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Improving water resource management in the western United States is critically needed to achieve sustainability between the competing demands of water supplies for cities and towns, for agriculture, particularly irrigated regions, by industries principally for generating electricity, and for the environment (i.e., providing adequate ecosystem services). The last decade marked by historically severe droughts revealed the need for new water management policies and environmental regulations. Moreover, the impact of climate change not only has exacerbated droughts but also may be causing episodic extreme wet periods requiring a new paradigm on water management strategies for surface water reservoirs and groundwater aquifers. The Sustainable Groundwater Management Act (SGMA) is an example of developing a water management policy for sustainability of water resources in California. California produces 80% of the world’s almonds, is the 4th largest wine producer worldwide while also providing three-quarters of the fruits and nuts in the U.S. Much of the production requires reliable water sources for irrigation. This has motivated research into designing networks of evapotranspiration (ET) flux tower measurements in grape and tree crop systems in conjunction with developing new remote sensing tools for mapping crop water use, ET, to efficiently use and conserve water resources across the over 1.5 million acres of woody perennial crop production fields. This acreage is largely irrigated and uses approximately 70% of freshwater resources in the region. The two projects, GRAPEX (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) and T-REX (Tree-crop Remote sensing of Evapotranspiration eXperiment) have the overall goal to identify management opportunities to maximize water use efficiency in vineyard, almond and other woody perennial crops. This presentation will describe the measurement network used for understanding the water and energy flux exchange of these complex cropping systems and in validating and refining remote sensing modeling tools from UAS and satellite platforms for estimating ET and crop water stress from plant and sub field to regional scales required for improving water management strategies of these agricultural systems.

COMPARAÇÃO DO NDVI OBTIDO POR MEIO DE DRONE E SATÉLITE NAS FASES FENOLÓGICAS DA VIDEIRA MARYJANE DINIZ DE ARAÚJO GOMES1; RODRIGO MÁXIMO SÁCHEZ ROMÁN2; ALFONSO TORRES-RUA3; ÉLVIS DA SILVA ÁLVES4 E MAC MCKEE3 1 Instituto Federal de Ciência e Tecnologia do Pará, BR 316, km 65, Campus Castanhal, s/n, CEP. 68740-970, Castanhal, Pará, Brasil, maryjane.gomes@ifpa.edu.br 2 Universidade Estadual Paulista “Júlio de Mesquita Filho” (UNESP), Campus de Botucatu – Faculdade de Ciências Agronômicas (FCA), Avenida Universitária, 3780, Altos do Paraíso – Fazenda Experimental Lageado CEP. 18610-034, Botucatu/SP, Brasil, rodrigo.roman@unesp.br 3 Utah State University, AggieAir Utah Water Research Laboratory, 1600 Canyon Road, Logan/UT, 84321, USA, alfonso.torres@usu.edu, mac.mckee@usu.edu 4Doutor em Engenharia Agrícola, Universidade Federal de Viçosa, CEP: 36570-000, Viçosa-MG, Brasil, Email: elvistv@gmail.com 1 RESUMO As diversas interações do meio biofísico, sejam por fatores bióticos ou abióticos, dificultam os estudos do comportamento das áreas cultivadas através de métodos padrões. A utilização de plataformas com sensores de alta resolução espacial possibilita detectar a heterogeneidade de culturas como a videira, por meio do NDVI. O objetivo do trabalho foi avaliar o NDVI obtido por imagens do VANT AggieAir Minion e do Landsat 8, em diferentes fases fenológicas da videira Pinot Noir. As imagens foram coletadas no Condado de Sacramento, Califórnia USA. O processamento das imagens foi realizado por meio do Quantum GIS (QGIS). Constatou-se uma subestimação do NDVI calculado por meio das imagens do Landsat 8, na fase de crescimento de baga, apresentando valores menores (0,68) do que os encontrados pelo VANT (0,87). Nessa fase os valores de NDVI devem ser mais próximos de 1,0, pois a planta encontra-se em pleno desenvolvimento vegetativo. O detalhamento das imagens depende da resolução espacial do sensor que a plataforma carrega, sendo assim considerando que a distância entre as linhas da videira era de 3,35 m, e devido ao tamanho do pixel do Landsat 8, foi detectado uma maior área de solo exposto devido à influência da máxima semelhança de veracidade que é utilizada pela linguagem do algoritmo utilizado no software de programação. Portanto, pode-se concluir que a resolução espacial influência em valores de índice vegetativo em fases fenológicas da videira e, consequentemente, nos parâmetros biofísicos estimados por ele, mostrando assim que as imagens capturadas por VANT expressam valores mais próximos do real para a avaliação do NDVI em videiras. Palavras-chave: VANT AggieAir, Landsat 8, Pinot Noir. COMPARISON OF NDVI OBTAINED BY DRONE AND SATELLITE IN THE PHENOLOGICAL PHASES OF GRAPEVINE GOMES, M. D. A.; SÁNCHEZ-ROMÁN, R. M; TORRES-RUA, A.; ÁLVES, E. S.; MCKEE, M 2 ABSTRACT Several interactions of the biophysical environment, whether by biotic or abiotic factors, hinder the study of crops' behavior using standard methods. The use of platforms with high spatial resolution sensors enables detecting heterogeneity of crops such as grapevine through NDVI. This study aimed to evaluate the NDVI obtained using images from the AggieAir Minion UAV and Landsat 8 in different phenological phases of the Pinot Noir grapevine. The images were collected in Sacramento County, California, USA. Image processing was performed using Quantum GIS (QGIS). We observed an underestimation of the NDVI calculated using Landsat 8 images in the berry growth phase, presenting lower values (0.68) than those obtained by the VANT (0.87). The NDVI values should be closer to 1.0 in this phase because the plant is in full vegetative development. The details of the images depends on the spatial resolution of the sensor that the platform carries. Thus, considering that the distance between the lines of the vine was 3.35 m, and due to the pixel size of the Landsat 8, a larger soil-exposed area was detected as a consequence of the maximum similarity of veracity used by the algorithm language employed in the programming software. The resolution influences the vegetative index values in phenological phases of grapevine and, consequently, the biophysical parameters estimated by them, showing that the UAV images convey values more similar to reality for NDVI evaluation in grapevines. Keywords: UAV AggieAir, Landsat 8, Pinot Noir.

