作者:Peng, JJ (Peng, Jingjing)[ 1 ] ; Liu, Q (Liu, Qiang)[ 2 ] ; Wang, LZ (Wang, Lizhao)[ 2 ] ; Liu, QH (Liu, Qinhuo)[ 1 ] ; Fan, WJ (Fan, Wenjie)[ 3 ] ; Lu, M (Lu, Meng)[ 4 ] ; Wen, JG (Wen, Jianguang)[ 1 ]

REMOTE SENSING

卷: 7  期: 6  页: 6886-6907

DOI: 10.3390/rs70606886

出版年: JUN 2015

摘要

The adjacency effect and non-uniform responses complicate the precise delimitation of the surface support of remote sensing data and their derived products. Thus, modeling spatial response characteristics (SRCs) prior to using remote sensing information has become important. A point spread function (PSF) is typically used to describe the SRCs of the observation cells from remote sensors and is always estimated in a laboratory before the sensor is launched. However, research on the SRCs of high-order remote sensing products derived from the observations remains insufficient, which is an obstacle to converting between multi-scale remote sensing products and validating coarse-resolution products. This study proposed a method that combines simulation and validation to establish SRC models of coarse-resolution albedo products. Two series of commonly used 500-m/1-km resolution albedo products, which are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data, were investigated using 30-m albedo products that provide the required sub-pixel information. The analysis proves that the size of the surface support of each albedo pixel is larger than the nominal resolution of the pixel and that the response weight is non-uniformly distributed, with an elliptical Gaussian shape. The proposed methodology is generic and applicable for analyzing the SRCs of other advanced remote sensing products.

通讯作者地址: Liu, Q (通讯作者)

       Beijing Normal Univ, Coll Global Change & Earth Syst Sci, 19 Xinjiekou Rd, Beijing 100875, Peoples R China.

地址:

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

       [ 2 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China

       [ 3 ] Peking Univ, Inst Remote Sensing & GIS, Beijing 100871, Peoples R China

       [ 4 ] Inst Geoinformat, D-48149 Munster, Germany