Droughts are major natural disasters that affect many parts of the world all years and recently affected one of the major conilon coffee-producing regions of the world in state of Espírito Santo, which caused a huge crisis in the sector. Therefore, the objective of this study was to conduct an analysis with technical-scientific basis of the real impact of drought associated with high temperatures and irradiances on the conilon coffee (Coffea canephora Pierre ex Froehner) plantations located in the north, northwest, and northeast regions of the state of Espírito Santo, Brazil. Data from 2010 to 2016 of rainfall, air temperature, production, yield, planted area and surface remote sensing were obtained from different sources, statistically analyzed, and correlated. The 2015/2016 season was the most affected by the drought and high temperatures (mean annual above 26 °C) because, in addition to the adverse weather conditions, coffee plants were already damaged by the climatic conditions of the previous season. The increase in air temperature has higher impact (negative) on production than the decrease in annual precipitation. The average annual air temperatures in the two harvest seasons that stood out for the lowest yields (i.e. 2012/2013 and 2015/2016) were approximately 1 °C higher than in the previous seasons. In addition, in the 2015/2016 season, the average annual air temperature was the highest in the entire series. The spatial and temporal distribution of Enhanced Vegetation Index values enabled the detection and perception of droughts in the conilon coffee-producing regions of Espírito Santo. The rainfall volume accumulated in the periods from September to December and from April to August are the ones that most affect coffee yield. The conilon coffee plantations in these regions are susceptible to new climate extremes, as they continue to be managed under irrigation and full sun. The adoption of agroforestry systems and construction of small reservoirs can be useful to alleviate these climate effects, reducing the risk of coffee production losses and contributing to the sustainability of crops in Espírito Santo.

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.

Accurate information about the spatiotemporal variability of actual crop evapotranspiration (ETa), crop coefficient (K_c) and water productivity (WP) is crucial for water efficient management in the agriculture. The Earth Engine Evapotranspiration Flux (EEFlux) application has become a popular approach for providing spatiotemporal information on ETa and Kc worldwide. The aim of this study was to quantify the variability of water consumption (ETa) and the K_c for an irrigated commercial planting of soybeans based on the EEFlux application in the western region of the state of Bahia, Brazil. The water productivity (WP) for the fields was also obtained. Six cloud-free images from Landsat 7 and 8 satellites, acquired during the 2016/17 soybean growing season were used and processed on the EEFlux platform. The ETa from EEFlux was compared to that of the modified FAO (MFAO) approach using the following statistical metrics: Willmot’s index of agreement (d-index), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE). The K_c from EEFlux was compared to the K_c used in the soybean field (K_c FAO-based) and to the K_c values obtained in different scientific studies using the d-index. A similar procedure was performed for WP. Our results reveal that EEFlux is able to provide accurate information about the variability of ETa and the K_c of soybean fields. The comparison between ETa EEFlux and ETa MFAO showed good agreement based on the d-index, with values of 0.85, 0.83 and 0.89 for central pivots 1, 2 and 3, respectively. However, EEFlux tends to slightly underestimate ETa. The K_c EEFlux showed good accordance with the K_c values considered in this study, except in phase II, where a larger difference was observed; the average WP of the three fields (1.14 kg m^-3) was higher than that in the majority of the previous studies, which is a strong indicator of the efficient use of water in the studied soybean fields. The study showed that EEFlux, an innovative and free tool for access spatiotemporal variability of ETa and Kc at global scale is very efficient to estimate the ETa and Kc on different growth stages of soybean crop.

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.

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (K_cb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The K_cb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the K_cb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R² = 0.74) was observed between the actual transpiration of the crop estimated by the K_cb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R² of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m⁻² and an R² of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m⁻², and the R² was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the K_cb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.