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A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data.

, , , , , and . Remote. Sens., 13 (2): 287 (2021)

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Prototyping Vegetation Traits Models in the Context of the Hyperspectral Chime Mission Preparation., , , , , , , , , and 8 other author(s). IGARSS, page 7678-7681. IEEE, (2021)Developing a Sandbox Environment for Prosail, Suitable for Education and Research., , , , and . IGARSS, page 783-786. IEEE, (2018)Hyperspectral Retrieval of Canopy Water Content Through Inversion of the Beer-Lambert Law., , , , and . IGARSS, page 3805-3808. IEEE, (2018)A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data., , , , , and . Remote. Sens., 13 (2): 287 (2021)Introducing the Potential of the EnMAP-Box for Agricultural Applications Using Desis and Prisma Data., , , , and . IGARSS, page 467-470. IEEE, (2021)Retrieval of aboveground crop nitrogen content with a hybrid machine learning method., , , , , , and . CoRR, (2020)Simulation of Spaceborne Hyperspectral Remote Sensing to Assist Crop Nitrogen Content Monitoring in Agricultural Crops., , , , , and . IGARSS, page 3801-3804. IEEE, (2018)Towards Quantifying Non-Photosynthetic Vegetation for Agriculture Using Spaceborne Imaging Spectroscopy., , , , , , , and . IGARSS, page 5822-5825. IEEE, (2021)