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A Comprehensive Comparison of Machine Learning and Feature Selection Methods for Maize Biomass Estimation Using Sentinel-1 SAR, Sentinel-2 Vegetation Indices, and Biophysical Variables.

, , , , , , , and . Remote. Sens., 14 (16): 4083 (2022)

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Rank-based tests of cross-sectional dependence in panel data models., , , and . Comput. Stat. Data Anal., (2021)Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery., , , , , , , , , and 3 other author(s). Int. J. Appl. Earth Obs. Geoinformation, (2019)Consensus Error Performance of Linear Multi-Agent Systems., , and . CDC, page 328-333. IEEE, (2022)Validation of Global Land Surface Satellite Phase-2 Surface Broadband Albedo Product., , , , and . IGARSS, page 1168-1171. IEEE, (2018)Comparison and Validation of Long Time Serial Global GEOV1 and Regional Australian MODIS Fractional Vegetation Cover Products Over the Australian Continent, , , and . Remote Sensing, 7 (5): 5718--5733 (May 5, 2015)A Comprehensive Comparison of Machine Learning and Feature Selection Methods for Maize Biomass Estimation Using Sentinel-1 SAR, Sentinel-2 Vegetation Indices, and Biophysical Variables., , , , , , , and . Remote. Sens., 14 (16): 4083 (2022)A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods., , , , , , and . Remote. Sens., 12 (9): 1470 (2020)Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing., , , , and . Remote. Sens., 10 (7): 1000 (2018)Comparison of fractional vegetation cover estimating methods using in-situ measurements and the PROSAIL model., , and . IGARSS, page 4351-4354. IEEE, (2016)Consensus Robustness under PI Protocols with Undirected Graphs., , , , and . ACC, page 1068-1073. IEEE, (2023)