Satellite Cloud Detection With Shortwave Channels: Algorithms, MISR Applications, and Three-Dimensional Radiative Effects

This thesis presents the studies that I have conducted on a most fundamental step in satellite remote sensing---cloud detection, which has been known as the largest source of error in the retrievals of satellite geophysical products. This thesis consists of two parts: the operational part and the theoretical part. Specifically, the operational part is on the selection, implementation, and validation of the Radiometric Camera-by-camera Cloud Mask (RCCM) over land algorithm for NASA's Multi-angle Imaging SpectroRadiometer (MISR) mission. This algorithm is now in operational use at the NASA Langley Distributed Active Archive Center (DAAC) by the MISR cloud, land, and aerosol remote sensing algorithms. The theoretical study is on the impacts of 3-D radiative effects on satellite cloud detection and their consequences on cloud fraction and aerosol optical depth retrieval. The results showed that 3-D radiative effects lead to overlaps between the distributions of clear and cloudy pixels through channeling, leakage, shadowing and cloud-surface interaction pathways. Due to this fact, perfect satellite cloud detection is practically impossible through single thresholding techniques even when there is no background variability and no instrument noise. The consequences of cloud masking on cloud fraction and aerosol optical depth retrievals are significant. For aerosol optical depth retrievals, the biases reach their peak at a solar zenith angle in the range of 30° to 50° when retrievals are based on the perfect cloud mask, the minimum classification error cloud mask, and the cloud fraction conservative cloud mask.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Atmospheric Sciences

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Yang, Yuekui

Contributors dc:contributor

• Di Girolamo, Larry

• Remote Sensing

Language dc:language

eng

Identifier

(MiAaPQ)AAI3290442

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/85972