Search Results (1,762)

Urban air pollution poses significant risks to human health, ecosystems, and the environment, highlighting the need for accurate monitoring of atmospheric pollutants. This study investigated the spatial and temporal variability of key tropospheric pollutants, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), nitrous acid (HONO), formaldehyde (HCHO), and ozone (O₃), in Brighton, UK, using an integrated approach that combined ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), Long-Path Differential Optical Absorption Spectroscopy (LP-DOAS), and Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) observations. Ground-based measurements comprised four MAX-DOAS campaigns conducted between 2021 and 2024 and a long-term LP-DOAS dataset spanning 2017–2023, complemented by coincident TROPOMI observations. The datasets were spatially co-located, temporally aligned, quality-controlled, and analysed using statistical methods, time-series analysis, and polar plot techniques to assess pollutant variability, identify emission sources, and evaluate the agreement between satellite and ground-based observations. The results revealed clear seasonal and diurnal variations in pollutant levels, with elevated NO₂ during winter and enhanced O₃ during summer, reflecting the influence of anthropogenic emissions and photochemical processes. Polar plot analysis further identified distinct wind-dependent pollutant patterns, indicating the importance of local emission sources. Comparisons between ground-based and satellite observations showed that TROPOMI successfully captured the temporal variability of NO₂ measured by means of LP-DOAS, with a moderate positive correlation (r_s = 0.55), but underestimated NO₂ relative to MAX-DOAS observations (r_s = 0.38), reflecting differences in measurement geometry, spatial resolution, and retrieval sensitivity. The overall findings demonstrate that integrating ground-based and satellite observations provides a more comprehensive understanding of urban air quality than either approach alone. This combined monitoring framework improves confidence in satellite-derived atmospheric products and supports more effective air quality assessment and management in Brighton and similar urban environments. Full article

Drought is a recurring agricultural hazard in Northeast Thailand’s floodplain environments, yet the actual values of remote sensing indices at confirmed drought locations remain poorly characterized. This study characterized six remote sensing indices (NDVI, VCI, SMI, NDMI, MNDWI, NSMI) at 541 agricultural drought-reporting locations in the Chi River Basin, Maha Sarakham Province, across three years representing different ENSO phases (La Niña 2020, El Niño 2023, neutral 2024). Drought-reporting frequency was classified based on village-level alert frequency: high frequency (six alerts, n = 100 villages) and low–moderate frequency (≤3 alerts, n = 441 villages). Sentinel-2 imagery was processed for the January–May dry season. Due to non-independence of observations (repeated measurements and spatial autocorrelation), analyses focused on descriptive statistics and effect sizes (Cohen’s d) rather than formal hypothesis testing. Results revealed remarkably small mean differences between frequency classes (0.008–0.024), with uniformly small effect sizes (Cohen’s d = 0.20–0.22). VCI and MNDWI showed negligible differences (Cohen’s d = 0.124 and 0.094, respectively). Index values at high-frequency locations showed stability across years (CV < 7% for all indices except NDMI), with limited year-to-year variation. SMI and NSMI were perfectly correlated (r = 1.00), indicating mathematical redundancy. Provisional descriptive reference points were derived from the three-year dataset (NDVI ≈ 0.21, VCI ≈ 0.52, SMI ≈ 0.41, MNDWI ≈ −0.33 at high-frequency locations), but these are descriptive summaries only and require validation with longer time series before they can be considered for operational use. These findings demonstrate that individual remote sensing indices have limited discriminatory power in this sandy soil floodplain environment, where local factors—soil properties, topography, and irrigation access—dominate over regional climate forcing. Five policy-relevant observations are proposed, including re-evaluation of threshold-based early warning systems and prioritized irrigation investments based on static vulnerability factors. This study contributes to SDG 2 (Zero Hunger), SDG 6 (Clean Water), and SDG 13 (Climate Action) through improved understanding of drought monitoring limitations in floodplain environments. Full article

