A generalization of spatial and temporal fusion methods for remotely sensed surface parameters

作者：Zhang, Hankui K.; Huang, Bo^*; Zhang, Ming; Cao, Kai; Yu, Le

来源：International Journal of Remote Sensing, 2015, 36(17): 4411-4445.

DOI：10.1080/01431161.2015.1083633

• RESOLUTION SATELLITE DATA
• LEAF-AREA INDEX
• SPATIOTEMPORAL REFLECTANCE FUSION
• MAPPING DAILY EVAPOTRANSPIRATION
• MODIS DATA FUSION
• NDVI TIME-SERIES
• LAND-COVER TYPE
• NOAA-AVHRR DATA
• IMAGE FUSION
• BLENDING LANDSAT

Remotely sensed surface parameters, such as vegetation index, leaf area index, surface temperature, and evapotranspiration, show diverse spatial scales and temporal dynamics. Generally the spatial and temporal resolutions of remote-sensing data should match the characteristics of surface parameters under observation. These requirements sometimes cannot be provided by a single sensor due to the trade-off between spatial and temporal resolutions. Many spatial and temporal fusion (STF) methods have been proposed to derive the required data. However, the methodology suffers from disorderly development. To better inform future research, this study generalizes the existing methods from around 100 studies as spatial or temporal categories based on their physical assumptions related to spatial scales and temporal dynamics. To be specific, the assumptions are related to the scale invariance of the temporal information and temporal constancy of the spatial information. The spatial information can be contexture or spatial details. Experiments are conducted using Landsat data acquired on 13 dates in two study areas and simulated Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results are presented to demonstrate the typical methods from each category. This study concludes the following. (1) Contexture methods depend heavily on how components maps (contexture) are defined. They are not recommended except when components maps can be estimated properly from observed images. (2) The spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) methods belong to the temporal and spatial categories, respectively. Thus, STARFM and ESTARFM should be better applied to temporal variance - dominated and spatial variance - dominated areas, respectively. (3) Non-linear methods, such as the sparse representation-based spatio-temporal reflectance fusion model, can successfully address land-cover changes in addition to phonological changes, thereby providing a promising option for STF problems in the future.

• 出版日期2015
• 单位清华大学