Search Results (633)

Accurate assessment of a crop’s water requirement is essential for optimising irrigation scheduling and increasing the sustainability of water use. The crop coefficient (K_c) is a dimensionless factor that converts reference evapotranspiration (ET₀) into actual crop evapotranspiration (ET_c) and is widely used for irrigation scheduling. The K_c reflects canopy cover, phenology, and crop type/variety, but is difficult to measure directly in heterogeneous perennial systems, such as vineyards. Remote sensing (RS) products, especially open-source satellite imagery, offer a cost-effective solution at moderate spatial and temporal scales, although their application in vineyards has been relatively limited due to the large pixel size (~100 m²) relative to vine canopy size (~2 m²). This study aimed to improve grapevine K_c predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing, with ground data obtained from canopy light interception measurements in three winegrape cultivars (Shiraz, Cabernet Sauvignon, and Chardonnay) in the Barossa and Eden Valleys, South Australia. A linear spectral mixture analysis approach was taken, which required estimation of vine canopy cover through beta regression models to improve the accuracy of vegetation indices that were used to build the K_c prediction models. Unmixing improved the prediction of seasonal K_c values in Shiraz (R² of 0.625, RMSE = 0.078, MAE = 0.063), Cabernet Sauvignon (R² = 0.686, RMSE = 0.072, MAE = 0.055) and Chardonnay (R² = 0.814, RMSE = 0.075, MAE = 0.059) compared to unmixed pixels. Furthermore, unmixing improved predictions during the early and late canopy growth stages when pixel variability was greater. Our findings demonstrate that integrating open-source satellite data with machine learning models and spectral unmixing can accurately reproduce the temporal dynamics of K_c values in vineyards. This approach was also shown to be transferable across cultivars and regions, providing a practical tool for crop monitoring and irrigation management in support of sustainable viticulture. Full article

Severe water scarcity has become a critical constraint to global agricultural development. Enhancing both the timeliness and accuracy of crop evapotranspiration (ET_c) retrieval is essential for optimizing irrigation scheduling. Addressing the limitations of conventional ground-based point-source measurements in rapidly acquiring two-dimensional ET_c information at the field scale, this study employed unmanned aerial vehicle (UAV) remote sensing equipped with multispectral and thermal infrared sensors to obtain high spatiotemporal resolution imagery of a representative citrus orchard (Citrus reticulata Blanco cv. ‘Yichangmiju’) in western Hubei at different phenological stages. In conjunction with meteorological data (air temperature, daily net radiation, etc.), ET_c was retrieved using two established approaches: the Seguin-Itier (S-I) model, which relates canopy–air temperature differences to ET_c, and the multispectral-driven single crop coefficient method, which estimates ET_c by combining vegetation indices with reference evapotranspiration. The thermal-infrared-driven S-I model, which relates canopy–air temperature differences to ET_c, and the multispectral-driven single crop coefficient method, which estimates ET_c by combining vegetation indices with reference evapotranspiration. The findings indicate that: (1) both the S-I model and the single crop coefficient method achieved satisfactory ET_c estimation accuracy, with the latter performing slightly better (accuracy of 80% and 85%, respectively); (2) the proposed multi-source fusion model consistently demonstrated high accuracy and stability across all phenological stages (R² = 0.9104, 0.9851, and 0.9313 for the fruit-setting, fruit-enlargement, and coloration–sugar-accumulation stages, respectively; all significant at p < 0.01), significantly enhancing the precision and timeliness of ET_c retrieval; and (3) the model was successfully applied to ET_c retrieval during the main growth stages in the Cangwubang citrus-producing area of Yichang, providing practical support for irrigation scheduling and water resource management at the regional scale. This multi-source fusion approach offers effective technical support for precision irrigation control in agriculture and holds broad application prospects. Full article

