Search Results (65)

A comparison of evapotranspiration between seasonally frozen and permafrost soils has important theoretical value for studying land surface processes and ecological environmental evolution on the Tibetan Plateau. In this work, the actual (ET_a) and reference (ET₀) evapotranspiration and crop coefficient (K_c) were calculated via eddy covariance data and meteorological gradient data from sites in the Naqu Prefecture and Tanggula Mountains. The variations, differences, and factors influencing the ET_a and ET₀ were analysed. The results revealed that at the two sites in 2008, the annual total ET_a values were 493.53 and 585.17 mm, which accounted for 83.58% and 144.39% of the total annual rainfall, respectively. The ET_a at the Naqu site was affected mainly by the Tibetan Plateau monsoon (TPM), whereas the ET_a at the Tanggula site was strongly affected by both the TPM and the freezing–thawing processes of the permafrost. The annual total ET₀ values at the two sites were 819.95 and 673.15 mm, respectively. The monthly total ET₀ at the Naqu site was greater than that at the Tanggula site. The ET_a and ET₀ values at the two sites were low in winter–spring, high in summer–autumn, and concentrated from May to October. When snow was present, the ET_a values at the Naqu site were relatively high, and the ET₀ values at both sites were very small and even negative at the Naqu site. The ET_a and ET₀ values at the two sites were significantly positively correlated with the net radiation (R_n), surface temperature (T₀), air temperature (T_a), water vapour pressure (e) and soil water content (smc), and negatively correlated with the wind speed (ws). The correlation between the ET_a and the T₀ at the Naqu site was the most significant, and the coefficient of partial correlation was 0.812; meanwhile, the correlation between the ET_a and the smc at the Tanggula site was the most significant, and the coefficient of partial correlation was 0.791. The R_n at the Naqu and Tanggula sites both had greater impacts on the ET₀. Full article

Accurate estimation of crop water use, which is expressed as evapotranspiration (ET) is an important task for effective irrigation and agricultural water management. Although direct field measurement of actual evapotranspiration (ETa) is the most reliable method, practical and economic limitations often make it difficult to acquire, especially in developing countries. Consequently, crop evapotranspiration (ETc) is calculated using reference evapotranspiration (ETo) and crop-specific coefficients (Kc) to support irrigation water management practices. Several ETo models have been developed to address varying environmental conditions; however, their transferability to new environments often leads to under or over estimation of ETo, which has an impact on ETc estimation. This study evaluated the accuracy of 30 ETo micrometeorological models to estimate ETc under different seasonal and micro-climatic conditions using ETa data directly measured using a smart field weighing lysimeter as a benchmark. Local Kc values were derived from field-based measurements, while statistical metrics were applied for the evaluation process. A cumulative ranking approach was used to assess the accuracy and consistency of the models across four cropping seasons. Results demonstrated the Penman–Monteith model to be the most consistent model in estimating ETc, which outperformed other models across all cropping seasons. The performance of alternative models differed significantly with seasonal conditions, indicating their susceptibility to seasonality. The findings demonstrated the Penman–Monteith model as the most reliable approach for estimating ETc, which justifies its application role as a benchmark for validating other ETo models in data-limited areas. The study emphasizes the importance of site-specific validation and calibration of ETo models to improve their accuracy, applicability, and reliability in diverse environmental conditions. Full article

Unevenly distributed rainfall leads to reduced potato water productivity (WP) under rainfed production conditions. Understanding the practices that can increase WP is vital. Our objectives were to understand (i) the seasonal variables that influence WP under rainfed conditions and (ii) the effect of the integration of cultivar x locality x mulch on potato WP. The study was undertaken in smallholder settings in two agroecological zones: Appelsbosch (Mbalenhle locality) and Swayimane (Stezi and Mbhava locality). A split plot, in a randomized complete block design experiment, included mulching (mulch and no mulch), four selected cultivars, and s three localities. Soil water content (SWC), yield, and climatic data were collected, and actual crop evapotranspiration (ETa) and WP were calculated. Rainfall, ETa, and crop growth and development had a significant influence on the seasonal WP. Cultivar × mulch × locality had an insignificant effect on the WP, however, locality × cultivar significantly altered the WP. The localities that had lower vapor pressure deficit (VPD), high relative humidity, and sandy soil had a higher potato WP for all cultivars, with the highest (18.38 kg m⁻³) being that from Electra. The findings suggest that using localities that have less atmospheric dryness and a cultivar (Electra) that shows stability of yield across the seasons can be an easy-to-apply practice for increasing potato WP in a resource-limited environment. Full article

