Métodos de estimativa da evapotranspiração da cultura da cana-de-açúcar em condições de sequeiro

Silva, Vicente de Paulo Rodrigues da; Garcêz, Silvia L. A.; Silva, Bernardo Barbosa da; Albuquerque, Monik F. de; Almeida, Rafaela Silveira Rodrigues

doi:10.1590/1807-1929/agriambi.v19n5p411-417

Cited by 16 publications

(5 citation statements)

References 19 publications

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“…Sugar cane total evapotranspiration during its cycle, found by Dingre and Gorantiwar [35], was 1388 mm. The one found by Silva et al [38] for the Brazilian Northeast conditions was 1600 mm. Thus, it can be noticed that the total evapotranspiration found in this work is close to the values found in Brazil.…”

Section: Models Applicationmentioning

confidence: 70%

Modeling Actual Evapotranspiration with MSI-Sentinel Images and Machine Learning Algorithms

et al. 2022

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The modernization of computational resources and application of artificial intelligence algorithms have led to advancements in studies regarding the evapotranspiration of crops by remote sensing. Therefore, this research proposed the application of machine learning algorithms to estimate the ETrF (Evapotranspiration Fraction) of sugar can crop using the METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) model with data from the Sentinel-2 satellites constellation. In order to achieve this goal, images from the MSI sensor (MultiSpectral Instrument) from the Sentinel-2 and the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors from the Landsat-8 were acquired nearly at the same time between the years 2018 and 2020 for sugar cane crops. Images from OLI and TIR sensors were intended to calculate ETrF through METRIC (target variable), while for the MSI sensor images, the explanatory variables were extracted in two approaches, using 10 m (approach 1) and 20 m (approach 2) spatial resolution. The results showed that the algorithms were able to identify patterns in the MSI sensor data to predict the ETrF of the METRIC model. For approach 1, the best predictions were XgbLinear (R² = 0.80; RMSE = 0.15) and XgbTree (R² = 0.80; RMSE = 0.15). For approach 2, the algorithm that demonstrated superiority was the XgbLinear (R² = 0.91; RMSE = 0.10), respectively. Thus, it became evident that machine learning algorithms, when applied to the MSI sensor, were able to estimate the ETrF in a simpler way than the one that involves energy balance with the thermal band used in the METRIC model.

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Section: Models Applicationmentioning

confidence: 70%

Modeling Actual Evapotranspiration with MSI-Sentinel Images and Machine Learning Algorithms

et al. 2022

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“…Physiological changes controlled by different mechanisms define different growth stages. In general, sugarcane growth is divided by the following four periods: an initial stage (30 days), tillering stage (90 days), development stage (150 days), and the final stage (90 days) (Doorenbos and Kassam, 1979;Silva et al, 2015). Throughout the analysis, we will refer to these growth stages.…”

Section: Sugarcane Growthmentioning

confidence: 99%

Observing Sucrose Accumulation With Sentinel-1 Backscatter

Besten

Steele‐Dunne

Aouizerats³

et al. 2021

Front. Remote Sens.

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In this study the impact of sucrose accumulation in Sentinel-1 backscatter observations is presented and compared to Planet optical observations. Sugarcane yield data from a sugarcane plantation in Xinavane, Mozambique are used for this study. The database contains sugarcane yield of 387 fields over two seasons (2018-2019 and 2019-2020). The relation between sugarcane yield and Sentinel-1 VV and VH backscatter observation is analyzed by using the Normalized Difference Vegetation Index (NDVI) data as derived from Planet Scope optical imagery as a benchmark. The different satellite observations were compared over time to sugarcane yield to understand how the relation between the observations and yield evolves during the growing season. A negative correlation between yield and Cross Ratio (CR) from Sentinel-1 backscatter was found while a positive correlation between yield and Planet NDVI was observed. An additional modeling study on the dielectric properties of the crop revealed how the CR could be affected by sucrose accumulation during the growing season and supported the opposite correlations. The results shows CR contains information on sucrose content in the sugarcane plant. This sets a basis for further development of sucrose monitoring and prediction using a combination of radar and optical imagery.

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“…Atualmente há vários métodos desenvolvidos para a estimativa da evapotranspiração de referência (Silva, 2015). Entre os vários métodos existentes na literatura, o método de Penman-Monteith vem sendo apontado mundialmente, como o mais adequado (Carvalho et al, 2011).…”

Section: Introductionunclassified

Comparação de dados meteorológicos obtidos por estação convencional e automática e estimativa da evapotranspiração de referência em Imperatriz/MA

Lima

Moraes²,

Fonseca³

et al. 2020

J. Env. Anal. Progr.

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O uso de dados meteorológicos na agricultura tem grande importância na previsão da evapotranspiração e projeção de cenários futuros, sendo necessário precisão nas estações de coletas desses dados. O estudo objetivou comparar os dados meteorológicos obtidos na estação convencional em relação aqueles obtidos na estação automática, bem como estimar a evapotranspiração de referência (E|To) pelo método de Penman-Monteith. Para alcançar tais objetivos, dados diários de temperatura máxima, mínima, umidade relativa máxima e mínima, velocidade do vento e radiação solar foram adquiridos de dois tipos de estação meteorológicas: uma convencional (EMC) e outra automática (EMA), pertencentes à rede de estações do Instituto Nacional de Meteorologia (INMET) ambas localizadas no município de Imperatriz/MA. Para comparação dos dados bem como da evapotranspiração obtida a partir desses dados, utilizou-se a análise de regressão linear e determinou-se o coeficiente de determinação (R²). Além disso, utilizou-se também a porcentagem relativa dos dados obtidos na EMC em relação aos dados obtidos na EMA (superestimativa/subestimativa) (%), o índice de concordância de Willmott (d), o coeficiente de correlação (r) e o coeficiente de confiança (c) proposto por Camargo e Sentelhas. Os resultados deste estudo mostraram que as estações apresentam divergências relativamente grandes quanto aos dados de temperatura máxima, temperatura mínima, velocidade do vento e radiação solar, sendo necessária a calibração do sensor da estação convencional. A temperatura média e a umidade relativa média do ar foram semelhantes apresentando a melhor correlação entre a EMC e EMA. A evapotranspiração de referência pelo método de Penman Monteith apresentou classificação muito alta segundo a classificação de Cohen, quanto ao coeficiente de correlação e desempenho sofrível pela classificação de Camargo e Sentelhas.

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Métodos de estimativa da evapotranspiração da cultura da cana-de-açúcar em condições de sequeiro

Cited by 16 publications

References 19 publications

Modeling Actual Evapotranspiration with MSI-Sentinel Images and Machine Learning Algorithms

Modeling Actual Evapotranspiration with MSI-Sentinel Images and Machine Learning Algorithms

Observing Sucrose Accumulation With Sentinel-1 Backscatter

Comparação de dados meteorológicos obtidos por estação convencional e automática e estimativa da evapotranspiração de referência em Imperatriz/MA

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