With global warming, the increasing frequency of drought events threatens the stability of ecosystems, so the development of a rational ecological drought monitoring and assessment model is urgently needed. In this study, an evapotranspiration deficit (ED) was added for the first time into the construction of an ecological drought index. Considering atmospheric water deficit (WD), soil moisture (SM) and runoff (RF), both the Copula method and a nonparametric method were used to construct a multivariate comprehensive drought index (MCDI) to monitor ecological drought. The MCDI was evaluated using Pearson, actual drought validation, Theil–Sen, Mann–Kendall and ExtraTrees+SHAP methods, in order to assess differences between construction methods, analyze the drivers and sensitivities of ecological drought in Xinjiang, China, and specifically explore the role of ED in ecological drought. The results showed that (1) ED based on the ratio form is more suitable for capturing SM changes; (2) the performance of the composite drought index was improved in all aspects when cumulative effects were considered, and the ecological drought index based on the nonparametric method was superior to the index using the Copula method; (3) soil moisture was identified as the main contributor to ecological drought in Xinjiang, with the strongest synergistic effect occurring between SM and ED; and (4) the sensitivity of ecological drought to soil moisture within the arid regions increased nonlinearly along the decreasing SM gradient. In addition, the sensitivity to all drivers increased over time, with the largest increase observed for RF, followed by SM and then ED. The findings of this paper provide a useful reference for constructing a comprehensive drought index at the global scale, since the nonparametric method requires considerably fewer computational resources compared with the Copula method. In addition, the identified synergistic effect of ED and SM offers a new theoretical basis for ecological drought prevention and management in arid regions. Full article

The Surface Cool Island (SCI) refers to localized reductions in land surface temperature (LST) produced by features that enhance evapotranspiration, shading, and energy flux regulation. In arid urban areas, vegetated parks play a key role in mitigating heat through these mechanisms. This study evaluates how park vegetation structure and spatial configuration influence SCI intensity (ΔTmax) and extent (Lmax) using multi-seasonal, day–night satellite observations in Mexicali, Mexico. A total of 435 parks were analyzed using Landsat 8/9 TIRS (30 m) for LST and Sentinel-2 MSI (10 m) for vegetation mapping via NDVI thresholding and supervised random forest (RF) classification. On average, parks lowered daytime LST by 0.81 °C (max: 6.41 °C), with a mean Lmax of 120 m; nighttime cooling was weaker (avg. ΔTmax: 0.37 °C; Lmax: 48 m). RF-derived metrics explained SCI variability more effectively (R² up to 0.64 for ΔTmax; 0.48 for Lmax) than NDVI-based metrics (R² < 0.35), highlighting the value of object-based land cover classification in capturing vegetation structure. This remote sensing framework offers a scalable method for assessing urban cooling performance and supports climate-adaptive green space design in hot-arid cities. Full article

Accurate estimation of daily reference evapotranspiration (ET_o) is crucial for sustainable water resource management and irrigation scheduling, especially in water-scarce regions like Arizona. The standardized Penman–Monteith (PM) method is costly and requires specialized instruments and expertise, making it generally impractical for commercial growers. This study developed 35 ET_o models to predict daily ET_o across Coolidge, Maricopa, and Queen Creek in Pinal County, Arizona. Seven input combinations of daily meteorological variables were used for training and testing five machine learning (ML) models: Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Support Vector Machine (SVM). Four statistical indicators, coefficient of determination (R²), the normalized root-mean-squared error (RMSE_n), mean absolute error (MAE), and simulation error (S_e), were used to evaluate the ML models’ performance in comparison with the FAO-56 PM standardized method. The SHapley Additive exPlanations (SHAP) method was used to interpret each meteorological variable’s contribution to the model predictions. Overall, the 35 ET_o-developed models showed an excellent to fair performance in predicting daily ET_o over the three weather stations. Employing ANN10, RF10, XGBoost10, CatBoost10, and SVM10, incorporating all ten meteorological variables, yielded the highest accuracies during training and testing periods (0.994 ≤ R² ≤ 1.0, 0.729 ≤ RMSE_n ≤ 3.662, 0.030 ≤ MAE ≤ 0.181 mm·day⁻¹, and 0.833 ≤ S_e ≤ 2.295). Excluding meteorological variables caused a gradual decline in ET-developed models’ performance across the stations. However, 3-variable models using only maximum, minimum, and average temperatures (T_max, T_min, and T_ave) predicted ET_o well across the three stations during testing (17.655 ≤ RMSE_n ≤ 13.469 and S_e ≤ 15.45%). Results highlighted that T_max, solar radiation (R_s), and wind speed at 2 m height (U₂) are the most influential factors affecting ET_o at the central Arizona sites, followed by extraterrestrial solar radiation (R_a) and T_ave. In contrast, humidity-related variables (RH_min, RH_max, and RH_ave), along with T_min and precipitation (P_r), had minimal impact on the model’s predictions. The results are informative for assisting growers and policymakers in developing effective water management strategies, especially for arid regions like central Arizona. Full article