The risk of soil salinization is prevalent in arid and semi-arid regions, posing a critical challenge to sustainable agriculture. This study addresses the need for accurate assessment of regional root-zone soil salt content (SSC) and understanding of underlying driving mechanisms, which are essential for developing effective salinization mitigation and water management strategies. A remote sensing inversion technique, initially proposed to estimate root-zone SSC in cotton fields, was adapted and validated more widely to non-cotton farmlands. Validation results (with a coefficient of determination R² > 0.53) were obtained using data from a three-year (2020–2022) regional survey conducted in the arid Manas River Basin (MRB), Xinjiang, China. Based on this adapted technique, we analyzed the spatiotemporal distributions of root-zone SSC across all farmlands in MRB from 2001 to 2022. Findings showed that root-zone SSC decreased significantly from 5.47 to 3.77 g kg⁻¹ over the past 20 years but experienced a slight increase of 0.15 g kg⁻¹ in recent five years (2017–2022), attributed to cultivated area expansion and reduced irrigation quotas due to local water shortages. The driving mechanisms behind root-zone SSC distributions were analyzed using an approach combined with two machine learning algorithms, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP), to identify influential factors and quantify their impacts. The approach demonstrated high predictive accuracy (R² = 0.96 ± 0.01, root mean squared error RMSE = 0.19 ± 0.03 g kg⁻¹, maximum absolute error MAE = 0.14 ± 0.02 g kg⁻¹) in evaluating SSC drivers. Factors such as initial SSC, crop type distribution, duration of film mulched drip irrigation implementation, normalized difference vegetation index (NDVI), irrigation amount, and actual evapotranspiration (ET_a), with mean ($\left|\mathrm{SHAP} \mathrm{value}\right|$) ≥ 0.02 g kg⁻¹, were found to be more closely correlated with root-zone SSC variations than other factors. Decreased irrigation amount appeared as the primary driver for recent increased root-zone SSC, especially in the mid- and down-stream sections of MRB. Recommendations for secondary soil salinization risk reduction include regulation of the planting structure (crop choice and extent of planting area) and maintenance of a sufficient irrigation amount. Full article

The injudicious use of water and fertilizer to maximize crop yield not only leads to environmental pollution, but also causes enormous economic losses. For this reason, we investigated the effect of nitrogen (N) (N0 (0), N60 (60 kg ha⁻¹), and N120 (120 kg ha⁻¹)) at different irrigation levels (I0 (0), I1200 (budding 600 m³ ha⁻¹ + kernel 600 m³ ha⁻¹), and I1800 (budding 900 m³ ha⁻¹ + kernel 900 m³ ha⁻¹)) on oilseed flax in the Loess Plateau of China in 2019 and 2020. The objective was to establish appropriate irrigation and fertilizer management strategies that enhance the grain yield (GY) of oilseed flax and maximize water and N productivity. The results demonstrated that irrigation and N application and their coupling effects promoted dry matter accumulation (DMA) and non-structural carbohydrate (NSC) synthesis, and increased the GY of oilseed flax. The contents of NSC in various organs of flax were closely related to grain yield and yield components. Higher NSC in stems was conducive to increased sink capacity (effective capsule number per plant (EC) and thousand kernel weight (TKW)), and the coupling of irrigation and N affected GY by promoting NSC synthesis. Higher GY was obtained by the interaction of irrigation and N fertilizer, with the increase rate ranging from 15.84% to 35.40%. Additionally, in the increased yield of oilseed flax, 39.70–78.06%, 14.49–54.11%, and −10.6–24.93% were contributed by the application of irrigation and nitrogen and the interaction of irrigation and nitrogen (I × N), respectively. Irrigation was the main factor for increasing the GY of oilseed flax. In addition, different climatic conditions changed the contribution of irrigation and N and their interaction to yield increase in oilseed flax. Drought and low temperature induced soluble sugar (SS) and starch (ST) synthesis to resist an unfavorable environment, respectively. The structural equation model showed that the key factors to increasing the GY of oilseed flax by irrigation and nitrogen fertilization were the differential increases in DMA, EC, and TKW. The increases in EC and TKW were attributed to the promotion of DMA and NSC synthesis in oilseed flax organs by irrigation, nitrogen fertilization, and their coupling effects. The I1200N60 treatment obtained higher water use efficiency (WUE) and N partial factor productivity (NPFP) due to lower actual evapotranspiration (ETa) and lower N application rate. Therefore, the strategy of 1200 m³ ha⁻¹ irrigation and 60 kg ha⁻¹ N application is recommended for oilseed flax in semi-arid and similar areas to achieve high grain yield and efficient use of resources. Full article

