Spatio-temporal fusion of MODIS and Landsat data aims to produce new data that have simultaneously the Landsat spatial resolution and MODIS temporal resolution. It is an ill-posed problem involving large uncertainty, especially for reproduction of abrupt changes and heterogeneous landscapes. In this paper, we proposed to incorporate the freely available 250 m MODIS images into spatio-temporal fusion to increase prediction accuracy. The 250 m MODIS bands 1 and 2 are fused with 500 m MODIS bands 3-7 using the advanced area-to-point regression kriging approach. Based on a standard spatio-temporal fusion approach, the interim 250 m fused MODIS data are then downscaled to 30 m with the aid of the available 30 m Landsat data on temporally close d...
Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, s...
Landsat images have been widely used in support of responsible development of natural resources, dis...
It is challenging to acquire satellite sensor data with both fine spatial and fine temporal resoluti...
Landsat and MODIS data have been widely utilized in many remote sensing applications, however, the t...
Spatial and temporal data fusion approaches have been developed to fuse reflectance imagery from Lan...
Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one...
The focus of the current study is to compare data fusion methods applied to sensors with medium- and...
Crop condition and natural vegetation monitoring require high resolution remote sensing imagery in b...
2016-2017 > Academic research: refereed > Publication in refereed journalbcrcAccepted ManuscriptSelf...
We present a new, simple, and efficient approach to fusing MODIS and Landsat images. It is well know...
In this study, three documented spatiotemporal data fusion models were applied to Landsat-7 and MODI...
Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolut...
Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Differen...
Spatiotemporal fusion of remote sensing data is essential for generating high spatial and temporal r...
Thesis (Ph.D.)--Boston UniversityPLEASE NOTE: Boston University Libraries did not receive an Authori...
Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, s...
Landsat images have been widely used in support of responsible development of natural resources, dis...
It is challenging to acquire satellite sensor data with both fine spatial and fine temporal resoluti...
Landsat and MODIS data have been widely utilized in many remote sensing applications, however, the t...
Spatial and temporal data fusion approaches have been developed to fuse reflectance imagery from Lan...
Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one...
The focus of the current study is to compare data fusion methods applied to sensors with medium- and...
Crop condition and natural vegetation monitoring require high resolution remote sensing imagery in b...
2016-2017 > Academic research: refereed > Publication in refereed journalbcrcAccepted ManuscriptSelf...
We present a new, simple, and efficient approach to fusing MODIS and Landsat images. It is well know...
In this study, three documented spatiotemporal data fusion models were applied to Landsat-7 and MODI...
Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolut...
Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Differen...
Spatiotemporal fusion of remote sensing data is essential for generating high spatial and temporal r...
Thesis (Ph.D.)--Boston UniversityPLEASE NOTE: Boston University Libraries did not receive an Authori...
Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, s...
Landsat images have been widely used in support of responsible development of natural resources, dis...
It is challenging to acquire satellite sensor data with both fine spatial and fine temporal resoluti...