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Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data.

, , , , , and . Remote. Sens., 11 (22): 2599 (2019)

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Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping., , , , and . Remote. Sens., 4 (3): 561-582 (2012)First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe., , and . Remote Sensing, 8 (3): 166 (2016)COSMO-SkyMed® for crops monitoring., , , , and . IGARSS, page 764-767. IEEE, (2014)Leaf Shape Evolution Through Duplication, Regulatory Diversification, and Loss of a Homeobox Gene, , , , , , , , , and 3 other author(s). Science, 343 (6172): 780-783 (2014)Cost-effectiveness of Vegetation Biophysical Parameters Retrieval from Remote Sensing Data., and . IGARSS, page 1949-1952. IEEE, (2006)Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas., , , , and . Remote Sensing, 5 (3): 1274-1291 (2013)Comparison of the Landsat Surface Reflectance Climate Data Record (CDR) and manually atmospherically corrected data in a semi-arid European study area., , and . Int. J. Appl. Earth Obs. Geoinformation, (2015)How much does multi-temporal Sentinel-2 data improve crop type classification?, , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2018)Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data., , , , , and . Remote. Sens., 14 (3): 541 (2022)Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples., , , , , , , , and . Remote. Sens., 8 (11): 938 (2016)