Spatio-temporal reflectance fusion via unmixing: accounting for both phenological and land-cover changes

• Published in: International journal of remote sensing Vol. 35; no. 16; pp. 6213 - 6233
• Main Authors: Huang, Bo, Zhang, Hankui
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.01.2014
• Subjects: Algorithms; Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Blending; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Mathematical models; Reflectance; Reflectivity; Satellite imagery; Spatial resolution; Teledetection and vegetation maps; Vegetation; models; Accounting; Space remote sensing; Landsat; Prediction; adaptation; Acquisition; Implementation; Type; Phenology; Limitation; techniques; imagery; spatial resolution; Plant cover; land cover; Reflectance
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2014.951097

Owing to technical limitations the acquisition of fine spatial resolution images (e.g. Landsat data) with frequent (e.g. daily) coverage remains a challenge. One approach is to generate frequent Landsat surface reflectances through blending with coarse spatial resolution images (e.g. Moderate Resolution Imaging Spectroradiometer, MODIS). Existing implementations for data blending, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced STARFM (ESTARFM), have their shortcomings, particularly in predicting the surface reflectance characterized by land-cover-type changes. This article proposes a novel blending model, namely the Unmixing-based Spatio-Temporal Reflectance Fusion Model (U-STFM), to estimate the reflectance change trend without reference to the change type, i.e. phenological change (e.g. seasonal change in vegetation) or land-cover change (e.g. conversion of a vegetated area to a built-up area). It is based on homogeneous change regions (HCRs) that are delineated by segmenting the Landsat reflectance difference images. The proposed model was tested on both simulated and actual data sets featuring phenological and land-cover changes. It proved more capable of capturing both types of change compared to STARFM and ESTARFM. The improvement was particularly observed on those areas characterized by land-cover-type changes. This improved fusion algorithm will thereby open new avenues for the application of spatio-temporal reflectance fusion.