Search Results (611)

South African irrigation schemes face critical challenges of water scarcity, infrastructure deterioration, and limited monitoring capacity, threatening agricultural productivity and food security. This scoping review systematically analyses 59 peer-reviewed publications (2000–2025) on sensor-based and acoustic irrigation monitoring technologies in South Africa, using transformer-based natural language processing (Sentence-BERT embeddings), unsupervised Machine Learning (UMAP dimensionality reduction, HDBSCAN clustering), and geospatial mapping applied to literature retrieved from Web of Science and Scopus. Results show that water quality monitoring (42.4% of studies) and remote sensing (25.4%) dominate the national research landscape, while soil moisture sensing and modelling remain comparatively limited. Notably, no peer-reviewed studies applying acoustic monitoring technologies to irrigation were identified, representing a critical gap despite proven international applications for leak detection (95–98% accuracy), widespread infrastructure aging (over 50% of schemes exceeding 30 years), and reported water losses of 30–60% in poorly managed systems. Reported experimental water savings range from 15% to 30%, yet applications remain largely confined to pilot-scale implementations concentrated within a limited number of Water Management Areas. Persistent adoption barriers include infrastructure unreliability, financial inaccessibility, limited digital literacy, and weak institutional coordination. The review recommends: (i) expanding research coverage across underrepresented regions and Water Management Areas; (ii) strengthening extension support and technical training to enable broader adoption; and (iii) integrating low-cost sensor networks with predictive, data-driven irrigation advisory systems. These priorities aim to support scalable, context-sensitive irrigation modernisation under increasing water scarcity pressures. Full article

Soil moisture content (SMC) is a critical factor in agricultural management; however, traditional monitoring methods face limitations regarding spatial resolution and the acquisition of regional dynamics. Unmanned Aerial Vehicle (UAV) remote sensing offers new opportunities for precision monitoring. This study proposes a UAV-based multi-modal remote sensing method for soil moisture estimation. Specifically, novel dual-band and three-band hyperspectral (HS) indices were constructed, and visible (RGB) and thermal infrared (TIR) information were integrated to form a multi-modal data system; simultaneously, multi-modal estimation models were developed by combining four AutoML methods: TPOT, AutoGluon, H2O AutoML, and FLAML. The results indicate that the H2O AutoML model, fusing multi-modal data, exhibited the best performance in estimating soil moisture at depths of 0–20 cm and 20–40 cm (R ≥ 0.72, RMSE 1.99–2.17%), demonstrating superior stability and generalization capabilities compared to other models. This study has made progress in hyperspectral index construction, multi-modal fusion, and soil moisture retrieval, providing a new technical approach for the refined management of agricultural water resources. Full article

Over six decades, satellite remote sensing of water resources has evolved from manual interpretation of weather photographs to AI systems that learn hydrologic predictions directly from satellite imagery. This review traces that evolution through the observation-to-inference arc—a framework for the progressively tightening coupling between what satellites observe and what hydrologists infer. Using illustrative applications in precipitation, evapotranspiration, soil moisture, snow, surface water, and groundwater, we show how early observations (1960–1985) remained disconnected from operational hydrology; how calibrated retrieval algorithms (1985–2000) established a one-way pipeline from satellites to models; how operational infrastructure (2000–2015), anchored by MODIS, GRACE, GPM, and Sentinel, achieved assimilative coupling through computational feedback between models and observations; and how deep learning (2015–present) is beginning to collapse this pipeline. Multi-source data fusion has been a recurring enabler at each stage. We articulate a four-level AI vision and research trajectory, from AI-assisted interpretation through AI-native retrieval and AI-driven inference to autonomous Earth observation intelligence. Persistent challenges in mission continuity, calibration, equity of access, and translating satellite-derived information into operational water management decisions provide essential context for evaluating both the promise and limits of this trajectory. Full article

