Search Results (125)

Accurate evapotranspiration (ET) estimation is critical for sustainable water management in arid environments. This study estimates actual ET over the Al-Hofuf region, Al-Ahsa Oasis, Saudi Arabia, during 2024 using a cloud-based remote sensing approach. Landsat 9 Level-2 imagery was combined with ERA5-Land meteorological data to quantify spatial and temporal ET variations across a 25 km buffer. Vegetation dynamics were characterized using the Normalized Difference Vegetation Index (NDVI) to derive crop coefficients (Kc) within a Kc–ET₀ framework, where reference ET (ET₀) was obtained from ERA5-Land potential evaporation. All processing utilized Python (Version 3.14) on Google Colab and Google Earth Engine for scalable computation. Eighty-eight cloud-free Landsat 9 scenes were processed following cloud and shadow masking. Mean NDVI, Kc, and daily ET values were compiled into a comprehensive time-series dataset. Model performance was evaluated through cross-validation with MODIS MOD16A2 and internal consistency checks, demonstrating strong statistical agreement (R² = 0.82, NSE = 0.71, PBIAS = +8.3%). Results revealed pronounced seasonal variability closely linked to vegetation activity and atmospheric demand, with peak ET occurring during summer months (June–July: 7.2–7.5 mm day⁻¹) and minima in winter (January–February: 2.0–2.6 mm day⁻¹). Findings demonstrate that cloud-based techniques provide reliable, cost-effective ET monitoring in data-scarce, groundwater-dependent regions. Validation confirms Kc-ET₀ estimates reliably capture spatial and temporal patterns, supporting practical irrigation management applications. This approach aids precision irrigation and long-term water sustainability planning in Al-Hofuf, contributing significantly to national water conservation objectives under Saudi Arabia’s Vision 2030 and National Water Strategy. Full article

Evapotranspiration (ET) is a fundamental process within the water cycle and the agricultural water balance, optimizing resource allocation, maintaining soil health, and enhancing ecosystem resilience to climate change. Because ET represents a primary consumptive use of irrigation on agricultural lands, enhancing water-use efficiency and sustainable water management requires accurate estimation of evapotranspiration to support long-term sustainability and productivity. This study offers an effective means to visualize spatial and temporal patterns of reference evapotranspiration (ETo) across various vegetation management practices. This study examined the impacts of agroforestry buffers (ABs), grass buffers (GBs), biofuel crops in an agroforestry watershed (BCa), and biofuel crops in a grass buffer watershed (BCg) on ETo, compared to a corn (Zea mays L.)–soybean (Glycine max L.) rotation (RC) for claypan soil in Northern Missouri, USA. The experimental watersheds were located at the Greenley Memorial Research Center, Missouri, USA. Campbell Scientific sensors and Photosynthetically Active Radiation (PAR) smart sensors were installed to measure net radiation, anemometers, humidity, and air temperature. All instruments were mounted on masts at a height of 2 m above ground level in crop, tree, grass, and biofuel areas. Measured meteorological data were recorded hourly from April to October during 2017 and 2018. Daily ETo predictions were calculated using the Penman–Monteith model. These ETo predictions were displayed across the landscape using Python-based GIS for selected dates (each Saturday) for the watersheds. The methodology was implemented using the software programs of Python 2.7.10 and ArcGIS 10.3.1. The results indicated that ETo increased by 11%, 17%, 18%, and 25% in 2017, and by 7%, 9%, 14%, and 20% in 2018 for AB, BCa, BCg, and GB, respectively, compared to RC management. This process may improve soil water recharge in perennial management systems. Accurate estimation of ET in agricultural regions is critical for understanding water balance, hydrological and ecosystem processes, and climate variability. Given that agriculture constitutes the majority of global water consumption, precise ET estimation is particularly significant for sustainable water management, especially in regions experiencing water scarcity. These outcomes may support effective planning and management of agricultural water resources by enabling optimized irrigation and agricultural production. Full article

