Search Results (146)

In satellite altimetry calibration, the atmosphere’s integrated water vapor content has been customarily derived through the Global Navigation Satellite Systems (GNSS), principally over land where the satellite radiometer is not operational. Progressively, several alternative methods have emerged to estimate this wet troposphere component with ground instruments, alternative satellite sensors, and global models. For any ground calibration facility, integration of various approaches is required to arrive at an optimum value of a calibration constituent and in accordance with the strategy of Fiducial Reference Measurements (FRM). In this work, different estimation methods and instruments are evaluated for wet troposphere delays, especially when transponder and corner reflectors are employed at the Permanent Facility for Altimetry Calibration of the European Space Agency. Evaluation includes, first, ground instruments with microwave radiometers and radiosondes; second, satellite sensors with the Ocean Land Color Instrument (OLCI) and the Sea Land Surface Temperature Radiometer (SLSTR) of the Copernicus Sentinel-3 altimeter, as well as the TROPOMI spectrometer on the Sentinel-5P satellite; and finally with global atmospheric models, such as the European Center for Medium-Range Weather Forecasts. Along these lines, multi-sensor and redundant values for the troposphere delays are thus integrated and used for the calibration of Sentinel-6 MF and Sentinel-3A/B satellite altimeters. All in all, the integrated water vapor value of the troposphere is estimated with an FRM uncertainty of ±15 mm. In the absence of GNSS stations, it is recommended that the OLCI and SLSTR measurements be used for determining tropospheric delays in daylight and night operations, respectively. Ground microwave radiometers can also be used to retrieve tropospheric data with high temporal resolution and accuracy, provided that they are properly installed and calibrated and operated with site-specific parameters. Finally, the synergy of ground radiometers with instruments on board other Copernicus satellites should be further investigated to ensure redundancy and diversity of the produced values for the integrated water vapor. Full article

Ultrahigh resolution fine particulate matter (PM_2.5) mass concentration remote sensing products are crucial for atmospheric environmental monitoring, pollution source verification, health exposure risk assessment, and other fine-scale applications in urban environments. This study developed an ultrahigh resolution retrieval algorithm to estimate 30 m resolution PM_2.5 mass concentrations over urban areas from Landsat-8 and Sentinel-2A/B satellite measurements. The algorithm utilized aerosol optical depth (AOD) products retrieved from the Landsat-8 OLI and Sentinel-2 MSI measurements from 2017 to 2020, combined with multi-source auxiliary data to establish a PM_2.5-AOD relationship model across China. The results showed an overall high coefficient of determination (R²) of 0.82 and 0.76 for the model training accuracy based on samples and stations, respectively. The model prediction accuracy in Beijing and Wuhan reached R² values of 0.86 and 0.85. Applications in both cities demonstrated that ultrahigh resolution PM_2.5 has significant advantages in resolving fine-scale spatial patterns of urban air pollution and pinpointing pollution hotspots. Furthermore, an analysis of point source pollution at a typical heavy pollution emission enterprise confirmed that ultrahigh spatial resolution PM_2.5 can accurately identify the diffusion trend of point source pollution, providing fundamental data support for refined monitoring of urban air pollution and air pollution prevention and control. Full article

