Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

Figueiredo, G. K. D. A.; Brunsell, Nathaniel A.; Higa, Breno Hiroyuki; Rocha, Jansle Vieira; Lamparelli, Rubens Augusto Camargo

doi:10.1590/0103-9016-2015-0215

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…Not only NDVI is used for yield forecasting. Other vegetation indices such as SR (Simple Ratio Index), RVI (Ratio Vegetation Index), EVI (Enhanced Vegetation Index), NDRE (Normalized Difference Red Edge Index), FAPAR (Fraction of Absorbed Photosynthetically Active Radiation), SAVI (Soiladjusted vegetation index, GNDVI (Green Normalized Difference Vegetation Index) are also used [23][24][25][26][27][28]. Yield forecasting at regional level, using satellite remote sensing, can be performed by using mean values of the vegetation indices for the total area or only for the total cropland or for the crop masks for individual crops [13,18,29,30].…”

Section: Introductionmentioning

confidence: 99%

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Panek

Gozdowski

2021

Agronomy

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In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

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Section: Introductionmentioning

confidence: 99%

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Panek

Gozdowski

2021

Agronomy

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“…Other approaches have also been developed to estimate grain yield, but they have a lower spatial resolution, limiting its application on a farm level. Among these approaches, the most common is the application of remote sensing techniques based on agro-meteorological [4] and satellite imagery data [5,6]. Regarding this kind of data, efforts have also been made applying advanced algorithms to estimate yield [7].…”

Section: Introductionmentioning

confidence: 99%

Soybean Yield Estimation and Its Components: A Linear Regression Approach

Wei

Molin

2020

Agriculture

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Soybean yield estimation is either based on yield monitors or agro-meteorological and satellite imagery data, but they present several limiting factors regarding on-farm decision level. Aware that machine learning approaches have been largely applied to estimate soybean yield and the availability of data regarding soybean yield and its components (number of grains (NG) and thousand grains weight (TGW)), there is an opportunity to study their relationships. The objective was to explore the relationships between soybean yield and its components, generate equations to estimate yield and evaluate its prediction accuracy. The training dataset was composed of soybean yield and its components’ data from 2010 to 2019. Linear regression models based on NG, TGW and yield were fitted on the training dataset and applied to a validation dataset composed of 58 on-field collected samples. It was found that globally TGW and NG presented weak (r = 0.50) and strong (r = 0.92) linear relationships with yield, respectively. In addition to that, applying the fitted models to the validation dataset, model based on NG presented the highest accuracy, coefficient of determination (R2) of 0.70, mean absolute error (MAE) of 639.99 kg ha−1 and root mean squared error (RMSE) of 726.67 kg ha−1.

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“…Enhanced Vegetation Index (EVI), um índice de vegetação amplamente utilizado para acompanhamento da saúde da vegetação (greenness), tal como para acompanhamento de safra agrícola (FIGUEIREDO et al, 2016;HATFIELD;PRUEGER, 2010;QIU et al, 2022). As Além disso, foram calculados os acumulados de precipitação utilizando os dados do CHIRPS.…”

Section: íNdice De Vegetação Melhorado (Evi)unclassified

Uso do sensoriamento remoto para avaliação de seca em áreas agrícolas de pequena escala: estudo de caso da seca na região central do Rio Grande do Sul

Oliveira Costa,

Fisher,

Cunha

2023

RBClima

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Este estudo avaliou o uso de índices de sensoriamento remoto para monitorar os impactos da seca na produção de soja em pequenas áreas (<2km²). O estudo se concentrou em sete áreas localizadas em Santiago (RS), abrangendo dois ciclos de cultivo (Safra 1: 2018-2019 e Safra 2: 2019-2020). Dados de precipitação derivados de satélite foram usados para calcular o Índice de Precipitação Padronizada (SPI) e caracterizar os eventos de seca que ocorreram entre 2018 e 2020 na área de estudo. Além disso, dados de temperatura foram utilizados para avaliar a ocorrência de eventos de calor extremo durante o período de análise. Os dados do Enhanced Vegetation Index (EVI) foram empregados para avaliar os efeitos do déficit hídrico na produção de soja. Os resultados indicaram três eventos de seca entre 2018 e 2020, sendo que o período mais intenso e severo ocorreu entre fevereiro e abril de 2020, coincidindo com a fase de plantio em todas as áreas. Além disso, foram identificadas seis ondas de calor, incluindo uma em março de 2020, que coincidiu com o evento de seca severa, formando um evento composto de seca-calor. A análise dos dados do EVI revelou um impacto mais significativo do déficit hídrico na Safra 2, particularmente em parcelas não irrigadas, resultando em menor produtividade. No geral, o estudo demonstrou a viabilidade de utilizar técnicas de sensoriamento remoto para monitorar os impactos da seca na produção agrícola, mesmo em pequenas áreas.

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Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

Cited by 13 publications

References 22 publications

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Soybean Yield Estimation and Its Components: A Linear Regression Approach

Uso do sensoriamento remoto para avaliação de seca em áreas agrícolas de pequena escala: estudo de caso da seca na região central do Rio Grande do Sul

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