@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 = "15 jun. 2024"
}