Ensemble learning-based crop yield estimation: a scalable approach for supporting agricultural statistics
Author:
Affiliation:
1. Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Braunschweig, Germany
Funder
funded by the German Federal Statistical Office
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/15481603.2024.2367808
Reference105 articles.
1. Testing of Satellite-Based Yield Estimation for Agricultural Statistics - the SatAgrarstat Project;Arnold J.;WISTA,2021
2. Bauer-Marschallinger, B., and S. Massart. 2023. Copernicus Global Land Operations “Vegetation and Energy, CGLOPS-1”. Quality Assessment Report. Update 2022. Soil Water Index Collection 1km. Version 1.0. Technical Report I1.00. TU Wien. https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_QAR2022_SWI1km_V1_I1.00.pdf.
3. Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering
4. A general conceptual framework for multi-dimensional spatio-temporal data sets
5. Hyperopt: A Python Library for Model Selection and Hyperparameter Optimization;Bergstra J.;Computational Science & Discovery,2015
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