Climate variability represents a major threat to agricultural systems, particularly in arid and semi-arid regions such as the Beni-Amir irrigated perimeter, located in the Tadla plain in central Morocco. In this perimeter, olive trees are exposed to multiple environmental and management-related factors that are associated with variations in phenology and vegetation vigor, such as successive drought episodes. This study represents a spatio-temporal assessment of drought impact on olive using satellite- derived vegetation indices from Sentinel-2 imagery and precipitation satellite data from CHIRPS over the period 2015–2024. The Standardized Precipitation Index (SPI-12) was used to identify wet and dry phases over this period. The results indicate an alternation of dry and wet periods between 2015 and 2021, followed by a predominance of dry conditions from September 2021. Over the same period, the time series of the Normalized Difference Vegetation Index (NDVI) and the other vegetation indices reveals marked interannual variability and a progressive degradation of olive tree phenological cycles. A land cover map derived from a supervised support vector machine (SVM) under three classification scenarios achieved high overall accuracies exceeding 94%. Post-classification change detection highlights a substantial reduction in mapped olive-growing areas between 2016 and 2024, with an estimated 72% loss of the initial area. The findings reported in this study indicate that the succession of drought episodes may have contributed to olive grove degradation, including disruptions in phenological cycles and a decline in maximum NDVI values. Even the most resilient olive groves appeared affected following the severe drought period after 2021. The study underscores the usefulness of satellite-derived vegetation indices and drought indicators for the effective monitoring of drought-related stress and supporting improved management practices under climate change. Full article

Land subsidence is one of the most critical geological hazards in Hainan Province, primarily driven by groundwater overexploitation. This study integrates regional-scale SBAS-InSAR deformation results with groundwater observations from nine representative monitoring wells to investigate the spatiotemporal evolution of land subsidence and its groundwater-related response mechanisms in Hainan Province. Sentinel-1A imagery from 2019 to 2023 was used to derive LOS deformation time series, which were converted into vertical land subsidence using local incidence-angle correction under the assumption of negligible horizontal displacement. Seasonal and Trend decomposition using Loess (STL), Pearson correlation analysis, dynamic time warping (DTW), and lag correlation analysis were applied to separate multiscale signals and examine groundwater–subsidence responses in representative hydrogeological settings. The results indicate that (1) land subsidence in Hainan Province is mainly concentrated in coastal plains, with Haikou, Wenchang, and Danzhou identified as the main subsidence centers, where local annual subsidence rates exceed −50 mm/yr; (2) representative well-based analysis shows that groundwater-level decline is closely synchronized with cumulative subsidence in major subsidence-sensitive areas, with DTW distances consistently below 10, indicating high temporal consistency; long-term groundwater depletion is an important driver of cumulative subsidence in these representative areas; (3) lag correlation analysis reveals spatially heterogeneous lag responses of 1–6 months between groundwater-level fluctuations and land subsidence, with lag time and phase relationship closely related to aquifer structure, low-permeability layer distribution, and groundwater extraction intensity. This study provides a scientific basis for land subsidence mitigation and sustainable groundwater management in tropical island regions. Full article

Accurate crop evapotranspiration (ET_c) estimation at high spatial and temporal resolution remains a major challenge for precision irrigation. This study presents a multi-sensor data fusion framework combining daily MODIS (250 m), Sentinel-2 (10 m), and Landsat 8/9 (30 m) imagery with FAO-56 Penman–Monteith reference evapotranspiration (ET₀) to generate pixel-wise daily ET_c maps for irrigated maize (Zea mays L.) near Nyírbátor, Hungary, over six growing seasons (2020–2025). The proposed Median Time Series Model exploits field-scale MODIS NDVI as a temporal backbone and derives pixel-wise linear transfer functions to reconstruct daily NDVI at 10–30 m resolution. Three gap-filling strategies were compared; the median approach yielded the highest agreement (NDVI reconstruction R² = 0.81; RMSE = 0.19 (NDVI units); pixel-wise correlation 0.70–0.85) and effectively suppressed sub-pixel spectral mixture artefacts. Sentinel-2 consistently outperformed Landsat 8/9 (pixel-wise R² = 0.36–0.78 vs. 0.001–0.91). A nonlinear power crop coefficient model (K_c = a · NDVI^b) proved more robust than linear rescaling (mean validation R² of 0.80 (power) vs. 0.71 (rescale) across Sentinel-2 seasons; both methods were positive in all six seasons after correcting an unconstrained-fit artefact). Seasonal ET_c ranged from 313 to 545 mm, with cumulative water deficits reaching −334 mm during the 2021 drought. Six-year mean seasonal ET_c (428–483 mm for Sentinel-2) falls within the 400–600 mm range published for irrigated maize under comparable continental conditions, with season-integrated ET_c/ET₀ ratios (rescale method mean 0.86; power method mean 0.84) consistent with expected FAO-56 K_c trajectories. Cross-validation against an independent MATLAB implementation confirmed algorithmic consistency (reference ET₀ (R² = 0.88–0.91, Pearson r = 0.97–1.00)) and daily ET_c while identifying meteorological input as the dominant source of absolute ET_c uncertainty (estimated at ±15–30% through first-order error propagation). Plausibility assessment was limited to comparison with published seasonal benchmarks and an independent algorithmic implementation; no eddy covariance or lysimeter measurements were available for direct ET_c validation. Full article

