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 (NDVI_nc) 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 NDVI_nc 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.

Western Bahia, Brazil, is a classic example of a region where intense irrigation growth has led to acute water stress situations in a few small basins. The water stress problem has the potential to grow regionally. However, there are currently no systematic field measurements of water withdrawn from rivers or groundwater to supply irrigation systems. In this work, we merge remote sensing and river gauge data to assess both the amount of water used for irrigation in Western Bahia and also its consequences for regional water stress, identifying water conflict situations and assessing water security. Remote sensing products used include time series of the normalized difference vegetation index, evapotranspiration, and rainfall. Field data include time series of river discharge and calibration data for crop status and actual evapotranspiration. From calibrated remote sensing products, three-day water balances were calculated for each center pivot using computations of irrigation depth and water uptake for irrigation, both individually at the center-pivot scale and integrated regionally. From these regional integrations, a simple water-use diagnostic indicated that three sub-basins presented the most critical conditions for water conflicts. An in-depth analysis of these sub-basins shows that, despite the high water stress, water use for irrigation has been steadily increasing, pushing the water use to its limits. This work demonstrates that the use of remote sensing products together with field data is a powerful tool for diagnosing water conflict situations. The limitations of this work relate to the absence of field data to validate the water uptake estimated and to the lack of additional long-term and high-quality river flow stations to provide diagnostics for all small basins in the region.

DYNAMICS OF ACTUAL CROP EVAPOTRANSPIRATION BASED IN THE COMPARATIVE ANALYSIS OF SEBAL AND METRIC-EEFLUX ROBERTO FILGUEIRAS1; EVERARDO CHARTUNI MANTOVANI2; DANIEL ALTHOFF3; RAYSSA BALIEIRO RIBEIRO4; LUAN PERONI VENANCIO5 E ROBSON ARGOLO DOS SANTOS6 1 Department of Agricultural Engineering, Federal University of Viçosa, Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, roberto.f.filgueiras@ufv.br. 2 Department of Agricultural Engineering, Federal University of Viçosa , Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, everardo@ufv.br. 3 Department of Agricultural Engineering, Federal University of Viçosa , Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, daniel.althoff@ufv.br. 4 Department of Agricultural Engineering, Federal University of Viçosa , Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, rayssa.ribeiro@ufv.br. 5 Department of Agricultural Engineering, Federal University of Viçosa , Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, luan.venancio@ufv.br. 6 Department of Agricultural Engineering, Federal University of Viçosa , Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, robson.argolo@ufv.br. 1 ABSTRACT Obtaining spatial evapotranspiration requires that the user has knowledge of the energy balance equation, as well as digital image processing. This fact has made researchers create and make available an actual evapotranspiration (ETa) product for scientific community, EEFLUX (Earth Engine Evapotranspiration Flux). Based on this, the present work aimed to compare ETa from the SEBAL (ETa-SEBAL) algorithm, with ETa based on the METRIC algorithm, which is available by EEFLUX (ETA-EEFLUX). For this, 14 Landsat images were used throughout the 2018 crop season, for maize crop irrigated by central pivot, in western Bahia, Brazil. The results showed that the product available by EEFLUX presents a higher estimate of evapotranspiration, when compared to SEBAL, for the areas with lower NDVI values and higher surface temperature, and the opposite was also observed. In addition, the SEBAL algorithm was more correlated with the NDVI variables and surface temperature. However, ET-EEFLUX showed agreement with the results obtained by the SEBAL algorithm, being an important information available to the scientific community and decision makers in the practice of irrigated agriculture, since it does not require in-depth technical knowledge. Keywords: water demand, crop monitoring, remote sensing FILGUEIRAS, R.; MANTOVANI, E. C.; ALTHOFF, D.; RIBEIRO, R. B.; VENANCIO, L. P.; SANTOS, R. A. DINÂMICA DA EVAPOTRANSPIRAÇÃO BASEADO NA ANÁLISE COMPARATIVA DO ALGORITMO SEBAL E DO METRIC-EEFLUX 2 RESUMO A obtenção da evapotranspiração espacializada requer que o usuário tenha conhecimento da equação do balanço de energia, bem como de processamento digital de imagens. Este fato fez com que pesquisadores criassem e disponibilizassem um produto de evapotranspiração real (ETa) para a comunidade científica, o EEFLUX (Earth Engine Evapotranspiration Flux). Baseado nisso, o presente trabalho teve como objetivo comparar a ETa proveniente do algoritmo SEBAL (ETa-SEBAL), com a ETa baseada no algoritmo METRIC, que está disponível pelo EEFLUX (ETa-EEFLUX). Para isso, utilizaram-se 14 imagens Landsat. Os resultados demonstraram que o produto disponível pelo EEFLUX apresenta uma estimativa maior de evapotranspiração, quando comparado com o SEBAL, para as áreas que apresentam valores de NDVI mais baixos e de temperatura da superfície mais elevada, sendo o contrário também observado. Além disso, o algoritmo SEBAL se mostrou mais correlacionado com as variáveis NDVI e temperatura de superfície. Entretanto, o ET-EEFLUX apresentou concordância com os resultados obtidos pelo algoritmo SEBAL, sendo uma importante informação disponível para comunidade científica e tomadores de decisão na prática da agricultura irrigada, visto que dispensa conhecimento técnico aprofundado. Palavras-chave: demanda hídrica, monitoramento de cultura, sensoriamento remoto.

