01581naa a2200397 a 450000100080000000500110000800800410001902400470006010000250010724501550013226000090028752002620029665000430055865000260060165000190062765000290064665000290067565000250070465300280072965300260075765300270078365300210081065300180083165300310084965300300088070000200091070000190093070000260094970000210097570000280099670000220102470000250104670000130107170000250108477300740110921513392023-08-11       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/ 10.3390/ijgi120200412DOI1 aPEREIRA, F. R. da S.  aImputation of missing parts in UAV orthomosaics using PlanetScope and Sentinel-2 databa case study in a grass-dominated área.h[electronic resource]  c2023  aIn this study, we propose a methodological framework to impute missing parts of UAV orthomosaics using PlanetScope (PS) and Sentinel-2 (S2) data and the random forest (RF) algorithm of an integrated crop-livestock system (ICLS) covered by grass at the time.  aNormalized difference vegetation index  aPrecision agriculture  aRemote sensing  aUnmanned aerial vehicles  aAgricultura de Precisão  aSensoriamento Remoto  aAprendizado de máquina  aData intercalibration  aÍndice de vegetação  aMachine learning  aRandom forest  aSpatial gap-filling method  aSpatial imputation method1 aREIS, A. A. dos1 aFREITAS, R. G.1 aOLIVEIRA, S. R. de M.1 aAMARAL, L. R. do1 aFIGUEIREDO, G. K. D. A.1 aANTUNES, J. F. G.1 aLAMPARELLI, R. A. C.1 aMORO, E.1 aMAGALHÃES, P. S. G.  tInternational Journal of Geo-Informationgv. 12, n. 2, 41, Feb. 2023.