Search Results (66)

Accurately partitioning evapotranspiration (ET) into soil evaporation (E) and plant transpiration (T) is fundamental for improving water resource management, yet robust ET partitioning remains challenging. This study proposes a two-step ET partitioning strategy that first extracts pure E and T samples from long-term ET observations and then uses these samples to independently constrain E and T sub-models. The strategy was implemented in three classical two-source ET models: Shuttleworth–Wallace (SW), Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), and FAO-56 dual crop coefficient (FAO56-DK), and was compared against the conventional one-step calibration approach. Results show that the two-step strategy consistently improves the estimation of ET components and the transpiration fraction (T/ET). For the PT-JPL model, RMSEs of E, T, and ET decreased from 0.04, 0.06, and 0.078 to 0.03, 0.03, and 0.04 mm/30 min, respectively. In FAO56-DK, R² values increased from 0.08, 0.55, and 0.65 to 0.10, 0.65, and 0.75. The RMSE of T/ET declined from 0.21 to 0.18 in SW and from 0.47 to 0.34 in FAO56-DK. The effectiveness of pure samples depends on model structure, with E samples most beneficial for SW, T samples for FAO56-DK, and both for PT-JPL. Overall, these results demonstrate that pure-sample constraints substantially enhance ET partitioning accuracy. Full article

Evapotranspiration (ET) plays a crucial role in the terrestrial water cycle, especially in arid and semi-arid agricultural regions where precise water management is essential. However, the limited spatial resolution and temporal frequency of existing ET products hinder their application in fine-scale agricultural monitoring. In this study, we first improved the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model by replacing the relative humidity-based soil moisture constraint with the land surface water index (LSWI), aiming to enhance model performance in water-limited environments. Second, we developed a Crop Unmixing and Weight Fusion Model for ET (CUWFM) to generate daily ET products at a 30 m spatial resolution by integrating high-resolution but infrequent PT-JPL-ET data with coarse-resolution but frequent PML-V2-ET data. The CUWFM employs a hybrid approach combining sub-pixel crop fraction decomposition with similarity-weighted regression, allowing for more accurate ET estimation over heterogeneous agricultural landscapes. The proposed methods were evaluated in the Changji region of Xinjiang, China, using field-measured ET data from two-flux-tower sites. The results show that the improved PT-JPL model increased ET estimation accuracy compared with the original version, with higher R² and Nash–Sutcliffe efficiency (NSE), and lower root mean square error (RMSE). The CUWFM outperformed benchmark spatiotemporal fusion methods, including STARFM, ESTARFM, and Fit-FC, in both pixel- and field-scale assessments, achieving the highest overall performance scores based on the All-round Performance Assessment (APA) framework. This study demonstrates the potential of integrating vegetation indices and crop-specific spatial decomposition into ET modeling, providing a feasible pathway for producing high spatiotemporal resolution ET datasets to support precision agriculture in arid and semi-arid regions. Full article

To further improve the prediction accuracy for greenhouse crop evapotranspiration (ET) under different irrigation conditions and enhance irrigation water use efficiency, this study proposes three methods to revise the Priestley–Taylor (PT) model coefficient α for calculating ET at different growth stages: (1) considering the leaf senescence coefficient f_S, plant temperature constraint parameter f_t, and soil water stress index f_sw to correct α (MPT model); (2) combining the Penman–Monteith (PM) model to inversely calculate α (PT-M model); (3) using the machine learning XGBoost algorithm to optimize α (PT-M_(XGB) model). Accordingly, this study observed the cumulative evaporation (Ep) of a 20 cm standard evaporation pan and set two different irrigation treatments (K0.9: 0.9Ep and K0.5: 0.5Ep). We conducted field measurements of meteorological data inside the greenhouse, tomato physiological and ecological indices, and ET during 2020 and 2021. The above three methods were then used to dynamically simulate greenhouse tomato ET. Results showed the following: (1) In 2020 and 2021, under K0.9 and K0.5 irrigation treatments, the MPT model mean coefficient α for the entire growth stage was 1.27 and 1.26, respectively, while the PT-M model mean coefficient α was 1.31 and 1.30. For both models, α was significantly lower than 1.26 (conventional value) during the seedling stage and the flowering and fruiting stage, rose rapidly during the fruit enlargement stage, and then gradually declined toward 1.26 during the harvest stage. (2) Predicted ET (ET_e) using the PT-M model underestimated the observed ET (ET_m) by 8.71~16.01% during the seedling stage and the harvest stage, and overestimated by 1.62~6.15% during the flowering and fruiting stage and the fruit enlargement stage; the errors compared to ET_m under both irrigation treatments over two years was 0.1~3.3%, with an R² of 0.92~0.96. (3) The PT-M_(XGB) model achieved higher prediction accuracy, with errors compared to ET_m under both irrigation treatments over two years of 0.35~0.65%, and R² above 0.98. The PT-M_(XGB) model combined with the XGBoost algorithm significantly improved prediction accuracy, providing a reference for the precise calculation of greenhouse tomato ET. Full article