One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r²), root mean square error (RMSE), Nash–Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images.

A equação para determinar a evapotranspiração de referência (ETo), método de Penman-Monteith, parametrizada pela FAO (PM-FAO56), requer dados que, muitas vezes, não estão disponíveis na maioria das estações climatológicas. Para superar o problema da disponibilidade de dados climáticos, o boletim 56 da FAO propõe vários procedimentos para estimar a ETo na ausência dos dados de radiação, umidade relativa e velocidade do vento, e posterior substituição. Baseado nisto, o objetivo do presente estudo foi comparar a ETo estimada na ausência de dados de velocidade do vento ou da umidade relativa ou radiação solar com a ETo estimada, com os dados completos para várias localidades de Minas Gerais. A comparação foi realizada utilizando o coeficiente angular da regressão, o coeficiente de determinação, o índice de concordância de Willmott e os erros associados a cada metodologia. Verificou-se que, na ausência de dados de umidade relativa e velocidade do vento, o método de Penman-Monteith apresenta pequenos erros de estimativa. Na ausência dos dados de radiação, os erros são maiores em relação às outras variáveis. Na disponibilidade apenas das temperaturas máximas e mínimas, o método de PM-FAO56 apresenta desempenho superior ao método de Hargreaves-Samani.

A crescente preocupação mundial com a produção eficiente de energia e com os recursos hídricos exige desenvolver estratégias que economizem água sem redução da produtividade. Assim, objetivou-se determinar as lâminas de irrigação que resultem na máxima produtividade e uso eficiente de água para duas cultivares de batata-doce. Lâminas equivalentes a 50, 75, 100 e 125% da evapotranspiração da cultura (ETc), calculadas por meio do software Irriplus, foram aplicadas via gotejamento. A produtividade de raízes tuberosas foi determinada após 187 e 208 dias de cultivo das cultivares de batata-doce Amanda e Duda, respectivamente. A maior produtividade (49,8 t ha-1) de raízes tuberosas pelas plantas da 'Amanda' foi obtida com uma lâmina acumulada de 325,5 mm (95,2% da ETc), enquanto que para 'Duda' a lâmina que proporcionou maior produtividade (67,1 t ha-1) foi de 347,0 mm (100,4% da ETc). A diferença de produtividade entre as cultivares foi de 25,8%, com aumento no consumo de água pela cultivar Duda de 21,5 mm (6,2% maior). Isso indica que 'Duda' apresentou maior eficiência no uso da água. O aumento na lâmina de água aplicada resultou no aumento da eficiência no uso da água (EUA), até atingir um valor máximo de 16,1 kg m-3, com a aplicação de 301,8 mm (87,3% da ETc) para 'Amanda' e de 20,0 kg m-3, com a aplicação de 332,4 mm (96,2% da ETc) para 'Duda'. A produtividade referente à máxima EUA foi de 48,6 t ha-1, com uma economia de 23,7 mm, representando redução de apenas 1,2 t ha-1 na produtividade da cultivar Amanda. Para 'Duda', a produtividade equivalente à máxima EUA foi de 66,3 t ha-1, com uma economia de 14,6 mm e redução de 0,8 t ha-1, quando comparada à produtividade real. As lâminas recomendadas para 'Amanda' e 'Duda', nas condições edafoclimáticas de condução desta pesquisa, foram de 301,8 e 332,4 mm, respectivamente.