author = "Bendini, Hugo do Nascimento and Fonseca, Leila Maria Garcia and 
                         Schwieder, Marcel and K{\"o}rting, Thales Sehn and Rufin, 
                         Philippe and Sanches, Ieda Del'Arco and Leit{\~a}o, Pedro J. and 
                         Hostert, Patrick",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and 
                         {Humboldt-Universit{\"a}t zu Berlin} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Humboldt-Universit{\"a}t zu 
                         Berlin} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Humboldt-Universit{\"a}t zu Berlin} and 
                         {Humboldt-Universit{\"a}t zu Berlin}",
                title = "Detailed agricultural land classification in the Brazilian cerrado 
                         based on phenological information from dense satellite image time 
              journal = "International Journal of Applied Earth Observation and 
                 year = "2019",
               volume = "82",
                pages = "UNSP 101872",
                month = "Oct.",
             keywords = "Big data, Time-Series mining, Random forest algorithm, Land use 
                         and Land cover mapping (LULC), Multi-Sensor.",
             abstract = "The paradox between environmental conservation and economic 
                         development is a challenge for Brazil, where there is a complex 
                         and dynamic agricultural scenario. This reinforces the need for 
                         effective methods for the detailed mapping of agriculture. In this 
                         work, we employed land surface phenological metrics derived from 
                         dense satellite image time series to classify agricultural land in 
                         the Cerrado biome. We used all available Landsat images between 
                         April 2013 and April 2017, applying a weighted ensemble of Radial 
                         Basis Function (RBF) convolution filters as a kernel smoother to 
                         fill data gaps such as cloud cover and Scan Line Corrector 
                         (SLC)-off data. Through this approach, we created a dense Enhanced 
                         Vegetation Index (EVI) data cube with an 8-day temporal resolution 
                         and derived phenometrics for a Random Forest (RF) classification. 
                         We used a hierarchical classification with four levels, from land 
                         cover to crop rotation classes. Most of the classes showed 
                         accuracies higher than 90%. Single crop and Non-commercial crop 
                         classes presented lower accuracies. However, we showed that 
                         phenometrics derived from dense Landsat-like image time series, in 
                         a hierarchical classification scheme, has a great potential for 
                         detailed agricultural mapping. The results are promising and show 
                         that the method is consistent and robust, being applicable to 
                         mapping agricultural land throughout the entire Cerrado.",
                  doi = "10.1016/j.jag.2019.05.005",
                  url = "http://dx.doi.org/10.1016/j.jag.2019.05.005",
                 issn = "0303-2434",
             language = "en",
           targetfile = "bendini_detailed.pdf",
        urlaccessdate = "18 abr. 2021"