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Physically-based retrievals of Norway spruce canopy variables from very high spatial resolution hyperspectral data.

, , , , , , , and . IGARSS, page 4057-4060. IEEE, (2007)

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Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data., , and . Int. J. Geogr. Inf. Sci., 29 (4): 624-642 (2015)Identifying Favorable Spatio-Temporal Conditions for West Nile Virus Outbreaks by Co-Clustering of Modis LST Indices Time Series., , , , , and . IGARSS, page 4670-4673. IEEE, (2018)On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction., , and . CoRR, (2024)Comparative Evaluation of XAI Methods for Transparent Crop Yield Estimation Using CNN., , and . IGARSS, page 7478-7482. IEEE, (2024)Characterizing the spatial and temporal variability of biophysical variables of a wheat crop using hyper-spectral measurements., , , and . IGARSS, page 2206-2208. IEEE, (2003)A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture., , , , , , , and . Remote. Sens., 9 (10): 1048 (2017)Harmonizing Machine Learning Based Phenological Modeling: A Unified Workflow for Comparative Analyses., , and . IGARSS, page 5333-5336. IEEE, (2024)Use of Guided Regularized Random Forest for Biophysical Parameter Retrieval., and . IGARSS, page 5776-5779. IEEE, (2018)Mapping specific crop- A multi sensor temporal approach., , , , and . IGARSS, page 3034-3037. IEEE, (2012)A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling., , , , , and . Int. J. Appl. Earth Obs. Geoinformation, 9 (2): 165-193 (2007)