@Article{BolfePSSBVSV:2023:MaLeAp,
               author = "Bolfe, {\'E}dson Luis and Parreiras, Taya Cristo and Silva, Lucas 
                         Augusto Pereira da and Sano, Edson Eyji and Bettiol, Giovana 
                         Maranh{\~a}o and Victoria, Daniel de Castro and Sanches, Ieda 
                         Del'Arco and Vicente, Luiz Eduardo",
          affiliation = "{Embrapa Agricultura Digital} and {Universidade Estadual de 
                         Campinas (UNICAMP)} and {Universidade Federal de Uberl{\^a}ndia 
                         (UFU)} and {Embrapa Cerrados} and {Embrapa Cerrados} and {Embrapa 
                         Agricultura Digital} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Embrapa Meio Ambiente}",
                title = "Mapping Agricultural Intensification in the Brazilian Savanna: A 
                         Machine Learning Approach Using Harmonized Data from Landsat 
                         Sentinel-2",
              journal = "ISPRS International Journal of Geo-Information",
                 year = "2023",
               volume = "12",
               number = "7",
                pages = "e263",
                month = "July",
             keywords = "agriculture, artificial intelligence, Cerrado, HLS, multisensor, 
                         remote sensing.",
             abstract = "Agricultural 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 
                         20212022 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.",
                  doi = "10.3390/ijgi12070263",
                  url = "http://dx.doi.org/10.3390/ijgi12070263",
                 issn = "2220-9964",
                label = "self-archiving-INPE-MCTIC-GOV-BR",
             language = "en",
           targetfile = "ijgi-12-00263.pdf",
        urlaccessdate = "16 jun. 2024"
}