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Journal of Applied Remote Sensing

Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model
Author(s): Mingquan Wu; Zheng Niu; Changyao Wang; Chaoyang Wu; Li Wang
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Paper Abstract

Due to cloud coverage and obstruction, it is difficult to obtain useful images during the critical periods of monitoring vegetation using medium-resolution spatial satellites such as Landsat and Satellite Pour l'Observation de la Terre (SPOT), especially in pluvial regions. Although high temporal resolution sensors, such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), can provide high-frequency data, the coarse ground resolutions of these sensors make them unsuitable to quantify the vegetation growth processes at fine scales. This paper introduces a new data fusion model for blending observations of high temporal resolution sensors (e.g., MODIS) and moderate spatial resolution satellites (e.g., Landsat) to produce synthetic imagery with both high-spatial and temporal resolutions. By detecting temporal change information from MODIS daily surface reflectance images, our algorithm produced high-resolution temporal synthetic Landsat data based on a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image at the beginning time (T1). The algorithm was then tested over a 185×185 km2 area located in East China. The results showed that the algorithm can produce high-resolution temporal synthetic Landsat data that were similar to the actual observations with a high correlation coefficient (r) of 0.98 between synthetic imageries and the actual observations.

Paper Details

Date Published: 7 March 2012
PDF: 14 pages
J. Appl. Remote Sens. 6(1) 063507 doi: 10.1117/1.JRS.6.063507
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
Mingquan Wu, Institute of Remote Sensing Applications (China)
Zheng Niu, Institute of Remote Sensing Applications (China)
Changyao Wang, Institute of Remote Sensing Applications (China)
Chaoyang Wu, Institute of Remote Sensing Applications (China)
Li Wang, Institute of Remote Sensing Applications (China)


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