Japan Aerospace Exploration Agency (JAXA) is conducting the Global Change Observation Mission (GCOM). The GCOM mission consists of two satellite missions: GCOM-W (called “SHIZUKU”) for observing water cycle and GCOM-C (called “SHIKISAI”) for observing carbon cycle and radiation budget. The GCOM-C satellite was launched from JAXA Tanegashima Space Center on December 23, 2017 (JST). It carries the Second generation Global Imager (SGLI). SGLI is a versatile, general purpose optical and infrared radiometer system covering the wavelength region from near ultraviolet to thermal infrared using two radiometers: Visible and Near Infrared Radiometer (SGLI-VNR) and the Infrared Scanning Radiometer (SGLI-IRS). SGLI is a successor of the Global Imager (GLI) on board the Advanced Earth Observing Satellite-II (ADEOS-II), which was launched in 2002.

GCOM-C provides 28 types of Standard products, including aerosols, clouds, vegetation, ocean chlorophyll, sea surface temperature (SST), snow and ice distribution, and snow grain size. In addition to these standard products, Research products are also released, supporting studies on global climate change and carbon cycle monitoring such as evapotranspiration index, fire detection index and snow surface albedo. Based on these products, the GCOM-C aims to provide comprehensive information of the Essential Climate Variables (ECVs) defined by Global Climate Observing System (GCOS). It observes 18 categories of geophysical parameters defined as ECSs related to the atmosphere, ocean, land, and cryosphere, contributing to climate change research. Since its launch, GCOM-C has been continuously observing for over eight years, generating a variety of geophysical data products. Furthermore, by integrating data from similar optical sensors such as MODIS, the mission has enabled the creation of continuous datasets spanning more than 25 years. These data are available through platforms such as G-Portal and JASMES, and for some products, API-based access is also becoming available.

How to cite: Shimada, R., Murakami, H., and Tanada, K.: Overview and latest updates of the GCOM-C/SGLI products and expanding data accessibility, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10439, https://doi.org/10.5194/egusphere-egu26-10439, 2026.

The Chlorophyll (Chl) content estimated from satellite observations often employs inversion of canopy radiative transfer models using other satellite estimates [leaf area index (LAI) and vegetation classification (VC)] and the leaf-carotenoid-content assumption, because its direct retrieval from the light absorption is difficult due to the rapid decrease in the Chl-absorption band reflectance reaching its lower limit at low Chl concentration as Chl increases in forests and grasslands. Such estimation largely depends on the accuracy of the input data, such as LAI, VC, and carotenoids, and thus cannot account for unexpected changes in VC and carotenoids. This study aims to develop a Chl-content estimation method directly from Global Change Observation Mission-Climate (GCOM-C) satellite observations, independent of other satellite estimates and assumptions. This study demonstrated that the SGLI green band (Band 6, 565 nm center wavelength) enables robust estimation due to its small but broad Chl sensitivity and lack of carotenoid sensitivity. In contrast, another green band (Band 5, 530 nm), which has the sensitivity to carotenoids, was not suitable for this purpose because carotenoid content varies across species and seasons and thus requires its assumption or estimation. In short, the green wavelength and its width were critically important. Using a green band (Band 6) and an NIR band (Band 11), one general estimation model of ground-area-based total Chl content (R²=0.87 and RMSE=0.55 g m⁻²) and three specific models for broad leaves, needle leaves, and grasses were created (R²=0.93, 0.91, and 0.94; RMSE=0.39 g m⁻², 0.44 g m⁻², and 0.36 g m⁻², respectively). The ground-area-based total Chl content estimation was independent of PFTs and LAI, whereas the leaf-area-based estimation required them. The general model, which only requires SGLI’s two-band reflectance, offers computational efficiency and near-real-time detection even in areas of unexpected change driven by natural and anthropogenic disturbances, because it is independent of the carotenoid assumption and other satellite-estimated results, such as VC (e.g., land cover classification) and LAI. Besides, it has less dependence on soil moisture, which affects vegetation background reflectance, and water area fraction in a vegetated pixel. Accordingly, SGLI ground-area-based Chl estimates are independent of those factors. Such independent ground-based estimates of Chl content can provide new insights into vegetation studies.