Accurately estimating and forecasting evapotranspiration is one of the most important tasks to strengthen water resource management, especially in desert areas such as La Yarada, Tacna, Peru, a region located at the head of the Atacama Desert. In this study, we used temperature, humidity, wind speed, air pressure, and solar radiation from a local weather station to forecast potential evapotranspiration (ETo) using machine learning. The Feedforward Neural Network (Multi-Layered Perceptron) algorithm for prediction was used under two approaches: “direct” and “indirect”. In the first one, the ETo is predicted based on historical records, and the second one predicts the climate variables upon which the ETo calculation depends, for which the Penman-Monteith, Hargreaves-Samani, Ritchie, and Turc equations were used. The results were evaluated using statistical criteria to calculate errors, showing remarkable precision, predicting up to 300 days of ETo. Comparing the performance of the approaches and the machine learning used, the results obtained indicate that, despite the similar performance of the two proposed approaches, the indirect approach provides better ETo forecasting capabilities for longer time intervals than the direct approach, whose values of the corresponding metrics are MAE = 0.033, MSE = 0.002, RMSE = 0.043 and RAE = 0.016.

Water conservation efforts for California’s agricultural industry are critical to its sustainability through severe droughts like the current one and others experienced over the last two decades. This is most critical for perennial crops, such as vineyards and orchards, which are costly to plant and maintain and constitute a significant fraction of the regional water use. It is no longer feasible to access groundwater for irrigation to replace deficit surface water resources during drought due to a significant overdraft of aquifers and new regulation limiting its use. To achieve significant water savings, the actual crop water use or evapotranspiration (ET) needs to be mapped from field to regional scales on a daily basis. This can only be achieved using remote sensing-based models, particularly thermal-based energy balance models that are sensitive to deficit irrigation conditions. The two-source energy balance (TSEB) model has been successfully applied over vineyards in California, but challenges still remain. In particular, much of the irrigated cropland in the California Central Valley is affected by advection of hot dry air masses from surrounding non-irrigated areas and the TSEB model appears to need modifications to adequately estimate ET under such conditions, as well as the partitioning between evaporation and transpiration. This study investigates the application of the TSEB model, using local observations in a vineyard having significant advection. Four versions of the transpiration algorithm in TSEB are applied and evaluated with tower eddy covariance measurements spanning 4 growing seasons. The results suggest the performance of the original transpiration algorithm based on Priestley–Taylor used in TSEB is satisfactory in all but the most extreme advective conditions, while a transpiration algorithm based on Shuttleworth–Wallace with a canopy resistance formula, which relates maximum stomata conductance to vapor pressure deficit (VPD), performs well in all cases. These modifications have potential for improving regional applications of the TSEB model in support of water management in the Central Valley., Funding and logistical support for the GRAPEX project were provided by E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant No. NNH17AE39I). This research was also supported in part by the U.S. Department of Agriculture, Agricultural Research Service.

Scientific and related management challenges in the water domain are inherently multi-disciplinary, requiring synthesis of data of multiple types from multiple domains. Many data manipulation, visualization, and analysis tasks performed by water scientists are difficult because (1) datasets are becoming larger and more complex; (2) standard data formats for common data types are not always agreed upon, and, when they are, they are not always mapped to an efficient structure for visualization and/or analysis within an analytical environment; and (3) water scientists generally lack training in data intensive scientific methods that would enable them to use new and existing tools to efficiently tackle large and complex datasets. This project advances Data Science and Analytics for Water (DSAW) through developing: (1) an advanced object data model that maps common water-related data types to performant data structures within the object-oriented Python language and analytical environment based upon standard file, data, and content types established by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare system; (2) two new Python packages that enable users to write Python code for automating retrieval of required water data, loading it into performant memory objects specified by the object data model we design, and performing analysis in a reproducible way that can be shared, collaborated around, and formally published for reuse. We have developed domain-specific data science applications to demonstrate how the new Python Packages can be paired with the powerful data science capabilities of existing Python packages like Pandas, numpy, and scikit-learn to develop advanced analytical workflows within a Python environment. By doing so, we are extending the data access, collaboration, and archival capabilities of the HydroShare data and model repository and promoting its use as a platform for reproducible water-data science., Funding Agency: National Science Foundation (US) Award Title: Collaborative Research: Elements: Advancing Data Science and Analytics for Water (DSAW) Award Number: 1931297 Award URL: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931297