Search Results (1,304)

This study presents an integrated framework for agricultural drought monitoring in data-scarce regions, utilizing the Google Earth Engine (GEE) platform to analyze multisource Earth observation data over the South Wollo highlands, Ethiopia, from 2001 to 2024. The analysis was complemented by Mann–Kendall trend testing, Sen’s slope estimation, and Pettitt change-point detection to identify and quantify long-term trends and abrupt shifts in drought dynamics. The methodology integrates climatic and satellite-derived indicators within a hybrid analytical framework. It incorporates the standardized precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), vegetation health index (VHI), temperature condition index (TCI), and land surface temperature (LST), which are derived from MODIS (NDVI, LST, PET) and CHIRPS precipitation datasets. The analysis focused on the main growing season (June–September) to capture critical crop growth and moisture-sensitive periods for agricultural production in the study area. The findings reveal pronounced interannual variability in drought occurrence and intensity across the study period. Severe agricultural drought conditions were most extensive in 2009 and 2014, with VHIs indicating 15% and 4% of the area under severe and extreme drought in 2009, respectively, and 2.6% and 2% in 2014, respectively. In contrast, 2001, 2005, 2020, and particularly 2024 were characterized by predominantly no-drought to mild-drought conditions, with no-drought coverage increasing from 86.7% (2009) to 98.0% (2024). Vegetation-based indices demonstrate that drought impacts are episodic rather than persistent and strongly controlled by rainfall timing and early-season moisture availability. The LST exhibited marked year-to-year variability (28.8 °C to 33.8 °C), with elevated temperatures coinciding with drought periods and suppressed evaporative cooling. Correlation analysis confirmed a strong positive relationship between the SPEI and VHI (r = 0.77), with moderate correlations for the VCI (r = 0.40) and TCI (r = 0.36), underscoring the sensitivity of integrated vegetation health to the climatic water balance. The study concludes that combining the SPEI with satellite-derived vegetation and thermal indices provides a robust, scalable approach for agricultural drought assessment in regions with limited ground-based observations. The integrated framework effectively captures both moisture deficits and thermal stress components, offering a scientific basis for improving drought early warning systems and climate-resilient agricultural planning in Ethiopia and similar environments. Full article

Climate change and increasing water scarcity are intensifying pressure on irrigated agriculture, which currently represents 70% of global freshwater withdrawals. Remote sensing technologies have become essential tools for monitoring soil moisture, evapotranspiration, crop growth, and irrigation performance across multiple spatial and temporal levels. This review synthesizes 83 peer-reviewed studies published between 2002 and 2025, focusing on the use of optical, thermal, and microwave sensors to support irrigation water management under climate variability. The analysis highlights progress in multi-sensor integration, UAV-based monitoring, crop and agro-hydrological modeling, and emerging machine learning approaches that enhance irrigation scheduling, soil moisture estimation, and crop water stress detection. Despite these advancements, several methodological challenges persist, including data integration constraints, sensor-specific limitations, model transferability issues, insufficient ground validation, and difficulties in translating remote sensing outputs into operational decision support systems. In addition, structural gaps at the policy level restrict the evaluation of irrigation efficiency and climate resilience. This review aims to clarify current limitations and outline priority research directions to enhance the climate resilience and sustainability of irrigated agricultural systems. Full article

