Resource
2026 EN
Rongguang Ni · Guangjian Yan · Xihan Mu
+4 more
Earth observation satellites are crucial in regional scale monitoring, but the effectiveness of optical satellite is often severely restricted by cloud cover. Most existing satellite scheduling methods at regional-scale ignore the impact of clouds, resulting in a waste of observation resources and degraded data quality. To address this problem, this study proposes a regional scale multi-satellite scheduling framework that considers a historical cloud cover for the first time. The core of the framework is a novel pixel-region integrated method (PRIM), which quantifies the functional relationship between observation success probability (OSP) and observation success fraction (OSF) using historical cloud products (MOD35) and satellite overpass information. We construct an optimization model to maximize OSF under a user-preset OSP (e.g., 95%) and use a genetic algorithm (GA) to solve it. Through experiments in three different climate and terrain regions in China, we compare the proposed method with two baseline strategies, namely, “nadir observation” and “maximum coverage”. The results show that the proposed method can not only achieve 100% geometric coverage, but also far exceeds the baseline strategy in terms of effectiveness in considering cloud effects. For example, at 95% OSP confidence level, the OSFs obtained by this method for the three regions are on average 14% higher than those of the maximum coverage strategy. In addition, through Monte Carlo simulation verification, the central limit theorem (CLT) approximation method we rely on improves computational efficiency by hundreds of times while ensuring accuracy. This framework can provide mission planners with decision-making solutions with clear probabilistic guarantees derived from long term historical cloud statistics, significantly improving the efficiency of satellite resource utilization and the ability to obtain effective data under the influence of cloud uncertainty.
Resource
2026 EN
Julian Chaubell · Simon Yueh · Andreas Colliander
+16 more
The Soil Moisture Active Passive mission (SMAP, since 2015) from The National Aeronautics and Space Administration's (NASA) and Soil Moisture and Ocean Salinity mission (SMOS, since 2009) from The European Space Agency's (ESA) measure polarimetric brightness temperature (TB) at L-band (1.4 GHz). They provide estimates of surface soil moisture (SM) and L-band vegetation optical depth (L-VOD) approximately every 2-3 days at the equator, with a spatial resolution of ∼40 km for a local overpass time of 6 AM/PM. Integrating the AM and PM TB observations from SMAP and SMOS satellite missions can reduce the revisit time to about 1 day over the equator, thus helping to address fast-response hydrologic processes that cannot be addressed with the 2-3 day revisits. This will allow the capture of the SM conditions more often and hence capture the rate of decline due to drainage and recharge to groundwater. This occurs early during dry down after storms. The integration of SMOS measurements also works to fill temporal gaps caused by missing data due to SMAP instrument outages. This paper details the integration of the SMAP and SMOS observations to achieve a combined SM and L-VOD product. The SMOS TB observations interpolated to 40° incidence angle were first relatively calibrated (RC) to generate SMAP-like SMOS TB (RCTB), making the combined TB records consistent spatially and temporally. The SMAP baseline SM and L-VOD retrieval algorithm was then applied to the RCTB records. We showed that after relative calibration (ARC), the bias between the SMAP and SMOS TBs was reduced from 0.5 K to -0.03 K for TB H and from 2.6 K to 0.014 K for TB V in the AM cases. For the PM cases the mean value of differences was reduced from 0.82 K to 0.27 K and from 2.88 K to 0.19 K for TB H and TB V respectively. The comparison of the core validation sites (CVS) in-situ SM to the retrieved SM from the combined TB record showed an unbiased root-mean-square-difference of 0.039 m3/m3 for both AM and PM cases and the retrieved L-VOD demonstrated consistency with independent biomass and tree height estimates. We also showed an improvement in temporal coverage and that the global mean number of visits to each grid went up from 283 (SMAP only) to 446 (SMAP+SMOS) when both AM and PM overpasses are considered.
