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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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Introducing the Potential of the EnMAP-Box for Agricultural Applications Using Desis and Prisma Data., , , , and . IGARSS, page 467-470. IEEE, (2021)A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data., , , , , and . Remote. Sens., 13 (2): 287 (2021)Retrieval of aboveground crop nitrogen content with a hybrid machine learning method., , , , , , and . CoRR, (2020)Towards Quantifying Non-Photosynthetic Vegetation for Agriculture Using Spaceborne Imaging Spectroscopy., , , , , , , and . IGARSS, page 5822-5825. IEEE, (2021)Simulation of Spaceborne Hyperspectral Remote Sensing to Assist Crop Nitrogen Content Monitoring in Agricultural Crops., , , , , and . IGARSS, page 3801-3804. IEEE, (2018)Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study., , , , , , and . Remote. Sens., 10 (1): 85 (2018)Untangling the Causal Links between Satellite Vegetation Products and Environmental Drivers on a Global Scale by the Granger Causality Method., , , , and . Remote. Sens., 15 (20): 4956 (October 2023)Remote and Proximal Assessment of Plant Traits., and . Remote. Sens., 13 (10): 1893 (2021)Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine., , , , , and . Remote. Sens., 15 (13): 3404 (July 2023)Hyperspectral Retrieval of Canopy Water Content Through Inversion of the Beer-Lambert Law., , , , and . IGARSS, page 3805-3808. IEEE, (2018)