Abstract Normalized Difference Vegetation Index (NDVI) data derived from Landsat satellites
are important resources for vegetation monitoring. However, Landsat NDVI time-series data …

Spatio-temporal fusion is a technique used to produce images with both fine spatial and
temporal resolution. Generally, the principle of existing spatio-temporal fusion methods can …

Thick-cloud contamination causes serious missing data in Landsat images, which
substantially limits applications of these images. To remove thick clouds from Landsat data …

Spatio-temporal image fusion aims to increase the frequency and resolution of multispectral
satellite sensor images in a cost-effective manner. However, practical constraints on input …

Abstract The Terra/Aqua MODerate resolution Imaging Spectroradiometer (MODIS) data
have been used widely for global monitoring of the Earth's surface due to their daily fine …

The low resolution of remote sensing images often limits the land cover classification (LCC)
performance. Super resolution (SR) can improve the image resolution, while greatly …

High spatiotemporal resolution normalized difference vegetation index (NDVI) time-series
imagery is required for monitoring vegetation dynamics with dense observations and spatial …

Spatiotemporal fusion (STF) is considered a feasible and cost-effective way to deal with the
trade-off between the spatial and temporal resolution of satellite sensors, and to generate …

Leaf area index (LAI) is an important variable for characterizing vegetation structure.
Contemporary satellite-based LAI products with moderate spatial resolution, such as those …

Despite the success of deep learning in land cover classification, high-resolution (HR) land
cover mapping remains challenging due to the time-consuming and labor-intensive process …