Traditional high-precision satellite orbits rely on globally dense and evenly distributed ground tracking stations, while the accuracy of precise orbit determination (POD) based on a regional network cannot compare with that of a global network. Low Earth orbit (LEO) satellites can serve as space-based monitoring stations to compensate for this. In response to the current regional integrated POD that only resolves the ambiguities of ground stations, this paper proposes an ambiguity resolution (AR) strategy related to LEO satellites to enhance GPS orbit accuracy. A joint observation network is established using seven International GNSS Service (IGS) stations within China and 10 LEO satellites, including GRACE-C/D, LuTan1-A/B, SWARM-A/B/C, Sentinel-3A/B, and Sentinel-6A. Experiments are conducted and analyzed from three aspects: independent baseline selection, the common view time, and the wide-lane (WL) threshold of double-differenced ambiguity. The ambiguity fixing strategy is determined to be a combination of inter-satellite and satellite–ground baselines, a common view time of 5 min, and a WL ambiguity threshold of 0.2 cycles. Taking the final products released by the IGS as the reference, the GPS orbit accuracy in the along-track, cross-track, radial, and 1D RMS is 3.23, 2.74, 2.36, and 2.89 cm, respectively, which represents improvements of 9.3%, 12.5%, 10.9%, and 10.8% compared with the solution that only fixes the ambiguities of ground stations. This result demonstrates that, in regional integrated POD, further implementation of LEO satellite-related ambiguity fixing significantly improves GPS orbit accuracy. Given the limitation that most LEO satellites can only receive GPS satellite signals, in the future, as more LEO satellites gain access to GNSS observations, the ambiguity fixing strategy presented in this paper can provide an effective and feasible approach. Full article

This study presents the “Mobile Triaxial Stabilized Water-leaving Reflectance Measurement System” (MTS-WRMS), a ship-borne radiometric system designed for high-precision acquisition of water-leaving radiance (L_w) and remote sensing reflectance (R_rs) in mobile aquatic environments. The system employs a triaxial stabilized gimbal to maintain the orientation of three spectrometers, effectively mitigating angular deviations. The system also features automatic azimuth adjustment to maintain the relative sun-sensor azimuth angle within the optimal range of 90° ≤ φ ≤ 135° and supports long-range wireless telemetry for autonomous real-time monitoring. The system’s accuracy was validated through the “direct approach” experiments, which demonstrated low systematic bias, with a mean weighted absolute percentage deviation (WAPD) of 4.42% in the 440–720 nm range, which covers 90% of radiant energy. Additionally, ground validation involving 296 matched spectra from Gaoyou and Zhuhai revealed that Sentinel-3 A/B OLCI products tend to overestimate R_rs in highly turbid waters, with weighted percentage deviation (WPD) and WAPD values of about 16% and 31%, respectively. The overestimation was particularly pronounced in the 400–443 nm range, likely due to low R_rs and inadequate atmospheric correction. The MTS-WRMS provides an advanced tool for accurate, real-time R_rs measurements, offering valuable insights into temporal and spatial variations in water bodies. Full article

Aquatic vegetation, including floating-leaved and emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV), and algal blooms (AB), are primary producers in eutrophic lake ecosystems and hold significant ecological importance. Aquatic vegetation and AB dominate in clear and turbid water states, respectively. Monitoring their dynamics is essential for understanding lake states and transitions. Sentinel imagery provides high-resolution data for capturing changes in aquatic vegetation and AB. However, the existing mapping algorithms for aquatic vegetation and AB based on Sentinel data only focused on one or two types. There are still limited algorithms that comprehensively reflect the dynamic changes of aquatic vegetation and AB. Additionally, the unique red-edge bands of Sentinel-2 MSI have not yet been fully exploited for mapping aquatic vegetation and AB. Therefore, we developed an automated mapping algorithm that utilizes Sentinel data, especially red-edge bands, to comprehensively reflect the dynamic changes of FEAV, SAV, and AB. The key indicator of the algorithm, the second principal component (PC2) derived from four red-edge bands and four other bands of Sentinel-2 MSI, can effectively distinguish between FEAV and AB. SAV was mapped by the Sentinel-based submerged aquatic vegetation index (SSAVI), which was constructed by fusing Sentinel-1 SAR and Sentinel-2 MSI data. The algorithm was tested in three representative lakes, including Lake Taihu, Lake Hongze, and Lake Chaohu, and yielded an average accuracy of 87.65%. The algorithm was also applied to track changes in aquatic vegetation and AB from 2019 to 2023. The results show that, over the past five years, AB coverage in all three lakes has decreased. The coverage of aquatic vegetation in Lake Taihu and Lake Hongze is also declining, while coverage remains relatively stable in Lake Chaohu. This algorithm leverages the high spatiotemporal resolution of Sentinel data, as well as its band advantages, and is expected to be applicable for large-scale monitoring of aquatic vegetation and AB dynamics. It will provide valuable technical support for future assessments of lake ecological health and state transitions. Full article

