气候变化领域集成服务门户(CCPortal): Kalman filter method for generating time-series synthetic Landsat images and their uncertainty from Landsat and MODIS observations

CCPortal

DOI	10.1016/j.rse.2019.111628
	Kalman filter method for generating time-series synthetic Landsat images and their uncertainty from Landsat and MODIS observations
	Zhou F.; Zhong D.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	The Landsat program, since its commencement in 1972, has acquired millions of images of our planet. Those images are one of the most valuable Earth Observation resources for local, regional and global land surface monitoring and study due to their moderate spatial resolution and rich spectral information. However, their applications are impeded largely by their relatively low revisit frequency and cloud contamination on images. In order to improve their usability, a number of studies have been conducted to blend Landsat images with Moderate Resolution Imaging Spectroradiometer (MODIS) images to take merits of the two sensors. All blending models reported that they can predict synthetic Landsat images with various degrees of accuracy. However, only a couple of models reported that they can explicitly estimate uncertainty for their blended images. In this study, we propose a new surface reflectance blending model based on a Kalman Filter algorithm (Kalman Filter Reflectance Fusion Model - KFRFM) to predict time-series synthetic Landsat images from Landsat and MODIS images, and simultaneously to estimate uncertainty of the predicted synthetic images to quantify the quality of the synthetic images. Using the model, we predicted a time-series of 38 synthetic Landsat images with a temporal interval of 4 days for a vegetation growing season spanning about 6 months from Nadir Bi-directional Reflectance Distribution Function adjusted MODIS product (MCD43A4), and their corresponding uncertainties. From this time-series, we calculated five vegetation indices involving all the spectral bands of the synthetic images, and compared them to those from Landsat observations. The results demonstrated that the proposed method is able to produce high quality synthetic Landsat images to meet various application demands for higher spatial and temporal resolution images. Uncertainty analysis reveals that cropland has the largest uncertainty followed by grassland while forests have the smallest uncertainties among the seven vegetation land cover types of the study area. For performance evaluation, we compared KFRFM to several published models. The comparison results reveal that KFRFM performs the best based on the assessed image quality indices. © 2020
英文关键词	Image fusion; Kalman filter; Landsat; MODIS; Time-series; Uncertainty
语种	英语
scopus关键词	Distribution functions; Image fusion; Image quality; Kalman filters; Radiometers; Reflection; Time series; Uncertainty analysis; Vegetation; Bidirectional reflectance distribution functions; Kalman filter algorithms; LANDSAT; Moderate resolution imaging spectroradiometer; MODIS; Spatial and temporal resolutions; Uncertainty; Vegetation-land cover type; Image enhancement; accuracy assessment; algorithm; growing season; Kalman filter; land surface; Landsat; MODIS; observational method; satellite imagery; spatial resolution; surface reflectance; time series analysis; uncertainty analysis
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179435
作者单位	Canada Center for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 6th floor, 560 Rochester Street, Ottawa, ON K1A 0E4, Canada
推荐引用方式 GB/T 7714	Zhou F.,Zhong D.. Kalman filter method for generating time-series synthetic Landsat images and their uncertainty from Landsat and MODIS observations[J],2020,239.
APA	Zhou F.,&Zhong D..(2020).Kalman filter method for generating time-series synthetic Landsat images and their uncertainty from Landsat and MODIS observations.Remote Sensing of Environment,239.
MLA	Zhou F.,et al."Kalman filter method for generating time-series synthetic Landsat images and their uncertainty from Landsat and MODIS observations".Remote Sensing of Environment 239(2020).