This study compares the performance of empirical and hybrid deep learning (DL) models in estimating daily potential evapotranspiration (PET) in the Nakdong River Basin (NRB), South Korea, with the FAO-56 Penman–Monteith (PM) method as a reference. Two empirical models, Priestley–Taylor (P-T) and Hargreaves–Samani (H-S), and two DL models, a standalone Long Short-Term Memory (LSTM) network and a hybrid Convolutional Neural Network Bidirectional LSTM with an attention mechanism, were trained on a meteorological dataset (1973–2024) across 13 meteorological stations. Four input combinations (C1, C2, C3, and C4) were tested to assess the model’s robustness under varying data availability conditions. The results indicate that empirical models performed poorly, with a basin-wide RMSE of 5.04–5.79 mm/day and negative NSE (−10.37 to −13.99), and are therefore poorly suited to NRB. In contrast, DL models achieved significant improvements in accuracy. The hybrid CNN-BiLSTM Attention Mechanism (C1) produced the highest performance, with R² = 0.820, RMSE = 0.672 mm/day, NSE = 0.820, and KGE = 0.880, which was better than the standalone LSTM (R² = 0.756; RMSE = 0.782 mm/day). The generalization of heterogeneous climates was also verified through spatial analysis, in which the NSE at the station level consistently exceeded 0.70. The hybrid DL model was found to be highly accurate in representing the temporal variability and seasonal patterns of PET and is therefore more suitable for operational hydrological modeling and water-resource planning in the NRB. Full article

Daily evapotranspiration (ET) estimated from an unoccupied aerial system (UAS) could help improve irrigation practices, but its spatial resolution needs to be upscaled to coarser pixel resolutions before applying surface energy balance models. The purpose of this study was to evaluate the impact of various energy balance-based aggregation schemes on generating spatially distributed latent heat flux (LE), and, in comparison, to existing occupied aircraft and satellite remote sensing platforms. In 2017, UAS multispectral and thermal imagery, along with ground truth data, were collected at various cotton growth stages. These data sources were combined to model LE using a Two-Source Energy Balance Priestley–Taylor (TSEB-PT) model. Several UAS aggregation schemes were tested, including the mode of aggregation (i.e., input image and output flux) as well as the averaging scheme (i.e., simple aggregation vs. Box–Cox). Results indicate that output flux aggregation with Box–Cox averaging produced the lowest relative upscaling pixel-scale variability in LE and the lowest absolute prediction errors (relative to eddy covariance flux tower measurements). Output flux aggregation with simple averaging was also more accurate in reproducing LE from occupied aircraft and satellite imagery. Although results are limited to a single site, UAS LE estimates were reliably aggregated to coarser pixel resolutions, which made for faster image processing for operational applications. Full article

