Search Results (340)

The mapping and monitoring of rice fields on a large scale using medium and high spatial resolution data (<10 m) is essential for efficient agricultural management and food security. However, challenges such as managing large volumes of data, addressing data gaps, and optimizing available data are key focuses in remote sensing research using automated machine learning models. In this sense, the objective of this study was to propose a pipeline to characterize and classify three different irrigated rice-producing regions in the state of Santa Catarina, Brazil. To achieve this, we used Sentinel-1 Synthetic Aperture Radar (SAR) polarizations and Sentinel-2 optical multispectral spectral bands along with multiple time series indices. The processing of input data and exploratory analysis were performed using a clustering algorithm based on Dynamic Time Warping (DTW), with K-means applied to the time series. For the classification step in the proposed pipeline, we utilized five traditional machine learning models available on the Google Earth Engine platform to determine which had the best performance. We identified four distinct irrigated rice cropping patterns across Santa Catarina, where the northern region favors double cropping, the south predominantly adopts single cropping, and the central region shows both, a flattened single and double cropping. Among the tested classification models, the SVM with Sentinel-1 and Sentinel-2 data yielded the highest accuracy (IoU: 0.807; Dice: 0.885), while CART and GTBoost had the lowest performance. Omission errors were reduced below 10% in most models when using both sensors, but commission errors remained above 15%, especially for patches in which rice fields represent less than 10% of area. These findings highlight the effectiveness of our proposed feature selection and classification pipeline for improving the generalization of irrigated rice mapping in large and diverse regions. Full article

This study investigates the impact of various water supply modes on the hydraulic reliability of large-scale irrigation networks. An EPANET hydraulic model was developed to simulate the performance of the irrigation network under three supply modes: segmented, uniform, and random water supply. Three key indicators were selected to evaluate the hydraulic reliability of the pipeline network under each mode: Water Supply Uniformity C_u, Pressure Reliability H_k, and Velocity Reliability $\left|v\right|$. These parameters were standardized using the min-max normalization method, and the resulting reliability scores were scaled to a unified range of 0–5, where higher values indicate greater system reliability. The results demonstrate that the EPANET model effectively simulates the hydraulic performance of large-scale irrigation networks. Specifically, under the segmented water supply mode, the reliability values for water supply uniformity, node pressure head, and flow velocity are 4.04, 0.84, and 0.64, respectively. Under this mode, significant flow deviations and pressure head fluctuations occur between the branches, with flow velocities typically exceeding the optimal range. Furthermore, the node pressure head at the branch inlets fails to meet the required minimum pressure head (H_min), indicating potential operational inefficiencies. In the uniform water supply mode, the highest reliability values are observed for water supply uniformity (4.76) and flow rate (4.49), with node pressure head reliability (0.94) slightly surpassing that of the segmented mode. Pressure head fluctuations and flow deviations are significantly reduced, with flow velocities generally aligning with the economic flow rates of the pipeline. However, despite these improvements, many nodes still fail to meet the required minimum pressure head, indicating limitations in meeting demand under peak conditions. In the random water supply mode, node pressure head reliability reaches its highest value (1.54), while water supply uniformity and flow rate reliabilities are 3.99 and 2.50, respectively. Flow deviations and pressure head fluctuations are comparable to those observed in the uniform supply mode. Notably, a higher proportion of nodes meet the minimum pressure head requirement compared to the uniform mode. Overall, the hydraulic reliability of the pipeline network is highest under the uniform water supply mode (2.83), followed by the random water supply mode (2.49), with the segmented water supply mode exhibiting the lowest hydraulic reliability (1.79). These findings provide valuable insights for the selection of optimal water supply modes and the assessment of hydraulic reliability in large-scale irrigation systems. Full article

The Weining Plain is in the semi-arid northwest region of China, with groundwater as its main source for various purposes. This research provided a detailed investigation into the groundwater exploitation status in the Weining Plain, analyzed the groundwater quality for different purposes, and estimated the groundwater recharges using water budget analysis with end member mixing analysis. The entropy water quality index was applied to assess the overall quality of drinking water, and the industrial water quality index and several agricultural water quality indicators were used for the assessment of groundwater quality for industrial and agricultural uses. The findings showed that the groundwater recharge in the research area primarily comes from irrigation infiltration and leakage of the irrigation canal system which account for approximately 50–60% of the total groundwater recharge. The overall drinking water quality is poor, with over 80% being of moderate to poor quality and requiring treatment. A large proportion of the groundwater in the research area is suitable for irrigation. However, groundwater has a corrosive effect on boilers, and there is a high risk of boiler scaling and foaming. Only 12.41% of the water samples have good water quality for industrial use, and the treatment of the water quality for industrial uses is needed. This study can help local decision-makers understand the availability of groundwater resources in the Weining Plain and manage groundwater resources reasonably. Full article

