2016
DOI: 10.1016/j.actaastro.2016.06.012
|View full text |Cite
|
Sign up to set email alerts
|

The evolution of Earth Observation satellites in Europe and its impact on the performance of emergency response services

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
30
0
1

Year Published

2017
2017
2021
2021

Publication Types

Select...
5
4
1

Relationship

0
10

Authors

Journals

citations
Cited by 57 publications
(32 citation statements)
references
References 0 publications
0
30
0
1
Order By: Relevance
“…Machine-learning methods have been applied across a range of science and engineering applications for more than 2 decades. A number of recent examples have targeted the (retrospective) prediction or retrieval of hydrological states and fluxes from single-and multi-satellite sources, including the estimation of typhoon rainfall over the ocean (Chen et al, 2011), the retrieval of surface soil moisture (Rodríguez-Fernández et al, 2016) and water vapour content (Aires et al, 2001), the estimation of river runoff (Rasouli et al, 2012;Deo andŞahin, 2016), the analysis of global hydro-climatic controls on vegetation (Papagiannopoulou et al, 2017), the training of high-resolution sensors for retrieval of NDVI (Houborg and McCabe, 2016), and the derivation of continental water and carbon fluxes using decision trees (Jung et al, 2009). Still, the application of these techniques to dynamically monitor hydrological events and processes using remote sensing remains an emerging field, with relatively limited existing applications.…”
Section: Cloud Computing and Data Analyticsmentioning
confidence: 99%
“…Machine-learning methods have been applied across a range of science and engineering applications for more than 2 decades. A number of recent examples have targeted the (retrospective) prediction or retrieval of hydrological states and fluxes from single-and multi-satellite sources, including the estimation of typhoon rainfall over the ocean (Chen et al, 2011), the retrieval of surface soil moisture (Rodríguez-Fernández et al, 2016) and water vapour content (Aires et al, 2001), the estimation of river runoff (Rasouli et al, 2012;Deo andŞahin, 2016), the analysis of global hydro-climatic controls on vegetation (Papagiannopoulou et al, 2017), the training of high-resolution sensors for retrieval of NDVI (Houborg and McCabe, 2016), and the derivation of continental water and carbon fluxes using decision trees (Jung et al, 2009). Still, the application of these techniques to dynamically monitor hydrological events and processes using remote sensing remains an emerging field, with relatively limited existing applications.…”
Section: Cloud Computing and Data Analyticsmentioning
confidence: 99%
“…For instance, Landsat-8 (which is around the size of a large car), had an estimated cost of USD 855 million to build and launch, and therefore producing multiple versions (not including associated launch costs) is not a realistic proposition. The 2014-2020 budget for the European Copernicus Earth observation program, which includes the Sentinel missions, is estimated at approximately EUR 4.3 billion, but does not include multi-satellite constellations beyond the Sentinel-2 pair (Denis et al, 2016). Here the key limitation in the replication of multiple sensing platforms relates to the satellites size and the associated price tag.…”
Section: The Rise Of the Cubesatmentioning
confidence: 99%
“…Because of the ever-increasing number of satellites, products and number of customers, the system should support custom-made business workflows and execution capabilities. As Earth observation satellites play a significant role in supporting the action of quick responders in case of emergency events (Denis, Boissezon, and Hosford 2016), more and more both professional and non-professional data application users need instant or near real-time services about satellite imaging and "area of interest" imagery. Recently, mobile devices have become very popular, as they provide convenient and shortcut methods to recognize and understand geospatial data products.…”
Section: User Requirementsmentioning
confidence: 99%