@Article{SilvaChLuSaAlAd:2024:SyRe,
               author = "Silva, Nildson Rodrigues de Fran{\c{c}}a e and Chaves, Michel 
                         Eust{\'a}quio Dantas and Luciano, Ana Cl{\'a}udia dos Santos and 
                         Sanches, Ieda Del'Arco and Almeida, Cl{\'a}udia Maria de and 
                         Adami, Marcos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Estadual Paulista (UNESP)} and {Universidade de 
                         S{\~a}o Paulo (USP)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Sugarcane Yield Estimation Using Satellite Remote Sensing Data in 
                         Empirical or Mechanistic Modeling: A Systematic Review",
              journal = "Remote Sensing",
                 year = "2024",
               volume = "16",
               number = "5",
                pages = "e863",
                month = "Mar.",
             keywords = "crop modeling, crop monitoring, crop yield, systematic literature 
                         review, text mining.",
             abstract = "The sugarcane crop has great socioeconomic relevance because of 
                         its use in the production of sugar, bioelectricity, and ethanol. 
                         Mainly cultivated in tropical and subtropical countries, such as 
                         Brazil, India, and China, this crop presented a global harvested 
                         area of 17.4 million hectares (Mha) in 2021. Thus, decision making 
                         in this activity needs reliable information. Obtaining accurate 
                         sugarcane yield estimates is challenging, and in this sense, it is 
                         important to reduce uncertainties. Currently, it can be estimated 
                         by empirical or mechanistic approaches. However, the models 
                         peculiarities vary according to the availability of data and the 
                         spatial scale. Here, we present a systematic review to discuss 
                         state-of-the-art sugarcane yield estimation approaches using 
                         remote sensing and crop simulation models. We consulted 1398 
                         papers, and we focused on 72 of them, published between January 
                         2017 and June 2023 in the main scientific databases (e.g., 
                         AGORA-FAO, Google Scholar, Nature, MDPI, among others), using the 
                         Preferred Reporting Items for Systematic Reviews and Meta-Analyses 
                         (PRISMA) methodology. We observed how the models vary in space and 
                         time, presenting the potential, challenges, limitations, and 
                         outlooks for enhancing decision making in the sugarcane crop 
                         supply chain. We concluded that remote sensing data assimilation 
                         both in mechanistic and empirical models is promising and will be 
                         enhanced in the coming years, due to the increasing availability 
                         of free Earth observation data.",
                  doi = "10.3390/rs16050863",
                  url = "http://dx.doi.org/10.3390/rs16050863",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-16-00863-v3.pdf",
        urlaccessdate = "18 jun. 2024"
}