Estimation of actual evapotranspiration (ET_a) based on reference evapotranspiration (ET_o) and the crop coefficient (K_c) remains one of the most widely used ET_a estimation approaches. However, its application in non-agricultural and natural environments has been limited, largely due to the lack of well-established K_c coefficients in these environments. Alternate K_c estimation approaches have thus been proposed in such instances, with techniques based on the use of leaf area index (LAI) estimates being quite popular. In this study, we utilised satellite-derived estimates of LAI acquired through the Google Earth Engine geospatial cloud computing platform and machine learning to quantify the water use of a commercial forest plantation situated within the eastern region of South Africa. Various machine learning-based models were trained and evaluated to predict K_c as a function of LAI, with the K_c estimates derived from the best-performing model then being used in conjunction with in situ measurements of ET_o to estimate ET_a. The ET estimates were then evaluated through comparisons against in situ measurements. An ensemble machine learning model showed the best performance, yielding RMSE and R² values of 0.05 and 0.68, respectively, when compared against measured K_c. Comparisons between estimated and measured ET_a yielded RMSE and R² values of 0.51 mm d⁻¹ and 0.90, respectively. These results were quite promising and further demonstrate the potential of geospatial cloud computing and machine learning-based approaches to provide a robust and efficient means of handling large volumes of data so that they can be optimally utilised to assist planning and management decisions. Full article

The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, we constructed a surface energy balance algorithm for land (SEBAL) model based on the Google Earth Engine platform to invert the actual evapotranspiration (ETa) in typical irrigation areas in Xinjiang, northwest China, during the growing season from 2005 to 2021. The inversion results were evaluated using the observed evaporation data and crop evapotranspiration estimated by the FAO Penman–Monteith method. The water stress index (WSI) was then calculated based on the simulated ETa. The impacts of climatic factors, hydrological conditions, land-use change, and irrigation patterns on ETa and WSI were analyzed. The results indicated the following: (1) The ETa simulated by the SEBAL model matched well with the observed data and the evapotranspiration estimated using the FAO Penman–Monteith approach, with correlation coefficients greater than 0.7. (2) The average ETa was 704 mm during the growing season, showing an increasing trend in the irrigation area of the Yanqi Basin (IAY), whereas for the irrigation area of Burqin (IAB) the average ETa was 677 mm during the growing season, showing an increasing trend. The land cover type mainly influenced the spatial distribution of ETa in the two study areas. (3) The WSI in both irrigation areas exhibited a decreasing trend, with the WSI in the IAY lower than that in the IAB. (4) Climate warming, increases in irrigation areas, and changes in cropping patterns led to increased ETa in the IAY and IAB; the overall decreasing trend in the WSI derived from the popularization of agricultural water-saving irrigation patterns in both regions, which reduces ineffective evapotranspiration and contributes positively to solving the water shortage problem in the basins. This study provides insight into water resource management in the Xinjiang irrigation areas. Full article

This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ET_index estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ET_index algorithm was adopted to estimate the monthly ETa for the period from November 2016 to October 2017 using a series of Landsat 8, Thermal Infrared Sensor (TIRS) Band 10 satellite imagery. The estimation accuracy was evaluated by comparing the results with other estimates of ETa, namely the mapping evapotranspiration with the internalized calibration (METRIC) model, the MODIS Global Evapotranspiration Project (MOD16), the surface energy balance system (SEBS) tools, and with the crop evapotranspiration under standard conditions (ETc) as estimated by the FAO-56 procedure. The evaluation was made for irrigated wheat, maize, rice, and orchards and for non-irrigated bare soil land. The comparison of ETa values showed good correlation among the GCOM-C, METRIC, and FAO-56, while the MOD16 and SEBS showed significantly lower values of ETa. The agreement with the METRIC ETa implies that the simple GCOM-C algorithm successfully estimated the ETa in the region and that the precision was similar to that of the METRIC. This study provides the first high-quality evapotranspiration data with the spatial resolution of Landsat Band 10 data for the southeastern part of Afghanistan. The estimation procedure is straightforward, and its results are anticipated to enhance the understanding of regional hydrology. Full article