The use of sophisticated software for greenhouse microclimate analysis often requires advanced modelling expertise and significant computational effort, which may not always be available to greenhouse designers. This study proposes an integrated and modular workflow aimed at supporting greenhouse design through coupled thermal and evapotranspiration simulations. The design methodology is based on three steps. In the initial phase, the greenhouse environmental conditions are evaluated through the implementation of a dynamic thermal analysis, which is conducted by the DesignBuilder software (version 4.2). Subsequently, a plant evapotranspiration model is employed in MATLAB/Simulink (version R2025b) to evaluate crop transpiration, moisture production, and irrigation water consumption. In the final phase, the simulated moisture production is used to estimate the required ventilation rates and to support the sizing of greenhouse systems, including irrigation and HVAC components. Plant moisture production is a crucial factor in determining the sizing of greenhouse subsystems, such as the irrigation system, the ventilation rate, and the HVAC system. Nonetheless, the implementation of the evapotranspiration model necessitates a bespoke calibration to a case study. Indeed, the proposed models are more generally applicable and must be adapted to real-world applications. The methodology was applied to a small greenhouse used for the cultivation of aeroponic lettuce (Lactuca sativa cv. Romana) in a Mediterranean environment. The aim of the study was to explore the potential of the proposed integrated modelling framework to estimate annual irrigation water demand and the minimum ventilation rate required to mitigate excess moisture production, using a coupled MATLAB/Simulink implementation. The proposed approach should be interpreted as an exploratory design-support methodology rather than a fully validated predictive model, intended to investigate system behaviour under the specific conditions of the case study. Full article

Soil surface moisture (SSM) is a critical indicator of agricultural drought, yet high-resolution projections under climate change remain scarce. This study develops a machine learning framework to predict and project SSM at 1 km resolution across five European Living Labs (LLs), encompassing vineyards, olive groves, and fruit tree systems. Historical Sentinel-1 SSM observations (2014–2024) were used to train ensemble models (Random Forest, XGBoost, ExtraTrees, LightGBM) incorporating climate variables, soil texture, topography, and land use. Tree-based models achieved R² values of 0.63–0.87. Vineyards showed the highest predictability (R² ≈ 0.87), reflecting their sensitivity to short-term atmospheric demand and surface water availability, whereas olive groves were the least predictable (R² ≈ 0.63–0.68), consistent with deeper rooting systems and greater drought buffering capacity. When forced with bias-corrected CMIP6 projections under SSP1-2.6 and SSP5-8.5 for 2041–2070, models indicate minimal changes under SSP1-2.6 but pronounced SSM declines of 8–24% under SSP5-8.5, with historically wetter regions experiencing the largest absolute losses. SHAP analysis confirmed precipitation and potential evapotranspiration as dominant predictors across all crops. This framework provides spatially explicit, crop-relevant SSM projections to support climate adaptation in European agricultural landscapes. Full article

The La Mancha region (a semi-arid area of southeast Spain) hosts the world’s highest concentration of vineyards and is also one of the regions with the largest areas devoted to almond tree cultivation. Viticulture and nut fruit trees (mainly almonds) are one of the region’s principal sources of economic revenue. The Two-Source Energy Balance (TSEB) model can assist management of water resources. A simplified version of the TSEB approach (STSEB) was previously tested in a vineyard and almonds to estimate sensible heat (H) and latent heat (LE) fluxes using a parallel scheme method based on the Monin–Obukov similarity theory (MOST). This study introduces a method based on Surface Renewal (SR) theory to partition the sensible heat flux using low-frequency measurements as input. The latter was friendlier than the parallel MOST method under unstable conditions and than the series SR and MOST methods. The objective was to compare the MOST and SR models within a parallel scheme method. During the 2014 and 2015 growing season, measurements were collected in a 4 ha row crop drip-irrigated Tempranillo vineyard. Hourly sensible heat flux measured by an eddy covariance (EC) system and evapotranspiration (ET) registered by a 9 m² monolithic large weighting lysimeter were used as a reference. ET estimates were obtained as a residual of the energy balance equation (known as the residual method) using three methods for estimating sensible heat flux, H_SR, H_MOST and H_EC, yielding ET_SR-RE, ET_MOST-RE and ET_EC-RE, respectively. For sensible heat flux, the index of agreement (IA expressed in %) for 2014 and 2015 was 93% and 83%, respectively, using SR, and 84% and 78%, respectively, for MOST. This represents a 6–10% improvement using SR. For evapotranspiration, the ET_SR-RE and ET_MOST-RE IA showed similar performance in both years (around 88%), while ET_EC-RE yielded the best results (92% and 89% for 2014 and 2015, respectively). In addition, half-hourly EC fluxes, during the growing season of 2017, were used as a reference in an almond orchard. The SR sensible heat flux performed better (IA = 93%) than MOST (IA = 86%) in this case, whereas for the latent heat flux, the residual method performed the best, resulting in an IA of 81% for SR and of 78% for MOST. Overall, SR performed better than MOST, particularly under unstable conditions with wind speeds above 1 ms⁻¹. Full article

