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Found 5 publication(s)

  1. 1

Orellana-Alvear, J.; Celleri, R.; Rollenbeck, R. & Bendix, J. (2019): Optimization of X-Band Radar Rainfall Retrieval in the Southern Andes of Ecuador Using a Random Forest Model. Remote Sensing 11(14), 1632.
Kühnlein, M.; Appelhans, T.; Thies, B. & Nauss, T. (2014): Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — A random forests-based approach applied to MSG SEVIRI. Remote Sensing of Environment 141, 129–143.
Meyer, H.; Lehnert, L.; Wang, Y.; Reudenbach, C.; Nauss, T. & Bendix, J. (2017): From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information?. International Journal of Applied Earth Observation and Geoinformation 55, 21-31.
Schulz, M.; Li, C.; Thies, B.; Chang, S. & Bendix, J. (2017): Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data. PLOS ONE 12(2), 1-17.
Turini, N.; Thies, B. & Bendix, J. (2019): Estimating High Spatio-Temporal Resolution Rainfall from MSG1 and GPM IMERG Based on Machine Learning: Case Study of Iran. Remote sensing 11(19), 2307.
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