TY - GEN
T1 - Blending NPP-VIIRS and landsat OLI images for flood inundation monitoring
AU - Huang, C.
AU - Chen, Y.
AU - Zhang, S. Q.
AU - Liu, R.
AU - Shi, K. F.
AU - Li, L. Y.
AU - Wu, J. P.
N1 - Funding Information:
This study is supported by National Major Scientific Research Program [Grant No. 2013CBA01806], the Special Trade Project for Commonweal of Water Resources [Grant No. 201401026], National Natural Science Foundation of China [Grant No. 41501460] and Shanxi Key Science and Technology Innovation Team [Grant No. 2014KCT-27].
Publisher Copyright:
© 2020 Proceedings - 21st International Congress on Modelling and Simulation, MODSIM 2015. All rights reserved.
PY - 2015
Y1 - 2015
N2 - Measuring surface water using remote sensing technology is an essential research topic in many research areas, including flood-related studies and water resource management. Recent advances in satellite remote sensing have provided more efficient ways of monitoring surface water from space. Several sensors, such as the Visible Infrared Imaging Radiometer Suite on board Suomi National Polar-orbiting Partnership (Suomi NPP-VIIRS) and Operational Land Imager (OLI) on board Landsat 8, have begun to monitor earth surface in recent years. They have been continuously providing enormous remotely sensed images. Nevertheless, it has to be noted that tradeoffs between spatial and temporal resolutions of these images still exist. Medium- to high-resolution images, such as Landsat OLI, are typically available fortnightly or less often, which limits their applications for intensively and continuously monitoring flood inundation dynamics. Whereas coarse-resolution sensors, such as NPP-VIIRS, scan the earth's surface once or several times a day, but their coarse spatial resolution hampers the correct mapping of flooded areas. This study, therefore, aims to blend NPP-VIIRS and Landsat OLI images in order to gain high spatial resolution from Landsat OLI and high temporal resolution from NPP-VIIRS simultaneously. Two classic fusion models, namely the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were tested and evaluated. Particularly, the fusion results of both models were compared with the actual Landsat images in order to evaluate the accuracy of these fusion results. It is hoped that this study will enlighten other studies that require remotely sensed data with both high spatial and temporal resolutions.
AB - Measuring surface water using remote sensing technology is an essential research topic in many research areas, including flood-related studies and water resource management. Recent advances in satellite remote sensing have provided more efficient ways of monitoring surface water from space. Several sensors, such as the Visible Infrared Imaging Radiometer Suite on board Suomi National Polar-orbiting Partnership (Suomi NPP-VIIRS) and Operational Land Imager (OLI) on board Landsat 8, have begun to monitor earth surface in recent years. They have been continuously providing enormous remotely sensed images. Nevertheless, it has to be noted that tradeoffs between spatial and temporal resolutions of these images still exist. Medium- to high-resolution images, such as Landsat OLI, are typically available fortnightly or less often, which limits their applications for intensively and continuously monitoring flood inundation dynamics. Whereas coarse-resolution sensors, such as NPP-VIIRS, scan the earth's surface once or several times a day, but their coarse spatial resolution hampers the correct mapping of flooded areas. This study, therefore, aims to blend NPP-VIIRS and Landsat OLI images in order to gain high spatial resolution from Landsat OLI and high temporal resolution from NPP-VIIRS simultaneously. Two classic fusion models, namely the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were tested and evaluated. Particularly, the fusion results of both models were compared with the actual Landsat images in order to evaluate the accuracy of these fusion results. It is hoped that this study will enlighten other studies that require remotely sensed data with both high spatial and temporal resolutions.
KW - Enhanced Spatial
KW - Image blending
KW - Image fusion
KW - Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM)
KW - Surface water
KW - Temporal Adaptive Reflectance Fusion Model (ESTARFM)
UR - http://www.scopus.com/inward/record.url?scp=85080886171&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85080886171
T3 - Proceedings - 21st International Congress on Modelling and Simulation, MODSIM 2015
SP - 2318
EP - 2324
BT - Proceedings - 21st International Congress on Modelling and Simulation, MODSIM 2015
A2 - Weber, Tony
A2 - McPhee, Malcolm
A2 - Anderssen, Robert
PB - Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ)
CY - Australia
T2 - 21st International Congress on Modelling and Simulation: Partnering with Industry and the Community for Innovation and Impact through Modelling, MODSIM 2015 - Held jointly with the 23rd National Conference of the Australian Society for Operations Research and the DSTO led Defence Operations Research Symposium, DORS 2015
Y2 - 29 November 2015 through 4 December 2015
ER -