Climate change is expected to increase the frequency and severity of drought events in Europe, necessitating the identification of more water-efficient cropping systems. This study compared the evapotranspiration dynamics, water-use efficiency, and yield performance of maize (Zea mays L.) and grain sorghum (Sorghum bicolor L. Moench) under controlled field conditions using a Thornthwaite–Mather-type compensation evapotranspirometer. Three water regimes (100%, 50%, and 30% of optimal water supply) were applied during the reproductive stage, combined with weed-free and weed-infested treatments. Under moderate water deficit (50% water supply), grain sorghum maintained stable grain yield, while maize grain yield decreased by 17.98%. Under severe water deficit (30% water supply), grain yield reductions reached 36.04% in maize and 42.80% in sorghum. Grain sorghum consistently required less water and used 2.87–38.17% less water to produce 1 kg of grain compared to maize across treatments. Weed interference was associated with a lower yield and water-use efficiency in both species, while severe water deficit (70%) caused substantial declines in all measured parameters. Evapotranspiration was primarily driven by solar radiation and temperature, with reduced sensitivity under increasing water limitation. Overall, the results suggest that grain sorghum may represent a viable alternative to maize under moderate drought conditions; however, both crops require supplemental irrigation under severe water scarcity. The study highlights the importance of integrated weed management and provides novel insights into crop water-use dynamics under combined abiotic and biotic stress conditions. Full article

Amid growing water scarcity, assessing agricultural water consumption and crop water footprint has become increasingly critical. This study aims to assess the water footprint of crops within the Shu–Talas River Basin, disaggregated into green and blue components. Using meteorological data from the 2000–2024 period, reference evapotranspiration (ET₀) and actual crop evapotranspiration (ET_c) were calculated according to the FAO methodology. The water footprint (WF_green, WF_blue, and WF_quant) was determined based on crop evapotranspiration, effective precipitation, and crop yields for maize, sugar beet, sunflower, and potato. It was found that total water consumption during the growing season ranges from 650 to 950 mm, with the blue water share exceeding 80%, reflecting the high dependence of agricultural systems on irrigation. The minimum WF_quant values were observed in sugar beet, while the maximum WF_quant values were recorded for sunflower. The study identifies crop yield, rather than absolute water consumption, as the key factor in water footprint formation. These findings and established patterns can be utilized to optimize cropping patterns and support sustainable agricultural water management in arid regions. Full article

Freshwater lakes in Mediterranean regions are highly sensitive to climatic variability, particularly to droughts intensified by rising temperatures and increasing atmospheric evaporative demand. This study investigates drought variability and ecosystem responses in the Trichonida basin, the largest natural freshwater system in Greece, using an integrated approach that combines the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple time scales with satellite-derived Normalized Difference Vegetation Index (NDVI), Crop Water Stress Index (CWSI), and lake surface water temperature. SPEI analysis revealed increasingly recurrent and persistent drought conditions in recent years, especially at medium- and long-term scales. NDVI exhibited pronounced seasonal variability and a moderate long-term increase at the basin scale, largely associated with agricultural activity and irrigation practices, while sharp declines were observed during severe drought episodes. CWSI showed strong seasonal patterns characterized by recurrent summer water stress events, but no significant long-term trend. Correlation analysis indicated positive relationships between NDVI and SPEI at medium- to long-term time scales, and significant negative correlations between CWSI and SPEI at short and medium time scales. A strong relationship between NDVI and CWSI further suggests the sensitivity of vegetation greenness to water stress, particularly during summer and autumn. Lake surface water temperature exhibited seasonal warming trends that coincided with periods of increased vegetation water stress. Drought-related water risks arise for calcareous fens dominated by Cladium mariscus in the Lake Trichonida system, a habitat of high conservation value, whose productivity is strongly seasonally controlled and closely linked to thermal dynamics. Overall, the combined multi-indicator analysis provides valuable insights into drought impacts and seasonal ecosystem vulnerability in Mediterranean lake basin environments, highlighting the importance of integrated monitoring frameworks for sustainable freshwater ecosystem management under increasing climatic variability. Full article

