Remotely sensed land surface temperature (LST) with spatial continuity and high
spatiotemporal resolution (hereafter referred to as high resolution) is a crucial parameter for …

The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture
Radar (SAR) images that can penetrate cloud cover. However, the large domain gap …

The existence of clouds is one of the main factors that contributes to missing information in
optical remote sensing images, restricting their further applications for Earth observation, so …

Remote sensing optical sensors onboard operational satellites cannot have high spectral,
spatial and temporal resolutions simultaneously. In addition, clouds and aerosols can …

Satellite remote sensing has now become a unique tool for continuous and predictable
monitoring of geosystems at various scales, observing the dynamics of different geophysical …

Optical remote sensing images are often contaminated by clouds and shadows, resulting in
missing data, which greatly hinders consistent Earth observation missions. Cloud and …

Cloud contamination is a common issue that severely reduces the quality of optical satellite
images in remote sensing fields. With the rapid development of deep learning technology …

Dense medium-resolution imagery is essential for fine-scale time-series applications. The
combined use of Landsat 8 and Sentinel-2 can derive 10-m time-series imagery at a …

Abstract The Sustainable Development Goals Satellite 1 (SDGSAT-1), equipped with the
Glimmer Imager (GLI), provides high-resolution nighttime light (NTL) data across multiple …

Remote sensing satellites provide observations of the Earth's surface, which are crucial data
for applications and analyses in several fields, including agriculture, environmental …