The measurement of available water for agricultural plants is a crucial parameter for farmers, particularly to plan irrigation. However, an area-wide measurement is often not trivial as there are several inputs and outputs of water into the system. Here, we present a high-resolution, remote sensing-based water balance model for starch potato cultivation, combining multispectral ground station data with UAV and satellite imagery. Over a three-year period (2021–2023), data from Arable Mark 2 ground stations, DJI Phantom 4 MS drones, PlanetScope satellites, and Sentinel-2 satellites were collected in Mecklenburg–Western Pomerania, Germany. The model utilizes NDVI-based crop coefficients (R² = 0.999) to estimate evapotranspiration and integrates on-farm irrigation and precipitation data for precise water balance calculations. A correlation with reference NDVI observations by Arable Mark 2 systems can be shown for UAV (R² = 0.94), PlanetScope satellite data (R² = 0.94), and Sentinel-2 satellite data (R² = 0.93). We demonstrate the model’s ability to capture intra-site heterogeneity on a precision farming scale. Our spatially comprehensive model enables farmers to optimize irrigation strategies, reducing water and energy use. Although the results are based on sprinkler irrigation, the model remains adaptable for advanced irrigation methods such as drip and subsurface systems. Full article

Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and 7 days ahead) and mid-term (14 and 30 days ahead) forecasts of SM at surface (0–10 cm) and subsurface (10–40 and 40–100 cm) soil layers across the contiguous U.S. The model was trained with five-year period (2018–2022) datasets including Soil Moisture Active Passive (SMAP) level 3 ancillary covariables, North American Land Data Assimilation System phase 2 (NLDAS-2) SM product, shortwave infrared reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS), and terrain features (e.g., elevation, slope, curvature), as well as soil texture and bulk density maps from the Soil Landscape of the United States (SOLUS100) database. To develop and evaluate the model, the dataset was divided into three subsets: training (January 2018–January 2021), validation (2021), and testing (2022). The outputs were validated with observed in situ data from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) soil moisture networks. The results indicated that the accuracy of SM forecasts decreased with increasing lead time, particularly in the surface (0–10 cm) and subsurface (10–40 cm) layers, where strong fluctuations driven by rainfall variability and evapotranspiration fluxes introduced greater uncertainty. Across all soil layers and lead times, the model achieved a median unbiased root mean square error (ubRMSE) of 0.04 cm³ cm⁻³ with a Pearson correlation coefficient of 0.61. Further, the performance of the model was evaluated with respect to both land cover and soil texture databases. Forecast accuracy was highest in coarse-textured soils, followed by medium- and fine-textured soils, likely because the greater penetration depth of microwave observations improves SM retrieval in sandy soils. Among land cover types, performance was strongest in grasslands and savannas and weakest in dense forests and shrublands, where dense vegetation attenuates the microwave signal and reduces SM estimation accuracy. These results demonstrate that the ConvLSTM framework provides skillful short- and mid-term forecasts of surface and subsurface soil moisture, offering valuable support for large-scale drought and flood monitoring. Full article

