Remote sensing as a tool to determine biophysical parameters of irrigated seed corn crop

Santos, Robson Argolo dos; Divincula, J. S.; Oliveira, Karine Rabelo de; Venâncio, Luan Peroni; Missio, Marcos Francisco; Filgueiras, Roberto; Cunha, Fernando França da; Aleman, Catariny Cabral

doi:10.5433/1679-0359.2020v41n2p435

Cited by 3 publications

(7 citation statements)

References 24 publications

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“…The second highest mean ET c value (4.8 mm d -1 ) occurred at 151 DOY, which corresponds to approximately 45 DAS and represents the period of greatest vegetative development in all center pivots (Figure 4). Santos et al (2020) described a similar result in an experiment in which the second-highest ETc identified in the seed maize crop was close to 5.0 mm d-1, at 46 DAS. It should be noted that, at 231 DOY, center pivots P23 and P24 exhibited low uniformity in their area (Figure 3), with a greater range of data.…”

Section: Considering the Center Pivots With Different Planting Dates (supporting

confidence: 67%

“…This is explained by the fact that this index grows with the development of the crop, having reached its maximum value around 78 DAS and starting to decline from this point until the senescence of the crop and harvest (Table 3), as the nutrients are displaced for the development of reproductive organs. Subsequently, with maturation, the plant ceases to grow (Alface et al, 2019;Santos et al, 2020). To some extent, NDVI has a positive correlation with biomass gain.…”

Section: Resultsmentioning

confidence: 99%

“…However, the irrigation amount is the same for the entire center pivot area. As a result, certain regions may have been under and/or over irrigated (Santos et al, 2020). terms of type, texture and physical and water characteristics of the soil under the pivot orientations, as well as plant development.…”

Section: Considering the Center Pivots With Different Planting Dates (mentioning

confidence: 99%

“…However, the irrigation amount is the same for the entire center pivot area. As a result, certain regions may have been under and/or over irrigated (Santos et al, 2020). Figure 5 shows the spatial and temporal distribution of the biomass values accumulated in the maize crop (BIO) in kilograms per hectare, on the days the images were acquired.…”

Section: Considering the Center Pivots With Different Planting Dates (mentioning

confidence: 99%

“…Irrigation management consists of the application of water at the right time and in the necessary amount for the crop (Bernardo, Mantovani, Silva, & Soares, 2019). Among the existing irrigation management methods, the weather-based approach is the most commonly used due to its easy execution and greater technical and economic viability (Althoff, Santos, Bazame, Cunha, & Filgueiras, 2019;Santos, Venancio, Filgueiras, & Cunha, 2020). In view of the importance of the crop coefficient (Kc) for weather-based irrigation management, various studies have examined with different planting dates, the crop evapotranspiration (ET c ) pixel values ranged from 0.0 to 6.0 mm d -1…”

Section: Introductionmentioning

confidence: 99%

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

Costa

Santos

et al. 2021

Semina: Ciênc. Agrár.

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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.

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Section: Considering the Center Pivots With Different Planting Dates (supporting

confidence: 67%

Section: Resultsmentioning

confidence: 99%

Section: Considering the Center Pivots With Different Planting Dates (mentioning

confidence: 99%

Section: Considering the Center Pivots With Different Planting Dates (mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Costa

Santos

et al. 2021

Semina: Ciênc. Agrár.

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Protocolo Metodológico para Obtenção dos Valores de Reflectância e de NDVI de Imagens Landsat 8/OLI Utilizando LEGAL

Krizek

Santos

2021

Rev. Bras. Geog. Fis.

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A obtenção dos valores de reflectância se mostra imprescindível para se calcular índices de vegetação, como o NDVI (Normalized Difference Vegetation Index). Este índice é utilizado para classificar a distribuição global da vegetação e para inferir variáveis ecológicas e ambientais, como a produção de fitomassa. Apesar disso, não é incomum encontrar trabalhos que utilizam os números digitais (ND) para a obtenção direta dos índices de vegetação; entretanto, tais números digitais não representam valores físicos reais e, portanto, não podem ser utilizados diretamente para o cálculo do NDVI. Assim, o objetivo deste artigo é demonstrar um protocolo metodológico para a conversão dos ND das imagens Landsat 8/OLI em valores de reflectância e a subsequente obtenção do NDVI, através da linguagem LEGAL (Linguagem Espacial para Geoprocessamento Algébrico), e, dessa forma, possibilitar a replicação e execução de outras pesquisas que visem obter esse índice de vegetação no software SPRING. Além disso, objetivou-se também demonstrar a importância da conversão dos ND em reflectância, a partir da comparação de uma imagem NDVI gerada através da reflectância com a mesma imagem NDVI gerada por meio dos dados brutos. Os resultados apontaram que a obtenção do NDVI através dos valores brutos de imagens de sensoriamento remoto, sem a necessária conversão dos números digitais em valores reais de reflectância, leva a resultados incorretos na estimativa de dados ecológicos da vegetação, subestimando a fitomassa. Dessa forma, esse trabalho ressalta a importância de se seguir um protocolo metodológico para a estimativa correta da fitomassa, produtividade e outros parâmetros da vegetação. Methodological protocol for obtaining reflectance and NDVI values from Landsat 8/OLI images using LEGALA B S T R A C TObtaining reflectance values is essential for calculating vegetation indices, such as the NDVI (Normalized Difference Vegetation Index). This index is used to classify the global distribution of vegetation and to infer the ecological and environmental parameters such as phytomass production. Nevertheless, it is common to find works that use digital numbers (DN) to directly obtain vegetation indices; however, such digital numbers do not represent actual physical values and therefore cannot be used directly for NDVI calculation. Thus, this paper aims to demonstrate a methodological protocol for DN conversion of Landsat 8/OLI images into reflectance values and then for obtaining NDVI through the LEGAL (Spatial Language for Algebraic Geoprocessing). Therefore, this protocol enables the replication and execution of other studies aimed to obtain this vegetation index using SPRING. In addition, the objective was also to demonstrate the importance of converting DN to reflectance by comparing an NDVI image generated from reflectance with the same NDVI image generated through the raw data. The results showed that obtaining the NDVI through the raw values of remote sensing images, without the conversion of digital numbers to real reflectance values, leads to incorrect results in the estimation of ecological vegetation data, underestimating phytomass, thus emphasizing the importance of following a methodological protocol for the correct estimation of biomass, productivity and other phytological parameters.Keywords: protocol, NDVI, reflectance, Landsat 8, SPRING

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Forage Mass Estimation in Silvopastoral and Full Sun Systems: Evaluation through Proximal Remote Sensing Applied to the SAFER Model

et al. 2023

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The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′ S, 47°50′ W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method.

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Remote sensing as a tool to determine biophysical parameters of irrigated seed corn crop

Cited by 3 publications

References 24 publications

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

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

Protocolo Metodológico para Obtenção dos Valores de Reflectância e de NDVI de Imagens Landsat 8/OLI Utilizando LEGAL

Forage Mass Estimation in Silvopastoral and Full Sun Systems: Evaluation through Proximal Remote Sensing Applied to the SAFER Model

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