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

Estimating the actual evapotranspiration (ET_{c act}) of cropland in arid areas, exploring the time trend, and analyzing periodic variation are the key to long-term assessment of water resource availability and regional drought. The Penman formula has a strong ability to characterize reference crop evapotranspiration (ET_o). However, the application of this formula may be limited in the absence of a complete set of climate data. While previous studies have investigated K_{c act} in China, few have employed localized K_c values to systematically analyze long-term periodic fluctuations in ET_{c act} under climate variability conditions. Therefore, this study aimed to evaluate the applicability of nine ET_o estimation models in the Loess Plateau of China, calculate actual crop coefficients (K_{c act}) for spring maize and winter wheat, and examine the temporal trend and periodicity of ET_{c act} for long-term (1961–2018) continuous cropping of spring maize and winter wheat in the study area. The Mann–Kendall test and continuous wavelet transform (CWT) were used to obtain the temporal trend and periodicity of ET_{c act}. The results were as follows: (1) Priestley–Taylor (Prs–Tylr), based on radiation, and the 1985 Hargreaves–Samani (Harg), based on temperature, can be used when meteorological data are limited. It should be noted that among the models evaluated in this study, except for FAO56-PM, only the Harg equation is compatible with K_c-ET_o due to established conversion factors. (2) The K_{c act} of spring maize at the seeding–jointing stage and the earning–filling stage was 12% and 10% lower than the value recommended by FAO, respectively. For K_{c act} of winter wheat, it was 65% higher, 31% lower, and 85% higher than the FAO experience values in the rejuvenation–jointing stage, heading–grouting stage, and grouting–harvest stage. (3) Winter wheat, through its ET_{c act} cycle synchronized with precipitation and excellent water balance, can effectively alleviate regional drought. It is recommended to be included in the promotion of drought resistance policies. Full article

In recent years, the Yellow River Basin has experienced frequent extreme climate events, with an increasing intensity and frequency of droughts, exacerbating regional water scarcity and severely constraining agricultural irrigation efficiency and sustainable water resource utilization. The accurate estimation of reference crop evapotranspiration (ET₀) is crucial for developing scientifically sound irrigation strategies and enhancing water resource management capabilities. This study utilized daily scale meteorological data from 31 stations across the Yellow River Basin spanning the period 1960–2023 to develop various machine learning models. The study constructed four machine learning models—random forest (RF), a Support Vector Machine (SVM), Gradient Boosting (GB), and Ridge Regression (Ridge)—using the meteorological variables required by the Priestley–Taylor (PT) and Hargreaves (HG) equations as inputs. These models represent a range of algorithmic structures, from nonlinear ensemble methods (RF, GB) to kernel-based regression (SVR) and linear regularized regression (Ridge). The objective was to comprehensively evaluate their performance and robustness in estimating ET₀ under different climatic zones and drought conditions and to compare them with traditional empirical formulas. The main findings are as follows: machine learning models, particularly nonlinear approaches, significantly outperformed the PT and HG methods across all climatic regions. Among them, the RF model demonstrated the highest simulation accuracy, achieving an R² of 0.77, and reduced the mean daily ET₀ estimation error by 0.057 mm/day and 0.076 mm/day compared to the PT and HG models, respectively. Under drought-year scenarios, although all models showed slight performance degradation, nonlinear machine learning models still surpassed traditional formulas, with the R² of the RF model decreasing marginally from 0.77 to 0.73, indicating strong robustness. In contrast, linear models such as Ridge Regression exhibited greater sensitivity to changes in feature distributions during drought years, with estimation accuracy dropping significantly below that of the PT and HG methods. The results indicate that in data-sparse regions, machine learning approaches with simplified inputs can serve as effective alternatives to empirical formulas, offering superior adaptability and estimation accuracy. This study provides theoretical foundations and methodological support for regional water resource management, agricultural drought mitigation, and climate-resilient irrigation planning in the Yellow River Basin. Full article

The Tibetan Plateau (TP) serves as a vital ecological safeguard and water conservation region in China. In recent decades, substantial efforts have been made to promote vegetation greening across the TP; however, these interventions have added complexity to the local water balance and evapotranspiration (ET) processes. To investigate these dynamics, we apply the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model to simulate ET components in the TP. Through model sensitivity experiments, we isolate the contribution of vegetation greening to ET variations. Furthermore, we analyze the role of climatic drivers on ET using a suite of statistical techniques. Based on satellite and climate data from 1982 to 2018, we found the following: (1) The PT-JPL model successfully captured ET trends over the TP, revealing increasing trends in total ET, canopy transpiration, interception loss, and soil evaporation at rates of 0.06, 0.39, 0.005, and 0.07 mm/year, respectively. The model’s performance was validated using eddy covariance observations from three flux tower sites, yielding R² values of 0.81–0.86 and RMSEs ranging from 6.31 to 13.20 mm/month. (2) Vegetation greening exerted a significant enhancement on ET, with the mean annual ET under greening scenarios (258.6 ± 120.9 mm) being 2.9% greater than under non-greening scenarios (251.2 ± 157.2 mm) during 1982–2018. (3) Temperature and vapor pressure deficit were the dominant controls on ET, influencing 53.5% and 23% of the region, respectively, as identified consistently by both multiple linear regression and dominant factor analyses. These findings highlight the net influence of vegetation greening and offer valuable guidance for water management and sustainable ecological restoration efforts in the region. Full article

