Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data

Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data

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) ...

Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data

An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data

The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this researc...

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat...

Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and tempo...