According to United Nations projections, the global population is expected to reach 9.7 billion by 2050, with 70% residing in urban areas, while arable land availability continues to decline. Vertical farming (VF) offers a promising pathway for sustainable urban food production by utilizing vertical space and controlled environments. Among emerging approaches, the adaptive vertical farm (AVF) introduces movable shelving systems that adjust to plant growth stages, allowing a higher number of cultivation shelves to be accommodated within the same rack height. In this study, we developed a computational model to quantify and compare the energy consumption of AVF and conventional VF systems under industrial-scale conditions. The reference scenario considered 272 multilevel racks, each hosting 8 shelves in the VF and 15 shelves in the AVF, with Lactuca sativa as the test crop. Energy consumption for thermohygrometric control and lighting was estimated under different sowing schedules, with crop growth dynamics simulated using scheduling algorithms. Plant heat loads were calculated through the Penman–Monteith model, enabling a robust estimation of evapotranspiration and its impact on indoor climate control. Simulation results show that the AVF achieves an average 22% reduction in specific energy consumption for climate control compared to the VF, independently of sowing strategies. Moreover, the AVF nearly doubles the number of cultivation shelves within the same footprint, increasing the cultivable surface area by over 400% compared to traditional flat indoor systems. This work provides the first quantitative assessment of AVF energy performance, demonstrating its potential to simultaneously improve land-use efficiency and reduce energy intensity, thereby supporting the sustainable integration of vertical farming in urban food systems. Full article

Both climate change and human activities have had a significant impact on hydrological processes. Quantification of affecting factors on river regime changes is scientifically essential for understanding hydrological processes and sustainable water resources management in the basins. This study investigates the features of variations in meteorological and hydrological variables in the Liuhe River Basin (LRB) from 1956 to 2020 based on various observed records and statistical methods. It then quantitatively identifies the possible impacts of climate variability and human activities on runoff in the LRB using the empirical methods and the Budyko framework. The results show that (1) the runoff demonstrates a significantly decreasing trend over the past 65 years, but the rainfall has no obvious trend with significant interannual fluctuations, and potential evapotranspiration exhibits a weekly decreasing trend, particularly in summer. (2) The runoff series can be divided into two periods, i.e., the baseline (1956–1969) and change (1970–2020) periods, and the change period can also be divided into two stages, i.e., stage I (1970–1999) and stage II (2000–2020). (3) Human activities are the dominant factors in the runoff decline in the LRB, with the contribution rates being greater than 80% in the change period, particularly for stage II. The analysis of this study can provide a reference for the rational utilization of water resources in the LRB. Full article

Crop aeroponic cultivation still faces issues such as insufficient precision in water supply control and scientifically-based irrigation scheduling. To address this challenge, the present study aims to establish a precision irrigation protocol adapted to the characteristics of crop aeroponic cultivation. Using coriander (Coriandrum sativum L.) as the experimental subject, crop water requirements were estimated utilizing both the FAO56 P-M equation and its revised form. The RMSE between the water requirement measured values and the calculated values using the P-M formula is 2.12 mm, the MAE is 2.0 mm, and the MAPE is 14.29%. The RMSE between the water requirement measured values and the calculated values using the revised P-M formula is 0.88 mm, the MAE is 0.82 mm, and the MAPE is 5.78%. The results indicate that the water requirement values calculated using the revised P-M formula are closer to the measured values. For model development, this study used coriander evapotranspiration as a basis. Major environmental variables influencing water requirement were selected as input features, and the daily reference water requirement served as the output. Three modeling approaches were implemented: Random Forest (RF), Bagging, and M5P Model Tree algorithms. The results indicate that, in comparing various input combinations (C1: air temperature, relative humidity, atmospheric pressure, wind speed, radiation, photoperiod; C2: air temperature, relative humidity, wind speed, radiation; C3: air temperature, relative humidity, radiation), the RF model based on C1 input demonstrated superior performance with RMSE = 0.121 mm/d, MAE = 0.134 mm/d, MAPE = 2.123%, and R² = 0.971. It significantly outperforms the RF models with other input combinations, as well as the Bagging and M5P models across all input scenarios, in terms of convergence rate, determination coefficient, and comprehensive performance. Its predictions aligned more closely with observed data, showing enhanced accuracy and adaptability. This optimized prediction model demonstrates particular suitability for forecasting water requirements in aeroponic coriander production and provides theoretical support for efficient, intelligent water-saving management in crop aeroponic cultivation. Full article

