Evapotranspiration estimates in a traditional irrigated area in semi-arid Mediterranean. Comparison of four remote sensing-based models

Elfarkh, Jamal; Simonneaux, Vincent; Jarlan, Lionel; Ezzahar, Jamal; Boulet, Gilles; Chakir, Adnane; Er-Raki, Salah

doi:10.1016/j.agwat.2022.107728

Cited by 7 publications

(2 citation statements)

References 90 publications

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“…LST fluctuations are more pronounced at higher wind speeds, especially in areas with heterogeneous land cover, such as surfaces in close proximity to water, as exemplified in the Sand-100 m flight, where the cool air over the water lowers the temperature of the surrounding surfaces (Figure 6). For crop fields, irrigation is a critical factor to consider when selecting the flight time, given the rapid change not just in the wet fraction of the soil but also in the transpiration process that cools down the plants [43][44][45]. Overall, it can be challenging to identify the causes responsible for generating LST variation because these factors often interact with one another in complex ways.…”

Section: Discussionmentioning

confidence: 99%

Quantifying Within-Flight Variation in Land Surface Temperature from a UAV-Based Thermal Infrared Camera

Elfarkh,

Johansen,

Angulo

et al. 2023

Drones

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Land Surface Temperature (LST) is a key variable used across various applications, including irrigation monitoring, vegetation health assessment and urban heat island studies. While satellites offer moderate-resolution LST data, unmanned aerial vehicles (UAVs) provide high-resolution thermal infrared measurements. However, the continuous and rapid variation in LST makes the production of orthomosaics from UAV-based image collections challenging. Understanding the environmental and meteorological factors that amplify this variation is necessary to select the most suitable conditions for collecting UAV-based thermal data. Here, we capture variations in LST while hovering for 15–20 min over diverse surfaces, covering sand, water, grass, and an olive tree orchard. The impact of different flying heights and times of the day was examined, with all collected thermal data evaluated against calibrated field-based Apogee SI-111 sensors. The evaluation showed a significant error in UAV-based data associated with wind speed, which increased the bias from −1.02 to 3.86 °C for 0.8 to 8.5 m/s winds, respectively. Different surfaces, albeit under varying ambient conditions, showed temperature variations ranging from 1.4 to 6 °C during the flights. The temperature variations observed while hovering were linked to solar radiation, specifically radiation fluctuations occurring after sunrise and before sunset. Irrigation and atmospheric conditions (i.e., thin clouds) also contributed to observed temperature variations. This research offers valuable insights into LST variations during standard 15–20 min UAV flights under diverse environmental conditions. Understanding these factors is essential for developing correction procedures and considering data inconsistencies when processing and interpreting UAV-based thermal infrared data and derived orthomosaics.

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Section: Discussionmentioning

confidence: 99%

Quantifying Within-Flight Variation in Land Surface Temperature from a UAV-Based Thermal Infrared Camera

Elfarkh,

Johansen,

Angulo

et al. 2023

Drones

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“…They found that Priestley-Taylor method gave better results, followed by Penman-Monteith method. Besides, researchers like Liou and Kar [18] and Elfarkh et al [19] have used remote sensing (RS) images and processes in geographical information system (GIS) to enhance evapotranspiration estimation.…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Eight Empirical and Four Hybrid Machine Learning Models for Estimating Daily Reference Evapotranspiration in Sub-Humid and Semi-Arid Climates

Acharki,

Raza,

Alshehri

et al. 2023

Preprint

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Improving reference evapotranspiration (RET) estimation accuracy contributes to effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The widely recommended FAO-56 Penman-Monteith (PM-FAO56) model for RET estimation often faces limitations due to incomplete meteorological data availability. To address this, we evaluate the ability of eight empirical models, four machine learning (ML) models and their hybrid models to estimate daily RET in Gharb and Loukkos irrigated perimeters in Morocco. These ML and hybrid models include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), RF-M5P, RF-XGBoost, RF-LightGBM and XGBoost-LightGBM. Additionally, six input combinations (based on Tmax, Tmin, RHmean, Rs and U2) were designed, with PM-FAO56 model considered as a target to models. Four statistical indicators including Kling Gupta Efficiency index (KGE), Coefficient of determination (R2), Mean Squared Error (RMSE), and Root relative squared error (RRSE) were applied to assess the models’ performance, across both training and testing phases. The findings reveal that Valiantzas 2013 (VAL2013b) model outperformed the other empirical models for all station, exhibiting high KGE and R2 (0.95–0.97), low RMSE (0.32–0.35 mm.day-1) and RRSE (8.14–10.30%). Additionally, the Hargreaves and Samani 1985 (HargS1985) model performed well in Gharb's stations, while the Valiantzas 2013 (VAL2013a) model showed good results in Loukkos' stations. Besides, the ML model’s performance RET estimation was higher when Tmax, Tmin, RHmean, Rs and U2 were used as inputs (combination 6). Among the ML and hybrid models, the XGBoost-LightGBM and RF-LightGBM achieved the highest accuracy (on average RMSE 0.015–0.097 mm.day-1), closely followed by the LightGBM and XGBoost models. However, M5P model had the lowest estimation accuracy RMSE ranged from 0.022 to 0.108 mm.day-1 on average. In summary, our study highlights the potential of ML models for RET estimation in subhumid and semi-arid areas, providing vital insights for improving water resource management, helping climate change research and optimizing irrigation scheduling for optimal agricultural water usage in the region.

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Monitoring Surface Energy Flux Dynamics of Irrigated Maize Using a Large Aperture Scintillometer in a Semi‐Arid Region

Singh,

Sehgal,

Dhakar

et al. 2024

CLEAN Soil Air Water

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Water, a crucial input in agricultural production, is distributed based on geographical and topographical patterns. However, anthropogenic climate change has intensified water scarcity in semi‐arid regions. This research aims to precisely estimate crop evapotranspiration (ET) and examine the diurnal and seasonal patterns of surface energy fluxes in maize (Zea mays) crops cultivated in a semi‐arid region. The precision of our methodology is underscored by the use of a large‐aperture scintillometer (LAS), which measured surface energy fluxes at 5‐min intervals over two crop‐growing seasons. The results, a testament to the accuracy of the LAS, indicated that during the rainy (Kharif) season of 2015–2016, the seasonal sensible heat flux (H) and latent heat flux (LE) values were 185.91 and 242.14 mm, respectively. In the rainy (Kharif) season of 2017–2018, these values were 151.57 mm for H and 373.63 mm for LE. LE values ranged from 0.40 to 6.83 MJ m−2 day−1 throughout the growing season. The findings, which highlight the LAS's ability to accurately estimate surface energy fluxes, provide a deeper understanding of their interactions with microclimatic factors, such as weather, soil, and crop management. These insights, with their significant implications for ecophysiological studies and improving agricultural practices in semi‐arid regions, underscore the importance of our research.

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Evapotranspiration estimates in a traditional irrigated area in semi-arid Mediterranean. Comparison of four remote sensing-based models

Cited by 7 publications

References 90 publications

Quantifying Within-Flight Variation in Land Surface Temperature from a UAV-Based Thermal Infrared Camera

Quantifying Within-Flight Variation in Land Surface Temperature from a UAV-Based Thermal Infrared Camera

Comparative Assessment of Eight Empirical and Four Hybrid Machine Learning Models for Estimating Daily Reference Evapotranspiration in Sub-Humid and Semi-Arid Climates

Monitoring Surface Energy Flux Dynamics of Irrigated Maize Using a Large Aperture Scintillometer in a Semi‐Arid Region

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