面向GF-1 WFV数据和MODIS数据的时空融合算法对比分析

对城市热岛效应、植物覆盖指数、叶面积指数等地表参数的高频次高精度反演,能更好地实现基于遥感手段的地表特征动态监测。然而,目前单一数据源的遥感影像还很难实现高时空分辨率数据的同步获取,时空融合技术是解决这个时空分辨率矛盾的有效方法。根据原理不同,时空融合算法可以分为基于线性模型的融合算法、基于光谱解混的融合算法等。高分卫星产品是近几年中国高分辨率对地观测系统重大专项天基系统中的首发星,对于该类数据的时空融合研究仍然较少。因此,本文拟采用4种常见的时空融合算法（STARFM、FSDAF、STDFA、Fit_FC）实现GF-1 WFV数据与MODIS数据的时空融合,分析这几种方法对GF-1 WFV数据时空融合的有效性和精度,从而为后续的研究提供一定依据。

Comparisons of Spatio-temporal Fusion Methods for GF-1 WFV and MODIS Data

Observations with high spatial resolution and frequency can better monitor land surface dynamics, such as urban heat island effect monitoring, normalized difference vegetation index, leaf area index assimilation. However, it is still difficult to acquire satellite data with high spatial and temporal resolution from one single satellite sensor. For example, Landsat TM/ETM+/OLI data at 30 m spatial resolution have been widely applied for various applications, however, their 16-day revisit-cycles limit their usage in dynamics monitoring; on the other hand, MODIS data can be widely used in land process monitoring at global or large-scale because of their daily revisit period, but the coarser spatial resolution of MODIS data cannot meet the fine-scale environment applications. The spatio-temporal fusion is an effective way to solve this trade-off problem. The spatio-temporal fusion methods can be grouped into linear model methods (STARFM, ESTARFM), unmixing methods (FSDAF, STDFA) and so on. These methods have been used to support various applications such as leaf area index assimilation, urban heat island effect monitoring, land surface temperature generating, crop types and dynamics mapping, land cover classification, and land surface water mapping. The GF-1 satellite was launched from China's Jiuquan Satellite Launch Center in April 2013. It carries two panchromatic cameras with pixel resolution of 2 m and two multi-spectral cameras with pixel resolutions of 8 m, and four wide-field view (WFV) cameras with 16 m pixel resolution. The WFV sensors capture ground features with four bands that cover the visible and near-infrared wavelength range and the swath width reaches 800 km when the four sensors are combined. The spatio-temporal fusion researches on GF-1 WFV are still insufficient, hence, in this study, we used four spatio-temporal fusion methods including STARFM, FSDAF, STDFA and Fit_FC to blend GF-1 WFV and MODIS data, and then the fusion validation and accuracies were analyzed for further researches.