Multiyear Mapping of Water Demand at Crop Level: An End-to-End Workflow Based on High-Resolution Crop Type Maps and Meteorological Data

Author:

Weikmann Giulio1ORCID,Marinelli Daniele2ORCID,Paris Claudia3ORCID,Migdall Silke4ORCID,Gleisberg Eva4,Appel Florian4ORCID,Bach Heike4,Dowling Jim5,Bruzzone Lorenzo1ORCID

Affiliation:

1. University of Trento, Trento, Italy

2. Fondazione Edmund Mach, San Michele All'Adige, Italy

3. University of Twente, Enschede, The Netherlands

4. VISTA Remote Sensing in Geosciences GmbH, Munich, Germany

5. Logical Clocks, Stockholm, Sweden

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Reference80 articles.

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2. How transferable are features in deep neural networks?;yosinski;Proc 27th Int Conf Neural Inf Process Syst,0

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4. Multi-year crop type mapping using pre-trained deep long-short term memory and Sentinel 2 image time series

5. Parameterisation of the land surface reflectance model SLC for winter rape using spaceborne hyperspectral CHRIS data;migdall;Proc Hyperspectral Workshop,0

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