For the management of the water cycle, it is essential to comprehend evapotranspiration (ET) and how it changes over time and space, especially in relation to vegetation. Here, using the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, we explored the spatiotemporal variations in ET across different time scales during 1982–2018 in the Wuding River Basin. We also quantitatively evaluated the driving mechanisms of climate and vegetation changes on ET changes. Results showed that the ET estimate by the PT-JPL model showed good agreement (R² = 0.71–0.84) with four ET products (PML, MOD16A2, GLASS, FLDAS). Overall, the ET increased significantly at a rate of 3.11 mm/year (p < 0.01). Spatially, ET in the WRB is higher in the southeast and lower in the northwest. Attribution analysis indicated that vegetation restoration (leaf area index) was the dominant driver of ET changes (99.93% basin area, p < 0.05), exhibiting both direct effects and indirect mediation through the Vapor Pressure Deficit. Temperature influences emerged predominantly through vegetation feedbacks rather than direct climatic forcing. These findings establish vegetation restoration as a key driver of regional ET, providing empirical support for optimizing revegetation strategies in semi-arid environments. Full article

Potential evapotranspiration (PET) is a critical component of the water cycle, driving plants’ growth and survival. This study focused on estimating the daily potential evapotranspiration (PET) in a forest site in Northern Greece and assessing the performance of nine empirical PET estimation methods. These methods, categorized into mass-transfer, temperature-based, and radiation-based models, were compared against the widely used FAO-56 Penman–Monteith benchmark. The results highlight significant seasonal and monthly variations in vegetation water requirements. Among the methods tested, radiation-based models, particularly the Makkink equation, outperformed the others, followed by the Turc and Priestley–Taylor models. Temperature-based methods showed moderate performance and could serve as viable alternatives in forests with limited data availability, though local calibration is advisable. Full article

Evapotranspiration (ET) and water availability (WA) are critical components of the global water cycle. Although the effects of ecological restoration on ET and WA have been widely investigated, quantifying the impacts of multiple environmental factors on plant water consumption and regional water balance in dryland areas remains challenging. In this study, we investigated the spatial and temporal trends of ET and WA and isolated the contributions of vegetation restoration and climate change to variations in ET and WA in the Beijing–Tianjin Sand Source Region (BTSSR) in Northern China from 2001 to 2021, using the remote sensing-based Priestley–Taylor-Jet Propulsion Laboratory (PT-JPL) model and scenario simulation experiments. The results indicate that the estimated ET was consistent with field observations and state-of-the-art ET products. The annual ET in the BTSSR increased significantly by 1.28 mm yr⁻¹ from 2001 to 2021, primarily driven by vegetation restoration (0.78 mm yr⁻¹) and increased radiation (0.73 mm yr⁻¹). In contrast, the drier climate led to a decrease of 0.56 mm yr⁻¹ in ET. In semiarid areas, vegetation and radiation were the dominant factors driving the variability of ET, while in arid areas, relative humidity played a more critical role. Furthermore, reduced precipitation and increased plant water consumption resulted in a decline in WA by −0.91 mm yr⁻¹ during 2001–2021. Climate factors, rather than vegetation greening, determined the WA variations in the BTSSR, accounting for 77.6% of the total area. These findings can provide valuable insights for achieving sustainable ecological restoration and ensuring the sustainability of regional water resources in dryland China under climate change. This study also highlights the importance of simultaneously considering climate change and vegetation restoration in assessing their negative impacts on regional water availability. Full article

