title: Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data.
abstract: Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolution remote sensing data. To address this problem, this study introduces a modified spatial and temporal data fusion approach (MSTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the limitations of the conditional spatial temporal data fusion approach (STDFA) including the constant window for disaggregation and the sensor difference. An adaptive window size selection method is proposed in this study to select the best window size and moving steps for the disaggregation of coarse pixels. The linear regression method is used to remove the influence of differences in sensor systems using disaggregated mean coarse reflectance by testing and validation in two study areas located in Xinjiang Province, China. The results show that the MSTDFA algorithm can generate daily synthetic Landsat imagery with a high correlation coefficient (R) ranged from 0.646 to 0.986 between synthetic images and the actual observations. We further show that MSTDFA can be applied to 250 m 16-day MODIS MOD13Q1 products and the Landsat Normalized Different Vegetation Index (NDVI) data by generating a synthetic NDVI image highly similar to actual Landsat NDVI observation with a high R of 0.97.
authors.affiliation: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. wumq@radi.ac.cn.
authors.forename: Mingquan
authors.initials: M
authors.lastname: Wu
authors.affiliation: Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. huangwj@radi.ac.cn.
authors.forename: Wenjiang
authors.initials: W
authors.lastname: Huang
authors.affiliation: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. niuzheng@radi.ac.cn.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Niu
authors.affiliation: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. wangcy508@radi.ac.cn.
authors.forename: Changyao
authors.initials: C
authors.lastname: Wang
keywords: FROM-GLC; Landsat; MODIS; remote sensing; spatial and temporal data fusion