02974naa a2200397 a 450000100080000000500110000800800410001902400460006010000170010624501650012326000090028852017110029765000160200865000280202465000190205265000160207165000120208765000250209965300330212465300280215765300340218565300080221965300290222765300310225665300210228765300250230865300160233370000210234970000230237070000160239370000190240970000230242870000250245170000190247677300810249521552142023-07-25       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.3390/ijgi120702632DOI1 aBOLFE, E. L.  aMapping agricultural intensification in the Brazilian savannaba machine learning approach using harmonized data from Landsat Sentinel-2.h[electronic resource]  c2023  aAgricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021-2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture.  aAgriculture  aArtificial intelligence  aRemote sensing  aAgricultura  aCerrado  aSensoriamento Remoto  aAgricultural Intensification  aAprendizado de máquina  aHarmonized Landsat Sentinel-2  aHLS  aInteligência artificial  aIntensificação agrícola  aMachine learning  aMapeamento agrícola  aMultisensor1 aPARREIRAS, T. C.1 aSILVA, L. A. P. da1 aSANO, E. E.1 aBETTIOL, G. M.1 aVICTORIA, D. de C.1 aDEL'ARCO SANCHES, I.1 aVICENTE, L. E.  tISPRS International Journal of Geo-Informationgv. 12, n. 7, 263, July 2023.