Accurate hourly crop evapotranspiration (ETc) estimation is important for data-driven irrigation management support in tropical horticulture, yet existing approaches are constrained by data requirements and an inability to capture multi-scale temporal dynamics. This study proposes a Transformer encoder model for one-step-ahead hourly FAO-56 PM-derived ETc estimation in a durian orchard in Chanthaburi Province, Eastern Thailand, using 36,528 hourly meteorological observations obtained from the Visual Crossing Weather API for the orchard location over four years, with ETc computed from these inputs using the FAO-56 Penman–Monteith equation. The model employs a 168-h (7-day) look-back window, three stacked encoder blocks with multi-head self-attention ($h=8$, $d_{\mathrm{model}}=128$), and five meteorological input features (air temperature, relative humidity, solar radiation, wind speed, and ETc). A SARIMA$(2,1,2){(1,0,0)}_{24}$ model trained on the same dataset served as the statistical baseline. The Transformer achieved an RMSE of 0.0308 mm/h, MAE of 0.0188 mm/h, and $R^2$ of 0.9018 on the 168-h test set, outperforming SARIMA (RMSE = 0.0717, MAE = 0.0593, $R^2$ = 0.4688), representing a 57.0% reduction in RMSE, a 68.3% reduction in MAE, and a 92.4% improvement in $R^2$. The Transformer also achieved a daytime-only RMSE of 0.0414 mm/h vs. 0.0791 mm/h for SARIMA, and a daily cumulative ETc MAE of 0.1599 mm/day vs. 0.5901 mm/day, demonstrating superior accuracy during agronomically critical periods. The Transformer accurately reproduced both the 24-h diurnal cycle and the 7-day weekly pattern of ETc, whereas SARIMA exhibited a damped amplitude response. A recursive 168-h heuristic simulation demonstrated that the model generates physically plausible ETc patterns under an approximated meteorological scenario, suggesting the approach warrants further investigation as a component of future irrigation decision-support research. These results highlight the potential of Transformer-based deep learning for site-specific, proof-of-concept ETc estimation from meteorological inputs in tropical fruit production, pending validation across diverse sites and seasons. Full article

Winter wheat production in the extremely arid oasis region of Xinjiang relies heavily on irrigation and fertilization, but conventional high-input management can induce luxury transpiration and non-productive water consumption, limiting the coordinated improvement of grain yield, water use efficiency (WUE), and economic benefits. To identify the threshold at which water–fertilizer inputs shift from efficient use to inefficient water consumption and to define a robust management range, a two-year field experiment was conducted in southern Xinjiang during the 2022–2023 and 2023–2024 growing seasons. Four irrigation levels, corresponding to 60%, 80%, 100%, and 120% of crop evapotranspiration (ET_c), and four fertilization levels were established to evaluate the effects of water–fertilizer interactions on canopy development, leaf gas exchange, evapotranspiration, yield, WUE, and economic benefits. Appropriate water and nutrient supply promoted canopy establishment and maintained higher photosynthetic capacity, thereby increasing grain yield, WUE, and net return. However, excessive inputs weakened yield gains and failed to synchronously improve WUE and economic benefits. Linear plateau models revealed clear thresholds in both the crop-stand scale evapotranspiration (ET)–dry matter accumulation (DM) relationship and the leaf-scale transpiration rate (Tr)–net photosynthetic rate (Pn) relationship. The seasonal ET thresholds were 504.59 and 553.87 mm in the two growing seasons, respectively, and the Tr threshold was 4.83 mmol m⁻² s⁻¹. Beyond these thresholds, additional water consumption was not effectively converted into photosynthetic assimilation or biomass accumulation, indicating luxury transpiration. Year-specific response surface analysis and TOPSIS evaluation showed that I3F3, namely 100% ET_c combined with 210–195–75 kg ha⁻¹ N–P₂O₅–K₂O, together with its adjacent range, sustained high grain yield, WUE, and economic benefits, with I3F3 achieving the best overall performance in both years. The intersection of the two-year high-performance regions further defined a robust interannual feasible range with an irrigation amount of 506.21–545.09 mm and a total fertilizer input of 369.54–628.33 kg ha⁻¹. Overall, maintaining water and fertilizer inputs within the I3F3-adjacent range can reduce non-productive water consumption and luxury transpiration risk while synergistically improving grain yield, WUE, and economic benefits in winter wheat. Full article

