Search Results (410)

The southern Red Sea coastal zone of Jizan Province in Saudi Arabia is increasingly exposed to seismic hazards, soil erosion, flash flooding, and tectonically shaped landscape instability, raising challenges for sustainable development. This paper integrates high-resolution 12.5 m ALOS PALSAR digital elevation model data, morphometric analyses, geomorphic interpretations, and the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion vulnerability and relative tectonic activity along 24 sub-basins. A total of 22 morphometric parameters were investigated, analyzed, and integrated into a weighted compound ranking key for prioritizing erosion-prone sub-basins. Geomorphic interpretation was assessed using the hypsometric integral, valley-floor width-to-height ratio, and basin shape, which were processed in the Relative Tectonic Activity (RTA) model. The results recognize sub-basins 8, 7, 22, 18, 23, 13, 3, and 9 as the highest-priority zones for soil conservation, while hypsometric integral values (0.04–0.48) reveal mature landscapes with geomorphic reactivation. High spatial correlation among morphometric prioritization, RTA interpretation, and the RUSLE model reveals that drainage characteristics, relief, lithology, and tectonic signatures indicate a significant spatial association with the soil erosion framework. The proposed model presents a reliable baseline key for sub-basin prioritization and climate-resilient mega-structure planning in data-poor settings, directly supporting SDG 9 and SDG 13. Full article

Accurate canopy-height mapping is essential for forest carbon monitoring, yet spatially continuous estimates over northern forests remain limited by sparse LiDAR sampling and unreliable optical data. We present a probabilistic deep learning framework that integrates seasonal Landsat optical, Sentinel-1 C-band SAR, and yearly ALOS-PALSAR-2 L-band SAR composites with GEDI training targets to produce 30 m canopy-height and pixel-level uncertainty maps over Ontario, Canada. A ResUNet ensemble is trained with a Laplace negative log-likelihood (NLL) loss; aleatoric and epistemic uncertainty are captured through a mixture of Laplace distributions, calibrated post hoc by Platt scaling and validated against airborne LiDAR (ALS P98). Under matched architecture and inputs, replacing Gaussian with Laplace NLL improved $R^2$ by 18.6%, reduced RMSE by 13.7%, and cut systematic bias from 1.43 to 0.14 m. The primary model reached $R^2=0.70$, RMSE = 3.65 m, and bias = 0.23 m, exceeding GEDI’s own footprint-level agreement with the independent ALS reference within Ontario’s managed forest zone. Seasonal compositing raised $R^2$ by 12.9% over summer-only inputs, and SAR-only configurations outperformed four global products in every forest class. The framework also delivers a calibrated, pixel-level uncertainty layer that reflects local structural difficulty beyond canopy height and can serve as a confidence layer for downstream carbon-stock and forest-management applications. Full article

Under the combined impacts of climate change and intensified human activities in coastal zones, mangrove ecosystems are increasingly exposed to degradation, fragmentation, and declines in ecological functions. It is therefore important to systematically examine the progress and evolution of the research hotspots in remote sensing monitoring of mangroves. In this study, 942 publications on mangrove remote sensing monitoring from 2000 to 2025 were retrieved from the China National Knowledge Infrastructure (CNKI) and the Web of Science Core Collection, comprising 485 CNKI records and 457 Web of Science records. CiteSpace 6.4.R2 was used to conduct bibliometric and knowledge-mapping analyses of publication trends, geographic distribution, author collaboration networks, keyword co-occurrence, keyword cluster timelines, and burst keywords. The results show that research on mangrove remote sensing monitoring generally increased over time, with marked growth after 2015. Research topics gradually shifted from early studies on mangrove distribution mapping, land-use change, and image classification to multi-source remote sensing applications, change detection, biomass estimation, blue carbon assessment, and machine learning- and deep learning-based methods. Author collaboration networks provide a descriptive overview of collaboration patterns and suggest that cross-team and cross-regional collaboration still needs to be strengthened. Keyword co-occurrence, cluster timeline, and burst keyword results indicate that remote sensing monitoring, machine learning, deep learning, random forest, support vector machine, object-based image analysis, ALOS PALSAR, ALOS-2 PALSAR-2, blue carbon, carbon stock, aboveground biomass, ecosystem services, and forest degradation are important themes in this field. Future research should further strengthen multi-source remote sensing data integration, cross-regional validation of intelligent algorithms, degradation monitoring indicator systems, uncertainty assessment, and long-term time-series analysis. These efforts will improve the accuracy, comparability, and management applicability of mangrove remote sensing monitoring and provide scientific support for coastal ecological conservation, mangrove restoration, and blue carbon governance. Full article

