ABSTRACT SAFER (Simple Algorithm for Evapotranspiration Retrieving) is a relatively new algorithm applied successfully to estimate actual crop evapotranspiration (ET) at different spatial scales of different crops in Brazil. However, its use for monitoring irrigated crops is scarce and needs further investigation. This study assessed the performance of SAFER to estimate ET of irrigated corn in a Brazilian semiarid region. The study was conducted in São Desidério, Bahia State, Brazil, in corn-cropped areas in no-tillage systems and irrigated by central pivots. SAFER algorithm with original regression coefficients (a = 1.8 and b = –0.008) was initially tested during the growing seasons of 2014, 2015, and 2016. SAFER performed very poorly for estimating corn ET, with RMSD values greater than 1.18 mm d –1 for 12 fields analyzed and NSE values < 0 in most fields. To improve estimates, SAFER regression coefficients were calibrated (using 2014 and 2015 data) and validated with 2016 data, with the resulting coefficients a and b equal to 0.32 and –0.0013, respectively. SAFER performed well for ET estimation after calibration, with r 2 and NSE values equal to 0.91 and RMSD = 0.469 mm d –1 . SAFER also showed good performance (r 2 = 0.86) after validation, with the lowest RMSD (0.58 mm d –1 ) values for the set of 14 center pivots in this growing season. The results support the use of calibrated SAFER algorithm as a tool for estimating water consumption in irrigated corn fields in semiarid conditions.

RESUMO Em regiões onde a área irrigada é crescente e a disponibilidade hídrica é reduzida, como o Oeste do Estado da Bahia, Brasil, o emprego de técnicas que contribuam para melhorar a eficiência de uso de água é primordial. Uma das formas de melhor adequação da irrigação é através da melhoria do cálculo da evapotranspiração atual (ETa), que entre outros fatores, é influenciada pelo secamento do solo, sendo importante entender essa relação, que geralmente é contabilizada nos modelos de manejo de irrigação por meio do coeficiente de estresse hídrico (Ks). Este estudo objetivou estimar o coeficiente de estresse hídrico (Ks) por meio de informações obtidas via sensoriamento remoto, combinadas com informações de campo. Para isto, realizou-se um estudo no município de São Desidério, área localizada no Oeste da Bahia, utilizando imagens do satélite Landsat-8. O Ks foi calculado pela relação entre a evapotranspiração da cultura e a ETa, calculada pelo Simple Algorithm for Evapotranspiration Retrieving (SAFER). O Ks estimado por sensoriamento remoto apresentou para os estádios em desenvolvimento e médio, erros médios da ordem de 5,50%. No estádio final de desenvolvimento do milho os erros obtidos foram de 23,2%.

ABSTRACT In regions where the irrigated area is increasing and water availability is reduced, such as the West of the Bahia state, Brazil, the use of techniques that contribute to improving water use efficiency is paramount. One of the ways to improve irrigation is by improving the calculation of actual evapotranspiration (ETa), which among other factors is influenced by soil drying, so it is important to understand this relationship, which is usually accounted for in irrigation management models through the water stress coefficient (Ks). This study aimed to estimate the water stress coefficient (Ks) through information obtained via remote sensing, combined with field data. For this, a study was carried out in the municipality of São Desidério, an area located in western Bahia, using images of the Landsat-8 satellite. Ks was calculated by the relationship between crop evapotranspiration and ETa, calculated by the Simple Algorithm for Evapotranspiration Retrieving (SAFER). The Ks estimated by remote sensing showed, for the development and medium stages, average errors on the order of 5.50%. In the final stage of maize development, the errors obtained were of 23.2%.

ABSTRACT Monitoring of large agricultural lands is often hampered by data collection logistics at field level. To solve such a problem, remote sensing techniques have been used to estimate vegetation indices, which can subsidize crop management decision-making. Therefore, this study aimed to select vegetation indices to detect variability in irrigated corn crops. Data were collected in São Desidério, Bahia State (Brazil), using an OLI sensor (Operational Land Imager) embedded to a Landsat-8 satellite platform. Five corn growing plots under central pivot irrigation were assessed. The following vegetation indices were tested: NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio), NDWI (Normalized Difference Water Index), and MSI (Moisture Stress Index). Among the tested indices, SR was more sensitive to high corn biomass, while GNDVI, NDVI, EVI, and SAVI were more sensitive to low values. Overall, all indices were found to be concordant with each other, with high correlations among them. Despite this, the use of a set of these indices is advisable since some respond better to certain peculiarities than others.