Climate change is reshaping yield formation and water use in cold-region rice production through rising air temperatures, altered precipitation patterns, and increasing atmospheric CO₂ concentrations. However, the responses of yield, crop evapotranspiration (ET_c), and water use efficiency (WUE) to climate forcing and elevated CO₂ remain insufficiently quantified for cold-region rice systems in Northeast China. This study simulated changes in rice yield, ET_c and WUE during the 2030s–2090s relative to the 2000–2020 baseline period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios at 10 agro-meteorological stations in Heilongjiang Province. Simulations were conducted using the AquaCrop model driven by CMIP6 multi-model climate data, and the contribution of elevated CO₂ was quantified by comparing the rising-CO₂ and fixed-CO₂ treatments. The results showed that under SSP5-8.5, the maximum air temperature in the 2090s is projected to increase by 5~6 °C relative to the baseline period, while precipitation is projected to range from −10% to 20%. Compared with the fixed-CO₂ treatment, rice yield under the rising-CO₂ treatment is projected to increase by 18.70%. Although ET_c showed an overall increasing trend, rising CO₂ attenuated its increase. Under SSP5-8.5 in the 2090s, ET_c increased by only 2.70% under rising-CO₂ treatment, compared with 11.61% under fixed-CO₂ treatment. As a result of increased yield and ET_c, the WUE improved by 15.42% and 14.28% under SSP2-4.5 and SSP5-8.5, respectively, in the 2090s, whereas it remained below the baseline level under the scenarios without CO₂ effects. These findings indicate that rising CO₂ may enhance yield and moderate ET_c increases, thereby providing useful information for regional grain-yield assessment, agricultural water-resource evaluation, and climate-change adaptation planning. Full article

Agricultural producers need affordable tools to estimate reference evapotranspiration (ET₀) in field conditions, especially in regions with limited access to complete weather data. In this study, the ET₀ for an alfalfa (Medicago sativa) crop was estimated using the SEG-SRM-V1 electronic system, based on the surface renewal method and validated using the FAO-56 Penman–Monteith method. The estimated ET₀ values for alfalfa ranged from approximately 4.0 to 8.5 mm day⁻¹ under the prevailing climatic conditions: periods of high temperature (21 °C to 33 °C), as measured by the system in the experimental area, with cloud cover, wind (1 to 8 m/s) and net radiation of 664 W/m² to 910 W/m². Comparisons between the two methods yielded determination coefficients of between 0.65 and 0.85, and the values of the errors (MSE, RMSE and MAE) tend to 0, which indicates that the estimates of ET₀ measured by the system (SEG-SRM-V1) are close to those obtained using the Penman–Monteith method. Similarly to the performance of open-field systems operating under atmospheric and vegetation cover conditions, these results demonstrate that high-frequency (10 Hz) air temperature measurements provide sufficient physical information to support the estimation of ET₀ in alfalfa, while the open architecture of the SEG-SRM-V1 system allows for flexibility and scalability for irrigation management applications in other crop types. Full article

