ADAPTIVE LEAST-SQUARES METHOD FOR ESTIMATION OF PARTIAL CLOUD COVERAGE WITHIN A PIXEL

被引：4

作者：

ARAI, K ^{[1
]}

TERAYAMA, Y ^{[1
]}

UEDA, Y ^{[1
]}

MORIYAMA, M ^{[1
]}

机构：

[1] NAGASAKI UNIV,DEPT MECH SYST,NAGASAKI 852,JAPAN

来源：

INTERNATIONAL JOURNAL OF REMOTE SENSING | 1995年 / 16卷 / 12期

关键词：

D O I：

10.1080/01431169508954551

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

A study of the estimation of partial cloud cover within a pixel has been conducted in order to be able to use pixels partially contaminated with cloud in sea surface temperature determination. The existing estimation methods based on the least squares method with constraints of minimizing the mixing ratio and observation vector, are theoretically compared and then an adaptive least squares method is proposed. In a comparative study the estimation accuracies for the proposed and other existing methods, including the maximum likelihood method, are compared with simulated and real satellite image data of NOAA AVHRR and MOS-1 VTIR. The results with the simulation data show that the maximum likelihood method is best followed by the adaptive least squares method, the least squares method and the observation vector, while the results with the real VTIR data show that the proposed adaptive least squares method is best followed by the least squares method, the maximum likelihood method and the observation vector but there is no significant differences between all these methods.

引用

页码：2197 / 2206

页数：10