Cotton (Gossypium hirsutum L.), as a significant economic crop, has undergone significant modernization in planting methods, and its smart irrigation management relies heavily on accurate cotton water content (CWC) estimation. Existing ground-based methods for measuring CWC are constrained by their limited scope and high monitoring costs. Although the development of unmanned aerial vehicle (UAV) technology has provided a new opportunity for large-scale CWC measurements, there remains a gap in the study of CWC estimation in cotton using multi-source and multi-stage data. In this study, we used UAV-based data, including texture features, vegetation indices, and a heat index, and applied four machine learning algorithms, i.e., partial least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XGB), to estimate CWC. The findings demonstrate that in a single growth stage, the boll setting stage performs the best, and multi-source and multi-stage inputs can improve the accuracy of CWC estimation, with the best performance of XGB (R² = 0.860). Overall, this study highlights that the synergistic use of multi-source and multi-stage data can effectively improve CWC estimation in cotton, suggesting UAV-based data will lead to a brighter future for precision agriculture. Full article

Water scarcity significantly challenges agricultural systems worldwide, especially in tropical areas such as the Dominican Republic. This study explores integrating satellite-based remote sensing technologies and field-based soil moisture sensors to assess water stress and optimize irrigation management in avocado orchards in Puerto Escondido, Dominican Republic. Using multispectral imagery from the Landsat 8 and 9 satellites, key vegetation indices (NDVI and SAVI) and NDWI, a water-related index that specifically indicates changes in crop water contents, rather than vegetation vigor, were derived to monitor vegetation health, growth stages, and soil water contents. Crop coefficient (Kc) values were calculated from these vegetation indices and combined with reference evapotranspiration (ETo) estimates derived from three meteorological models (Hargreaves–Samani, Priestley–Taylor, and Blaney–Criddle) to assess crop water requirements. The results revealed that soil moisture data from sensors at 30 cm depth strongly correlated with satellite-derived estimates, reflecting avocado trees’ critical root zone dynamics. Additionally, seasonal patterns in the vegetation indices showed that NDVI and SAVI effectively tracked vegetative growth stages, while NDWI indicated changes in the canopy water content, particularly during periods of water stress. Integrating these satellite-derived indices with field measurements allowed a comprehensive assessment of crop water requirements and stress, providing valuable insights for improving irrigation practices. Finally, this study demonstrates the potential of remote sensing technologies for large-scale water stress assessment, offering a scalable and cost-effective solution for optimizing irrigation practices in water-limited regions. These findings advance precision agriculture, especially in tropical environments, and provide a foundation for future research aimed at enhancing data accuracy and optimizing water management practices. Full article

Groundwater provides a strategic resource in the face of uncertain climate conditions in arid and semi-arid regions. Solar-based groundwater pumping is quickly gaining ground across rural sub-Saharan Africa, promoted by national and international organizations as the new technology of choice for water supply and irrigation. A crucial question in large-scale developments is whether pre-existing boreholes can be fitted with solar pumps. Based on data from southern Madagascar, this paper provides an automated method to deal with this. Our approach relies on a combination of hydrogeological criteria, including well screen depth, drawdown in relation to the static water column, and pumping efficiency. The results show that 60% of the existing boreholes in the study region are potentially suitable for the installation of solar pumps. Out of these, 54% would be able to supply water to large rural communities (>1000 people), whereas the remaining 46% present the potential to provide water to medium communities (500 to 1000 people). There are, however, concerns as to whether the information contained in the dataset is fully representative of current borehole conditions. Furthermore, the potential for installation of solar-based supplies must be placed in the context of the available resources and local capacities in order to ensure future sustainability. Full article

Accurately monitoring soil moisture content (SMC) in the field is crucial for achieving precision irrigation management. Currently, the development of UAV platforms provides a cost-effective method for large-scale SMC monitoring. This study investigates silage corn by employing UAV remote sensing technology to obtain multispectral imagery during the seedling, jointing, and tasseling stages. Field experimental data were integrated, and supervised classification was used to remove soil background and image shadows. Canopy reflectance was extracted using masking techniques, while Pearson correlation analysis was conducted to assess the linear relationship strength between spectral indices and SMC. Subsequently, convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and partial least squares regression (PLSR) models were constructed to evaluate the applicability of these models in monitoring SMC before and after removing the soil background and image shadows. The results indicated that: (1) After removing the soil background and image shadows, the inversion accuracy of SMC for CNN, BPNN, and PLSR models improved at all growth stages. (2) Among the different inversion models, the accuracy from high to low was CNN, PLSR, BPNN. (3) From the perspective of different growth stages, the inversion accuracy from high to low was seedling stage, tasseling stage, jointing stage. The findings provide theoretical and technical support for UAV multispectral remote sensing inversion of SMC in silage corn root zones and offer validation for large-scale soil moisture monitoring using remote sensing. Full article