Water scarcity significantly challenges agricultural systems worldwide, especially in tropical areas such as the Dominican Republic. This study explores integrating satellite-based remote sensing technologies and field-based soil moisture sensors to assess water stress and optimize irrigation management in avocado orchards in Puerto Escondido, Dominican Republic. Using multispectral imagery from the Landsat 8 and 9 satellites, key vegetation indices (NDVI and SAVI) and NDWI, a water-related index that specifically indicates changes in crop water contents, rather than vegetation vigor, were derived to monitor vegetation health, growth stages, and soil water contents. Crop coefficient (Kc) values were calculated from these vegetation indices and combined with reference evapotranspiration (ETo) estimates derived from three meteorological models (Hargreaves–Samani, Priestley–Taylor, and Blaney–Criddle) to assess crop water requirements. The results revealed that soil moisture data from sensors at 30 cm depth strongly correlated with satellite-derived estimates, reflecting avocado trees’ critical root zone dynamics. Additionally, seasonal patterns in the vegetation indices showed that NDVI and SAVI effectively tracked vegetative growth stages, while NDWI indicated changes in the canopy water content, particularly during periods of water stress. Integrating these satellite-derived indices with field measurements allowed a comprehensive assessment of crop water requirements and stress, providing valuable insights for improving irrigation practices. Finally, this study demonstrates the potential of remote sensing technologies for large-scale water stress assessment, offering a scalable and cost-effective solution for optimizing irrigation practices in water-limited regions. These findings advance precision agriculture, especially in tropical environments, and provide a foundation for future research aimed at enhancing data accuracy and optimizing water management practices. Full article

A sophisticated precision irrigation system was created to precisely determine the water requirements of crops and implement effective irrigation control strategies for automated, real-time, and targeted crop irrigation in the semi-arid regions of India. This system incorporates ZigBee technology, wireless sensor networks, and fuzzy logic-based control methodologies. This system discussed by the author actively gathers data for the most prominent parameters of the targeted area, such as soil water potential and meteorological conditions, encompassing ambient temperature, humidity, solar radiation, and wind speed. These data obtained from the sensors then processed with the fuzzy logic-based algorithms is utilized to transmit precise irrigation control instructions to the system. Moreover, this proposed system employs the Priestley and Taylor model (PTM) so as to calculate farmland evapotranspiration (ET). This algorithm has been chosen instead of the Penman & Monteith model (PMM) because of its better accuracy and simple calculations. Both field evapotranspiration and soil water potential serve as crucial inputs for the suggested fuzzy controller-based system. A comprehensive multi-factor control rule library is established, facilitating the implementation of fuzzy control mechanisms for regulating crop irrigation water requirements with enhanced performance. The testing results obtained from this proposed system demonstrate the system’s economic viability and practicality, underscoring its reliability in communication, high control accuracy, and suitability for precision irrigation in semi-arid regions in India that, in turn, enhances the crop yield. Full article

The quantitative assessment of the impact of vegetation restoration on evapotranspiration and its components is of great significance in developing sustainable ecological restoration strategies for water resources in a given region. In this study, we used the Priestley-Taylor Jet Pro-pulsion Laboratory (PT-JPL) to simulate the ET components in the Helong section (HLS) of the Yellow River basin. The effects of vegetation restoration on ET and its components, vegetation transpiration (Et), soil evaporation (Es), and canopy interception evaporation (Ei) were separated by manipulating model variables. Our findings are as follows: (1) The simulation results are compared with the ET calculated by water balance and the annual average ET of MODIS products. The R² of the validation results are 0.61 and 0.78, respectively. The results show that the PT-JPL model tracks the change in ET in the HLS well. During 2000–2018, the ET, Ei, and Es increased at a rate of 1.33, 0.87, and 2.99 mm/a, respectively, while the Et decreased at a rate of 2.52 mm/a. (2) Vegetation restoration increased the annual ET in the region from 331.26 mm (vegetation-unchanged scenario) to 338.85 mm (vegetation change scenario) during the study period, an increase of 2.3%. (3) TMP (temperature) and VPD (vapor pressure deficit) were the dominant factors affecting ET changes in most areas of the HLS. In more than 37.2% of the HLS, TMP dominated the change affecting ET, and vapor pressure difference (VPD) dominated the area affecting ET in 30.5% of the HLS. Overall, the precipitation (PRE) and VPD were the main factors affecting ET changes. Compared with previous studies that directly explore the relationship between many influencing factors and ET results through correlation research methods, our study uses control variables to obtain results under two different scenarios and then performs difference analysis. This method can reduce the excessive interference of influencing factors other than vegetation changes on the research results. Our findings can provide strategic support for future water resource management and sustainable vegetation restoration in the HLS region. Full article