An object-oriented based daytime over land fog detection approach using EOS/MODIS data

Xiongfei Wen; Liangming Liu; Wei Li; Pei Dong

doi:10.1117/12.830163

29 September 2009 An object-oriented based daytime over land fog detection approach using EOS/MODIS data

Xiongfei Wen, Liangming Liu, Wei Li, Pei Dong

Proceedings Volume 7475, Remote Sensing of Clouds and the Atmosphere XIV; 747516 (2009) https://doi.org/10.1117/12.830163
Event: SPIE Remote Sensing, 2009, Berlin, Germany

Abstract

A new algorithm is presented for land fog detection from daytime image of Earth Observation System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) data. Due to its outstanding spatial and spectral resolutions, this image is an ideal data source for fog detection. The algorithm utilizes an object-oriented technique to separate fog from other cloud types. In this paper, MOD35 product is first introduced to exclude cloud-free areas, and high clouds are removed with MODIS 26 band, and then a parameter named Normalized Difference Fog Index (NDFI) is proposed based on Streamer radiative model and MODIS data for fog detection. Through segmenting NDFI image into regions of pixels, and computing attributes (e.g. mean value of brightness temperature) for each region to create objects, each object could be identified based on the attributes selected to determine whether belongs to fog or cloud. Algorithm's performance is evaluated against ground-based measurements over China in winter. The algorithm is proved to be effective in detecting fog accurately based on two different test cases.

Citation Download Citation

Xiongfei Wen, Liangming Liu, Wei Li, and Pei Dong "An object-oriented based daytime over land fog detection approach using EOS/MODIS data", Proc. SPIE 7475, Remote Sensing of Clouds and the Atmosphere XIV, 747516 (29 September 2009); https://doi.org/10.1117/12.830163

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available