Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

Land cover classification of Landsat images is one of the most important applications
developed from Earth observation satellites. The last four decades were marked by different …

Because of the internal malfunction of satellite sensors and poor atmospheric conditions
such as thick cloud, the acquired remote sensing data often suffer from missing information …

Multiple factors regulate urban land surface temperature (LST), including land cover,
climate, and urban form, among which urban form is now receiving more and more attention …

Longer time high-resolution, high-frequency, consistent, and more detailed land cover data
are urgently needed in order to achieve sustainable development goals on food security …

Because of sensor malfunction and poor atmospheric conditions, there is usually a great
deal of missing information in optical remote sensing data, which reduces the usage rate …

Remote sensing satellite sensors feature a tradeoff between the spatial, temporal, and
spectral resolutions. In this paper, we propose an integrated framework for the spatio …

Thick cloud and its shadow severely reduce the data usability of optical satellite remote
sensing data. Although many approaches have been presented for cloud and cloud shadow …

The trade-off between the temporal and spatial resolutions, and/or the influence of cloud
cover, makes it difficult to obtain continuous fine-scale satellite data for surface urban heat …

Fundamentals of Satellite Remote Sensing: An Environmental Approach, Third Edition, is a
definitive guide to remote sensing systems that focuses on satellite-based remote sensing …