Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — A random forests-based approach applied to MSG SEVIRI
Open Access
- 1 February 2014
- journal article
- research article
- Published by Elsevier BV in Remote Sensing of Environment
- Vol. 141, 129-143
- https://doi.org/10.1016/j.rse.2013.10.026
Abstract
No abstract availableKeywords
Funding Information
- German Research Foundation (DFG)
This publication has 73 references indexed in Scilit:
- Random Forests for Genetic Association StudiesStatistical Applications in Genetics and Molecular Biology, 2010
- An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests.Psychological Methods, 2009
- Newer classification and regression tree techniques: Bagging and random forests for ecological predictionEcosystems, 2006
- Investigation of summertime convective rainfall in Western Europe based on a synergy of remote sensing data and numerical modelsArchiv für Meteorologie, Geophysik und Bioklimatologie Serie A, 2001
- Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 2. Cloud thermodynamic phaseJournal of Geophysical Research: Atmospheres, 2000
- IR‐based satellite and radar rainfall estimates of convective storms over northern ItalyMeteorlogical Applications, 2000
- Discriminating clear sky from clouds with MODISJournal of Geophysical Research: Atmospheres, 1998
- Classification trees: an alternative to traditional land cover classifiersInternational Journal of Remote Sensing, 1996
- Land cover classification by an artificial neural network with ancillary informationInternational Journal of Geographical Information Science, 1995
- Determination of Rainfall Rates from GOES Satellite Images by a Pattern Recognition TechniqueJournal of Atmospheric and Oceanic Technology, 1985