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Sound water resources planning and management requires adequate data with sufficient spatial and temporal resolution. This is especially true in the context of irrigated agriculture, which is one of the main consumptive users of the world’s freshwater resources. Existing remote sensing methods for the management of irrigated agricultural systems are often based on empirical cropland data that are difficult to obtain, and that put into question the transferability of mapping algorithms in space and time. Here we implement an automatic irrigation mapping procedure in Google Earth Engine that uses surface reflectance satellite imagery from different sensors. The method is based on unsupervised training of a pixel-by-pixel classification algorithm within image regions identified through unsupervised object-based segmentation, followed by multi-temporal image analysis to distinguish productive irrigated fields from non-productive and non-irrigated areas. Ground-based data are not required. The final output of the mapping algorithm are monthly and annual irrigation maps (30 m resolution). The novel method is applied to the Central Asian Chu and Talas River Basins that are shared between upstream Kyrgyzstan and downstream Kazakhstan. We calculate the development of irrigated areas from 2000 to 2017 and assess the classification results in terms of robustness and accuracy. Based on seven available validation scenes (in total more than 2.5 million pixels) the classification accuracy is 77–96%. We show that on the Kyrgyz side of the Talas basin, the identified increasing trends over the years are highly significant (23% area increase between 2000 and 2017). In the Kazakh parts of the basins the irrigated acreages are relatively stable over time, but the average irrigation frequency within Soviet-era irrigation perimeters is very low, which points to a poor physical condition of the irrigation infrastructure and inadequate water supply.

Irrigated areas are major vectors of agricultural development and components of ecosystems. The groundwater level maintains the irrigated areas’ ecology safety and sustainable development. Under the influence of irrational irrigation practices—such as flood irrigation or extreme water saving without consideration of ecological impact—different areas within an irrigation district may experience anomalies in groundwater levels (either too deep or too shallow). It is of great significance to carry out research on water resource allocation and future water-saving strategies, taking into consideration groundwater depths. In this study, a method for the optimal allocation of irrigation water resources that considered groundwater level was used to regulate irrational irrigation practices and to reveal the future direction of water saving. Helan County in Ningxia province, an ecologically fragile and arid irrigated area, was selected as a case study. Multiple scenarios of different water use and different degrees of water-saving were analyzed. The results showed that non-engineering water-saving measures (such as adjusting the planting structure and controlling the amount of irrigation for rice) had better benefits compared to engineering measures (such as efficient water-saving irrigation and channel lining). When implementing only one water-saving measure, the strategy of replacing 75% of the rice area with corn yielded the best results. This approach can reduce the irrigation water shortage rate to 11% and increase by 4.58% the acreage where the groundwater level is reasonable. When multiple water-saving measures are implemented together, the most effective strategy for future water-saving efforts involves the joint implementation of several measures: replacing 75% of the rice area with corn, limiting irrigation for rice to no more than 11.85 thousand m3/ha, adopting high-efficiency water-saving irrigation in 90% of the pump-diverted water irrigation region and 40% of the channel-diverted water irrigation region, and maintaining the channel’s water utilization coefficient at 0.62. This strategy can keep the irrigation water shortage below 3.66% and increase the acreage where the groundwater level is reasonable, by 4.58% per year. The conclusions and research approaches can provide references for the formulation of water-saving measures for irrigated areas’ sustainable development.

Accurate monitoring of irrigation areas is of great significance to ensure national food security and rational utilization of water resources. The low resolution of the Moderate Resolution Imaging Spectroradiometer and Landsat data makes the monitoring accuracy insufficient for actual demand. Thus, this paper proposes a method of extracting the irrigated area in arid regions based on Sentinel-2 long time-series imagery to realize the accurate monitoring of irrigation areas. In this paper, a typical irrigation area in the arid region of Northwest China–Xinjiang Santun River is selected as the study area. The long time series Sentinel-2 remote sensing data are used to classify the land use of the irrigation area. The random forest, CART decision tree, and support vector machine algorithms are used to combine the field collection of the typical irrigation point and non-irrigated sample points. The irrigation area is extracted by calculating the Normalized Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) time series data as the classification parameters. The results show that (1) the irrigated area of the dryland irrigation region can be effectively extracted using the SAVI time-series data through an object-oriented approach combined with the random forest algorithm. (2) The extracted irrigated areas were 44,417, 42,915, 43,411, 48,908, and 47,900 hm2 from 2019 to 2023, and the overall accuracies of the confusion matrix validation were 94.34%, 90.22%, 92.03%, 93.23%, and 94.63%, with kappa coefficients of 0.9011, 0.8887, 0.8967, 0.9009, and 0.9265, respectively. The errors of the irrigated area compared with the statistical data were all within 5%, which demonstrated the effectiveness of the method in extracting the irrigated area. This method provides a reference for extracting irrigated areas in arid zones.

