A Rigorously-Weighted Spatiotemporal Fusion Model with Uncertainty Analysis

Abstract

Abstract: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.

Improvement of Clustering Methods for Modelling Abrupt Land Surface Changes in Satellite Image Fusions

“…A technical challenge is that the usability of fine spatial-resolution images (e.g., Landsat and Sentinel-2 images) are still largely constrained by their low revisit frequency and cloud contamination. Developing advanced methods for fusing satellite images from high-temporal frequency (e.g., MODIS) and fine spatial-resolution sensors (e.g., Landsat Operational Land Imager or Sentinel-2 Multispectral Imager) has become a well-established research field [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

“…A number of satellite image fusion methods have been developed to predict synthetic Landsat-like images from dense time series of MODIS images and a limited set of fine-resolution Landsat images. Examples include the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) [1], the Enhanced version of STARFM (ESTARFM) [2], the Spatial Temporal Adaptive Algorithm for Mapping Reflectance Change (STAARCH) [3], the Unmixing-based Spatial-Temporal Reflectance Fusion Model (U-STFM) [4,5], the Flexible Spatiotemporal Data Fusion (FSDAF) model [6], the Spatiotemporal image-fusion model (STI-FM) for enhancing the temporal resolution [7], the Hybrid Color Mapping (HCM) approach [8], the Rigorously-Weighted Spatiotemporal Data Fusion Model (RWSTFM) [9], the Prediction Smooth Reflectance Fusion Model (PSRFM) [10]. These studies have demonstrated that the fusion methods are mostly limited to predicting spectral changes in reflectance due to gradual vegetation phenological changes [6,7].…”

A Prediction Smooth Method for Blending Landsat and Moderate Resolution Imagine Spectroradiometer Images

“…In the last decade, a number of image fusion methods have been developed to predict synthetic Landsat-like images from frequent MODIS images and a limited set of Landsat images. Examples include the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) [1], the Enhanced version of STARFM (ESTARFM) [2], the Spatial Temporal Adaptive Algorithm for Mapping Reflectance Change (STAARCH) [3], the Unmixing-based Spatial-Temporal Reflectance Fusion Model (U-STFM) [4,5], the Flexible Spatiotemporal Data Fusion (FSDAF) model [6], the Spatiotemporal image-fusion model (STI-FM) for enhancing the temporal resolution [7], the Hybrid Color Mapping (HCM) Approach [8] and the Rigorously-Weighted Spatiotemporal Data Fusion Model (RWSTFM) [9] and so forth. However, studies have demonstrated that all of the above reflectance fusion methods have their advantages and disadvantages.…”

“…However, studies have demonstrated that all of the above reflectance fusion methods have their advantages and disadvantages. Some of them are only effective or applicable for certain applications [1][2][3][4][5][6][7][8][9]. Interested readers can find concise reviews for these methods in [6,7].…”

“…Equations (3), (4), (9) and (10) are formulas required to predict gradual vegetation phenological change ∆R 01 . According to the error propagation law, we can also estimate their corresponding uncertainties for each pixel belonging to cluster c as followŝ…”