Atmospheric artifacts can obscure tectonic deformation in interferometric synthetic aperture radar (InSAR) observations, while conventional correction methods often depend on external atmospheric data. This study proposes a supervised residual-learning framework that directly predicts noise components in unwrapped InSAR maps rather than reconstructing deformation signals. Physically informed synthetic datasets were generated by combining Okada and Mogi deformation models with topography-correlated tropospheric delays, spatially correlated turbulent noise, and long-wavelength ramps. A network-depth sensitivity analysis identified a 20-layer denoising convolutional neural network as the optimal balance between accuracy and model complexity. Tests on independent synthetic datasets showed that the model reliably distinguished deformation from noise when the signal-to-noise ratio exceeded approximately 10⁻¹, whereas performance degraded under extremely noise-dominated conditions. The framework was further evaluated using ALOS/PALSAR and ALOS-2/PALSAR-2 observations of post-eruptive deformation at Mt. Ontake, Japan, and coseismic deformation associated with the 2009 L’Aquila earthquake, Italy. Compared with uncorrected, GACOS-corrected, and linear-corrected results, the CNN correction reduced topography-correlated and long-wavelength artifacts, improved temporal consistency, and generally achieved closer agreement with GNSS observations. These results suggest that residual noise learning provides an efficient approach for automatic atmospheric correction of unwrapped InSAR observations, with the potential for transferability across different deformation-source settings. Full article

Forest cover change alters land–atmosphere exchanges of energy, water, and carbon, thereby influencing local and regional climate. This study assessed climatic patterns associated with afforestation in Khyber Pakhtunkhwa, Pakistan, from 2003 to 2023 using remote sensing data and Google Earth Engine. Land surface temperature (LST) was treated as the primary response variable, while evapotranspiration (ET) was analyzed as a secondary response variable. Air temperature; precipitation; vegetation indices, including the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI); and elevation were used as supporting variables to interpret the broader climatic and biophysical responses of afforestation. MODIS land-cover, LST, ET, and vegetation-index products, together with climate research unit (CRU) climate data and ALOS-PALSAR DEM, were used to evaluate spatiotemporal trends and variable relationships. The results showed that mean LST increased by 0.520 ± 0.070 °C across KP during 2003–2023; however, areas classified as forest gain showed a localized cooling pattern of 0.490 ± 0.050 °C during the 2013–2023 forest-cover transition assessment window. Afforested areas also exhibited increased ET, whereas forest-loss areas showed reduced ET and higher LST. Specifically, ET increased by 0.013 ± 0.002 mm/8-day in afforested areas, whereas forest-loss areas showed a decline of 0.005 ± 0.001 mm/8-day. CRU-derived regional air temperature showed an increasing tendency of 0.310 ± 0.050 °C, whereas precipitation showed only a weak and statistically non-significant regional tendency; therefore, precipitation was used only as background climatic context. The NDVI and the EVI were negatively correlated with daytime LST, and elevation showed a strong negative relationship with LST. Overall, the findings indicate that forest-cover gain was associated with localized surface cooling patterns and improved vegetation–climate regulation indicators in the study area. Full article

Deterministic empirical formulas often fail to capture the epistemic uncertainties of dam failure mechanisms, leading to biased risk estimations. To address this, we propose the Probabilistic Hydro-Risk Forecasting System (PHRFS), a stochastic framework integrating Latin Hypercube Sampling (LHS) with HEC-RAS 2D hydrodynamic modeling. Applied to the Honghu-Erji Reservoir (Inner Mongolia), an ensemble of 60 stratified scenarios was generated based on a multi-model envelope of breach parameters and simulated over a $12.5\mathrm{m}$ ALOS PALSAR DEM using the Full Momentum Shallow Water Equations with the Eulerian–Lagrangian method (SWE-ELM). The SWE-ELM simulations reveal a heavy-tailed distribution of peak discharge (mean $2.65\times {10}^4{\mathrm{m}}^3/\mathrm{s}$; max $5.85\times {10}^4{\mathrm{m}}^3/\mathrm{s}$), significantly exceeding deterministic estimates. Sensitivity analysis identifies a physical dichotomy: breach depth ($D_b$) primarily controls flood magnitude ($r\approx 0.7$), whereas formation time ($t_f$) governs the arrival timeline. This temporal uncertainty propagates downstream, creating a distinct longitudinal gradient in SWE-ELM-derived warning time—ranging from rapid onset ($33.0\pm 19.3\min$) in proximal zones to a substantial lead time ($33.1\pm 4.2\mathrm{h}$) in the distal Hailar District. Consequently, a moderate coupling ($r=0.65$) emerges between economic loss and loss of life, while their spatial patterns remain strongly differentiated by warning time and exposure. Upstream settlements face high mortality risks due to insufficient evacuation windows, whereas the downstream urban center faces high economic exposure (∼1.57 billion CNY) but limited life loss (∼12.15 persons). These findings provide a scientific basis for differentiating emergency strategies, shifting from immediate life-saving in upstream reaches to asset protection in downstream areas. 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