The Himalayan belt, formed due to the Cenozoic convergence between the Eurasian and Indian craton, acts as a storehouse of large amounts of strain, resulting in large earthquakes from the Western to the Eastern Himalayas. Glaciers also occur over a major portion of the high-altitude Himalayan region. The impact of earthquakes can be easily studied in the plains and plateaus with the help of well-distributed seismogram networks and these regions’ accessibility is helpful for field- and lab-based studies. However, earthquakes triggered close to high-altitude Himalayan glaciers are tough to investigate for the impact over glaciers and glacial deposits. In this study, we attempt to understand the impact of earthquakes on and around Himalayan glaciers in terms of vertical displacement and coherence change using space-borne synthetic aperture radar (SAR). Eight earthquake events of various magnitudes and hypocenter depths occurring in the vicinity of Himalayan glacial bodies were studied using C-band Sentinel1-A/B SAR data. Differential interferometric SAR (DInSAR) analysis is applied to capture deformation of the glacial surface potentially related to earthquake occurrence. Glacial displacement varies from −38.9 mm to −5.4 mm for the 2020 Tibet earthquake (M_w 5.7) and the 2021 Nepal earthquake (M_w 4.1). However, small glacial and ground patches processed separately for vertical displacements reveal that the glacial mass shows much greater seismic displacement than the ground surface. This indicates the possibility of the presence of potential site-specific seismicity amplification properties within glacial bodies. A reduction in co-seismic coherence around the glaciers is observed in some cases, indicative of possible changes in the glacial moraine deposits and/or vegetation cover. The effect of two different seismic events (the 2020 and 2021 Nepal earthquakes) with different hypocenter depths but with the same magnitude at almost equal distances from the glaciers is assessed; a shallow earthquake is observed to result in a larger impact on glacial bodies in terms of vertical displacement. Earthquakes may induce glacial hazards such as glacial surging, ice avalanches, and the failure of moraine-/ice-dammed glacial lakes. This research may be able to play a possible role in identifying areas at risk and provide valuable insights for the planning and implementation of measures for disaster risk reduction. Full article

The Chinese HuanjingJianzai-2 (HJ-2) A/B satellites are equipped with advanced sensors, including a Multispectral Camera (MSC) and a Polarized Scanning Atmospheric Corrector (PSAC). To address the challenges of atmospheric correction (AC) for the MSC’s wide-swath, wide-field images, this study proposes a pixel-by-pixel method incorporating Bidirectional Reflectance Distribution Function (BRDF) effects. The approach uses synchronous atmospheric parameters from the PSAC, an atmospheric correction lookup table, and a semi-empirical BRDF model to produce surface reflectance (SR) products through radiative, adjacency effect, and BRDF corrections. The corrected images showed significant improvements in clarity and contrast compared to pre-correction images, with minimum increases of 55.91% and 35.63%, respectively. Validation experiments in Dunhuang and Hefei, China, demonstrated high consistency between the corrected SR and ground-truth data, with maximum deviations below 0.03. For surface types not covered by ground measurements, comparisons with Sentinel-2 SR products yielded maximum deviations below 0.04. These results highlight the effectiveness of the proposed method in improving image quality and accuracy, providing reliable data support for applications such as disaster monitoring, water resource management, and crop monitoring. Full article