Resource
2026 EN
Zhiwei Chen · Wei Zheng · Wenjie Yin
+1 more
Temporal gravity field models derived from the Gravity Recovery and Climate Experiment and its Follow-On mission [GRACE(-FO)] observations effectively detect global surface mass changes. However, the 11-month gap between the GRACE(-FO) missions introduces biases into the research analysis and limits further application. Therefore, ensuring the continuity of the GRACE(-FO) products is crucial for accurately analyzing regional terrestrial water storage anomalies (TWSA). Here, we predict the water storage changes in the Yangtze River Basin during the GRACE(-FO) missions gap by using a geographically weighted-based reconstruction method (GW-based) and compare its accuracy with the results obtained from Region-based and Grid-based methods. The results reveal that the GW-based method achieves an average root mean square error (RMSE) of 12.24 mm and a correlation coefficient (CC) of 0.94 across the 13 test periods. These results outperform those from the Region-based and Grid-based methods, underscoring the superior accuracy of the GW-based method. Specifically, the spatial distribution of the GW-based results is more reasonable than that of the Grid-based results, and the CC and RMSE of the GW-based results are improved by 25.15% and 33.53%, respectively. The comparison with soil water storage changes derived from the GLDAS exhibited that the accuracy of the GW-based results is consistent across both the gap and test periods. Furthermore, the GW-based results strongly agree with the three published reconstruction results. Except for the lower reaches of the Yangtze River, where the CC is comparatively lower, those for the remaining regions exceed 0.938. Hence, the preceding analyses indicate the exceptional reliability of the GW-based method for predicting the water storage change within the Yangtze River Basin during the gap period.
Resource
2026 EN
Elissar Al Aawar · Sofien Resifi · Juan Felipe Mendez Espinosa
+1 more
Accurate building footprint delineation and height estimation are critical for urban mapping, infrastructure monitoring, and risk assessment. We present SwinH-Fuse , a hierarchical satellite remote sensing framework that integrates data from the European Space Agency's (ESA) Sentinel-1 synthetic aperture radar (SAR) mission and Sentinel-2 multi-spectral optical mission to jointly extract building footprints and estimate heights. The framework employs a dual-branch Swin-UNet transformer for city-scale classification, a UNet for neighborhood-scale refinement, log-scale regression, and bias correction for height estimation. Monte Carlo dropout is integrated to quantify predictive uncertainty. Across diverse test regions in Europe and North America, SwinH-Fuse achieved a height RMSE of 1.22 m with a Pearson correlation coefficient of 0.73 with reference values. In an out-of-sample case study on the King Abdullah University of Science and Technology (KAUST) campus, the regression module maintained strong correlation despite classification challenges in sparse desert conditions. The results demonstrate that SwinH-Fuse provides accurate, scalable, and uncertainty-aware urban mapping suitable for sustainable planning and risk assessment.
Resource
2026 EN
Arnab Muhuri · Natascha Oppelt
Accurate shallow-water bathymetric mapping is essential for understanding coastal dynamics, supporting marine infrastructure design, and managing shallow-water ecosystems. While both spectral and bathymetric data can be collected from airborne platforms, bathymetric LiDAR surveys are expensive, limited in coverage, and not routinely performed everywhere. NASA's ICESat-2 mission, equipped with a photon-counting Advanced Topographic Laser Altimeter System, enables cost-effective opportunities for depth retrieval in optically shallow waters by detecting photon returns from subaqueous surface features. To overcome the challenges of sparse coverage and variable signal quality inherent to ICESat-2 bathymetry, we introduce a photon confidence-aware multimodal machine-learning framework that integrates spaceborne ICESat-2 photon returns with high-resolution airborne multispectral data. Using ICESat-2 depth estimates, high-resolution MagicBathyNet multispectral imagery, and co-registered reference bathymetry from our primary site in Puck Lagoon, Baltic coast, Poland, we trained and validated random forest (RF) and extreme gradient boosting (XGB) models. We observed that RF was more robust when training labels were sparse or noisy, whereas XGB achieved slightly lower errors when high-confidence labels were abundant. We further propose a scene-level bias correction step using sparse, high-confidence ICESat-2 photons that can reduce prediction errors, highlighting the importance of photon quality in depth retrieval. When the correction pings were selected from the higher-confidence YAPC regime (150–255) rather than the lower-confidence regime (0–150), the reduction in mean absolute error and root mean squared error was generally larger, indicating that ICESat-2 photon-ping confidence influences the effectiveness of bias correction applied to predicted depths. RF achieved the lowest corrected errors when both training samples and correction pings were drawn from the high-YAPC category (MAE $ 0.38$ m, RMSE $ 0.68$ m). A cross-site validation over Agia Napa, Cyprus, indicated that model performance was driven by the same confidence regime ranking as at the primary site under a reference-masked validation. Our framework provides a scalable and cost-effective alternative to traditional LiDAR surveys and establishes practical guidelines for confidence-aware photon selection, bias correction, and cross-site validation. This enables more reliable and resource-efficient shallow-water mapping in data-sparse regions lacking dense bathymetric coverage.