In recent years, the Cyclone Global Navigation Satellite System (CYGNSS) of NASA has attracted widespread attention for the retrieval of freeze/thaw (F/T) states through the analysis of reflected signals. F/T variations in high-altitude regions have long been a focal point in this field. However, these areas lack benchmark observational data with high temporal and spatial resolution. A model named Partial Convolution–Time Convolutional Network (PTCN) is proposed in this paper to reconstruct CYGNSS data at a 3 km resolution. This model integrates partial convolution with a time convolutional network (TCN) and does not rely on any auxiliary data. Partial convolution is employed to distinguish valid pixels, with the interference of missing values being removed. TCN is employed to capture temporal features, which results in the reconstruction of observational data. Compared with the original observational data (at a 3 km resolution), the coverage of the reconstructed data is six times that of the original. A simulation of missing data is applied for the first time in the quantitative evaluation of observational data reconstruction. The results show that the value of R for the reconstructed data reaches 0.92, and the value of the root mean square error (RMSE) reaches 2.7. The reconstructed data is used for daily F/T retrieval. At both 36 km and 9 km resolutions, the F/T retrieval accuracy after reconstruction is comparable to that before reconstruction. The temporal resolution is improved by 256%, which successfully fills 92% of the observational gaps in soil moisture passive–active (SMAP) data. Compared with ground-based F/T retrievals, the reconstructed F/T accuracies are 87.71% at 36 km and 82.3% at 9 km.The model successfully reconstructs high-temporal and spatial resolution CYGNSS data while maintaining accuracy. In the future, this method holds significant potential for the application of global GNSS-R high-temporal and spatial resolution remote sensing observations. Full article

The aim of the current study was to select and test the appropriate model and input parameters for remote sensing retrieval of surface soil moisture (SSM) in the case of bare Chernozems on flat and sloping terrains in northern Bulgaria under different tillage systems. Normalized synthetic aperture radar (SAR) measurements from Sentinel-1 C-band dual-pol products (Gamma-Nought in VV, ratio) were utilized in two ways to delineate SSM from environmental factors that bias determination. The accuracy of the obtained SSM prediction was evaluated against ground-based volumetric water content (VWC) measured in the 0–3.8 cm soil layer at multiple points using a TDR meter. The TDR VWC data were preliminarily calibrated against gravimetric measurements in the 0–5 cm soil layer. The obtained data for soil water retention curves in all studied variants were used to determine the range of soil moisture variation. The measured ground-based data for surface roughness generally correlate with the co-pol Gamma-Nought in VV. The data modeled with the surface soil moisture script in Sentinel Hub (SSM-SH) was calibrated using the ground-based data. Incidence angle normalization of Sentinel-1 products improved the relationship between SAR observables and SSM, when expressed as the ratio of soil moisture to total porosity (rVWC). The modeling indicated the highest importance of the optical indices, together with the temporal differences of radar descriptors sensitive to variations in soil moisture over time. Although the applied Random Forest Regression (RFR) model achieved higher accuracy during training (nRMSE of 7.27%, R² of 0.86), the Gaussian Process Regression (GPR) model provided better generalization performance on the independent validation dataset. The results proved the advantages of the joint utilization of temporal Sentinel-1 SAR measurements with Sentinel-2 optical acquisitions to determine SSM in different bare soil conditions for achieving high accuracy. Full article

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides a low-cost, all-weather approach for continuous soil moisture content (SMC) retrieval. However, in single-constellation, multi-satellite applications, the optimal satellite number and the combined effects of multiple environmental factors on retrieval accuracy and stability remain insufficiently quantified. To address these issues, this study develops a dual-frequency GNSS-IR SMC retrieval framework that explicitly incorporates multiple environmental factors. Entropy-based fusion (EFM) is used to adaptively weight dual-frequency phase-delay observations, and a marginal-gain criterion is introduced to determine a suitable number of participating satellites. On this basis, univariate linear regression (ULR) and random forest (RF) models are established, and the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation are incorporated into the RF model to improve retrieval robustness and quantify the relative contributions of environmental factors. The results show that multi-satellite combinations significantly improve SMC retrieval performance, while the incremental gain exhibits clearly diminishing returns and converges when the number of participating satellites reaches about 5–6 within a single constellation. Dual-frequency fusion consistently outperforms single-frequency schemes across different GNSS constellations, demonstrating the complementary value of multi-frequency information under multi-satellite conditions. In addition, the environmentally informed nonlinear model achieves higher accuracy and stability than the linear model, and the dominant environmental drivers differ across stations. Overall, this study provides quantitative support for configuring single-constellation multi-satellite GNSS-IR soil moisture monitoring schemes and for improving retrieval robustness under complex environmental conditions. Full article

As climate change increasingly impacts the water cycle across the Alpine region, monitoring surface soil moisture is essential for hydrological models and drought early warning. Yet operational products either mask steep terrain, or lack the spatial resolution to capture the surface soil moisture (SSM) spatial variability of the Alpine catchments. This study presents a novel retrieval approach aggregating Sentinel-1 radiometric terrain-corrected backscatter (${\gamma }^0$) into 100 m elevation bands per sub-basin and aspect across the Austrian Alps. The resulting Alpine backscatter product is processed through an orbit-wise change detection to derive over 34,000 SSM timeseries, evaluated using ERA5-Land and compared to 264 precipitation stations from Geosphere for the period from 2016 to 2024. The results show satisfactory agreement with ERA5-Land (Pearson correlation > 0.46 below 400 m) and capture in situ precipitation-driven anomalies with the strongest performance below 400 m (Spearman correlation > 0.47), particularly over grasslands and south-facing slopes. Despite its limitations at high elevation and over dense vegetation, Sentinel-1 provides consistent and elevation-stratified information across more than 80% of the Austrian Alps, typically excluded from operational products. The new Alpine SSM product highlights Sentinel-1’s potential to support hydrological modeling, drought monitoring, and water resource management across complex topography such as the Alps. Full article