Accurate crop mapping in middle-to-high latitude agricultural basins is essential for food security, agricultural management, and sustainable land-use planning. However, crop classification in these regions remains challenging because fragmented field patterns, mixed pixels, and overlapping phenological stages often lead to severe spectral confusion among major dryland crops. To address this issue, this study developed a Phenology–Spectral Dual-Constrained Strategy (PS-DCS) by integrating agronomic knowledge with physically constrained spectral features. The proposed framework identified August as the optimal observation window based on crop phenological divergence. Wheat was first extracted using a spectral fingerprint combining the Chlorophyll Index Red Edge (CI_RE) and Redness index. Subsequently, maize and soybean were separated within the non-wheat mask using the B6 red-edge band selected through feature separability analysis. Validation based on Sentinel-2 time-series imagery and 1056 independent field samples collected in 2025 yielded an Overall Accuracy of 95.36% with a Kappa coefficient of 0.928. Compared with RF, XGBoost, and CNN models, PS-DCS maintained competitive classification performance while substantially reducing dependence on large training datasets and complex parameter tuning. Cross-year validation during 2022–2024 further demonstrated stable spatial transferability without threshold recalibration. These results indicate that translating agronomic mechanisms into physically interpretable remote sensing rules provides an effective and transparent framework for high-precision crop mapping and long-term agricultural monitoring in complex agricultural landscapes. Full article

Accurate monitoring of oil palm plantations is important for balancing economic development with environmental conservation in Southeast Asia. However, existing plantation maps often suffer from low spatial resolution and a lack of recent temporal coverage, impeding effective surveillance of rapid land-use changes. In this study, we propose a deep learning framework to generate 10 m resolution oil palm plantation maps for Indonesia and Malaysia from 2020 to 2024, utilizing Sentinel-2 imagery without requiring new manual annotations. To address the resolution mismatch between coarse 100 m historical labels and 10 m imagery, we employ a U-Net architecture optimized with Determinant-based Mutual Information (DMI). This approach effectively mitigates the influence of label noise. We validated our method against 2058 manually verified points, achieving overall accuracies of 70.64%, 63.53%, and 60.06% for the years 2020, 2022, and 2024, respectively. The gradual decline in accuracy with time is consistent with a growing temporal mismatch between the 2016 historical reference labels and the later prediction years. At the regional scale, the mapped oil palm area suggests a peak in 2022 followed by a lower mapped extent in 2024. Land cover transition analysis further indicates exchanges with cropland and flooded vegetation, which should be interpreted together with the reported accuracy and uncertainty. Given the moderate per-year accuracies and the temporal mismatch between the 2016 supervision and the later prediction years, these results should be interpreted as regional-scale indicators rather than pixel-level change maps. The generated maps can support regional monitoring, sustainability assessment, and prioritization of areas for further validation. Full article