The application of digital technology offers new opportunities to promote the green transformation and upgrading of agriculture. Farmers’ digital literacy, as a critical link between digital technology and agricultural green development, significantly influences their production decisions. Whether digital literacy serves as an enabling factor driving farmers’ adoption of agricultural green production technologies warrants further exploration. This paper uses the entropy method to measure farmers’ digital literacy levels and employs a Probit model for empirical analysis of survey data from 643 farmers in Shandong and Shaanxi provinces, examining how farmers’ digital literacy influences their adoption of green production technologies. The baseline regression result indicates that digital literacy can significantly increase farmers’ adoption of green production technologies. A mechanism analysis reveals that enhanced farmers’ digital literacy promotes the adoption of green production technologies through three pathways: enhancing farmers’ risk perception, expanding farmers’ digital social capital, and strengthening the effectiveness of technology promotion. Heterogeneity analysis demonstrates that improved digital literacy significantly enhances the adoption of four technologies—water-saving irrigation, pest control, pollution-free pesticide, and straw return to fields—and exerts a stronger impact on large-scale and middle-generation farmers. Accordingly, this study suggests improving digital village infrastructure, enhancing farmers’ digital literacy comprehensively, and formulating differentiated extension policies. Full article

The cultivation of Japanese plums (Prunus salicina Lindl.) in South Africa has increased over the years, yet their water use is unknown. Their cultivation in the Western Cape Province of South Africa is highly dependent on supplementary irrigation, indicating their high water use demand. This study used remote sensing techniques to estimate the actual evapotranspiration (ET_{c act}) of the Japanese plums to assess their water use on a large scale. The accuracy of the procedure had to be validated before getting to tangible conclusions. The eddy covariance was used to measure ET_c act in an African Delight plum orchard to validate the FruitLook remote sensing data for the 2023–2024 hydrological year and irrigation season. The seasonal and annual plum crop water requirements measured using the eddy covariance system were 751 and 996 mm, while those estimated by FruitLook were 744 and 948 mm, respectively. Although FruitLook slightly underestimated plum ET_{c act} by a Pbias of −6.15%, it performed well with a Nash–Sutcliffe efficiency (NSE) of 0.91. FruitLook underestimated evapotranspiration mainly during the peak summer season with full vegetation cover when the model may inaccurately represent irrigation impacts, soil moisture availability, and localized advection effects, better captured by the eddy covariance system. Based on the results, FruitLook proved to be sufficiently accurate for large-scale applications to estimate evapotranspiration in Japanese plum orchards in the Western Cape. Full article

Accurate and precise prediction of actual evapotranspiration (AET) on a large scale is a fundamental issue in natural sciences such as forestry (especially in species selection and planning), hydrology, and agriculture. With the estimation of AET, controlling dams, agriculture, and irrigation and providing potable and utility water supply for industry would be possible. Gathering reliable AET data is possible only with a sufficient weather station network, which is rarely established in many countries like Turkey. Therefore, climate models must be developed for reliable AET data, especially in countries with complex terrains. This study aimed to generate spatiotemporal AET surfaces using the Australian National University spline (ANUSPLIN) model and compare the results with the maps generated by the inverse distance weighting (IDW) and co-kriging (KRG) interpolation techniques. Findings from the interpolated surfaces were validated in three ways: (1) some diagnostics from the surface fitting model include measures such as signal, mean, root mean square predictive error, root mean square error estimate, root mean square residual of the spline, and the estimated standard deviation of noise in the spline; (2) a comparison of common error statistics between the interpolated surfaces and withheld climate data; and (3) evaluation by comparing model results with other interpolation methods using metrics such as mean absolute error, mean error, root mean square error, and adjusted R² (R²_adj). The correlation between AET and normalized difference vegetation index (NDVI) was also evaluated. ANUSPLIN outperformed the other techniques, accounting for 73 to 94% (RMSE: 3.7 to 26.1%) of the seasonal variation in AET with an annual value of 83% (RMSE: 10.0%). The correlation coefficient between observed and predicted AET based on NDVI ranged from 0.49 to 0.71 for point-based and 0.62 to 0.83 for polygon-based data. The generated maps at a spatial resolution of 0.005° × 0.005° could provide valuable insights to researchers and practitioners in the natural resources management domain. Full article