Sea surface salinity (SSS) is a critical climate variable influencing ocean circulation, deep water formation, and the global hydrological cycle. This study evaluates a broad suite of satellite-derived SSS products against in situ measurements collected at 4.5 m depth along a transect conducted in 2021 from western Greenland to Sardinia, spanning the subpolar North Atlantic and western Mediterranean Sea. All satellite products capture the large-scale salinity increase from high latitudes to the Mediterranean and show generally high correlations with in situ data. However, differences exist among specific products and at different latitudes. Multi-mission and optimally interpolated global products exhibit the smallest discrepancies, remaining close to the in situ reference along most of the transect, whereas single-mission Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity (SMOS) products show larger and more variable differences, especially in dynamically complex or coastal areas. Regional products provide additional insights: the European Space Agency (ESA) CCI-Salinity Northern Hemisphere product and the Barcelona Expert Center Arctic Version 4 dataset are examined near Greenland and the subpolar North Atlantic, while the ESA 4D Mediterranean V3 product performs consistently in the western Mediterranean, highlighting scale and representativeness effects. A simple multi-product ensemble approach reduces product-specific noise and provides a balanced representation across diverse regimes and latitudes. These findings underline persistent regional challenges in satellite SSS retrievals and emphasise the need for more in situ observations and for further development of multi-product approaches. Full article

The development of passive microwave sensors traces back to Robert Dicke’s pioneering experiments in the 1940s. Since then, microwave radiometry has evolved into a key tool for Earth observation, strengthened by data from multiple satellite missions operating across different wavelengths. This paper reviews the state of the art in microwave radiometry for monitoring land surfaces. After introducing the theoretical foundations underpinning current missions, we present an overview of major satellite instruments. We then examine early theoretical advances in retrieving soil moisture and snow properties, two applications that contributed to the future development of satellite microwave radiometry missions for the observation of surface variables. Particular attention is given to radiative transfer theory and its solutions, which model the effects of roughness, vegetation, and snow cover. These approaches form the basis of today’s retrieval algorithms and remain central to future missions. Subsequent sections highlight the use of passive microwave data for estimating a variety of surface variables, the role of passive microwave in data assimilation systems and forthcoming missions dedicated to land monitoring. The review concludes with key achievements, ongoing challenges, and open issues—such as soil moisture retrieval under dense vegetation or snow property retrieval in melting conditions. Addressing these limitations is critical to fully exploiting microwave radiometry in the context of climate research and mitigation strategies. Full article

In arid northwest China, water scarcity is the primary constraint on agricultural sustainability. Accurate prediction of soil moisture under vegetation is essential for optimizing water use and enabling precision irrigation. Furthermore, water and nitrogen management are often studied in isolation, and their spatiotemporal synergy in regulating soil moisture remains unclear, which hinders the development of optimized coupled strategies. To address this, this study integrated UAV hyperspectral (450–950 nm), multispectral remote sensing, and ground sensor networks to systematically conduct field experiments covering three irrigation levels: full irrigation (W1) at 100% of maintaining soil moisture content; mild deficit irrigation (W2), with soil moisture content set at three-quarters of W1; and severe deficit irrigation (W3), with soil moisture content set at half of W1 and three nitrogen application rates (N1: 350, N2: 250, and N3: 150 kg/ha) in a field experiment. Through sensitive band extraction and spectral index optimization, triple-band indices (RES: Reflectance Extraction Index, MSR: Moisture Sensitive Ratio Index, two novel triple-band spectral indices developed based on Kubelka–Munk and Hapke models) were innovatively developed to enhance signals and suppress noise. Random Forest algorithms were employed to construct soil moisture inversion models for different soil layers. Rigorous comparative analysis comprehensively evaluated performance differences between hyperspectral and multispectral technologies in the indirect retrieval of soil moisture based on crop physiological response and detecting soil moisture at varying depths (10–100 cm). The results indicate that the 450–760 nm visible band represents the optimal spectral region for soil moisture detection. The two indices (MSR and RES) constructed within this range demonstrated prediction correlations 18–32% higher than traditional indices. Hyperspectral technology exhibited comprehensive advantages, particularly in monitoring deep soil layers (>80 cm) (R² = 0.49 vs. 0.18 for multispectral). The spatiotemporal dynamics of soil moisture are primarily governed by irrigation intensity, while nitrogen fertilizers indirectly influence water redistribution through physiological processes such as root architecture regulation, rather than directly altering soil water-holding capacity. This study demonstrates the efficacy of a UAV-based hyperspectral system for precision soil moisture monitoring in vegetated farmland, and it provides a critical scientific basis for optimizing water–nitrogen management and enhancing water use efficiency in arid agriculture. Full article