How to cite: Akitsu, T., Kume, A., Lai, R., Kobayashi, H., Nakaji, T., Hanba, Y., and Murakami, H.: Development of GCOM-C/SGLI ground-area-based chlorophyll content estimation: a computationally efficient algorithm free from LAI and vegetation classification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16127, https://doi.org/10.5194/egusphere-egu26-16127, 2026.

How to cite: Bekaert, D., Lacaze, R., Camacho, F., De Munck, D., Dutra, E., Eklundh, L., Gebruers, S., Jin, H., Lopes, F., Padilla, M., Raml, B., Sanchez-Zapero, J., Swinnen, E., Toté, C., Verger, A., and Wagner, W.: Consistent Multi-Mission Time Series for Global Assessment of Ecosystems Dynamics and Disturbance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16277, https://doi.org/10.5194/egusphere-egu26-16277, 2026.

The spectral reflectance of the snow-covered or bare sea ice in the Arctic is a critical parameter for determining the surface energy budget and for developing satellite passive remote sensing of clouds and aerosol particles. For static land surfaces, the bidirectional reflectance distribution function (BRDF) is acquired by sampling reflectance over multiple overpasses. The aggregated reflectance data are then fitted by a kernel-based approach. While the kernels were originally developed for vegetated surfaces, they have been extended to other surface types and the algorithm has been operationally implemented for imagery by Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). However, applying these kernels to the snow surface results in poor fitting due to its highly anisotropic reflectance. Even recently developed snow kernels, whose performance is yet to be validated against field observations, do not consider processes unique to sea ice such as surface roughness or floe drift. They are also confronted with sparse temporal and viewing angle sampling that stems from the sun-synchronous satellite orbits of MODIS and VIIRS. As a first step toward development of a kernel-based sea-ice BRDF retrieval, this study focuses on the packed, homogeneous sea ice surface before the melt onset and evaluates the performance of snow kernels. Specifically, we examine the impact of surface roughness on the directional and hemispherical reflectance using novel aircraft datasets.

We used data from the NASA Arctic Radiation-Cloud-Aerosol-Surface Interaction Experiment (ARCSIX), an aircraft campaign that took place near the northern coast of Greenland from May to August 2024. Particularly, we used airborne nadir-looking all-sky camera imagery, spectral irradiance by the Solar Spectral Flux Radiometer (SSFR) and laser altimetry data by the Land, Vegetation and Ice Sensor (LVIS). The camera imagery was radiometrically and geometrically calibrated to derive the directional reflectance. The imagery was then collocated against surface roughness derived by camera imagery and by LVIS when available, as well as against hemispherical albedo obtained from SSFR. We found that the snow kernels adequately capture the anisotropy of the camera-derived reflectance within the observed range of roughness. The kernel fit coefficients and predicted albedo showed high sensitivity to roughness, which modulated albedo by up to 10% in the shortwave infrared wavelength range. However, when limiting the viewing angle to the subset of angles that are accessible to satellite imagers, there is not enough information for the kernels to accurately predict the anisotropic reflectance and albedo perturbed by roughness. Additional constraints would likely be needed as a next step toward the retrieval of the sea ice BRDF influenced by surface roughness, leveraging other data such as multi-angle imagery (e.g., PACE, 3MI) and laser altimetry (e.g., ICESat-2, CryoSat).

How to cite: Hirata, K., Schmidt, K. S., Nataraja, V., and Hofton, M.: Impact of Arctic Sea Ice Heterogeneity on Surface Reflectance Evaluated with Airborne Imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15031, https://doi.org/10.5194/egusphere-egu26-15031, 2026.

How to cite: Nataraja, V., Hirata, K., Chen, H., Chen, Y.-W., Meyer, K., Peterson, C., and Schmidt, S.: Go with the floe: Spatio-temporal evolution of Arctic sea ice albedo using satellite imagery in a Lagrangian framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13633, https://doi.org/10.5194/egusphere-egu26-13633, 2026.

