Medium-resolution remote-sensing images with tens of metre spatial resolutions have spatial and spectral characteristics that are suited for mapping a range of structural and compositional properties of vegetation. However, many factors, such as the long revisit cycles and frequent
cloud contamination, limit the availability of images for the monitoring and time-series analysis of vegetation. Thus, there is a strong incentive to combine data from more than one observation system in order to fill the gaps in observation and enhance the capability of remote sensing to
monitor dynamics. In this paper, we introduce a framework for the normalization of the normalized difference vegetation index (NDVI) from different sensor systems by the use of synchronous coarse-resolution NDVI data. A new model called the Local Cluster-specific Linear Model (LCLM) is proposed.
This model is designed to build the specific relationships for different clusters, block by block, considering the spatial heterogeneity of the influencing factors. To improve the stability of the parameter estimation, an M-estimation method is utilized to solve the coefficients. Based on
an analysis of the previous evaluation methods, new schemes are designed for evaluating the accuracy of the parameter normalization. Different assessment experiments were undertaken with the new evaluation schemes, to validate the performance of the LCLM method. The results indicate that the
LCLM method performs better than the existing methods. An application experiment was also undertaken, in which synchronous NDVI from Landsat ETM+ and Terra ASTER sensors were normalized by the use of a coarse-resolution MODIS product.
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Document Type: Research Article
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, 430079, PR China
School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, 430079, PR China
Publication date: November 2, 2014
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