ABSTRACT Surface temperature (Ts) is a determining factor to obtain energy balance parameters, being relevant to understand the influence of this variable on the estimation of evapotranspiration. Thus, the objective of this study was to simulate errors in Ts estimation to verify the consequences of actual evapotranspiration (ETa) estimated by the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model. For this, an image of the Landsat-8 satellite was used to induce errors from 0.2K to 10K in the variable Ts, allowing verifying the consequences in the ETa data. After the estimations of Ts and ETa, the quantitative consequences and dynamics of Ts impact on the ETa data were verified along the different land uses in the study area. The results showed that the precise estimation of Ts is essential to obtain ETa accurately. The image of ETa errors presented the highest relative errors on the surface with exposed soils and with high Ts values. However, the highest residuals of ETa images occurred on the surfaces with milder Ts and higher evapotranspiration rates (irrigated surfaces).

RESUMO O uso de imagens de satélites como complemento na gestão de irrigação constitui uma base primordial no processo de tomada de decisão para a agricultura irrigada. Nesse contexto, o objetivo do trabalho foi monitorar, por meio do Índice de Vegetação por Diferença Normalizada (IVDN), a cultura da cana-de-açúcar irrigada pertencente à empresa Mafambisse, localizada no Distrito de Nhamatanda/Sofala, República de Moçambique; e estabelecer sua relação com o coeficiente da cultura estabelecido pela FAO (kcFAO) e ajustar um modelo de regressão para estimar o coeficiente de cultivo (kc) a partir da relação entre IVDN e kcFAO. Para a condução da pesquisa foi utilizada uma série de imagens Sentinel-2A/MSI, referente ao período de outubro de 2016 a outubro de 2017. Por meio das imagens geradas de NDVI foi possível fazer o acompanhamento da cana-de açúcar em campo e analisar a sensibilidade do índice no vigor vegetativo da cultura. Observou-se padrão semelhante entre os perfis de kcFAO e os valores do NDVI, tornando possível a realização do ajuste, demostrando que esse índice é uma alternativa para obtenção do coeficiente de cultivo.

ABSTRACT The use of satellite images as a complement in irrigation management constitutes a primordial basis in the decision-making process for irrigated agriculture. In this context, the present study aimed to monitor through Normalized Difference Vegetation Index (NDVI) an irrigated sugarcane field belonging to the Mafambisse company, located at the District of Nhamatanda/Sofala, Republic of Mozambique, and establish its relationship with the crop coefficient established by FAO (kcFAO) and fit a regression model to estimate crop coefficient (kc) from the relationship between NDVI and kcFAO. The study was conducted using a series of Sentinel-2A/MSI images, relative to the period from October 2016 to October 2017. Based on the NDVI images generated, it was possible to monitor the sugarcane crop in the field and analyse the sensitivity of the index to its vegetative vigor. A similar pattern was observed between kcFAO profiles and NDVI values, which allowed the adjustment to be performed, demonstrating that this index is an alternative to obtain the crop coefficient.

ABSTRACT One of the main factors that determine the success of decision-making in the fields is the climatic factor. This way, the geostatistical techniques have been used to represent and understand the spatial or temporal dynamics of meteorological parameters. Therefore, the aim of this research was to represent temporally through thematic maps, the average daily behavior for meteorological variables and the hydric balance for the municipality of Patos de Minas - MG. The climatic data were acquired from the automatic station INMET from the years 1990 to 2015. Later, it was calculated the evapotranspiration and the hydric balance for different capacities of available water in the soil (CAW): 24 mm, 48 mm, 80 mm and 112 mm. The climate variables showed temporal dependence, and through the thematic maps, derived from the ordinary Kriging, it was possible to identify the seasons of the year that are favorable for the production for the different crop groups.