Identifying the impact of climate change and changes in underlying surface conditions on river runoff changes is critical for sustainable water resource use and watershed management in arid regions. The Kriya River is not only a key support for water resources in the arid environment of the Tarim Basin, but also a solid foundation for the survival and development of agricultural oases. In this study, the Kriya River Basin in Xinjiang, China, was taken as the research object, and the Mann–Kendall, Sen’s Slope, Cumulative Sum, and other methods were used to systematically analyze the temporal evolution law and multi-modal characteristics of runoff in the basin. Based on the Budyko hydrothermal coupling equilibrium equation, the contribution of temperature, evaporation, and the underlying surface to runoff variation was quantitatively interpreted. The study found that the annual runoff depth of the Kriya River Basin has shown a significant positive evolution trend in the past 60 years, with an increase rate of 0.5189 mm/a (p ≤ 0.01). Through the identification of mutation points, the runoff time series of the Kriya River was divided into the base period 1957–1999 and the change period 2000–2015. Without considering the supply of snowmelt runoff, the contribution rate of precipitation to runoff change was 75.23%, followed by the change in underlying surface (23.08%), and the potential evapotranspiration was only 1.69%. The results of this study provide a good scientific reference for water resources management and environmental governance in the Kriya River Basin. Full article

Evapotranspiration (ET) variation is typically influenced by climatic factors, which are considered the primary drivers of agricultural water requirements. Any changes in ET rates directly affect crop water demands. In this study, temporal trends and magnitudes of key climatic variables, and their impacts on reference evapotranspiration (ETo) during 1981–2020, were evaluated across 36 districts of Punjab, Pakistan. Positive serial correlations, ranging from 0.29 to 0.48, were identified and removed using the pre-whitening technique. Increasing trends in maximum temperature (Tmax) and wind speed (WS) across Punjab and its subregions were observed, while relative humidity (RH) exhibited both increasing and decreasing trends. No significant trends were detected for the minimum temperature (Tmin). On a monthly scale, in the Southern Punjab (SP) region, Sen’s slope estimated an increase in ETo, ranging from 0.239 mm/year in November to 0.636 mm/year in May, at a significance level of α = 0.05 (5%). At the provincial scale, significant upward trends in ETo were observed for the annual, Kharif, and autumn seasons, with Z-values of 2.04, 2.16, and 3.13, respectively, at α = 0.05 and 0.01. It was determined that, on an annual scale in Punjab, ETo sensitivity to climatic parameters followed the following order: Tmax > wind speed (WS) > Tmin > RH. The best-fitted models for Tmax, Tmin, WS, and RH were Gaussian, exponential, and spherical. ETo was found to increase spatially from North to South Punjab, with an approximate rise of 70–80 mm/decade. The results provide a scientific basis for understanding hydroclimatic drivers of ETo in semi-arid regions and contribute to improving climate impact assessments on agricultural water use. The observed ETo increases, particularly in South Punjab and lower Central Punjab, highlight the need for region-specific irrigation scheduling and water allocation. These findings can guide cropping calendars, improve irrigation efficiency, and increase canal water supplies to high-ETo areas, supporting adaptive strategies against climate variability in Punjab. Full article