The global capacity for wind power has grown rapidly in recent years, yet uncertainties in wind power density (WPD) assessments still hinder effective climate change mitigation efforts. One major challenge is the significant underestimation of WPD when using coarser temporal resolutions (∆t) of wind speed data. Here, we show that using daily ∆t results in an average underestimation of 35.6% in global onshore WPD compared to hourly ∆t. This discrepancy arises from the exponential decay of WPD with increasing ∆t, reflecting the intrinsic properties of wind speed distributions, particularly in regions with weaker winds. To address this, we propose a calibration method that introduces a correction coefficient to reduce biases and harmonize WPD estimates across temporal resolutions. Applying this method to future wind energy projections under the Shared Socioeconomic Pathway 585 scenario increases global onshore WPD estimates by 25% by 2100, compared to uncorrected daily data. These findings highlight the effectiveness of calibration in reducing uncertainties, enhancing WPD assessments, and facilitating robust policy action toward carbon neutrality.

How to cite: Hou, C.: Detecting and calibrating large biases in global onshore wind power assessment across temporal scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1416, https://doi.org/10.5194/egusphere-egu26-1416, 2026.

Low-level wind shear is a frequent hazardous phenomenon at airports in the Xinjiang region of China, mainly due to complex terrain and highly variable weather conditions. It poses a significant risk to aircraft operations, particularly during take-off and landing. In this study, Doppler wind lidar observations are used to detect and identify low-level wind shear in the vicinity of airports, with a focus on improving the performance of existing identification algorithms under complex terrain conditions. Several commonly used wind shear detection algorithms are implemented, evaluated, and further refined. Based on their complementary strengths, a joint warning algorithm is developed to provide more reliable wind shear alerts. In addition, machine learning methods are explored to directly extract wind shear signals from raw lidar data, aiming to achieve faster detection without relying on full wind field retrieval. The results show that the joint warning algorithm clearly improves warning performance compared to individual algorithms, with fewer false alarms and missed events. The machine learning approach also demonstrates promising capability for rapid wind shear identification. These results suggest that combining multi-algorithm warning strategies with data-driven methods can effectively enhance future airport wind shear warning systems in the Xinjiang region. Further improvements are expected by training machine learning models with expanded libraries of representative wind shear cases.

How to cite: He, B.: Observation and Warning Algorithms for Low-Level Wind Shear at Airports in Xinjiang, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15353, https://doi.org/10.5194/egusphere-egu26-15353, 2026.

In offshore wind energy assessment, the vertical profile of near-surface wind speed is frequently modulated by regional atmospheric phenomena, including low-level jets (LLJs), variations in atmospheric stability, and land–sea thermal contrasts, thereby inducing pronounced variability in the synoptic wind field. Under certain conditions, wind speed may even decrease with height, exhibiting negative wind shear. Such non-monotonic wind profiles are particularly common over monsoon-dominated marine regions such as the Taiwan Strait. Previous studies have indicated that conventional vertical extrapolation approaches and Monin–Obukhov similarity theory (MOST) often fail to adequately represent the actual wind field under these conditions, consequently reducing the reliability of wind resource assessment and power generation forecasting.

This study uses near-surface meteorological data from the TReAD, a dynamical downscaling model(2km spatial resolution) developed by the National Science and Technology Center for Disaster Reduction (NCDR), along with MOST to estimate wind speed at hub height. The estimates are compared against observations from the nearest meteorological mast (approximately 725 m away) to develop a data-driven bias-correction approach. A bias prediction model is trained on 2019 data and then applied to independent datasets from 2022 and 2023 to evaluate its generalization across interannual variability and varying boundary-layer conditions.

The results show that, even when trained on data from a single year, the bias-corrected MOST wind speeds consistently reduce overall errors and improve the error across all evaluated years, suggesting that the proposed method can effectively correct MOST's systematic biases under negative wind shear and cross-year nonstationary conditions. Overall, this study presents a MOST bias-correction algorithm integrated with a dynamical downscaling model to produce near-surface wind speed estimates that more closely align with meteorological mast observations, thereby providing a practical approach for offshore wind energy assessment in the Taiwan Strait.

How to cite: Ho, C.-Y., Lin, C.-C., and Tsai, I.-W.: Bias-Corrected MOST Hub-Height Wind Estimates from Dynamical Downscaling for Offshore Wind Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15848, https://doi.org/10.5194/egusphere-egu26-15848, 2026.