@Article{LucianoPiDuRoLeMa:2021:EmMoFo,
               author = "Luciano, Ana Cl{\'a}udia dos Santos and Picoli, Michelle Cristina 
                         Ara{\'u}jo and Duft, Daniel Garbellini and Rocha, Jansle Vieira 
                         and Leal, Manoel Regis Lima Verde and le Maire, Guerric",
          affiliation = "{Universidade de S{\~a}o Paulo (USP)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade de S{\~a}o Paulo 
                         (USP)} and {Universidade Estadual de Campinas (UNICAMP)} and 
                         {Universidade Estadual de Campinas (UNICAMP)} and Eco\&Sols, Univ 
                         Montpellier, CIRAD, INRA, IRD",
                title = "Empirical model for forecasting sugarcane yield on a local scale 
                         in Brazil using Landsat imagery and random forest algorithm",
              journal = "Computers and Electronics in Agriculture",
                 year = "2021",
               volume = "184",
                pages = "e106063",
                month = "May",
             keywords = "Crop yield, Remote sensing, Vegetation indices, Machine 
                         learning.",
             abstract = "Sugarcane plays an important role in food and energy production in 
                         Brazil and worldwide. The large availability of satellite sensors 
                         and advanced techniques for processing data have improved the 
                         forecasting sugarcane yield on a local and global scale, but more 
                         work is needed on exploiting the synergy between remote sensing, 
                         meteorological and agronomic data. In this study, we combined such 
                         data sources to forecast sugarcane yield using a random forest 
                         (RF) algorithm on an extensive area of 50,000 ha, over four years. 
                         Images from Landsat satellites were processed to time series of 
                         surface reflectance and spectral indices. The approach focused on 
                         the development of predictive models which only used data acquired 
                         and accessible several months before the harvest. First, three RF 
                         models were calibrated with different predictors to forecast the 
                         sugarcane yield at harvest: using Landsat satellite images and 
                         meteorological data (RF1); agronomic and meteorological data 
                         (RF2); a combination of Landsat satellite images, agronomic and 
                         meteorological data (RF3). As a comparison, we also tested the 
                         influence of including knowledge on the future harvest date in the 
                         models RF2 and RF3 (RF4 and RF5). The average values of R2 for 
                         RF1, RF2, and RF3 were 0.66, 0.50 and 0.74, respectively. The 
                         model with the highest values of R2 (RF3) had a Root Mean Square 
                         Error (RMSE) of 9.9 ton ha\−1 on yield forecast, 
                         approximately 15% of the yield average. Including the harvest date 
                         improved the RF2 and RF3 models to reach R2 = 0.69 and RMSE = 10.8 
                         ton ha\−1 for RF4, and R2 = 0.76 and RMSE of 9.4 ton 
                         ha\−1 for RF5. A blind forecasting test for the 2016 yields 
                         showed similar prediction than the forecast made by in situ field 
                         expertise. This result has the potential to assist management of 
                         sugarcane production.",
                  doi = "10.1016/j.compag.2021.106063",
                  url = "http://dx.doi.org/10.1016/j.compag.2021.106063",
                 issn = "0168-1699",
             language = "en",
           targetfile = "1-s2.0-S0168169921000818-main.pdf",
        urlaccessdate = "20 maio 2024"
}