The North China Plain is a crucial agricultural region in China, but irregular precipitation patterns have led to significant water shortages. To address this, analyzing the high-resolution dynamics of root-zone soil moisture transport is essential for optimizing irrigation strategies and improving water resource efficiency. The Richards equation is a robust model for describing soil moisture transport dynamics across multiple soil layers, yet its application at large spatial scales is hindered by its sensitivity to boundary conditions and model parameters. This study introduces a novel approach that, for the first time, employs a continuous time series of near-surface soil moisture as the upper boundary condition in the Richards equation to estimate high-resolution root-zone soil moisture in the North China Plain, thus enabling its large-scale application. Singular spectrum analysis (SSA) was first applied to reconstruct site-specific time series, filling in missing and singular values. Leveraging observational data from 617 monitoring sites across the North China Plain and multiple spatial covariates, we developed a machine learning model to estimate near-surface soil moisture at a 1 km resolution. This high-resolution, continuous near-surface soil moisture series then served as the upper boundary condition for the Richards equation, facilitating the estimation of root-zone soil moisture across the region. The results indicated that the machine learning model achieved a correlation coefficient (R) of 0.92 for estimating spatial near-surface soil moisture. Analysis of spatial covariates showed that atmospheric forcing factors, particularly temperature and evaporation, had the most substantial impact on model performance, followed by static factors such as latitude, longitude, and soil texture. With a continuous time series of near-surface soil moisture, the Richards equation method accurately predicted multi-layer soil moisture and demonstrated its applicability for large-scale spatial use. The model yielded R values of 0.97, 0.78, 0.618, and 0.43, with RMSEs of 0.024, 0.06, 0.08, and 0.11, respectively, for soil layers at depths of 10 cm, 20 cm, 40 cm, and 100 cm across the North China Plain. Full article

Global climate change and the associated increasing impact of droughts on crops challenges researchers to rapidly assess plant health on a large scale. Photosynthetic activity is one of the key physiological parameters related to future crop yield. The present study focuses on the search for reflectance parameters for rapid screening of wheat genotypes with respect to photosynthetic activity under drought conditions. The development of drought stress modelled in laboratory conditions by stopping irrigation caused changes in chlorophyll fluorescence parameters that corresponded to a decrease in photosynthetic activity. In particular, a decrease in the photochemical quantum yield of photosystem II (Φ_PSII), which characterizes the rate of linear electron transport in the photosynthetic electron transport chain and is one of the most sensitive parameters responding at the early stages of drought stress, was observed. Along with the measurement of the photosynthetic activity, spectral characteristics of wheat plants were recorded using hyperspectral imaging. Normalized difference indices (NDIs) were calculated using the reflectance intensity of wheat shoots in the range from 400 to 1000 nm. Four NDIs that showed a strong correlation with the level of photosynthetic activity estimated by Φ_PSII were selected from different wavelength ranges (NDI_610/450, NDI_572/545, NDI_740/700, and NDI_820/630). The indices NDI_572/545 and NDI_820/630 showed the best combination of sensitivity to soil moisture deficit and strong relationship with photosynthetic activity under drought stress. Possible molecular and physiological causes of this relationship are discussed. The use of the proposed indices will allow to monitor in detail the specific features of wheat plant response and can serve as one of the criteria for selection of the most promising genotypes in breeding of drought-tolerant cultivars. Full article

Soil salinization is a serious land degradation phenomenon, posing a severe threat to regional agricultural resource utilization and sustainable development. It has been a mainstream trend to use machine-learning methods to achieve monitoring of large-scale salinized soil quickly. However, machine learning model training requires many samples and hyper-parameter optimization and lacks solvability. To compare the performance of different machine-learning models, this study conducted a soil sampling experiment on saline soils along the south bank of the Yellow River in Dalate Banner. The experiment lasted two years (2022 and 2023) during the spring bare soil period, collecting 304 soil samples. The soil salinity was estimated with the multi-source remote sensing satellite data by combining the extreme gradient boosting model (XGBoost), Optuna hyper-parameter optimization, and Shapley addition (SHAP) interpretable model. Correlation analysis and continuous variable projection were employed to identify key inversion factors. The regression effects of partial least squares regression (PLSR), geographically weighted regression (GWR), long short-term memory networks (LSTM), and extreme gradient boosting (XGBoost) were compared. The optimal model was selected to estimate soil salinity in the study area from 2019 to 2023. The results showed that the XGBoost model fitted optimally, the test set had high R² (0.76) and the ratio of performance to deviation (2.05), and the estimation results were consistent with the measured salinity values. SHAP analysis revealed that the salinity index and topographic factors were the primary inversion factors. Notably, the same inversion factor influenced varying soil salinity estimates at different locations. The saline soils of the study area in 2019 and 2023 were 65% and 44%, respectively, and the overall trend of soil salinization decreased. From the viewpoint of spatial distribution, the degree of soil salinization showed a gradually increasing trend from south to north, and it was most serious on the side near the Yellow River. This study is of great significance for the quantitative estimation of salinized soil in the irrigated area on the south bank of the Yellow River, the prevention and control of soil salinization, and the sustainable development of agriculture. Full article

