학술논문
Clear-Air Anomaly Detection Using Modified Kalman Temporal Filter from Geostationary Multispectral Data
Document Type
Conference
Source
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2018 - 2018 IEEE International. :6879-6882 Jul, 2018
Subject
Language
ISSN
2153-7003
Abstract
A multispectral temporal-based remote sensing technique based on a modified Kalman filter is presented for clear-air detection by using Geostationary visible-infrared radiometric passive measurements. The Kalman estimate relies on a model of the daily measurement cycle of the considered pixel in clear-sky conditions. If the measurement significantly deviates from the predicted value, an anomaly is detected, which is interpreted as a non-clear air scenario. The add-on value of such approach is to be able to provide a-priori estimates, making the algorithm applicable in a global way. The Meteosat Second Generation satellite has been used over a large sample area in West Africa and a test period of three months. An inter-comparison with respect to the EUMETSAT cloud mask product has been carried out showing promising results in terms of detecting clear-air scenarios and percentages of matching around 90% over the entire period.