Maps of irrigated areas are essential for Ghana’s agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20–57% higher than irrigated areas reported by Ghana’s Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.

Soil salinisation is a critical problem in northern China’s arid and semi-arid irrigated regions, posing a substantial impediment to the sustainable advancement of agriculture in these areas. This research utilises the Donghaixin Irrigation District, located on the southern bank of the Yellow River in Inner Mongolia, as a case study. This study examines the spatial distribution and determinants of soil salinisation through macro-environmental variables and micro-ion composition, integrating regression models and groundwater ion characteristics to elucidate the patterns and causes of soil salinisation systematically. The findings demonstrate that soil salinisation in the study region displays notable spatial clustering, with surface water-irrigated regions exhibiting greater salinisation levels than groundwater-irrigated areas. More than 80% of the land exhibits moderate salinity, predominantly characterised by the ions Cl−, HCO3−, and SO42−. The hierarchy of ion concentration variation with escalating soil salinity is as follows: Na+ > K+ > SO42− > Cl− > Mg2+ > HCO3− + CO32− > Ca2+. The susceptibility of ions to soil salinisation is ordered as follows: Ca2+ > Na+ > HCO3− + CO32− > Mg2+ > K+ > Cl− > SO42−. In contrast to the ordinary least squares (OLS) model, the geographic weighted regression (GWR) model more effectively elucidates the geographical variability of salinity, evidenced by an adjusted R2 of 0.68, particularly in high-salinity regions, where it more precisely captures the trend of observed values. Ecological driving elements such as organic matter (OM), pH, groundwater depth (GD), total dissolved solids (TDS), digital elevation model (DEM), normalised difference vegetation index (NDVI), soil moisture (SM), and potential evapotranspiration (PET) govern the distribution of salinisation. In contrast, anthropogenic activities affect the extent of salinisation variation. Piper’s trilinear diagram demonstrates that Na cations mainly characterise groundwater and soil water chemistry. In areas irrigated by surface water, the concentration of SO42− is substantially elevated and significantly affected by agricultural practises; conversely, in groundwater-irrigated regions, Cl− and HCO3− are more concentrated, primarily driven by evaporation and ion exchange mechanisms.

Evaluating groundwater quality in irrigated areas is crucial for sustainable agriculture, especially as limited water resources and climate change pose significant threat to groundwater resources. Spatial information on groundwater quality is essential for effective management and utilization of water resources, particularly in intensive cropping areas such as irrigated regions in IBIS, Pakistan. However, recent advancements in machine learning (ML) techniques have highlighted that conventional groundwater quality assessment methods are costly and time-consuming, especially for developing nations. Accurate and efficient ML models can address this challenge in agricultural water management by optimally identifying the categories of water quality. This study is conducted to predict groundwater quality using an innovative ensemble-boosting methodology. The data is collected from Rahim Yar Khan’s irrigation system by the Scarp Monitoring Organization. Four irrigation water quality indicators, including sodium adsorption ratio, total dissolved solids, residual sodium carbonate, and electrical conductivity, are used to predict groundwater quality by applying four ML models. The performance of the ML models is assessed using mean squared error, correlation coefficients (r), and root mean square error measures. The proposed Gradient Boosting (GB) approach combines the advantages of interpretable tree models and boosting approaches. Experimental results validate the utility of the proposed approach with a 99% accuracy in predicting groundwater quality, compared to conventional ML techniques. Based on the proposed GB model and the inverse distance weighting interpolation technique, the groundwater quality distribution in the Hazardous Area is 17.34%, the Marginal Area is 79.36%, and the Safe Area is 3.30%. Enhancement and validation of groundwater quality index predictions are carried out using k-fold validation and hyperparameter tuning. Results indicate that the ML models have the potential to accurately delineate different groundwater quality zones for managing water resources and ensuring sustainable agriculture. Water quality assessment through the proposed approach can help managing the groundwater for the regions susceptible to deterioration of water quality thus contributing to better irrigation governance.

Currently, up to 80% of agricultural land needs watering in the Russian Federation. Therefore, the state of irrigated agriculture affects the efficiency and effectiveness of the country's food sector. The subject of this article is the studying of the basic indicators’ dynamics of irrigated agriculture (total irrigated area, its condition: good, satisfactory and unsatisfactory, costs per hectare of irrigated area and actually watered area) and their interrelations in all federal districts of the Russian Federation for 2013-2016. Methods of economic and statistical analysis were used in the work, in particular, analysis of the time series, correlation and cluster analyses. As a result of the studying, it was established that the state and development of irrigated agriculture in the Russian Federation is unstable and cannot be regulated solely by climatic factors.

