02008naa a2200325 a 450000100080000000500110000800800410001902400550006010000180011524501170013326000090025052010690025965000140132865000190134265000140136165000250137565300510140065300110145165300100146265300220147265300110149465300160150570000170152170000220153870000220156070000270158270000160160970000230162577300340164821363502022-04-26       2021    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1080/10106049.2021.20006482DOI1 aOLDONI, L. V.  aExtraction of crop information through the spatiotemporal fusion of OLI and MODIS images.h[electronic resource]  c2021  aABSTRACT. Spatiotemporal data fusion algorithms have been developed tofuse satellite imagery from sensors with different spatial and tempoporal resolutions and generate predicted imagery. In this study, we compare the predictions of three spatiotemporal data fusion algorithms in blending Landsat-8/OLI and Terra-Aqua/MODIS images for mapping soybean and corn under five classification scenarios. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and Flexible Spatiotemporal Data Fusion (FSDAF) algorithms were compared to generate images for the 2016/2017 summer crop-year. Classifications including phenological metrics extracted from FSDAF- and STARFM-predicted EVI time series had overalls accuracies higher than the other scenarios, 93.11% and 91.33%, respectively. The results show that phenological metrics extracted from predicted images are an interesting alternative to overcome cloud cover frequency limitations for soybean and corn mapping in tropical areas.  aPhenology  aRemote sensing  aFenologia  aSensoriamento Remoto  aAlgoritmos de fusão de dados espaço-temporal  aESTARM  aFSDAF  aSéries temporais  aSTARFM  aTime series1 aMERCANTE, E.1 aANTUNES, J. F. G.1 aCATTANI, C. E. V.1 aSILVA JUNIOR, C. A. da1 aCAON, I. L.1 aPRUDENTE, V. H. R.  tGeocarto International, 2021.