English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The purpose of this study is the exploitation of Synthetic Aperture Radar (SAR) satellite data for land cover monitoring. The properties of the recorded radar data heavily depend on the type of land cover, season and weather conditions. Therefore, it is possible to utilize the variability of these factors, in order to develop various techniques and methodologies that can be used for classifying land surfaces. In this context, this paper proposes approaches, which include the application of mathematical expressions and application of thresholds on multi-temporal data, for recognizing and classifying various types of land cover, on the basis of ERS and Envisat C-band SAR backscatter and coherence properties. These can be useful for any kind of contemporary SAR data, such as those of the current two Sentinel-1 satellites. Although this study focuses on four main land cover types (urban, mountainous, agricultural-low vegetation and forested areas) over specific areas in Europe, the same principles can be extended worldwide, leading to useful insights for designing future SAR satellite missions or for establishing guidelines for in-depth studies of specific land cover types.

Approaches for land cover monitoring over Europe based on backscatter and coherence properties of Envisat and ERS SAR data