The temporal variations of evapotranspiration (ET) and its controlling factors occur across time scales ranging from seconds to decades, with significant differences in the lag effects of ET drivers under varying water conditions. Therefore, identifying the dominant time scales of the relationships between ET and its controlling factors under varying water conditions is crucial for optimizing irrigation strategies of crops grown in a greenhouse. In our study, we utilized two years of continuous lysimeter observations of greenhouse tomato ET, and applied two water treatments: well-irrigated (0.9E_pan, E_pan is the cumulative pan evaporation) and deficit-irrigated (0.5E_pan). Wavelet transform technology served as the core method to systematically examine the temporal variations of ET and its controlling factors. Observations indicated that the power spectra of ET featured pronounced peaks at daily and seasonal scales. The cospectra between ET and soil water content for greenhouse tomato revealed strong temporal correlation at 2~5 day scales, confirming the regulatory effect of irrigation cycles on ET. Moreover, ET variations were largely synchronous with net radiation, with ET lagging net radiation but leading vapor pressure deficit and air temperature at daily scales. In addition, significant disparities in phase angles between ET and individual meteorological variables were identified under 0.9E_pan and 0.5E_pan water conditions. Partial wavelet coherence revealed that net radiation was the primary meteorological driver of greenhouse tomato ET across multiple time scales, particularly at the daily scale, followed by vapor pressure deficit. These findings provide scientific evidence for selecting appropriate ET models at different time scales and offer valuable insights for optimizing water-saving irrigation for crops grown in greenhouses. Full article

Flash droughts, characterized by rapid onset and intensification, are occurring more frequently under global warming. Accurately identifying the frequency and hazard severity of flash droughts remains challenging, as they are influenced by multiple hydroclimatic drivers, including precipitation deficits, temperature increases, and soil moisture depletion. We developed a daily-scale Flash Drought Hazard Index (FDHI) by integrating the interactive effects of multiple driving factors, aiming to assess the spatiotemporal patterns of flash drought hazard in the Black Soil Region of Northeast China during the period 2000–2020. The FDHI employs the daily Standardized Precipitation Evapotranspiration Index, Standardized Soil Moisture Index, Standardized Soil Temperature Index, and Standardized Runoff Index to characterize short-term anomalies in multiple hydrometeorological variables. Results showed that flash droughts occurred most frequently in the southern part of the Black Soil Region of Northeast China, particularly in the Songnen Plain and the Liaohe Plain, with annual frequencies of 5.98 and 5.80 events, respectively. Flash drought severity in the Liaohe Plain exhibited a significant increasing trend during the past decade. Moreover, the dominant driving factors varied substantially among regions. Flash droughts in the Liaohe Plain were mainly associated with precipitation deficits and enhanced evapotranspiration, whereas soil moisture depletion and temperature anomalies played a more important role in the Songnen Plain. These results reveal pronounced regional heterogeneity in flash drought mechanisms across the Black Soil Region of Northeast China and demonstrate the effectiveness of the proposed FDHI for daily-scale agricultural flash drought monitoring. The study provides scientific support for agricultural drought risk management and disaster mitigation under climate change. Full article

Actual evapotranspiration is a primary pathway for crop water consumption in irrigation districts, including the Shijin irrigation district, where understanding the impacts of irrigation is crucial for managing water resources in this large-scale, water-scarce region. However, existing studies on evapotranspiration driving mechanisms often overlook irrigation activities and lack an analysis of synergistic effects among different environmental factors, with such research remaining particularly limited for this area. This study investigates the synergistic impact mechanisms of multiple drivers on evapotranspiration. Using data from 2003 to 2017, a projection pursuit model was employed to quantitatively assess the contributions of meteorological factors, Leaf Area Index, and irrigation to evapotranspiration evolution. The results indicate a significant structural shift in evapotranspiration, and the reduction in soil evaporation plays an important role in driving the variation of total evapotranspiration. Among the various factors, Leaf Area Index and irrigation exhibited the highest contribution rates to evapotranspiration. Furthermore, irrigation primarily acts in synergy with crop growth to enhance evapotranspiration. This study provides critical scientific insights for evidence-based water resource management and policy optimization in the Shijin irrigation district. Full article