Integrating semi-transparent photovoltaics (STPVs) into greenhouses offers a dual-use solution for land efficiency, although matching electricity generation with crop spectral needs remains a challenge. To address this, this study assesses the optical and microclimatic impact of Cadmium Telluride (CdTe, 50% transparency) and amorphous Silicon (a-Si, 20%) technologies compared to a conventional control in a semi-arid Mediterranean climate. Spectral analysis revealed that CdTe aligned with chlorophyll absorption peaks, preserving a transparency window that yielded a 66% relative gain in biologically useful radiation over the blue-blocking a-Si. Furthermore, while both technologies significantly reduced Photosynthetically Active Radiation (PAR), this shading served as a protective filter against supra-optimal irradiance, stabilizing the internal microclimate. In the control prototype, extreme vapour pressure deficits (VPDs approaching 9.0 kPa) drove maximum reference evapotranspiration (ET₀) above 4.6 mm/day. In contrast, the STPV systems effectively capped ET₀ at approximately 3.09 mm/day (CdTe) and 1.64 mm/day (a-Si) through their radiative attenuation, despite internal VPDs still reaching 6.5–7.0 kPa during peak summer. This decoupling resulted in drastic average ET₀ reductions of 31.4% and 61.3%, respectively, while mitigating soil overheating by up to 17.8%. These findings demonstrate that specific STPV technologies transcend mere shading to function as passive climate resilience tools, naturally enforcing water conservation and physically disarming atmospheric aridity in high-radiation environments. Full article

Against the backdrop of ongoing climate change, the Qinghai–Tibet Plateau, a region highly sensitive to climatic variation, exhibits intricate spatiotemporal patterns in reference crop evapotranspiration (ET_O), with significant implications for regional water-resource planning. This study selected four agro-climatic zones across the plateau region (TSA, TSH, TAZ, and WCH). Long-term daily observations from 28 meteorological stations were used to estimate ET_O via the FAO 56 Penman–Monteith equation. This extensive dataset enabled robust trend analysis using the Mann–Kendall test, alongside a cloud-model framework, and analyses of sensitivity and contributions to evaluate ET_O’s spatiotemporal evolution, its distributional uncertainty, and the underlying drivers. Results reveal pronounced regional heterogeneity in the interannual variability of ET_O. Annual ET_O declined in TSH and TSA (trend rates of −1.12 and −6.58 mm·10a⁻¹, respectively) and increased in TAZ and WCH (15.76 and 10.74 mm·10a⁻¹, respectively). At monthly and seasonal timescales, ET_O exhibited an unimodal pattern, with the greatest stability in winter and spring and lower stability in summer and autumn. The cloud-model parameter He indicates that ET_O stability is greatest in TSH and weakest in WCH, with He values of 7.15 and 12.29 mm, respectively. Contribution-rate analyses identify T_max and T_mean as the principal determinants of rising ET_O across all study zones, reflecting the largest individual contributions. Temperature-related factors together account for the majority of ET_O variability across the regions, with their absolute contributions ranging from 5.61% to 8.63%, well above those of aerodynamic factors (0.62–1.78%). Stability assessments indicate that ET_O is generally more unstable than its meteorological drivers, with substantial regional disparities, implying that ET_O evolution cannot be explained by a single controlling factor. Overall, the study characterizes the uncertainty in ET_O variations under complex terrain, highlights the value of the cloud model for refined hydrological assessments, and provides a scientific basis for adaptive agricultural water-resource management in the region. Full article

Accurate and reliable estimation of reference crop evapotranspiration (ET₀) in the North Henan Plain is crucial for agricultural water resource management, production, and food supply in China. This study aims to evaluate the performance of deep learning (DL) methods in ET₀ estimation and assess the applicability of the developed DL model beyond the training domain. This study utilized historical meteorological data from Zhengzhou City, northern Henan, spanning 2010–2024. Meteorological variables were selected through correlation analysis and maximum information coefficient (MIC). A novel DL model—the TCN-Attention model (TA)—was constructed by incorporating a self-attention mechanism into the temporal convolutional network (TCN) model. This model was compared with two classical DL models—Long Short-Term Memory (LSTM) and TCN. Results indicate: (1) Sunshine duration ($n$), relative humidity ($\mathrm{R}\mathrm{H}$), and maximum temperature (T_max) are the three most significant features influencing summer maize evapotranspiration; (2) prediction accuracy under the same input scenarios: TA model > TCN model > LSTM model; (3) in scenarios where only temperature data is input, the TA model has the highest prediction accuracy, surpassing the H-S empirical method; and (4) for limited meteorological data, the combination of temperature and humidity was found to be most effective, showing good adaptability and accuracy at different time steps (hourly: R² = 0.982; daily: R² = 0.975; weekly: R² = 0.928). This study highlights the potential of the TA model for estimating reference crop evapotranspiration in the northern Henan Plain, which may provide theoretical guidance for crop irrigation management under future climate change. 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

