作者:Wang, YT (Wang, Yiting)[ 1,2 ] ; Xie, DH (Xie, Donghui)[ 1 ] ; Liu, S (Liu, Song)[ 3 ] ; Hu, RH (Hu, Ronghai)[ 1,4 ] ; Li, YH (Li, Yahui)[ 1 ] ; Yan, GJ (Yan, Guangjian)[ 1 ]

REMOTE SENSING

卷: 8  期: 4

文獻號: 310

DOI: 10.3390/rs8040310

出版年: APR 2016

摘要

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical biophysical parameter in eco-environmental studies. Scaling of FAPAR from the field observation to the satellite pixel is essential for validating remote sensing FAPAR product and for further modeling applications. However, compared to spatial mismatches, few studies have considered temporal mismatches between in-situ and satellite observations in the scaling. This paper proposed a general methodology for scaling FAPAR from the field to the satellite pixel considering the temporal variation. Firstly, a temporal normalization method was proposed to normalize the in-situ data measured at different times to the time of satellite overpass. The method was derived from the integration of an atmospheric radiative transfer model (6S) and a FAPAR analytical model (FAPAR-P), which can characterize the diurnal variations of FAPAR comprehensively. Secondly, the logistic model, which derives smooth and consistent temporal profile for vegetation growth, was used to interpolate the in-situ data to match the dates of satellite acquisitions. Thirdly, fine-resolution FAPAR products at different dates were estimated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data using the temporally corrected in-situ data. Finally, fine-resolution FAPAR were taken as reference datasets and aggregated to coarse resolution, which were further compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) FAPAR product. The methodology is validated for scaling FAPAR from the field to the satellite pixel temporally and spatially. The MODIS FAPAR manifested a good consistency with the aggregated FAPAR with R-2 of 0.922 and the root mean squared error of 0.054.

通訊作者地址: Xie, DH (通訊作者)

Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Natl Marine Data & Informat Serv, Tianjin 300171, Peoples R China

[ 3 ] Navy Press, Tianjin 300450, Peoples R China

[ 4 ] Univ Strasbourg, UMR CNRS 7357, ICube Lab, F-67412 Illkirch Graffenstaden, France