Resource
2026 EN
Emma Tronquo · Hans Lievens · Susan C. Steele-Dunne
+2 more
Terrestrial evaporation ( $E$ ) plays a crucial role in the water, energy, and carbon cycles and modulates climate change through multiple feedback mechanisms. While process-based models estimate $E$ using satellite-derived drivers, they typically operate at daily or lower temporal resolutions. Key components of $E$ , such as transpiration and interception loss, exhibit strong diurnal variability, especially under water stress and during or shortly after precipitation events. Therefore, capturing the sub-daily variability of these variables is essential for improved process understanding and $E$ monitoring at fine temporal resolutions. Sub-daily microwave observations offer the potential to resolve these short-term processes while providing all-sky retrievals. The Sub-daily Land Atmosphere INTEractions (SLAINTE) mission, proposed as part of ESA's New Earth Observation Mission Ideas, aims to provide sub-daily Synthetic Aperture Radar (SAR) observations of surface soil moisture (SSM), vegetation optical depth (VOD), and wet/dry canopy state (WDCS). These observations are expected to enhance the estimation of $E$ beyond current capabilities. This study explores the added value of such observations through Observing System Simulation Experiments conducted at four European eddy-covariance forest sites, constraining a sub-daily version of the Global Land Evaporation Amsterdam Model (GLEAM) with synthetic sub-daily microwave observations. Three experiments assess the impact of: (1) sub-daily SSM on bare soil evaporation and transpiration, (2) sub-daily VOD on transpiration, and (3) sub-daily WDCS on interception loss. Results demonstrate that prospective sub-daily microwave data can substantially improve $E$ estimates and its components, showing average relative improvements in terms of $\Delta \text{RMSE}$ of up to 25% for interception loss when assimilating sub-daily WDCS, and up to 33% for transpiration when using sub-daily VOD. Our results highlight the need for satellite missions that provide sub-daily microwave data to better understand forest responses to environmental stress.
Resource
2026 EN
Xiaohui Li · Xinhai Han · Jingsong Yang
+4 more
To fully exploit the advantages of GNSS-R small satellites (large quantity, short revisit cycle) and address key challenges of multi-source GNSS-R data—track-wise distribution, uneven spatial coverage, and observation gaps, this study proposes a FusionGAN model integrating spatio-temporal attention mechanisms and physical constraints. This model enables end-to-end reconstruction of full-coverage gridded sea surface wind fields directly from sparse GNSS-R observations in the China Seas region. A two-stage training strategy is adopted: pre-training using 31+ years of CCMP wind dataset to lay a foundation for learning how to fill data gaps based on input orbit data, followed by fine-tuning with real GNSS-R observations from CYGNSS, FY-3 series satellites, and Tianmu-1 to enhance adaptability to practical data. Experimental results, validated through comparisons across low-to-medium wind, high wind, and typhoon scenarios, demonstrate the effectiveness of the proposed model: Finetuned-FusionGAN achieves an overall Bias of –0.03 m/s, an RMSE of 1.38 m/s (22% reduction), and a correlation coefficient $R$ of 0.90. Specifically, by leveraging physics-constrained losses and spatio-temporal attention, the model reduces input GNSS-R observational data RMSE by 28.5% to 1.18m/s and improves R by 8.2% to 0.92. In non-orbit regions, it generates physically consistent wind fields with an RMSE of 1.45m/s, a correlation coefficient $R$ of 0.89, comparable to input GNSS-R observational data accuracy. Moreover, benefiting from its physics-constrained design, the proposed model effectively mitigates observational discrepancies in multi-source GNSS-R observations, fills spatial gaps, providing an effective solution for multi-source GNSS-R sea surface wind field fusion.