Mining-induced land subsidence poses significant geohazard risks to critical on-site mining operational support infrastructure, such as tailings storage facilities (TSFs). This study investigates the Doornpoort TSF subsidence at the South Deep gold mine in South Africa, using multi-temporal Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and Non-negative Matrix Factorisation (NMF) algorithm approach to split the superimposed subsidence contributing drivers, alongside the incorporation of Global Navigation Satellite System (GNSS) data and underground mining layout plans. 78 Sentinel-1A Satellite Aperture Radar (SAR) ascending acquisitions between May 2022 and December 2024 were obtained and processed to determine the average annual deformation rates and cumulative time-series displacement for the study area. The InSAR-derived subsidence rates at the designated three benchmarks on the embankments of the Doornpoort TSF and TSF 1&2 are −26.09 mm/year, −13.40 mm/year and −16.25 mm/year, while the maximum cumulative subsidence was −57.45 mm, −42.76 mm and −36.44 mm. A comparison of the InSAR results with the GNSS-derived subsidence results showed correlation standard deviations of 1.87 mm, 0.93 mm, and 1.05 mm, respectively. The InSAR results revealed spatially coherent subsidence patterns and a good correlation between deformation boundaries and underground mining layouts, suggesting that mining-induced stress redistribution is the primary driver of regional surface subsidence. The NMF decomposition of the InSAR-derived deformation result at a selected benchmark on the Doornpoort TSF embankment, whose average annual deformation with a cumulative time series deformation of −26.09 mm/year and −57.45 mm, respectively, revealed that 92% of the observed cumulative deformation is associated directly with the underground mining, whilst the remaining 8% is associated with TSF embankment consolidation. Furthermore, the selected decomposition benchmark within the TSF basin showed that underground mining alone accounted for 100% of the observed subsidence there. These findings support a coupled deformation framework in which deep mining activities influence regional subsidence, while localised geological conditions modulate its surface manifestation. Full article

High-accuracy parcel-level agricultural mapping is fundamental to precision agriculture. However, in fragmented agricultural regions of the Yangtze River Delta, identifying cropping patterns at the parcel level faces two compounding challenges: asynchronous multi-source observations and mixed-pixel effects in small parcels. When historical archive records are used as training labels, inter-annual cropping pattern changes further introduce label noise that undermines model reliability. To address these challenges and the label noise issue, we propose PAST (Parcel-level Asynchronous SpatioTemporal), a parcel-level cropping pattern classification framework comprising three stages: K-Shape-based label quality control, parallel dual-branch classification, and decision-level fusion. PAST employs a dual-branch architecture: the temporal branch achieves interpolation-free cross-modal phenological fusion of Sentinel-1 and Sentinel-2 data, while the image branch extracts canopy texture features from 0.8 m high-resolution imagery to partially address mixed-pixel interference. Experiments in a typical fragmented agricultural region of the Yangtze River Delta demonstrate that PAST achieves an overall F1 score of 0.926 and a small-parcel F1 score of 0.906, outperforming mainstream time-series baselines. These results confirm that combining K-Shape label quality control at the data level with a dual-branch interference-robust architecture at the model level provides a complete integrated three-stage pipeline for fine-grained crop mapping under weakly supervised historical archive label conditions. Full article

Accurate maize distribution information is critical for crop-area statistics, food-security assessment, and agricultural monitoring, but large-scale maize-mapping remains difficult in regions with limited reference samples, heterogeneous crop calendars, and frequent optical data gaps. This study proposes a phenology-adaptive maize mapping framework based on Sentinel-2 time-series imagery and an Enhanced Red-edge NDVI (ENDVIre). ENDVIre was constructed from the Sentinel-2 red-edge 4 and red-edge 2 bands to enhance the spectral response of maize during the silking-to-grain-filling stage, when maize develops a dense canopy and high chlorophyll content but is often confused with soybean. The framework first reconstructed the NDVI time series using an upper-envelope-constrained Whittaker smoother to identify key phenological stages, including sowing–emergence, vigorous growth, and maturity–harvest. NDVI, ENDVIre, and LSWI were then integrated into an interpretable decision-tree model with phenology-aligned time windows to distinguish maize from soybean, rice, wheat, and other non-maize backgrounds. The method was evaluated in six representative maize-growing regions across the United States, Brazil, China, Kenya, and Ukraine, covering different crop calendars, field sizes, and agricultural systems. The mean overall accuracy, F1-score, and Kappa coefficient across the six regions reached 93.27%, 93.14%, and 0.8652, respectively. Cross-year experiments in a winter-wheat–summer-maize rotation region from 2020 to 2024 achieved overall accuracies of 89.80–96.80%, while spatial-transfer experiments in six independent regions achieved overall accuracies of 87.40–95.40%. A comparison with existing high-resolution maize products in the Huang-Huai-Hai Plain further showed that the proposed method better balanced omission and commission errors. These results indicate that ENDVIre-based phenology rules provide an interpretable and transferable solution for maize mapping under limited-sample conditions, although persistent cloud contamination and fragmented smallholder landscapes remain important challenges. Full article

