TY - JOUR

T1 - Spatial-temporal fraction map fusion with multi-scale remotely sensed images

AU - Zhang, Yihang

AU - Foody, Giles M.

AU - Ling, Feng

AU - Li, Xiaodong

AU - Ge, Yong

AU - Du, Yun

AU - Atkinson, Peter M.

PY - 2018/8

Y1 - 2018/8

N2 - Given the common trade-off between the spatial and temporal resolutions of current satellite sensors, spatial-temporal data fusion methods could be applied to produce fused remotely sensed data with synthetic fine spatial resolution (FR) and high repeat frequency. Such fused data are required to provide a comprehensive understanding of Earth's surface land cover dynamics. In this research, a novel Spatial-Temporal Fraction Map Fusion (STFMF) model is proposed to produce a series of fine-spatial-temporal-resolution land cover fraction maps by fusing coarse-spatial-fine-temporal and fine-spatial-coarse-temporal fraction maps, which may be generated from multi-scale remotely sensed images. The STFMF has two main stages. First, FR fraction change maps are generated using kernel ridge regression. Second, a FR fraction map for the date of prediction is predicted using a temporal-weighted fusion model. In comparison to two established spatial-temporal fusion methods of spatial-temporal super-resolution land cover mapping model and spatial-temporal image reflectance fusion model, STFMF holds the following characteristics and advantages: (1) it takes account of the mixed pixel problem in FR remotely sensed images; (2) it directly uses the fraction maps as input, which could be generated from a range of satellite images or other suitable data sources; (3) it focuses on the estimation of fraction changes happened through time and can predict the land cover change more accurately. Experiments using synthetic multi-scale fraction maps simulated from Google Earth images, as well as synthetic and real MODIS-Landsat images were undertaken to test the performance of the proposed STFMF approach against two benchmark spatial-temporal reflectance fusion methods: the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Flexible Spatiotemporal Data Fusion (FSDAF) model. In both visual and quantitative evaluations, STFMF was able to generate more accurate FR fraction maps and provide more spatial detail than ESTARFM and FSDAF, particularly in areas with substantial land cover changes. STFMF has great potential to produce accurate time-series fraction maps with fine-spatial-temporal-resolution that can support studies of land cover dynamics at the sub-pixel scale.

AB - Given the common trade-off between the spatial and temporal resolutions of current satellite sensors, spatial-temporal data fusion methods could be applied to produce fused remotely sensed data with synthetic fine spatial resolution (FR) and high repeat frequency. Such fused data are required to provide a comprehensive understanding of Earth's surface land cover dynamics. In this research, a novel Spatial-Temporal Fraction Map Fusion (STFMF) model is proposed to produce a series of fine-spatial-temporal-resolution land cover fraction maps by fusing coarse-spatial-fine-temporal and fine-spatial-coarse-temporal fraction maps, which may be generated from multi-scale remotely sensed images. The STFMF has two main stages. First, FR fraction change maps are generated using kernel ridge regression. Second, a FR fraction map for the date of prediction is predicted using a temporal-weighted fusion model. In comparison to two established spatial-temporal fusion methods of spatial-temporal super-resolution land cover mapping model and spatial-temporal image reflectance fusion model, STFMF holds the following characteristics and advantages: (1) it takes account of the mixed pixel problem in FR remotely sensed images; (2) it directly uses the fraction maps as input, which could be generated from a range of satellite images or other suitable data sources; (3) it focuses on the estimation of fraction changes happened through time and can predict the land cover change more accurately. Experiments using synthetic multi-scale fraction maps simulated from Google Earth images, as well as synthetic and real MODIS-Landsat images were undertaken to test the performance of the proposed STFMF approach against two benchmark spatial-temporal reflectance fusion methods: the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Flexible Spatiotemporal Data Fusion (FSDAF) model. In both visual and quantitative evaluations, STFMF was able to generate more accurate FR fraction maps and provide more spatial detail than ESTARFM and FSDAF, particularly in areas with substantial land cover changes. STFMF has great potential to produce accurate time-series fraction maps with fine-spatial-temporal-resolution that can support studies of land cover dynamics at the sub-pixel scale.

KW - Land cover

KW - Fraction maps

KW - Spatial-temporal fusion

KW - Spectral unmixing

KW - Super-resolution mapping

U2 - 10.1016/j.rse.2018.05.010

DO - 10.1016/j.rse.2018.05.010

M3 - Journal article

VL - 213

SP - 162

EP - 181

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -