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Open AccessArticle

A Rigorously-Weighted Spatiotemporal Fusion Model with Uncertainty Analysis

by Jing Wang and Bo Huang *
Institute of Space and Earth Information Science, Chinese University of Hong Kong, Shatin, Hong Kong, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(10), 990; https://doi.org/10.3390/rs9100990
Received: 14 August 2017 / Revised: 18 September 2017 / Accepted: 21 September 2017 / Published: 25 September 2017
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
Interest has been growing with regard to the use of remote sensing data characterized by a fine spatial resolution and frequent coverage for the monitoring of land surface dynamics. However, current satellite sensors are fundamentally limited by a trade-off between their spatial and temporal resolutions. Spatiotemporal fusion thus provides a feasible solution to overcome this limitation, and many blending algorithms have been developed. Among them, the popular spatial and temporal adaptive reflectance fusion model (STARFM) is based on a weighted function; however, it uses an ad hoc approach to estimate the weights of surrounding similar pixels. Additionally, an uncertainty analysis of the predicted result is not provided in the STARFM or any other fusion algorithm. This paper proposes a rigorously-weighted spatiotemporal fusion model (RWSTFM) based on geostatistics to blend the surface reflectances of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-5 Thematic Mapper (TM) imagery. The RWSTFM, which is based on ordinary kriging, derives the weights in terms of a fitted semivariance-distance relationship and calculates the estimation variance, which is a measure of the prediction uncertainty. The RWSTFM was tested using three datasets and compared with two commonly-used spatiotemporal reflectance fusion algorithms: the STARFM and the flexible spatiotemporal data fusion (FSDAF) method. The fusion results show that the proposed RWSTFM consistently outperformed the other algorithms both visually and quantitatively. Additionally, more than 70% of the squared error was accounted for by the estimation variance of the RWSTFM for all three of the datasets. View Full-Text
Keywords: rigorously-weighted spatiotemporal fusion; ordinary kriging; prediction uncertainty; MODIS; Landsat rigorously-weighted spatiotemporal fusion; ordinary kriging; prediction uncertainty; MODIS; Landsat
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Wang, J.; Huang, B. A Rigorously-Weighted Spatiotemporal Fusion Model with Uncertainty Analysis. Remote Sens. 2017, 9, 990.

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