The paper addresses the spatiotemporal variability of anthropogenic film pollution (AFP) in Avacha Gulf near the Kamchatka Peninsula based on satellite synthetic-aperture radar (SAR) imagery. Coastal waters of the study area are subject to significant anthropogenic impacts associated with intensive marine traffic, as well as the flow of household and industrial wastewater from factories located on the coast. A quantitative approach to the registration and quantitative analysis of spatiotemporal AFP distributions was applied. This approach is based on the processing of long-term time series of SAR imagery, taking into account inhomogeneous observation coverage and changing hydrometeorological conditions of different regions of water areas in various time periods. In total, 318 cases of AFP were detected in 2014–2023 in Avacha Gulf, covering 332 km² of the total area (~3% of the water area) based on the 1134 processed radar Sentinel-1A/B scenes. The average value of AFP exposure, e, was about 93 ppm, evidencing the high level of AFP in the studied water area (comparable to areas of the Black Sea with intensive marine traffic, for which e was previously determined to be between ~90 and ~130 ppm). An interannual positive trend was revealed, indicating that over the 10-year period under study, the exposure of the waters of Avacha Bay (the most polluted part of Avacha Gulf) to AFP increased ~3-fold. An analysis of AFP spatial distributions and marine traffic maps indicates that this type of activity is a significant source of anthropogenic film pollution in Avacha Gulf (including Avacha Bay). It was shown that the generated quantitative information products using the introduced AFP exposure concept can be interpreted and used, for example, for making management decisions. Full article

The latest satellite in the Landsat series, Landsat-9, was successfully launched on 27 September 2021, equipped with the Operational Land Imager-2 (OLI-2) sensor, continuing the legacy of OLI/Landsat-8. To evaluate the uncertainties in water surface reflectance derived from OLI-2, this study conducts a comprehensive performance assessment of six atmospheric correction (AC) methods—DSF, C2RCC, iCOR, L2gen (NIR-SWIR1), L2gen (NIR-SWIR2), and Polymer—using in-situ measurements from 14 global sites, including 13 AERONET-OC stations and 1 MOBY station, collected between 2021 and 2023. Error analysis shows that L2gen (NIR-SWIR1) (RMSE ≤ 0.0017 sr⁻¹, SA = 6.33°) and L2gen (NIR-SWIR2) (RMSE ≤ 0.0019 sr⁻¹, SA = 6.38°) provide the best results across four visible bands, demonstrating stable performance across different optical water types (OWTs) ranging from clear to turbid water. Following these are C2RCC (RMSE ≤ 0.0030 sr⁻¹, SA = 5.74°) and Polymer (RMSE ≤ 0.0027 sr⁻¹, SA = 7.76°), with DSF (RMSE ≤ 0.0058 sr⁻¹, SA = 11.33°) and iCOR (RMSE ≤ 0.0051 sr⁻¹, SA = 12.96°) showing the poorest results. By comparing the uncertainty and consistency of Landsat-9 (OLI-2) with Sentinel-2A/B (MSI) and S-NPP/NOAA20 (VIIRS), results show that OLI-2 has similar uncertainties to MSI and VIIRS in the blue, blue-green, and green bands, with RMSE differences within 0.0002 sr⁻¹. In the red band, the OLI-2 uncertainties are lower than those of MSI but higher than those of VIIRS, with an RMSE difference of about 0.0004 sr⁻¹. Overall, OLI-2 data processed using L2gen provide reliable surface reflectance and show high consistency with MSI and VIIRS, making it suitable for integrating multi-satellite observations to enhance global coastal water color monitoring. Full article

The integration of the Crop Growth Model (CGM), Radiative Transfer Model (RTM), and Machine Learning Algorithm (MLA) for estimating crop traits represents a cutting-edge area of research. This integration requires in-depth study to address RTM limitations, particularly of similar spectral responses from multiple input combinations. This study proposes the integration of CGM and RTM for crop trait retrieval and evaluates the performance of CGM output-based RTM spectra generation for multiple crop traits estimation without biased sampling using machine learning models. Moreover, PROSAIL spectra as training against Harmonized Landsat Sentinel-2 (HLS) as testing was also compared with HLS data only as an alternative. It was found that satellite data (HLS, 80:20) not only consistently performed better, but PROSAIL (train) and HLS (test) also had satisfactory results for multiple crop traits from uniform training samples in spite of differences in simulated and real data. PROSAIL-HLS has an RMSE of 0.67 for leaf area index (LAI), 5.66 µg/cm² for chlorophyll ab (Cab), 0.0003 g/cm² for dry matter content (Cm), and 0.002 g/cm² for leaf water content (Cw) against the HLS only, with an RMSE of 0.40 for LAI, 3.28 µg/cm² for Cab, 0.0002 g/cm² for Cm, and 0.001 g/cm² for Cw. Optimized machine learning models, namely Extreme Gradient Boost (XGBoost) for LAI, Support Vector Machine (SVM) for Cab, and Random Forest (RF) for Cm and Cw, were deployed for temporal mapping of traits to be used for wheat productivity enhancement. Full article

