and after calibration, which show that the calibration error of this new approach is consistent within 5%. Compared with most cross -calibration methods, our new method is generic and better consider on BRDF models; furthermore, the well-built BRDF of the calibration site and simultaneously retrieved AOD make the method fully automatic and cost effective for the calibration of most moderate resolution remote sensors, especially Chinese sensors.

通讯作者地址: Zhong, B (通讯作者)

Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China

[ 2 ] Satellite Environm Ctr, Environm Protect Dept, Beijing 100094, Peoples R China