Accurately estimating reference crop evapotranspiration (ET₀) is essential for agricultural water-resource management, yet the traditional Penman–Monteith (PM) method requires multiple meteorological variables and is difficult to apply in data-sparse regions. To explore more data-efficient alternatives, this study systematically evaluates several machine-learning (ML) models capable of capturing nonlinear relationships, using daily observations from 698 meteorological stations across China. In addition, we incorporate SHapley Additive exPlanation (SHAP), a game-theory-based interpretability approach, to quantify the contribution of input variables at both national and regional scales. The results show that the Random Forest (RF) model performs best (coefficient of determination, R² = 0.957; mean absolute percentage error, MAPE = 9.214%), significantly outperforming multiple linear regression and approaching the accuracy of the PM method. SHAP analysis indicates that maximum temperature, sunshine duration, and month are the most influential factors nationwide. Geographic variables contribute less overall but become important in specific regions, such as Southwest China. The study also reveals pronounced spatial heterogeneity in the drivers of ET₀, highlighting the necessity of regionalized interpretations. Furthermore, sensor-reduction experiments demonstrate that reasonable estimation accuracy can be maintained even without radiation or wind-speed observations, offering guidance for low-cost monitoring scenarios. Overall, this study provides transparent model comparisons for ML-based ET₀ estimation, uncovers regional differences in controlling factors, and offers practical insights for designing meteorological monitoring strategies in data-limited environments. Full article

Reference crop evapotranspiration (ET₀) is crucial for water management. Although the FAO56-PM method is widely used to estimate ET₀, its input variables of solar radiation (R_s) and net longwave radiation (R_nl) are not readily available. Currently, mere calibration of the formula for R_s is assumed to be effective in improving FAO56-PM’s performance. To test this hypothesis, all input variables for FAO56-PM were measured in Lhasa, Linzhi, and Bange over the Tibetan Plateau (TP) to assess how different calculation methods affect ET₀ estimates. Compared to the original FAO56-PM, calibration of both R_s and R_nl formulas yielded the best model performance. Mere calibration of the formula for R_nl notably improved ET₀ estimation accuracy, while mere calibration of the formula for R_s reduced its accuracy. The general calibration model was slightly less effective than calibration of both R_s and R_nl formulas but obviously outperformed the original FAO56-PM. This model showed that ET₀ increased from east to west, ranging from 569.4 mm/year to 1118.5 mm/year. Trend analysis indicated rapid increases in ET₀ in the eastern region and significant decreases in the western region of the TP over recent decades. The findings are useful for the regional application of FAO56-PM to achieve sustainable development in Tibet. Full article

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

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

The Saiss plain in northern Morocco covers an area of 2300 km² and is one of the main agricultural contributors to the national economy. However, climate change and water scarcity reduce the region’s agricultural yields. Conventional methods of estimating evapotranspiration (ET) provide localized results but cannot capture regional-scale variations. This study aims to estimate the spatiotemporal evolution of daily crop ET (olives, fruit trees, cereals, and vegetables) across the Saiss plain. The METRIC model was adapted for the region using Landsat 8 data and was calibrated and validated using in situ flux tower measurements. The methodology employed an energy balance approach to calculate ET as a residual of net radiation, soil heat flux, and sensible heat flux by using hot and cold pixels for calibration. METRIC-ET ranged from 0.1 to 11 mm/day, demonstrating strong agreement with reference ET (R² = 0.76, RMSE = 1, MAE = 0.78) and outperforming MODIS-ET in accuracy and spatial resolution. Olives and fruit trees showed higher ET values compared to vegetables and cereals. The results indicated a significant impact of ET on water availability, with spatiotemporal patterns being influenced by vegetation cover, climate, and water resources. This study could support the development of adaptive agricultural strategies. Full article

