Landsat and MODIS Data Fusion products based phenology analysis of dryland in Shan Dong province

Abstract

Current remote sensing sensors can only provide data either with high spatial resolution or with high temporal resolutions. It's a challenge to fully characterize the patchy phenology change in the dryland region. The Flexible Spatiotemporal Data Fusion (FSDAF) method, can produce synthesized frequent high spatial resolution images through blending Landsat 30 m data with MODIS 500 m data to produce synthetic imagery at Landsat spatial resolution and MODIS time steps. In this study, we evaluated the feasibility of using FSDAF to produce the synthetic imagery over a dryland vegetation study area in Shan Dong province, in order to track the phenological changes. In this study we assembled subsets of six Landsat-5 TM scenes and temporally-coincident MODIS datasets(MOD09Q1) spanning the 2001 January–December including the growing season in Shan Dong, which is noted as a crop producing province in china. In order to investigate the effects of temporal compositing and explore the goodness of the algorithm, we also adopt the spatial and temporal adaptive reflectance fusion model (STARFM). The STARFM and FSDAF algorithm both were applied to each MODIS data series to produce up to twelve synthetic images corresponding to each Landsat image. The accuracy of the synthetic images were evaluated by comparing the reflectance values with the corresponding pixel values of the reference Landsat image on a band-by-band basis. Our results indicate the effect of the FSDA algorithm in improving spatial and temporal resolution, although the results is slightly better, when compared with the STARFM model. This study demonstrates the feasibility of using FSDAF algorithm to assemble an imagery time series at MODIS temporal resolution and Landsat spatial resolution in crop-populated dryland ecosystems.