Accurate monitoring of inundation in rice paddies is essential for optimizing water use efficiency and mitigating methane emissions; yet, detecting water beneath dense rice canopies remains a major challenge. This study proposed a reliable classification approach applied to the Winter–Spring 2025 season in An Giang province, Vietnam, by integrating multi-temporal ALOS-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) SAR data with in situ field surveys. Time-series Sentinel-1 observations were used to estimate rice phenology (rice age), while multi-polarization backscatter from ALOS-2 PALSAR-2 was analyzed to discriminate inundated from non-inundated conditions across different growth stages. Results demonstrated that L-band signals, particularly in VV polarization, penetrated dense vegetation effectively, enabling classification of inundated vs. non-inundated fields with an overall accuracy of 81% and a Kappa coefficient of 0.77. The resulting multi-date inundation maps revealed distinct flooding regimes consistent with local field survey observations. These findings demonstrated the potential of L-band VV SAR data for characterizing sub-canopy inundation conditions under rice canopies. Crucially, the approach provides essential data for greenhouse gas inventories and supports the verification of low-emission water management practices, such as Alternate Wetting and Drying (AWD). Overall, the study demonstrated the value of multi-frequency SAR integration for advancing agricultural monitoring and climate-smart management in rice-growing regions. Full article

Achieving scalable monitoring of Alternate Wetting and Drying (AWD) for methane mitigation in rice cultivation depends on establishing field benchmarks for drainage behavior and demonstrating that satellite observations can reliably detect corresponding changes in water status. We analyzed about two million high-frequency in situ water-level observations from hundreds of sensors deployed in rice fields across the Philippines and Japan to quantify drainage duration from near-surface conditions to 15 cm below the soil surface and to test the sensitivity of open-access PALSAR-2 dual-polarization L-band SAR to vertical water-level variations. Across 564 drainage events, the median drainage duration was 19.0 h, and only 0.9% of events exceeded 240 h, indicating that drainage happens generally within a day. Seasonal differences were evident in Pangasinan, while small Chiba and Cagayan samples suggested exploratory longer-duration patterns; multiple drainage events occurred in 48.0% of Philippine dry-season fields but only 21.6% of wet-season fields. PALSAR-2 data showed a statistical significance in detecting inundation at Mid crop growth stage with cross-polarization band, but the significant overlap induces challenges in operational applications. These results provide empirical benchmarks for AWD-related drainage dynamics while showing that dual-polarization PALSAR-2 alone is unlikely to support robust field-scale monitoring of rice-field water status. Full article

Worldwide, flash floods are among the most unpredictable and hazardous hydrological phenomena, particularly in arid and semi-arid regions such as the Kingdom of Saudi Arabia, where sudden heavy rainfall follows prolonged periods of drought. This work presents an effective integrated model for flood hazard evaluation in the Hanefah Catchment, a socioeconomically vital area in the central part of Saudi Arabia that includes the capital city, Riyadh. Using high-resolution ALOS PALSAR 12.5 m Digital Elevation Model spatial data, we extracted and investigated indicative linear, areal, and relief morphometric keys of 64 sub-catchments. This paper employs a dual-method concept that integrates a multi-criteria ranking method and the El-Shamy approach in conjunction with morphotectonic analysis to model flood-susceptibility zones. Furthermore, this paper suggests a comparative assessment of low-cost morphometric models under data-scarce conditions, assessing the multi-criteria ranking method against El-Shamy’s approach, using the topographic position index (TPI) as an internal terrain scale benchmark. The ranking method successfully assigned 85.7% of the historically recorded flood locations to the high-hazard zone that covers ~24.22% of the Hanefah catchment. In contrast, the El-Shamy approach systematically underestimated flood susceptibility because regional tectonic activity increases bifurcation ratios, resulting in just ~42.9% of the historical floods being assigned to the high-hazard zone. The final results highlight the northern and northwestern parts of the catchment as high-hazard zones, characterized by high drainage density and steep relief. This study provides a refined, cost-effective model that aligns with the strategic objectives of Saudi Vision 2030 for sustainable water resources management and significant urban development. Full article