The planting area of the maize–soybean strip intercropping system has been increasing annually in the Hexi Corridor oasis irrigation area of China. However, long-term irrational water resource utilization and the excessive mono-application of fertilizers have led to significantly low water and nitrogen use efficiency in this cropping system. To explore the sustainable production model of high yield and high water–nitrogen productivity in maize–soybean strip intercropping, we established three irrigation levels (low: 60%, medium: 80%, and sufficient: 100% of reference crop evapotranspiration) and three nitrogen application levels (low: maize 230 kg ha⁻¹, soybean 29 kg ha⁻¹; medium: maize 340 kg ha⁻¹, soybean 57 kg ha⁻¹; and high: maize 450 kg ha⁻¹, soybean 85 kg ha⁻¹) for maize and soybean, respectively. Three irrigation levels without nitrogen application served as controls. The effects of different water–nitrogen combinations on multiple indicators of the maize–soybean strip intercropping system, including yield, water–nitrogen productivity, and quality, were analyzed. The results showed that the irrigation amount and nitrogen application rate significantly affected the kernel quality of maize. Specifically, the medium nitrogen and sufficient water (N2W3) combination achieved optimal performance in crude fat, starch, and bulk density. However, excessive irrigation and nitrogen application led to a reduction in the content of lysine and crude protein in maize, as well as crude fat and crude starch in soybean. Appropriate irrigation and nitrogen application significantly increased the yield in the maize–soybean strip intercropping system, in which the N2W3 treatment had the highest yield, with maize and soybean yields reaching 14007.02 and 2025.39 kg ha⁻¹, respectively, which increased by 2.52% to 138.85% and 5.37% to 191.44% compared with the other treatments. Taking into account the growing environment of the oasis agricultural area in the Hexi Corridor and the effects of different water and nitrogen supplies on the yield, water–nitrogen productivity, and kernel quality of maize and soybeans in the strip intercropping system, the highest target yield can be achieved when the irrigation quotas for maize and soybeans are set at 100% ET0 (reference crop evapotranspiration), with nitrogen application rates of 354.78~422.51 kg ha⁻¹ and 60.27~71.81 kg ha⁻¹, respectively. This provides guidance for enhancing yield and quality in maize–soybean strip intercropping in the oasis agricultural area of the Hexi Corridor, achieving the dual objectives of high yield and superior quality. 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

Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management. Full article

Drip irrigation technology, known for its advantages in high water use efficiency and yield increase, has been a focal point of research regarding its combined effects with the plastic film-mulching technique on field water consumption and crop growth. To accurately quantify the water-saving effect of plastic film-mulching techniques and investigate the mechanisms of mulching on evaporation (E) and transpiration (T), this study was conducted on soybean using the Bowen ratio–energy balance system and micro-lysimeters as the observation means and the MSW model as the data partitioning tool, during 2019–2021 in arid northwest China. We compared evapotranspiration (ET) under the film-mulched drip irrigation (FM) and non-mulched drip irrigation (NM) treatments. The results show that ET, E, and T under FM were reduced by 32.6 mm, 76.1 mm, and −43.5 mm, respectively. Moreover, mulching increased the leaf area index (LAI) by 20.7%, soybean yield from 2727.0 kg ha⁻¹ to 3250.5 kg ha⁻¹, and WUE from 0.64 kg m⁻³ to 0.83 kg m⁻³ on average, which means mulching reduced water consumption in the field by decreasing soil evaporation and improved water use efficiency by promoting crop growth. Further analysis indicated that mulching has strengthened the connection between soil temperature and humidity and weakened the effect of soil temperature on soybean leaf growth. Soil water content (SWC) and LAI had a direct negative effect on E, with LAI causing a stronger effect on E under the FM treatment. Mulching has weakened the direct effect of SWC on T, so that only LAI and soil temperature had a significant direct positive effect on T. Following the rapid growth of soybean LAI, the isolating effect of the mulch was gradually replaced by the dense leaf canopy. The results provide a reference for further exploring the water-saving and yield-increasing benefits of plastic film-mulching techniques, and to facilitate wider promotion of the plastic film-mulching techniques and the water–fertilizer integration technology in arid regions. Full article