Soil salinization is a significant global ecological issue that leads to soil degradation and is recognized as one of the primary factors hindering the sustainable development of irrigated farmlands and deserts. The integration of remote sensing (RS) and machine learning algorithms is increasingly employed to deliver cost-effective, time-efficient, spatially resolved, accurately mapped, and uncertainty-quantified soil salinity information. We reviewed articles published between January 2016 and December 2023 on remote sensing-based soil salinity prediction and synthesized the latest research advancements in terms of innovation points, data, methodologies, variable importance, global soil salinity trends, current challenges, and potential future research directions. Our observations indicate that the innovations in this field focus on detection depth, iterations of data conversion methods, and the application of newly developed sensors. Statistical analysis reveals that Landsat is the most frequently utilized sensor in these studies. Furthermore, the application of deep learning algorithms remains underexplored. The ranking of soil salinity prediction accuracy across the various study areas is as follows: lake wetland (R² = 0.81) > oasis (R² = 0.76) > coastal zone (R² = 0.74) > farmland (R² = 0.71). We also examined the relationship between metadata and prediction accuracy: (1) Validation accuracy, sample size, number of variables, and mean sample salinity exhibited some correlation with modeling accuracy, while sampling depth, variable type, sampling time, and maximum salinity did not influence modeling accuracy. (2) Across a broad range of scales, large sample sizes may lead to error accumulation, which is associated with the geographic diversity of the study area. (3) The inclusion of additional environmental variables does not necessarily enhance modeling accuracy. (4) Modeling accuracy improves when the mean salinity of the study area exceeds 30 dS/m. Topography, vegetation, and temperature are relatively significant environmental covariates. Over the past 30 years, the global area affected by soil salinity has been increasing. To further enhance prediction accuracy, we provide several suggestions for the challenges and directions for future research. While remote sensing is not the sole solution, it provides unique advantages for soil salinity-related studies at both regional and global scales. Full article

The NAC gene family plays a crucial role in plant growth, development, and responses to biotic and abiotic stresses. Paspalum Vaginatum, a warm-season turfgrass with exceptional salt tolerance, can be irrigated with seawater. However, the NAC gene family in seashore paspalum remains poorly understood. In this study, genome-wide screening and identification were conducted based on the NAC (NAM) domain hidden Markov model in seashore paspalum, resulting in the identification of 168 PvNAC genes. A phylogenetic tree was constructed, and the genes were classified into 18 groups according to their topological structure. The physicochemical properties of the PvNAC gene family proteins, their conserved motifs and structural domains, cis-acting elements, intraspecific collinearity analysis, GO annotation analysis, and protein–protein interaction networks were analyzed. The results indicated that the majority of PvNAC proteins are hydrophilic and predominantly localized in the nucleus. The promoter regions of PvNACs are primarily enriched with light-responsive elements, ABRE motifs, MYB motifs, and others. Intraspecific collinearity analysis suggests that PvNACs may have experienced a large-scale gene duplication event. GO annotation indicated that PvNAC genes were essential for transcriptional regulation, organ development, and responses to environmental stimuli. Furthermore, the protein interaction network predicted that PvNAC73 interacts with proteins such as BZIP8 and DREB2A to form a major regulatory hub. The transcriptomic analysis investigates the expression patterns of NAC genes in both leaves and roots under varying durations of salt stress. The expression levels of 8 PvNACs in roots and leaves under salt stress were examined and increased to varying degrees under salt stress. The qRT-PCR results demonstrated that the expression levels of the selected genes were consistent with the FPKM value trends observed in the RNA-seq data. This study established a theoretical basis for understanding the molecular functions and regulatory mechanisms of the NAC gene family in seashore paspalum under salt stress. Full article