This study proposes a method for retrieving ocean sea surface salinity (SSS) using C/X-band ocean emissivities in coastal regions, aiming to verify the performance of these unconventional frequencies for SSS retrieval in warm, high-salinity-variation coastal oceans. Since C/X-band brightness temperatures are less sensitive to sea surface salinity than L-band brightness temperatures, it becomes particularly important to develop a sophisticated and effective method for extracting salinity-related signals from C/X-band brightness temperatures. To this end, a wind effect correction process is developed to remove rough sea surface emissivity contributions from total emissivity and derive calm sea emissivity from WindSat’s brightness temperatures. The wind-induced effects are modeled with a third-order polynomial. Then, based on emissivity analysis, a weighted combination of C/X-band calm sea emissivities (with parameter λ) is introduced to reduce SST sensitivity. This λ-based combination is used to retrieve SSS in the Bay of Bengal. Based on the triple-match method and buoy data, the salinity retrieval results are verified and compared with the Soil Moisture Active Passive (SMAP) SSS and Argo in situ SSS. The results show that the use of parameter λ reduces the RMS error of SSS by 0.1–0.2 psu. The RMSE of SSS retrieval is about 0.64 psu, which is comparable to the error of SMAP data. Simultaneously, the SSS retrieval accuracy is significantly influenced by offshore distance. At an offshore distance of 100 km, the salinity retrieval error exceeds 1 psu, while when the offshore distance exceeds 500 km, the salinity retrieval error is better than 0.6 psu. Full article

The Water Cloud Model (WCM) provides a simple and effective framework for quantifying vegetation canopy absorption and scattering components and has been widely applied to active microwave-based soil moisture retrieval on vegetated areas. However, under conditions of dense vegetation, the WCM neglects soil–vegetation interaction scattering, which limits its retrieval accuracy. To mitigate this limitation, this study analyzes the active microwave radiative transfer process under vegetated conditions and proposes an approach to explicitly quantify soil–vegetation interaction scattering by incorporating the first-order soil–vegetation-scattering component into the WCM, thereby enhancing the performance of the WCM at high vegetation coverage. The effectiveness of the proposed model is validated using in situ observations from three study areas with different vegetation characteristics: (a) a pure farmland area, (b) a mixed landscape with small forest and shrubland patches and large cropland areas, and (c) a mixed landscape with large forest and shrubland patches and small cropland areas. Data from 2020–2022 were used for model training and parameter calibration, while independent datasets from 2023 and 2024 were employed to validate the model performance. In both the model training and validation phases, the proposed model improved the soil moisture retrieval accuracy across all study areas while exhibiting slight differences in the backscatter simulation performance. During the model training period, the root-mean-square error (RMSE) between simulated and measured backscatter in study area (a) increased slightly by 1.9%, whereas it decreased by 2.79% and 2.0% in study areas (b) and (c), respectively. In terms of soil moisture retrieval, the RMSEs in study areas (a), (b), and (c) decreased by 6.66%, 1.18%, and 6.03%, respectively. In the validation experiments, for the year 2023, the RMSEs of simulated versus observed backscatter in study areas (a), (b), and (c) were reduced by 9.6%, 1.51%, and 4.35%, respectively, while the corresponding soil moisture retrieval RMSEs decreased by 12.6%, 4.53%, and 7.24%. For the year 2024, the backscatter RMSE in study area (a) increased by 6.07%, whereas it decreased by 2.17% and 6.47% in study areas (b) and (c), respectively; meanwhile, the soil moisture retrieval RMSEs were reduced by 2.81%, 3.69%, and 9.45%, respectively. In summary, this study improves the accuracy of active microwave remote sensing-based soil moisture retrieval in areas with different vegetation cover by explicitly quantifying soil–vegetation interaction scattering. Full article

