01762naa a2200409 a 450000100080000000500110000800800410001902400440006010000280010424501610013226000090029352004740030265000160077665000160079265300290080865300280083765300250086565300240089065300090091465300380092365300270096165300090098865300160099765300180101365300290103165300420106065300160110270000170111870000210113570000220115670000250117870000200120370000260122370000250124970000280127477300500130221524952023-03-21       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.3390/rs150411302DOI1 aTORO, A. P. S. G. D. D.  aSAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.h[electronic resource]  c2023  aIn this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested.  aAgriculture  aAgricultura  aAgricultura regenerativa  aAprendizado de máquina  aAprendizado profundo  aFloresta aleatória  aICLS  aIntegrated Crop-livestock systems  aLong short-term memory  aLSTM  aMultisource  aRandom forest  aRegenerative agriculture  aSistemas integrados lavoura-pecuária  aTransformer1 aBUENO, I. T.1 aWERNER, J. P. S.1 aANTUNES, J. F. G.1 aLAMPARELLI, R. A. C.1 aCOUTINHO, A. C.1 aESQUERDO, J. C. D. M.1 aMAGALHÃES, P. S. G.1 aFIGUEIREDO, G. K. D. A.  tRemote Sensinggv. 15, n. 4, 1130, Feb. 2023.