Agricultural land consolidation (ALC) is a critical instrument for protecting the environment and expanding cropland. However, implementing different consolidation methods, scales, and technologies may have adverse effects on ecological and environmental factors. The ecological effects of ALC are evaluated in this investigation, with the Manas River Basin in China as the research object. Initially, the research examined the changes in land use that occurred during various periods of ALC in the basin using land cover data (CLCD). Secondly, an enhanced water benefit-based ecological index (SWBEI) for arid regions was developed using the Google Earth Engine (GEE) platform. The spatiotemporal variations in ecological environment quality (EEQ) during various ALC periods were analysed. Ultimately, the effects of a variety of factors on EEQ were disclosed. The research results show that: (1) The principal land-use types in the Manas River Basin are barren land, grassland, and cropland, with substantial fluctuations in area. Cropland area is increasing, with the majority being converted from grassland and desolate land. During the initial phase of farmland consolidation, the most rapid growth was observed, with expansion occurring both inward and outward from existing cropland. (2) The SWBEI outperforms the water benefit-based ecological index (WBEI) in arid regions. (3) The EEQ of the basin and cropland typically exhibits an “increasing–decreasing–increasing trend”, with deterioration predominantly occurring during early-stage ALC and a gradual improvement in EEQ during late-stage ALC. The Gobi Desert belt at the foothills of mountains and high-altitude frigid regions exhibit a deteriorating trend in the EEQ, whereas the oasis areas in the middle reaches of the basin exhibit an improving trend. (4) The most significant explanatory power for the basin’s EEQ is attributed to climate factors, followed by topographic factors, hydrological factors, and human factors. The influence of human factors and hydrological factors on the basin’s EEQ is increasing. The primary factors that influence the EEQ of a basin are the actual evapotranspiration, temperature, and elevation. The explanatory power of these two factors for the basin’s EEQ is augmented by their interaction. In the long term, ALC helps improve the EEQ of the basin and cropland. This study provides a reference for improving ALC methods and approaches, enhancing the ecological environment of river basins, and balancing agricultural production efficiency. Full article

Accurate estimations of reference evapotranspiration (ETo) are critical for hydrologic studies, efficient crop irrigation, water resources management and sustainable development. The evaluation of an empirical method was carried out to estimate hourly ETo, utilizing short-wave radiation and relative humidity as a surrogate of vapor pressure deficit (VPD), calibrated under semi-arid conditions and validated for different climatic regimes (hyper-arid, arid, subhumid) using American Society of Civil Engineers Penman–Monteith (ASCE PM) (2005) values as a standard, for the state of California. For hyper-arid climatic conditions, the empirical method resulted in underestimation and had coefficient of determination (R²) values of 0.88–0.95 and root mean square error (RMSE) values of 0.062–0.115 mm h⁻¹. Hyper-arid climatic conditions correspond to lower R² and different relations between the vapor pressure deficit (VPD) and the relative humidity function (1/lnRH) that the empirical method utilizes. For the other climatic regimes (arid, semi-arid, subhumid), the empirical method performed satisfactorily. The RMSE was calculated for groups of empirical estimates corresponding to various wind velocity values, and it was satisfactory for >99% of wind speed values (u₂). The RMSE was also calculated for grouped values of the estimates of the empirical method corresponding to observed VPDs and was satisfactory for >97% of all observed values of VPD, except for hyper-arid stations (59% of u₂ and 60% of all observed values of VPD). Full article

Reference crop evapotranspiration (ETo) is a crucial component in calculating crop water requirements, and its accurate prediction is vital for effective agricultural water management and irrigation planning. Generally, the FAO Penman-Monteith 56 equation is recommended as the benchmark’s method for calculating Eto, but it requires extensive meteorological data—posing challenges in regions with sparse monitoring infrastructure. This review addresses a critical gap: the lack of systematic comparative analysis of machine learning (ML) methods for ETo estimation under data-limited conditions. We review 325 studies searched by Web of Science from 2001 to 2024, focusing on applications of machine learning models in ETo modeling and prediction. Then, this review evaluates these models regarding their characteristics, accuracy, and applicability, including artificial neural networks (ANN), support vector machines (SVM), ensemble learning (EL), and deep learning (DL). Crucially, EL models demonstrate superior stability and cost-effectiveness, with typical performance metrics of R² > 0.95 and RMSE ranging from 0.1 to 0.6 mm·d⁻¹. Notably, DL methods achieve the highest accuracy under conditions of data scarcity. Using only temperature data, they attain competitive performance (R² = 0.81, RMSE = 0.56 mm·d⁻¹). Additionally, we further synthesize optimal input variables, performance metrics, and domain-specific implementation guidelines. In summary, this study provides a comprehensive and up-to-date overview of machine learning methods for ETo modeling, thereby offering valuable insights for researchers in the field of evapotranspiration. Full article