The application of the Multispectral Instrument (MSI) aboard Sentinel-2A/B constellation for assessing water quality in Chilean lakes represents an emerging area of research, particularly for the environmental monitoring of optically complex water bodies. Similarly, atmospheric correction processors applied to aquatic environments, such as the Case 2 Networks (C2RCC-Nets), are notably underrepresented. This study evaluates the capability of C2RCC-Nets using different neural networks—Case-2 Regional/Coast Color (C2RCC), C2X-Extreme (C2X), and C2X-Complex (C2XC)—to estimate Secchi depth in Lake Lanalhue (eutrophic), Lake Villarrica (oligo-mesotrophic), and Lake Panguipulli (oligotrophic). The evaluation used different statistical methods such as Spearman’s correlation and normalized error metrics (nRMSE, nMAE, and nbias) to assess the agreement between satellite-derived data and in situ measurements. C2XC demonstrated the best fit for Lake Lanalhue, with an nRMSE = 33.13%, nMAE = 23.51%, and nbias = 8.57%, in relation to the median ground truth values. In Lake Villarrica, the C2XC neural network displayed a moderate correlation (r_s = 0.618) and error metrics, with an nRMSE of 24.67% and nMAE of 20.67%, with an nbias of 4.21%. In the oligotrophic Lake Panguipulli, no relationship was observed between estimated and measured values, which could be related to the fact that the selected neural networks were developed for very case 2 waters. These findings highlight the need for methodological advancements in processing satellite-derived water quality products for Chile’s optical water types, particularly for very clear waters. Nonetheless, this study underscores the need for model-specific calibration of C2RCC-Nets, as lakes with different optical water types and trophic states may require tailored training ranges for inherent optical properties. Full article

Research on glacier movement is helpful for comprehensively understanding the laws behind this movement and can also provide a scientific basis for glacier change and analyses of the dynamic mechanisms driving atmospheric circulation and glacier evolution. Sentinel-1 series data were used in this study to retrieve the three-dimensional (3D) surface motion displacement of the Muz Taw glacier from 22 August 2017, to 17 August 2018. The inversion method of the 3D surface motion displacement of glaciers has been verified by the field measurement data from Urumqi Glacier No. 1. The effects of topographic factors, glacier thickness, and climate factors on the 3D surface displacement of the Muz Taw glacier are discussed in this paper. The results show that, during the study period, the total 3D displacement of the Muz Taw glacier was between 0.52 and 13.19 m, the eastward displacement was 4.27 m, the northward displacement was 4.07 m, and the horizontal displacement was 5.90 m. Areas of high displacement were mainly distributed in the main glacier at altitudes of 3300–3350 and 3450–3600 m. There were significant differences in the total 3D displacement of the Muz Taw glacier in each season. The displacement was larger in summer, followed by spring, and it was similar in autumn and winter. The total 3D displacement during the whole study period and in spring, summer, and autumn fluctuated greatly along the glacier centerline, while the change in winter was relatively gentle. Various factors such as topography, glacier thickness, and climate had different influences on the surface motion displacement of the Muz Taw glacier. Full article