Coherent space-based InSAR observations over snow- and ice-covered areas have been a valuable resource for cryospheric research. Coherence is considered a critical parameter for evaluating the quality of InSAR observations. This study evaluates the coherence characteristics of snow- and ice-covered areas using mainly fully polarimetric (quad-pol) X-band TerraSAR-X (TSX) and L-band ALOS PALSAR observations. The TSX data were acquired systematically during the Dual Receive Antenna campaign in 2010, while the quad-pol ALOS PALSAR L-band observations were acquired in 2007. A total of 57 TSX quad-pol images acquired over 17 areas at latitudes higher than 60° N were analyzed. The results across all study areas show relatively high coherence levels, ranging from 0.38 to 0.57, with the highest values observed in VV, followed by HH, and the lowest in HV. Interestingly, the highest coherence was found in the VV polarization, whereas HH coherence is typically higher than VV coherence in most InSAR applications. A comparative coherence analysis using quad-pol ALOS PALSAR L-band observations over selected snow- and ice-covered areas showed very similar coherence levels for both HH and VV polarizations. These results suggest that VV polarization is the most suitable for X-band InSAR applications over snow- and ice-covered areas. Full article

Water-level monitoring in rice paddies supports sustainable farming, responsible water management, and greenhouse gas emission mitigation. SAR-based remote sensing is an effective alternative for estimating water levels, especially in regions where optical observations are limited. This study evaluates ten ALOS-2/PALSAR-2 L-band SAR-derived polarimetric parameters for their contribution and effectiveness in water-level estimation across rice-growing phases using random forest regression in the Subang District, which is one of the largest rice-yield areas in West Java, Indonesia. Overall, L-band polarimetric information is clearly related to water-level dynamics throughout the rice-growing cycle, confirming its strong potential for quantitative water-level retrieval. The highest estimation accuracy was achieved by integrating all polarimetric parameter groups (MAE = 1.37 cm, RMSE = 1.79 cm, R² = 0.52, r = 0.73), indicating that no single group can adequately represent the complex scattering mechanisms governing water-level variability across an entire cropping season. Variable importance analysis shows a relatively uniform contribution (7.63–12.90%), suggesting synergies across parameters in water-level estimation. Phase-specific evaluation further reveals that Phase 2, corresponding to the vegetative-to-generative transition, is the optimal temporal window for L-band SAR-based water-level retrieval due to enhanced double-bounce scattering and reduced signal saturation. While Phase 2 data maximizes physical sensitivity and correlation, whole-phase modeling provides greater robustness and lower absolute errors, making it more suitable for L-band SAR-based operational water-level monitoring applications. Full article