Resource
2026 EN
Patrick Klenk · Kersten Schmidt · Jakob Giez
+4 more
Sentinel-1 C is the third satellite of the Sentinel-1 mission. Launched in late 2024, it ensures seamless continuity of C-band SAR data for global monitoring of the Earth surface in the framework of the Copernicus program. In parallel to the commissioning of Sentinel-1 C by the European Space Agency (ESA), an independent verification of the system calibration has been performed by the German Aerospace Center (DLR) under an ESA contract. Based on an efficient calibration strategy, this paper describes the different activities performed and discusses in detail the results and main findings of DLR during the commissioning phase (CP) of Sentinel-1 C. The main goal is to provide a comprehensive compendium of the SAR calibration and performance status of S1C at the beginning of its operational mission: At the end of the commissioning phase we find that S1C achieves or even exceeds all SAR performance requirements put forth and is ready for its nominal mission operations.
Resource
2026 EN
Menghua Wang · Wei Shi · Lide Jiang
In this study, we propose a new technique to monitor and evaluate the performance of the on-orbit radiometric calibration for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) for deriving global ocean color (OC) products. Based on the fact that the open ocean is black (i.e., no normalized water-leaving radiance nL w ( λ ) contributions) at the near-infrared (NIR) and shortwave infrared (SWIR) bands, and atmosphere and ocean conditions in the central regions of ocean gyres are generally clear and stable with the stable interannual and decadal variability, we compute the Rayleigh-corrected reflectance ρ rc ( λ ) and its de-cycled reflectance Δ ρ rc ( λ ) from VIIRS-SNPP, and use them to evaluate the best ocean regions to monitor the long-term sensor calibration performance at the NIR and SWIR bands. Of the six studied ocean locations in the Marine Optical Bouy (MOBY) in Hawaii, South Pacific Gyre (SPG), North Pacific Gyre (NPG), South Atlantic Gyre (SAG), North Atlantic Gyre (NAG), and Indian Ocean Gyre (IOG), the SPG is the most optimal site for the least trending of ρ rc ( λ ) and Δ ρ rc ( λ ) with significantly low interannual variations, and nearly flat trending due to its relative long-term stability for atmosphere properties. The slopes of the trending (2012–2024) from VIIRS-SNPP-measured Δ ρ rc ( λ ) are 0.42%, 0.10%, 0.21%, 0.32%, and 0.37% per year at its spectral bands of 745 nm, 862 nm, 1238 nm, 1601 nm, and 2258 nm, respectively. On the other hand, significantly downward Δ ρ rc ( λ ) trending at the MOBY site and strong interannual Δ ρ rc ( λ ) variations at the IOG site are attributed to the multi-year aerosol optical thickness (AOT) variations at these sites. From VIIRS-SNPP-measured ρ rc ( λ ) and Δ ρ rc ( λ ) at the SPG in 2012–2024, we show that the sensor has been well calibrated for both the NIR and SWIR bands, as well as for the visible bands. Stable and high-quality Level-1B data from other two VIIRS sensors on the NOAA-20 and NOAA-21 satellites are also evaluated. Results demonstrate that, with well radiometric calibrations, highly consistent mission-long OC products at the SPG site can be derived for three VIIRS sensors.
Resource
2026 EN
Federica Cotugno · Nestor Yague-Martinez · Paolo Berardino
+9 more
In recent years, small Synthetic Aperture Radar (SAR) satellite constellations have emerged as a viable solution due to their ease of design and relatively low launch costs. These next generation systems aim to meet the growing needs of the Differential Interferometric SAR (DInSAR) community, including high spatial resolution and temporal acquisition frequency. Nevertheless, despite their benefits, small satellites face drawbacks such as low power budgets and limited imaging capabilities, needing the exploration of new orbital configurations to meet specific mission objectives. Among these, mid-inclination orbits (MIOs) offer the unique advantage of enabling the retrieval of North-South surface displacements, overcoming a key limitation of conventional sun-synchronous orbits (SSOs). In this study, we analyze three SAR datasets acquired by Capella Space over the Campi Flegrei (CF) caldera (Italy), exploiting a 45° MIO. The presented results, validated against GNSS measurements, show a mean standard deviation of 3-4 mm between DInSAR and GNSS LOS-projected time series, while the uncertainty for the North South component is estimated to be less than 5 mm. Furthermore, we retrieve, for the first time, comprehensive North-South deformation products of the CF caldera, including both a high resolution map and displacement time series. These outcomes represent a precursor for the upcoming Italian SAR constellation NIMBUS, part of the IRIDE program, which will be launched in a similar MIO configuration and become operational during 2027.