On 21 May 2021, an Mw 6.1 earthquake occurred in Yangbi County, Dali Bai Autonomous Prefecture, Yunnan Province, with the epicenter located in an unmapped blind fault approximately 7 km west of the Weixi-Qiaohou fault (WQF) on the southeastern margin of the Qinghai–Tibetan Plateau. While numerous studies have been conducted to map the coseismic slip distribution by using the Global Navigation Satellite System (GNSS), Interferometric Synthetic Aperture Radar (InSAR) and seismic data as well as their combinations, the understanding of deformation characteristics during the postseismic stage remains limited, mostly due to the long revisiting time interval and large uncertainty of most SAR satellites. In this study, we refined coseismic slip and afterslip distributions with nonlinear inversions for both fault geometry and relaxation time. First, we determined the fault geometry and coseismic slip distribution of this earthquake by joint inversion for coseismic offsets in the line-of-sight (LOS) direction of both Sentinel-1A/B ascending and descending track images and GNSS data. Then, the descending track time series of Sentinel-1 were further fitted using nonlinear least squares to extract the coseismic and postseismic deformations. Finally, we obtained the refined coseismic slip and afterslip distributions and investigated the spatiotemporal evolution of fault slip by comparing the afterslip with aftershocks. The refined coseismic moment magnitude, which was of Mw 6.05, was smaller than Mw 6.1 or larger, which was inferred from our joint inversion and previous studies, indicating a significant reduction in early postseismic deformation. In contrast, the afterslip following the mainshock lasted for about six months and was equivalent to a moment release of an Mw 5.8 earthquake. These findings not only offer a novel approach to extracting postseismic deformation from noisy InSAR time series but also provide valuable insights into fault slip mechanisms associated with the Yangbi earthquake, enhancing our understanding of seismic processes. Full article

Salinity is an essential parameter for evaluating water quality and plays a crucial role in maintaining the stability of lake ecosystems, particularly in arid and semi-arid climates. Salinity responds to changes in climate and human activity, with significant impacts on water quality and ecosystem services. In this study, Sentinel-2A/B Multi-Spectral Instrument (MSI) images and quasi-synchronous field data were utilized to estimate lake salinity using machine learning approaches (i.e., XGB, CNN, DNN, and RFR). Atmospheric correction for MSI images was tested using six processors (ACOLITE, C2RCC, POLYMER, MUMM, iCOR, and Sen2Cor). The most accurate model and atmospheric correction method were found to be the extreme gradient boosting tree combined with the ACOLITE correction algorithm. These were used to develop a salinity model (N = 70, mean absolute percentage error = 9.95%) and applied to eight lakes in Inner Mongolia from 2016 to 2024. Seasonal and interannual variations were explored, along with an examination of potential drivers of salinity changes over time. Average salinities in the autumn and spring were higher than in the summer. The highest salinities were observed in the lake centers and tended to be consistent and homogeneous. Interannual trends in salinity were evident in several lakes, influenced by evaporation and precipitation. Climate factors were the primary drivers of interannual salinity trends in most lakes. Full article

Land use/land cover (LULC) data are essential for Earth science research. Due to the high fragmentation and heterogeneity of landscapes, machine learning-based LULC classification frequently emphasizes results such as classification accuracy, efficiency, and variable importance analysis. However, this approach often overlooks the intermediate processes, and LULC mapping that relies on a single classifier typically does not yield satisfactory results. In this paper, to obtain refined LULC classification products at the watershed scale and improve the accuracy and efficiency of watershed-scale mapping, we propose a subregion ensemble learning classification framework. The Huangshui River watershed, located in the transition belts between the Qinghai-Tibet Plateau and Loess Plateau, is chosen as the case study area, and Sentinel-2A/B multi-temporal data are selected for ensemble learning classification. Using the proposed method, the block classification scale is analyzed and illustrated at the watershed, and the classification accuracy and efficiency of the new method are compared and analyzed against three ensemble learning methods using several variables. The proposed watershed-scale ensemble learning framework has better accuracy and efficiency for LULC mapping and has certain advantages over the other methods. The method proposed in this study provides new ideas for watershed-scale LULC mapping technology. Full article