Earthquake-induced landslides in active orogens such as the Nepal Himalaya pose severe threats to lives, infrastructure, and post-disaster recovery. While machine learning (ML) and deep learning (DL) approaches to coseismic landslide susceptibility mapping have advanced considerably, spaceborne interferometric synthetic aperture radar (InSAR) products, particularly line-of-sight (LOS) displacement and coherence-based damage proxy maps (DPMs), remain underutilized in event-based frameworks. This study develops and evaluates a multi-factor coseismic landslide probability model that integrates InSAR-derived deformation metrics with geomorphic and hydrologic predictors to support rapid post-earthquake hazard assessment. Using the 25 April 2015 Mw 7.8 Gorkha earthquake as a case study, LOS displacement was derived from ALOS-2 PALSAR-2 ScanSAR interferometry, and the normalized channel steepness index (K_sn) was computed from a digital elevation model. Fourteen conditioning factors were used to train five architectures: Random Forest (RF), XGBoost, CNN, U-Net, and DeepLabV3. Spatial autocorrelation was mitigated using a leave-one-basin-out three-fold spatial cross-validation strategy, with models evaluated on a patch-based domain comprising 655,360 pixels at a positive-class prevalence of 6.35%, establishing a no-skill AUC-PR baseline of 0.0635. InSAR integration consistently improved model performance under high class imbalance, increasing AUC-PR across all models by 7.8% to 17.3%. Random Forest achieved the highest AUC-PR (0.7940, nearly 12.5 times the baseline) and CSI (0.3027), providing the best balance between landslide recall (88.09%) and non-landslide specificity (88.68%) with the lowest false alarm rate (11.32%). XGBoost attained the highest AUC-ROC (0.9501) but exhibited lower recall (83.73%) and poorer calibration (Brier = 0.1397). Among DL models, DeepLabV3 produced the best-calibrated probabilities (Brier = 0.0693) and the highest CSI (0.2307), while U-Net offered the most balanced DL performance and CNN achieved the highest recall (92.40%) at the expense of elevated false alarms. Permutation feature importance identified K_sn as the dominant predictor, highlighting the strong tectono-geomorphic control on coseismic landslide occurrence. These results demonstrate that integrating InSAR-derived products substantially enhances landslide hazard assessment and supports more reliable rapid response in the Nepal Himalaya. Full article

Paddy field water management and rice phenology strongly affect crop productivity and environmental processes, requiring continuous and quantitative monitoring. This study combined satellite synthetic aperture radar (SAR) observations and ground-based Global Navigation Satellite System (GNSS) interferometric reflectometry (GNSS-IR) over a paddy field to analyze their sensitivities to water-level variations and phenological dynamics. Sentinel-1 (C-band) and ALOS-2/PALSAR-2 (L-band) SAR time series were compared with continuous GNSS-IR observations acquired using geodetic-grade instrumentation. For GNSS-IR, Lomb–Scargle periodogram (LSP) analysis of SNR data was applied to derive two indicators: (i) the dominant spectral peak (f_water) frequency associated with the effective reflecting surface, and (ii) a normalized spectral integral (GNSS Phenology Indicator, GPI) representing vegetation-induced scattering and attenuation effects. The temporal evolution of LSP spectra exhibited systematic changes with rice phenological progression, including peak broadening and the emergence of multiple peaks as vegetation developed. For water level variations, L-band SAR co-polarized backscatter (VV and HH) and the GNSS-IR spectral peak exhibited comparable relationships with in situ water level, whereas C-band SAR showed weaker sensitivity. For phenological dynamics, GPI showed temporal behavior similar to that of the SAR polarization ratio (VH/VV), with clear responses around key growth stages, such as heading and harvest. These results suggest that SAR polarization-based indicators and GNSS-IR spectral characteristics can be interpreted within a consistent electromagnetic framework: co-polarized L-band SAR responses correspond to the water-surface-related GNSS-IR peak, whereas cross-polarized indicators correspond to GPI. This study demonstrated the potential of GNSS-IR as complementary information for physically interpreting SAR scattering mechanisms, highlighting a pathway toward more integrated microwave-based monitoring of land surface processes. Full article

Most of the dam structures around the world are approaching the end of their economic life of 50 to 70 years, especially due to sediment accumulation in reservoir areas. This situation necessitates the development of proactive infrastructure management strategies. This study presents an original framework for the process of renewal of aging dams that blends remote sensing techniques and meta-intuitive optimization methods. Within the scope of the study, the Hasanlar Dam located in Düzce was selected as a sample, and a new dam axis was determined in the upper part of the basin. A detailed volume–height curve was created using 12.5 m resolution ALOS PALSAR numerical height models (DEM) and GIS-based spatial data curation to calculate the reservoir storage capacity in precise increments of 2 m. To maximize the structural efficiency of the proposed “New Hasanlar Dam”, the cross-sectional area has been minimized through seven current algorithms such as Genetic Algorithm (GA), Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Crayfish Optimization Algorithm (CAO), and Cheetah Optimizer (CO). The findings obtained prove that the PSO and CAOs achieved a significant reduction in cross-sectional area by 29.36% and successfully approached the global optimum. The replacement of the 55.5 million m³ capacity of the existing Hasanlar Dam with a new structure with a height of 78 m will guarantee sustainability and structural safety in water management. As a result, this study reveals that the integration of high-resolution remote sensing data and advanced heuristic methods is a cost-effective and powerful tool in the strategic renovation of aging hydraulic infrastructures. Full article