RESUMO A importância da estimativa precisa da evapotranspiração está diretamente relacionada ao uso sustentável da água. Uma vez que a agricultura representa 70% do consumo de água no Brasil, a aplicação adequada e eficiente de água reduz os conflitos sobre o uso da água entre os múltiplos usuários. Considerando a importância de uma estimativa precisa da evapotranspiração, o objetivo do presente estudo foi modelar e comparar a evapotranspiração de referência a partir de diferentes metodologias heurísticas. O método padrão Penman-Monteith foi utilizado como referência para evapotranspiração, porém, para avaliar as metodologias heurísticas com dados escassos, avaliou-se o desempenho de métodos difundidos na literatura em relação à Penman-Monteith. Os métodos utilizados para estimar a evapotranspiração a partir de dados escassos foram Priestley-Taylor, Thornthwaite. As técnicas computacionais Regressão Stepwise (SWR), Random Forest (RF), Cubist (CB), Rede Neural com Regularização Bayesiana (BRNN) e Máquinas de Vetor de Suporte (SVM) foram utilizados para estimar a evapotranspiração, tanto com dados meteorológicos escassos, quanto com dados completos. Os resultados mostram a robustez dos métodos de aprendizagem de máquina na predição da evapotranspiração. Os critérios de desempenho desses métodos para dados meteorológicos completos variaram de 0,14 a 0,22 mm d-1 para erro absoluto médio (MAE), de 0,21 a 0,29 mm d-1 para raiz do erro quadrático médio (RMSE) e de 0,95 a 0,99 para o coeficiente de determinação (r²). As técnicas computacionais mostraram desempenho superior em todos os cenários em relação aos métodos estabelecidos na literatura. A BRNN apresentou o melhor desempenho geral.

ABSTRACT The importance of the precise estimation of evapotranspiration is directly related to sustainable water usage. Since agriculture represents 70% of Brazil’s water consumption, adequate and efficient application of water may reduce the conflicts over the use of water among the multiple users. Considering the importance of accurate estimation of evapotranspiration, the objective of the present study was to model and compare the reference evapotranspiration from different heuristic methodologies. The standard Penman-Monteith method was used as reference for evapotranspiration, however, to evaluate the heuristic methodologies with scarce data, two widely known methods had their performances assessed in relation to Penman-Monteith. The methods used to estimate evapotranspiration from scarce data were Priestley-Taylor and Thornthwaite. The computational techniques Stepwise Regression (SWR), Random Forest (RF), Cubist (CB), Bayesian Regularized Neural Network (BRNN) and Support Vector Machines (SVM) were used to estimate evapotranspiration with scarce and full meteorological data. The results show the robustness of the heuristic methods in the prediction of the evapotranspiration. The performance criteria of machine learning methods for full weather data varied from 0.14 to 0.22 mm d-1 for mean absolute error (MAE), from 0.21 to 0.29 mm d-1 for root mean squared error (RMSE) and from 0.95 to 0.99 coefficient of determination (r²). The computational techniques proved superior performance to established methods in literature, even in scenarios of scarce variables. The BRNN presented the best performance overall.

ABSTRACT: The measurements obtained by remote sensing are influenced by issues related to relief. Based on that, this paper aims to analyze the spatial and temporal dynamics of the net radiation (Rn), considering the influence of topographic parameters on it, in Edgárdia Experimental Farm (FEE). It was used the methodology based on the SEBAL algorithm (Surface Energy Balance Algorithm for Land) to estimate the net radiation by orbital images. In order to assess the topographic influence on the computing of net radiation, three methodologies were applied: without topographic correction (RnSem), cosine correction (RnCos) and C correction (RnC). These methodologies were applied on 21 images of the orbital platform Landsat-5/TM, from 1985 up to 2010. Due to the complex relief of the FEE, the cosine correction did not show effectiveness on the attenuation of topographic influence. For the values of RnC, we noticed an average reduction of 5.97% for the standard deviation, relative to the images without correction. These results show the importance of considering the relief, to achieve higher accuracies on the estimation of biophysical parameters with orbital images.