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@Article{BreunigGDDPSDC:2020:DeMaZo,
               author = "Breunig, F{\'a}bio Marcelo and Galv{\~a}o, L{\^e}nio Soares and 
                         Dalagnol da Silva, Ricardo and Dauve, Carlos Eduardo and Parraga, 
                         Adriane and Santi, Ant{\^o}nio Luiz and Della Flora, Diandra 
                         Pinto and Chen, Shuisen",
          affiliation = "{Universidade Federal de Santa Maria (UFSM)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Fazenda Vila Morena} and 
                         {Universidade Estadual do Rio Grande do Sul (UERGRS)} and 
                         {Universidade Federal de Santa Maria (UFSM)} and {Universidade 
                         Federal da Grande Dourados (UFGD)} and {Guangzhou Institute of 
                         Geography}",
                title = "Delineation of management zones in agricultural fields using 
                         cover–crop biomass estimates from PlanetScope data",
              journal = "International Journal of Applied Earth Observation and 
                         Geoinformation",
                 year = "2020",
               volume = "85",
                pages = "e102004",
                month = "Mar.",
             keywords = "Precision agriculture, Remote sensing, Biomass, Satellite, Machine 
                         learning, Crop yield.",
             abstract = "Several methods have been proposed to delineate management zones 
                         in agricultural fields, which can guide interventions of the 
                         farmers to increase crop yield. In this study, we propose a new 
                         approach using remote sensing data to delineate management zones 
                         at three farm sites located in southern Brazil. The approach is 
                         based on the hypothesis that the measured aboveground biomass 
                         (AGB) of the cover crops is correlated with the measured cash-crop 
                         yield and can be estimated from surface reflectance and/or 
                         vegetation indices (VIs). Therefore, we used seven different 
                         statistical models to estimate AGB of three cover crops (forage 
                         turnip, white oats, and rye) in the season prior to cash-crop 
                         planting. Surface reflectance and VIs were used as predictors to 
                         test the performance of the models. They were obtained from high 
                         spatial and temporal resolution data of the PlanetScope (PS) 
                         constellation of satellites. From the time series of 30 images 
                         acquired in 2017, we used the PS data that matched the dates of 
                         the field campaigns to build the models. The results showed that 
                         the satellite AGB estimates of the cover crops at the date of 
                         maximum VI response at the beginning of the flowering stage were 
                         useful to delineate the management zones. The cover-crop AGB 
                         models that presented the highest coefficient of determination 
                         (R-2) and the lowest root mean square (RMSE) in the validation and 
                         test datasets were Support Vector Machine (SVM), Cubist (CUB) and 
                         Stochastic Gradient Boosting (SGB). For most models and cover 
                         crops, the Enhanced Vegetation Index (EVI) and the Normalized 
                         Difference Vegetation Index (NDVI) were the two most important AGB 
                         predictors. At the date of maximum VI at the beginning of the 
                         flowering stage, the correlation coefficients (r) between the 
                         cover-crop AGB and the cash-crop yield (soybean and maize) ranged 
                         from +0.70 for forage turnip to +0.78 for rye. The fuzzy 
                         unsupervised classification of the cover-crop AGB estimates 
                         delineated two management zones, which were spatially consistent 
                         with those obtained from cash-crop yield. The comparison between 
                         both maps produced overall accuracies that ranged from 61.20% to 
                         68.25% with zone 2 having higher cover-crop AGB and cash-crop 
                         yield than zone 1 over the three sites. We conclude that satellite 
                         AGB estimates of cover crops can be used as a proxy for generating 
                         management zone maps in agricultural fields. These maps can be 
                         further refined in the field with any other type of method and 
                         data, whenever necessary.",
                  doi = "10.1016/j.jag.2019.102004",
                  url = "http://dx.doi.org/10.1016/j.jag.2019.102004",
                 issn = "0303-2434",
             language = "en",
           targetfile = "breunig_delineation.pdf",
        urlaccessdate = "11 abr. 2021"
}


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