Variabilidade Espaço-Temporal Da Condição Da Vegetação Na Agricultura Irrigada Por Meio De Imagens Sentinel-2ª

Ribeiro, Rayssa Balieiro; Filgueiras, Roberto; Ramos, Maria Camila Alves; Almeida, Laura Thebit de; Generoso, Tarcila Neves; Monteiro, Luane Inês Brito

doi:10.7127/rbai.v11n600648

Cited by 7 publications

(7 citation statements)

References 3 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is necessary to adopt more efficient mechanisms in agricultural monitoring to identify vulnerable regions in crops, for the purpose of avoiding economic losses [1]. Remote sensing products have become significant in this matter, since they help with the monitoring of large areas and the identification of crop conditions in a systematic and fast way [2].…”

Section: Introductionmentioning

confidence: 99%

Crop NDVI Monitoring Based on Sentinel 1

et al. 2019

Self Cite

View full text Add to dashboard Cite

Monitoring agricultural crops is necessary for decision-making in the field. However, it is known that in some regions and periods, cloud cover makes this activity difficult to carry out in a systematic way throughout the phenological cycle of crops. This circumstance opens up opportunities for techniques involving radar sensors, resulting in images that are free of cloud effects. In this context, the objective of this work was to obtain a normalized different vegetation index (NDVI) cloudless product (NDVInc) by modeling Sentinel 2 NDVI using different regression techniques and the Sentinel 1 radar backscatter as input. To do this, we used four pairs of Sentinel 2 and Sentinel 1 images on coincident days, aiming to achieve the greatest range of NDVI values for agricultural crops (soybean and maize). These coincident pairs were the only ones in which the percentage of clouds was not equal to 100% for 33 central pivot areas in western Bahia, Brazil. The dataset used for NDVInc modeling was divided into two subsets: training and validation. The training and validation datasets were from the period from 24 June 2017 to 19 July 2018 (four pairs of images). The best performing model was used in a temporal analysis from 02 October 2017 to 08 August 2018, totaling 55 Sentinel 2 images and 25 Sentinel 1 images. The selection of the best regression algorithm was based on two validation methodologies: K-fold cross-validation (k = 10) and holdout. We tested four modeling approaches with eight regression algorithms. The random forest was the algorithm that presented the best statistical metrics, regardless of the validation methodology and the approach used. Therefore, this model was applied to a time series of Sentinel 1 images in order to demonstrate the robustness and applicability of the model created. We observed that the data derived from Sentinel 1 allowed us to model, with great reliability, the NDVI of agricultural crops throughout the phenological cycle, making the methodology developed in this work a relevant solution for the monitoring of various regions, regardless of cloud cover.

show abstract

Section: Introductionmentioning

confidence: 99%

Crop NDVI Monitoring Based on Sentinel 1

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Reference evapotranspiration was highest because the meteorological variables were decisive for its estimate, with incident solar radiation (282 W m -2 ) being the parameter of greatest influence (Pandey, Dabral, & Pandey, 2016 Table 3 shows the biophysical parameters throughout the irrigated maize cultivation cycle, for the different center pivots. The variables of α s , NDVI and T s are results of the status of the agroecosystems and allow the management of the crop to be directed to the more critical regions, generating significant gains to the production system (Ribeiro et al, 2017;Teixeira & Leivas, 2017). The highest mean αs (0.19 to 0.25) and Ts (35.15 to 39.65 ºC) occurred before sowing and after 168 DAS.…”

Section: Resultsmentioning

confidence: 99%

“…Given that the phenological stage for the maize crop was characterized from the methodology proposed in the FAO-56 report (Allen et al, 1998) and in view of the sensitivity of NDVI to the presence of pigments that participate in photosynthetic and biomass processes in the aerial part (Formaggio & Sanches, 2017;Ribeiro et al, 2017), we estimated the crop coefficient (Kc) from NDVI (Figure 2A). The equation was fitted based on the relationship between NDVI and Kc FAO , considering the area of the center pivots under study.…”

Section: Seminamentioning

confidence: 99%

Spatio-temporal variability of biophysical parameters of irrigated maize using orbital remote sensing

Costa

Santos

et al. 2021

Semina: Ciênc. Agrár.

Self Cite

View full text Add to dashboard Cite

This study proposes to estimate the actual crop evapotranspiration, using the SAFER model, as well as calculate the crop coefficient (Kc) as a function of the normalized difference vegetation index (NDVI) and determine the biomass of an irrigated maize crop using images from the Operational Land Imager (OLI) and Thermal Infrared (TIRS) sensors of the Landsat-8 satellite. Pivots 21 to 26 of a commercial farm located in the municipalities of Bom Jesus da Lapa and Serra do Ramalho, west of Bahia State, Brazil, were selected. Sowing dates for each pivot were arranged as North and South or East and West, with cultivation starting firstly in one of the orientations and subsequently in the other. The relationship between NDVI and the Kc values obtained in the FAO-56 report (KcFAO) revealed a high coefficient of determination (R2 = 0.7921), showing that the variance of KcFAO can be explained by NDVI in the maize crop. Considering the center pivots with different planting dates, the crop evapotranspiration (ETc) pixel values ranged from 0.0 to 6.0 mm d-1 during the phenological cycle. The highest values were found at 199 days of the year (DOY), corresponding to around 100 days after sowing (DAS). The lowest BIO values occur at 135 DOY, at around 20 DAS. There is a relationship between ETc and BIO, where the DOY with the highest BIO are equivalent to the days with the highest ETc values. In addition to this relationship, BIO is strongly influenced by soil water availability.

show abstract

“…Neste estudo, os valores que representaram solo exposto foram de 0,16 no SAVI a 0,23 no NDVI, ou seja, os menores valores dos índices de vegetação. Resultado semelhante foi verifi cado por Ribeiro et al (2017) que utilizando imagens sentinel em pivô central obteve valores de solo exposto de 0,20 para o NDVI.…”

Section: Resultsunclassified

Estudo Teórico Sobre Como Realizar Um Processo De Obtenção De Melado De Algaroba (Prosopis Juliflora Sw Dc)

Falcão¹,

Texeira²,

Silva³

et al. 2019

As Ciências Exatas E Da Terra No Século XXI 2

View full text Add to dashboard Cite

Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição 4.0 Internacional (CC BY 4.0).O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais.

show abstract

Variabilidade Espaço-Temporal Da Condição Da Vegetação Na Agricultura Irrigada Por Meio De Imagens Sentinel-2ª

Cited by 7 publications

References 3 publications

Crop NDVI Monitoring Based on Sentinel 1

Crop NDVI Monitoring Based on Sentinel 1

Spatio-temporal variability of biophysical parameters of irrigated maize using orbital remote sensing

Estudo Teórico Sobre Como Realizar Um Processo De Obtenção De Melado De Algaroba (Prosopis Juliflora Sw Dc)

Contact Info

Product

Resources

About