The present research explores land use dynamics and irrigation practices in the Lower Mejerda Valley in Tunisia, a region deeply influenced by agricultural activities and water resource management. Since the United Nations Conference of Stockholm 1972, studies on land use and land cover have become essential for monitoring environmental changes, especially in the context of global warming. Agrarian reform in Tunisia since the 1960s has profoundly transformed its agricultural landscape, making irrigation a cornerstone of the national economy. This research focuses on the effects of prolonged droughts and revised water policies since the 1980s, which have impacted the irrigated areas in the Lower Medjerda Valley. By integrating multisource and multi-resolution satellite data, including Sentinel-1, Sentinel-2, and SPOT, the study aims to provide a detailed cartographic analysis of land use and irrigation practices. Using NDVI and RVI indices derived from optical and radar data, it evaluates the health and extent of vegetative cover over a five-year period (2016–2020). Spatio-temporal distribution indicated a significant chlorophyll activity for July 2017, 2019, and 2020, with NDVI values around 0.6 and 0.8 for irrigated plots. The RVI index, calculated from Sentinel-1 radar data, also shows correlation, albeit with slight noise due to sensor quality, with values around 2.4. The evaluation of the SAM method on SPOT 7 images shows it effectively discriminates land cover in agricultural environments. Despite some limitations in differentiating similar classes, the results justify using SAM for detailed land cover mapping. The results reveal significant fluctuations in vegetation indices, correlated with variations in water reserves and irrigation management practices. This comprehensive analysis highlights the crucial link between water resource availability and agricultural productivity, offering insights for optimizing water use and enhancing sustainable agricultural practices in the region.

Mercury (Hg) can evaporate and enter the plants through the stomata of plant leaves, which will cause a serious threat to local food safety and human health. For the risk assessment, this study aimed to investigate the concentration and accumulation of total gaseous mercury (TGM) in five typical leafy vegetables (Chinese chives ( Allium tuberosum Rottler), amaranth ( Amaranthus mangostanus L.), rape ( Brassica campestris L.), lettuce ( Lactuca sativa L.), and spinach ( Spinacia oleracea L.)) grown on sewage-irrigated areas in Tianjin, China. The following three sites were chosen to biomonitor Hg pollution: a paddy field receiving sewage irrigation (industrial and urban sewage effluents) for the last 30 years, a vegetable field receiving sewage irrigation for 15 years, and a grass field which did not receive sewage irrigation in history. Results showed that the total Hg levels in the paddy (0.65 mg kg −1 ) and vegetation fields (0.42 mg kg −1 ) were significantly higher than the local background level (0.073 mg kg −1 ) and the China national soil environment quality standard for Hg in grade I (0.30 mg kg −1 ). The TGM levels in ambient air were significantly higher in the paddy (71.3 ng m −3 ) and vegetable fields (39.2 ng m −3 ) relative to the control (9.4 ng m −3 ) and previously reported levels (1.45 ng m −3 ), indicating severe Hg pollution in the atmospheric environment of the sewage-irrigated areas. Furthermore, gaseous mercury was the dominant form of Hg uptake in the leaves or irreversibly bound to leaves. The comparison of Hg uptake levels among the five vegetables showed that the gradient of Hg accumulation followed the order spinach > red amaranth > Chinese chives > rape > lettuce. These results suggest that gaseous Hg exposure in the sewage-irrigated areas is a dominant Hg uptake route in leafy vegetables and may pose a potential threat to agricultural food safety and human health.

Salinity and sodicity have been a major environmental hazard of the past century since more than 25% of the total land and 33% of the irrigated land globally are affected by salinity and sodicity. Adverse effects of soil salinity and sodicity include inhibited crop growth, waterlogging issues, groundwater contamination, loss in soil fertility and other associated secondary impacts on dependent ecosystems. Salinity and sodicity also have an enormous impact on food security since a substantial portion of the world’s irrigated land is affected by them. While the intrinsic nature of the soil could cause soil salinity and sodicity, in developing countries, they are also primarily caused by unsustainable irrigation practices, such as using high volumes of fertilizers, irrigating with saline/sodic water and lack of adequate drainage facilities to drain surplus irrigated water. This has also caused irreversible groundwater contamination in many regions. Although several remediation techniques have been developed, comprehensive land reclamation still remains challenging and is often time and resource inefficient. Mitigating the risk of salinity and sodicity while continuing to irrigate the land, for example, by growing salt-resistant crops such as halophytes together with regular crops or creating artificial drainage appears to be the most practical solution as farmers cannot halt irrigation. The purpose of this review is to highlight the global prevalence of salinity and sodicity in irrigated areas, highlight their spatiotemporal variability and causes, document the effects of irrigation induced salinity and sodicity on physicochemical properties of soil and groundwater, and discuss practical, innovative, and feasible practices and solutions to mitigate the salinity and sodicity hazards on soil and groundwater.