Accurate estimations of actual evapotranspiration (ETa) are essential to various environmental issues. Artificial intelligence-based models are a promising alternative to the most common direct ETa estimation techniques and indirect methods by remote sensing (RS)-based surface energy balance models. Artificial Neural Networks (ANNs) are proven to be suitable for predicting reference evapotranspiration (ETo) and ETa based on RS data. This study aims to develop a methodology based on ANNs for estimating daily ETa values using NDVI and land surface temperature, coupled with limited site-specific climatic variables in a large irrigation catchment. The ANN model has been applied to the two different scenarios. Data from only the 38 days of satellite overpass dates was selected in Scenario I, while in Scenario II all datasets, i.e., the 769-day data were used. An irrigation scheme, located in the Mediterranean region of Turkiye, was selected, and a total of 38 Landsat images and local climatic data collected in 2021 and 2022 were used in the ANN model. The ETa results by the ANN model for Scenarios I and II showed that the R² values for training (0.79 and 0.86), testing (0.75 and 0.81), and the entire dataset (0.76 and 0.84) were all remarkably high. Moreover, the results of the new ANN model in two scenarios showed an acceptable agreement with ETa-METRIC values. The proposed ANN model demonstrated the potential for obtaining daily ETa using limited climatic data and RS imagery. As a result, the suggested ANN model for daily ETa computation offers a trustworthy way to determine crop water usage in real time for sustainable water management in agriculture. It may also be used to assess how crop evapotranspiration in drought-prone areas will be affected by climate change in the 21st century. Full article

This article aims to estimate the water footprint (WF) of cereals—specifically, wheat and barley—in the Kairouan plain, located in central Tunisia. To achieve this objective, two components must be determined: actual evapotranspiration (ET_a) and crop yield. The study covers three growing seasons from 2010 to 2013. The ET_a estimation employed the S-SEBI (simplified surface energy balance index) model, utilizing Landsat 7 and 8 optical and thermal infrared spectral bands. For yield estimation, an empirical model based on the normalized difference vegetation index (NDVI) was applied. Results indicate the effectiveness of the S-SEBI model in estimating ET_a, demonstrating an R² of 0.82 and an RMSE of 0.45 mm/day. Concurrently, yields mapped over the area range between 6 and 77 qx/ha. Globally, cereals’ average WF varied from 1.08 m³/kg to 1.22 m³/kg over the three study years, with the majority below 1 m³/kg. Notably in dry years, the importance of the blue WF is emphasized compared to years with average rainfall (WF_b-2013 = 1.04 m³/kg, WF_b-2012 = 0.61 m³/kg, WF_b-2011 = 0.41 m³/kg). Moreover, based on an in-depth agronomic analysis combining yields and WF, four classes were defined, ranging from the most water efficient to the least, revealing that over 30% of cultivated areas during the study years (approximately 40% in 2011 and 2012 and 29% in 2013) exhibited low water efficiency, characterized by low yields and high WF. A unique index, the WFI, is proposed to assess the spatial variability of green and blue water. Spatial analysis using the WFI highlighted that in 2012, 40% of cereal plots with low yields but high water consumption were irrigated (81% blue water compared to 6% in 2011). Full article

The tea plant (Camellia sinensis), as a major, global cash crop providing beverages, is facing major challenges from droughts and water shortages due to climate change. The accurate estimation of the actual evapotranspiration (ET_a) of tea plants is essential for improving the water management and crop health of tea plantations. However, an accurate quantification of tea plantations’ ET_a is lacking due to the complex and non-linear process that is difficult to measure and estimate accurately. Ensemble learning (EL) is a promising potential algorithm for accurate evapotranspiration prediction, which solves this complexity through the new field of machine learning. In this study, we investigated the potential of three EL algorithms—random forest (RF), bagging, and adaptive boosting (Ad)—for predicting the daily ET_a of tea plants, which were then compared with the commonly used k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perceptron (MLP) algorithms, and the experimental model. We used 36 estimation models with six scenarios from available meteorological and evapotranspiration data collected from tea plantations over a period of 12 years (2010–2021). The results show that the combination of R_n (net radiation), T_mean (mean air temperature), and RH (relative humidity) achieved reasonable precision in assessing the daily ET_a of tea plantations in the absence of climatic datasets. Compared with other advanced models, the RF model demonstrated superior performance (root mean square error (RMSE): 0.41–0.56 mm day⁻¹, mean absolute error (MAE): 0.32–0.42 mm day⁻¹, R²: 0.84–0.91) in predicting the daily ET_a of tea plantations, except in Scenario 6, followed by the bagging, SVM, KNN, Ad, and MLP algorithms. In addition, the RF and bagging models exhibited the highest steadiness with low RMSE values increasing (−15.3~+18.5%) in the validation phase over the testing phase. Considering the high prediction accuracy and stability of the studied models, the RF and bagging models can be recommended for estimating the daily ET_a estimation of tea plantations. The importance analysis from the studied models demonstrated that the R_n and T_mean are the most critical influential variables that affect the observed and predicted daily ET_a dynamics of tea plantations. Full article