Accurate estimation of topsoil moisture (TSM) is essential for optimizing agricultural practices, particularly in the context of precision farming. This study evaluates the use of high-resolution synthetic aperture radar (SAR) imagery from TerraSAR-X (X-band) and Radarsat-2 (C-band) for estimating TSM over bare agricultural soils, at both plot and intra-plot spatial scales. The experiment was conducted over a 420 km² area in southwest France, comprising 29 agricultural plots with varying topography, soil texture, and land management practices. Extensive in situ measurements of TSM, soil texture, and surface roughness were collected over multiple dates. A random forest regression model was developed to estimate soil moisture, using radar backscatter coefficients, incidence angles, soil texture components (clay, silt, sand), and roughness parameters (Hrms, correlation length) as input features. The modeling approach was applied at multiple spatial scales by extracting satellite signals within circular buffers of varying radius (5 to 30 m), as well as at the plot scale. Results indicate that estimation performance improves with increasing buffer size, with the best results achieved at the 30 m intra-plot scale (R² > 0.8, RMSE < 4 m³·m⁻³), outperforming plot-scale estimates. Both C-band and X-band data provided reliable results, with a slight advantage when combining data from multiple incidence angles. The inclusion of surface roughness and soil texture significantly improved model accuracy, underlining the importance of accounting for local soil properties in radar-based moisture retrieval. The intra-plot variability of TSM was found to be substantial, often exceeding inter-plot differences, highlighting the necessity for high spatial resolution in moisture monitoring. This study demonstrates the value of combining ground observations with multi-frequency SAR data and machine learning for high-resolution soil moisture mapping. The approach supports more precise water management strategies and contributes to sustainable agricultural development through informed decision-making. Full article

Accurate estimation of snowpack microwave backscatter is critical for retrieving key physical properties of snow, such as snow depth (SD) and snow water equivalent (SWE), typically modeled using radiative transfer models (RTM). Among the various sources of uncertainty in RTM simulations, snow–ground reflectivity—used as a boundary condition—plays a critical role in influencing the accuracy of simulated backscatter. This study leverages high-resolution X- and Ku-band synthetic aperture radar (SAR) backscatter aircraft measurements using SWESARR and SnowSAR from NASA’s SnowEx campaigns, co-located with in situ snow pit observations in Grand Mesa, Colorado, and uses a Bayesian MCMC parameter optimization model with RTM framework to estimate the key ground parameters such as surface roughness, moisture content, and specular-to-total reflectivity ratio (STRR) governing the estimation of the snow–ground reflectivity and quantify the uncertainties associated with them. At the X-band, increasing ground surface roughness reduced the simulated backscatter by ~1.5 dB across the tested range, increasing the STRR produced an additional ~1.0 dB decrease while the dielectric properties of the ground are highly sensitive to the moisture content of frozen soil, and increasing the moisture content even by 2% increased the backscatter by 2–3 dB. The retrieval sensitivity to the STRR is minimized in the 0.6–0.7 range and it can be fixed at 0.65 without having discernible impact. The Bayesian inversion reveals that the extreme parameter values act as diagnostic indicators of unmodeled complexity rather than retrieval failures, with representativeness error often dominating over instrument noise. The study provides a robust methodology for the estimation of the snow–ground backscatter boundary condition for forward modeling, ultimately aiding SWE and SD retrieval from active microwave observations. While this study relied on Grand Mesa, the framework developed here is general and, along with the model uncertainty, is directly transferable and broadly applicable to other snow-dominated mountain regions where active microwave observations can be used for snowpack monitoring. Full article

Climate variability threatens smallholder maize production in semi-arid Southern Africa, necessitating accurate irrigation management. We developed an Earth Observation–machine learning framework integrating Sentinel-1 SAR, TU Wien retrievals, and meteorological data to generate daily 10 m resolution root-zone soil moisture estimates (0–100 cm) for South Africa’s Vhembe District (2017–2022). Five algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Multivariate Adaptive Regression Splines (MARS)—were calibrated using ~50,000 observations from two monitoring stations across six depths and five growing seasons. RF and XGBoost achieved highest accuracy (R² = 0.96–0.97, RMSE < 0.025 cm³/cm³), detecting critical irrigation thresholds (management allowable depletion = 0.23 cm³/cm³, field capacity = 0.35 cm³/cm³) with operational precision (nRMSE < 0.05). Depth-stratified validation revealed strong SAR surface correlations (r = 0.84–0.85 at 10 cm) declining systematically with depth (r < 0.2 below 40 cm), confirming ML models integrate satellite observations at shallow layers with meteorological gap-filling at depth. District mapping showed 79–94% of maize areas required irrigation during dry years (2017–2019, 2021–2022) versus 32% in wet 2020–2021. The framework provides a transferable pathway for precision irrigation in smallholder systems, pending vegetation-corrected retrievals and expanded validation. Full article