Iberian reservoirs are highly vulnerable to droughts, warming temperatures, and agricultural runoff, which accelerate eutrophication. Monitoring these dynamics is crucial for sustainable management. This study investigated long-term trends in chlorophyll-a (Chl-a) and water transparency Secchi depth and developed empirical models for the Alto Rabagão (Rb) and Aguieira (Ag) reservoirs in Portugal. We used Sentinel-2 Level-2A reflectance data coupled with 153 in situ observations (2014–2024) for model calibration (n = 95) and validation (n = 58). Temporal trends were assessed using linear regression and Mann–Kendall analyses. Empirical models based on spectral indices (TBDO1, TBDO, MCI, NDWI) were evaluated using walk-forward time-series cross-validation. Results revealed a significant Chl-a increase (0.38 µg L⁻¹ year⁻¹; p = 0.016) and a simultaneous decline in transparency (p < 0.001) in Rb, indicating progressive eutrophication. In contrast, no significant trends were detected in Ag. Reservoir-specific models achieved moderate-to-high predictive performance, particularly for Chl-a (R² up to 0.75; cross-validated R² = 0.67–0.68, RMSE = 1.1 µg L⁻¹, MAE = 0.82 µg L⁻¹). Models using combined datasets showed lower accuracy, highlighting the importance of site-specific calibration. Wilcoxon signed-rank tests confirmed the absence of systematic bias between observed and predicted values. Ultimately, Sentinel-2 imagery combined with time-series cross-validation provides a reliable and cost-effective framework for the long-term monitoring of inland water quality. Full article

Accurate crop mapping in fragmented agricultural landscapes is challenged by overlapping crop calendars and redundancy among multi-source time-series variables. Using Sentinel-1/2 imagery from December 2022 to December 2023, we constructed 275 season-specific spectral–phenological feature–month variables (125 for summer crops and 150 for winter crops) for rice, maize, soybean, winter wheat, and winter rapeseed in Jiangsu Province, China. An auxiliary binary Random Forest (RF) was used to estimate out-of-bag (OOB) permutation-based predictive contributions and construct search priors. A prior-guided genetic algorithm (GA) then identified compact subsets, with crop-specific five-class RF models used both to evaluate candidate subsets and to produce the final classifications. A fixed stratified 80/20 development–validation split was maintained throughout the analysis, with the validation subset reserved for final assessment. August and April were the principal discriminative periods for summer and winter crops, respectively, while VH backscatter and SWIR-related indices, particularly STI and NDTI, showed recurrent predictive contributions across crops. On the independent validation subset, the optical/vegetation-index scheme, SAR-only scheme, and the complete feature library achieved mean target-crop $F_1$-scores of 78.42%, 83.74%, and 86.96%, respectively. The GA-selected subsets retained 9–39 variables and achieved a mean five-class overall accuracy of 91.77% and a mean target-crop $F_1$-score of 93.95%. After non-target classes were merged into a single background class, the integrated seasonal maps achieved overall accuracies of 81.20–95.03% on the same validation subset. Supplementary classifier comparisons indicated that subset effects depended on the crop and learning algorithm. The findings support crop-specific, interpretable dimensionality reduction within the RF workflow, while broader transferability requires multi-year and multi-region evaluation. Full article