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R² value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions. Full article

Determining evapotranspiration (ET) is essential for water accounting and for the management of irrigated agriculture from farm to region. We describe here a collaborative initiative aimed at establishing a prototype ET network in six countries of North Africa and the Near East (NENA region). The network utilizes a low-cost and open-source system, termed the CORDOVA-ET, consisting of a base station and sensing nodes to collect the weather data needed to determine the reference and actual ET (ET_o and ET_a). Here, we describe the network-deployment processes, system architecture, data-collection methodology, quality-control procedures, and some of the ET results obtained so far during a four-year period, starting in 2018. The network has been developed as an iterative and collaborative process, where training and capacity building have been the main drivers. The feedback and experiences gathered from the users have helped improve the different versions of the prototypes and enhance their assembly, deployment, reliability, and ease of operation. At the same time, the involvement in the construction, maintenance, and data analysis has also provided valuable insight into calculating ET from energy-balance methods. The network operated during six cropping seasons and the results were mixed, while data integrity (hourly and daily) varied from 95 to 23% depending on the country and season. Validation of the ET estimates was performed using the ECMWF ERA5 dataset as an independent reference. The energy-balance algorithm implemented in the system to determine the ET_a was validated using the OpenCropLib Python library. While the results of the data validation demonstrated the reliability and accuracy of the CORDOVA-ET system, network operations required significant support and special motivation on the part of the users. It is concluded that collaboration among users, together with the support services and participation of different stakeholders interested in agricultural water management, would be essential elements to ensure the sustainability of the ET network. Full article

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m³), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California’s Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ET_a; mm/d) to determine Crop Water Use (CWU; m³/m²), and 2. Crop Productivity (CP; kg/m²) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m² study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m³ of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant. Full article

Seasonal quantification of a crop’s evapotranspiration (ET) and water footprint (WF) is essential for sustainable agriculture. Therefore, this study was conducted to estimate the ET and WF of an irrigated potato crop using satellite imagery of Landsat and Sentinel-2 sensors. The Simplified Surface Energy Balance (SSEB) algorithm was used to evaluate the crop water use (ET_a) for potato fields belonging to the Saudi Agricultural Development Company, located in the Wadi-Ad-Dawasir region, Saudi Arabia. Normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and land surface temperature (LSD) were computed for Landsat and Sentinel-2 datasets, which were used as inputs for mapping the potato tuber yield and, subsequently, the WF. The results indicated that the NDVI showed the best accuracy for the prediction of the potato tuber yield (R² = 0.72, P > F = 0.021) followed by the SAVI (R² = 0.64, P > F = 0.018), compared to the field harvested actual yield (Y_A). A comparison between the satellite-based ET_a and the actual amount of water applied (W_A) for irrigation showed a good correlation (R² = 0.89, RMSE = 4.4%, MBE = 12.9%). The WF of the potatoes in the study area was estimated at values between 475 and 357 m³ t⁻¹ for the early (September–December) and late (December–April) growing periods, respectively. A major portion (99.2%) of the WF was accounted for from irrigation with variations of 18.5% and 3.5% for early- and late-planted potatoes, respectively, compared to the baseline (crop planted in season). In conclusion, the results showed the possibility of satisfactorily estimating the WF using the SSEB algorithm by integrating the Landsat-8 and Sentinel-2 datasets. In general, the high rates of ET in the early planting season led to higher WF values compared to the in-season and late planting dates; this will help in selecting suitable planting dates for potato crops in the study area and areas with similar environments, which enhances the opportunities for sustainable management of irrigation water. Full article