Evapotranspiration Importance in Water Resources Management Through Cutting-Edge Approaches of Remote Sensing and Machine Learning Algorithms

Raza, Ali; Hu, Yongguang; Acharki, Siham; Buttar, Noman Ali; Ray, Ram L.; Khaliq, Aftab; Zubair, Nadeem; Zubair, Muhammad; Syed, Neyha Rubab; Elbeltagi, Ahmed

doi:10.1007/978-3-031-29394-8_1

Springer Hydrogeology

2023

DOI: 10.1007/978-3-031-29394-8_1

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Evapotranspiration Importance in Water Resources Management Through Cutting-Edge Approaches of Remote Sensing and Machine Learning Algorithms

Ali Raza,

Yongguang Hu,

Siham Acharki

et al.

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Cited by 4 publications

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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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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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Use of gene expression programming to predict reference evapotranspiration in different climatic conditions

Raza,

Vishwakarma,

Acharki

et al. 2024

Appl Water Sci

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Evapotranspiration plays a pivotal role in the hydrological cycle. It is essential to develop an accurate computational model for predicting reference evapotranspiration (RET) for agricultural and hydrological applications, especially for the management of irrigation systems, allocation of water resources, assessments of utilization and demand and water use allocations in rural and urban areas. The limitation of climatic data to estimate RET restricted the use of standard Penman–Monteith method recommended by food and agriculture organization (FAO-PM56). Therefore, the current study used climatic data such as minimum, maximum and mean air temperature (Tmax, Tmin, Tmean), mean relative humidity (RHmean), wind speed (U) and sunshine hours (N) to predict RET using gene expression programming (GEP) technique. In this study, a total of 17 different input meteorological combinations were used to develop RET models. The obtained results of each GEP model are compared with FAO-PM56 to evaluate its performance in both training and testing periods. The GEP-13 model (Tmax, Tmin, RHmean, U) showed the lowest errors (RMSE, MAE) and highest efficiencies (R2, NSE) in semi-arid (Faisalabad and Peshawar) and humid (Skardu) conditions while GEP-11 and GEP-12 perform best in arid (Multan, Jacobabad) conditions during training period. However, GEP-11 in Multan and Jacobabad, GEP-7 in Faisalabad, GEP-1 in Peshawar, GEP-13 in Islamabad and Skardu outperformed in testing period. In testing phase, the GEP models R2 values reach 0.99, RMSE values ranged from 0.27 to 2.65, MAE values from 0.21 to 1.85 and NSE values from 0.18 to 0.99. The study findings indicate that GEP is effective in predicting RET when there are minimal climatic data. Additionally, the mean relative humidity was identified as the most relevant factor across all climatic conditions. The findings of this study may be used to the planning and management of water resources in practical situations, as they demonstrate the impact of input variables on the RET associated with different climatic conditions.

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Smart Resource Management: An Innovative Way of Balancing Materials, Energy, and Waste for a Green Future

Sagar,

Awasthi,

Dwivedi

et al. 2023

E3S Web Conf.

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In an era defined by environmental consciousness and resource scarcity, the concept of smart resource management has emerged as a linchpin in achieving a sustainable and green future. This abstract explores the multifaceted landscape of resource utilization, energy management, and waste minimization, collectively shaping our journey toward a more ecologically responsible world. The manuscript delves into the critical dimensions of this paradigm shift, examining innovative practices and technologies across diverse sectors. From precision agriculture optimizing crop yields with minimal environmental impact to the integration of renewable energy sources reshaping our energy landscape, the narrative unfolds . This highlights the profound impact of circular economy principles, which prioritize resource conservation and recycling. These principles, coupled with resource-sharing platforms, redefine how we perceive and utilize materials, fostering economic growth while reducing waste. Amidst these transformative tasks, challenges and barriers to implementation come to light. The abstract acknowledges these obstacles and explores emerging technologies and trends that promise to surmount them. Ultimately, the abstract underscores the imperative of smart resource management in the pursuit of a green future. It encapsulates the essence of a global movement where sustainability, economic prosperity, and environmental stewardship converge, offering hope and inspiration for generations to come.

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Evapotranspiration Importance in Water Resources Management Through Cutting-Edge Approaches of Remote Sensing and Machine Learning Algorithms

Cited by 4 publications

References 77 publications

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

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

Use of gene expression programming to predict reference evapotranspiration in different climatic conditions

Smart Resource Management: An Innovative Way of Balancing Materials, Energy, and Waste for a Green Future

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