Reliable quantification of crop evapotranspiration (ET_c) at field resolution is a prerequisite for evidence-based irrigation scheduling in agricultural systems subject to nitrate leaching constraints. This study presents and evaluates a multi-sensor data fusion framework integrating MODIS Terra (500 m, daily) and Sentinel-2 (10–20 m, 5-day revisit) imagery to generate cloud-robust, daily ET_c maps for an 87.5 ha irrigated maize field in Nyírbátor, Hungary, during the 2020 and 2021 growing seasons. Three gap-filling strategies for missing Sentinel-2 NDVI observations were systematically compared: (i) co-regionalisation with cokriging, (ii) local time series interpolation of MODIS pixel centres using ordinary kriging, and (iii) a median time series of cotemporal MODIS pixels—a novel approach developed to suppress sub-pixel spectral contamination from roads and irrigation infrastructure. For field-mean temporal reconstruction, the median approach consistently outperformed the alternatives (adjusted R² = 0.81, NRMSE = 0.15–0.17; pixel-wise correlation 0.70–0.85), effectively filtering heterogeneous landscape artefacts. Daily crop coefficients (K_c) derived from fused NDVI time series via the FAO-56 framework yielded ET_c ranging from 0.99 mm day⁻¹ (initial stage) to 6.40 mm day⁻¹ (peak crop development). Seasonal precipitation–ET_c deficit analyses revealed contrasting patterns: near balance in 2020 versus an 85 mm mid-season deficit at critical nodes in 2021, demonstrating the potential utility of spatially explicit daily ET_c monitoring for irrigation scheduling. These deficit estimates represent irrigation demand indicators; a complete water balance would additionally require measured irrigation volumes, soil water storage changes, deep percolation, and surface runoff data. The methodology provides a proof-of-concept framework for EU Nitrates Directive compliance monitoring, relying solely on freely available satellite data. Independent ET_c validation is required before operational deployment, and transferability to other crops and regions requires validation across contrasting pedoclimatic conditions. Full article

In this study, we explored how early bark beetle attacks can be detected using weekly aggregated Sentinel-2 data in combination with static data, such as geo- and forestry data. We used an XGBoost classifier, known for its strength and reliability in classification, and compared three sets of data: static data only, satellite data only, and using both together. Having trained the models on cumulative weekly data, we were able to track changes in model performance and feature importance over time, identifying key weeks for the detection of bark beetle attacks. A systematic overview of feature importance identified the Red-edge 3 and blue Sentinel-2 bands as the most important when combined with static data; it also showed changes in feature importance compared to using satellite-only data, e.g., adding static features reduced the importance of red and red-edge 2 bands. Among the static features, land cover and landforms were the most important. Evaluating the temporal features for the combined model highlighted certain weeks as containing key information for detection: week 19, which was the main swarming week; week 25, which is 6 weeks after swarming and just before the second generation is completed; and weeks 31 and 33, more than 3 months after the tree was attacked and well after the new generation has swarmed. The study shows that combining static features with cumulative Sentinel-2, accumulated across weeks, are all important for improving the detection of bark beetle attacks, and that such ideas form an important part of early warning systems. Full article

Rice cultivation is a major source of methane (CH₄) emission in the agricultural sector, with a significantly higher global warming potential than carbon dioxide. Accurate and scalable quantification of CH₄ from rice paddies is essential for carbon accounting. This study presents an automated framework for estimating rice CH₄ emissions from irrigated paddies in the central plain of Thailand, integrating multi-sensor Synthetic Aperture Radar (SAR) observations with the IPCC methodology. The framework combines Sentinel-1 C-band SAR time series for phenological detection, ALOS-2 PALSAR-2 L-band full-polarimetric SAR for water regime classification, and IPCC water-scaling factors corresponding to Continuous Flooding, Single Drainage, or Multiple Drainage regimes. Evaluated across five stratified holdout sets, the phenology detection algorithm achieved planting and harvesting date Mean Absolute Errors of 6.1 ± 1.4 and 8.3 ± 1.7 days, with a 97.0% ± 2.7% operational detection rate. Water regime classification employed rice growth stage-specific Support Vector Machine classifiers with Radial Basis Function kernels (SVM-RBF), achieving per-stage test Balanced Accuracy ranging from 0.59 to 0.89. End-to-end integration using a four-track counterfactual decomposition yielded a full-pipeline mean absolute error of 18.5 ± 4.5 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$ (21.4% of the mean ground-based CH₄ calculation) and a mean bias of 3.5 ± 5.8 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$. Water level classification was confirmed as the dominant algorithmic uncertainty source, while the IPCC Tier 1 emission factor structural range (−32% to +48% of the default) exceeded all algorithmic errors combined. The proposed framework provides a spatially explicit approach for integrating multi-frequency SAR data into IPCC-compliant methane estimation, supporting Monitoring, Reporting, and Verification applications. Full article