Brian Barrett
University of Glasgow
39 Papers
115 Citations
Brian Barrett is an academic researcher from University of Glasgow. The author has contributed to research in topics: Environmental science & Land cover. The author has an hindex of 13, co-authored 31 publications. Previous affiliations of Brian Barrett include University College Cork & University of Edinburgh.
Chat about Author
Papers
Surface soil moisture retrievals from remote sensing: Current status, products & future trends
TL;DR: It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments.
423
Satellite remote sensing of grasslands: from observation to management
TL;DR: In this article, the authors reviewed the current status of grassland monitoring/observation methods and applications based on satellite remote sensing data, and identified the key remaining challenges and some new upcoming trends for future development.
Soil moisture retrieval from active spaceborne microwave observations: an evaluation of current techniques.
TL;DR: The purpose of this paper is to review the current status of soil moisture determination from active microwave remote sensing systems and to highlight the key areas of research that will have to be addressed to achieve routine use of the proposed retrieval approaches.
238
Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series
TL;DR: The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results, despite the high impact of outliers from the general climatic pattern.
136
Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change
TL;DR: Google Earth Engine (GEE) as mentioned in this paper is a cloud-based computing platform for planetary-scale geospatial analyses, which enables fluvial geomorphologists to take their algorithms to petabytes worth of data.
115