Evaluating the performance of the TSEB model for sorghum evapotranspiration estimation using time series UAV imagery

Tunca, Emre

doi:10.1007/s00271-023-00887-2

Cited by 2 publications

(1 citation statement)

References 97 publications

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“…The ultimate objective is to achieve larger yields of superior-quality products per unit of agricultural land. This imperative has led to the widespread adoption of cuttingedge production techniques, including automation and mechanization systems [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Modeling Environmental Conditions in Poultry Production: Computational Fluid Dynamics Approach

Küçüktopçu,

Cemek,

Simsek

2024

Animals

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In recent years, computational fluid dynamics (CFD) has become increasingly important and has proven to be an effective method for assessing environmental conditions in poultry houses. CFD offers simplicity, efficiency, and rapidity in assessing and optimizing poultry house environments, thereby fueling greater interest in its application. This article aims to facilitate researchers in their search for relevant CFD studies in poultry housing environmental conditions by providing an in-depth review of the latest advancements in this field. It has been found that CFD has been widely employed to study and analyze various aspects of poultry house ventilation and air quality under the following five main headings: inlet and fan configuration, ventilation system design, air temperature–humidity distribution, airflow distribution, and particle matter and gas emission. The most commonly used turbulence models in poultry buildings are the standard k-ε, renormalization group (RNG) k-ε, and realizable k-ε models. Additionally, this article presents key solutions with a summary and visualization of fundamental approaches employed in addressing path planning problems within the CFD process. Furthermore, potential challenges, such as data acquisition, validation, computational resource requirements, meshing, and the selection of a proper turbulence model, are discussed, and avenues for future research (the integration of machine learning, building information modeling, and feedback control systems with CFD) are explored.

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Section: Introductionmentioning

confidence: 99%

Modeling Environmental Conditions in Poultry Production: Computational Fluid Dynamics Approach

Küçüktopçu,

Cemek,

Simsek

2024

Animals

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Evapotranspiration estimation using high-resolution aerial imagery and pySEBAL for processing tomatoes

Peddinti,

Nicolas,

Raij-Hoffman

et al. 2024

Irrig Sci

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The use of high-resolution aerial imagery for assessing actual crop evapotranspiration $$ \left({ET}_{a}\right)$$ holds the potential to optimize the use of limited water resources in agriculture. Despite this potential, there is a shortage of information regarding the effectiveness of energy balance algorithms, initially designed for satellite remote sensing in estimating $$ {ET}_{a}$$ using aerial imagery. This study addresses this gap by employing the remote sensing model pySEBAL (Surface Energy Balance Algorithm for Land) in conjunction with high-resolution aerial imagery to estimate $$ {ET}_{a}$$ for processing tomatoes. Throughout the 2021 growing season, an aircraft captured multispectral and thermal imagery over a processing tomato field near Esparto, California, USA. Simultaneously, an eddy covariance flux tower within the field measured high-frequency turbulent fluxes and low-frequency biometeorology variables essential for evaluating the energy balance. The comprehensive assessment of energy balance components, including $$ {ET}_{a}$$, yielded compelling evidence that pySEBAL accurately estimated $$ {ET}_{a}$$ at high spatial resolution. The root mean square error (RMSE) and normalized RMSE for various energy balance components were as follows: 33 W m− 2 (12%) for latent heat flux, 29 W m− 2 (35%) for sensible heat flux, 24 W m− 2 (4%) for net radiation, and 10 W m− 2 (15%) for soil heat flux. Additionally, $$ {ET}_{a}$$ exhibited an RMSE and NRMSE of 0.26 mm d− 1 (6%). Moreover, the spatial mapping of $$ {ET}_{a}$$ across the processing tomato field visually depicted the spatial variability associated with irrigation scheduling, crop development, areas affected by disease, and soil heterogeneity. This research underscores the value of high resolution spatial aerial imagery and pySEBAL algorithm for estimating $$ {ET}_{a}$$ variability in the field, a crucial aspect for guiding precision irrigation management and ensuring the optimal use of limited water resources in agriculture.

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Evaluating the performance of the TSEB model for sorghum evapotranspiration estimation using time series UAV imagery

Cited by 2 publications

References 97 publications

Modeling Environmental Conditions in Poultry Production: Computational Fluid Dynamics Approach

Modeling Environmental Conditions in Poultry Production: Computational Fluid Dynamics Approach

Evapotranspiration estimation using high-resolution aerial imagery and pySEBAL for processing tomatoes

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