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The surface shortwave cloud radiative effect (CRE) plays a critical role in modulating the Earth's energy balance and climate change. However, accurately quantifying the CRE remains challenging due to significant uncertainties in downwelling surface shortwave radiation (DSSR) and cloud parameter estimates, especially in the Arctic. This paper introduces a novel approach that enhances the accuracy of CRE estimation by constructing a computationally efficient, long-term gridded surface cloud fraction radiative kernels (GCF-CRKs) and integrating refined DSSR estimates and a high-precision cloud fraction (CF). By leveraging the correlation between the top-of-atmosphere (TOA) shortwave radiative parameters and surface radiation, combined with high-precision fused CF datasets from multiple satellite sources, we construct a CF-dependent model to refine DSSR estimates. Based on this model, we construct GCF-CRKs using the CF as the sole perturbation parameter to isolate the CF CRE. Our results indicate that this method significantly improves the accuracy of DSSR estimation under partially cloudy conditions (0-2, and the bias was reduced by 1.23 Wm-2, which was an improvement of 8.7 % (reduction of RMSE) against the CERES-EBAF. The even greater improvements were achieved at stations in Greenland (RMSE reduced by 4.53 Wm-2 and a bias reduced by ~6.89 Wm-2, with an accuracy improved about 11.1%). The GCF-CRKs exhibit similar signs and patterns and enhanced stability compared to existing kernels. The sensitivity analysis results reveal that seasonal and interannual variations introduce GCF-CRK uncertainties of approximately 1 Wm-2 %-1 and 0.1 Wm-2 %-1, respectively, while spatial variations within the same latitude range can cause CRK uncertainties of 0.2–1.2 Wm-2 %-1. These uncertainties can result in CRE biases ranging from 5 to 50 Wm-2, which demonstrates the limitations of existing methods that utilize short-term, small-area parameter data to produce global CRKs. Using these GCF-CRKs, we estimated the spatiotemporal properties of the surface shortwave CRE in the Arctic over a 21-year period (2000–2020), and the trend result indicates that despite the increasing influence of the CF on the Arctic DSSR, the smaller magnitude and interannual trend of the annual average surface shortwave CRE suggest that previous studies may have overestimated the magnitude and rate of the cooling effect of clouds on the Arctic DSSR by up to 4 Wm-2 and 0.5 Wm-2 per decade, particularly in Greenland. This study provides a more accurate and efficient assessment of the CRE, and the results underscore the need for more effective measures to mitigate the impact of Arctic amplification on the surface radiative energy balance, which is crucial for understanding and addressing regional and global climate change. The GCF-CRKs can be freely available to the public at https://doi.org/10.5281/zenodo.13907217 (Liu, 2024). [ABSTRACT FROM AUTHOR]

Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VIopt) and the differential radar information (DRI) derived from SAR data. Targeting the estimation of corn plant height (H) and the BBCH (Biologische Bundesanstalt, Bundessortenamt and CHemical industry) phenological parameters, this study compared the estimation accuracies of various multispectral vegetation indexes (VIopt) and the corresponding VIDRI (vegetation index corrected by DRI) indexes in inverting the corn growth parameters. (1) When comparing the estimation accuracies of four multispectral vegetation indexes (NDVI, NDVIre1, NDVIre2, and S2REP), NDVI showed the lowest estimation accuracy, with a normalized root mean square error (nRMSE) of 20.84% for the plant height, while S2REP showed the highest estimation accuracy (nRMSE = 16.05%). In addition, NDVIre2 (nRMSE = 16.18%) and S2REP (16.05%) exhibited a higher accuracy than NDVIre1 (nRMSE = 19.27%). Similarly, for BBCH, the nRMSEs of the four indexes were 24.17%, 22.49%, 17.04% and 16.60%, respectively. This confirmed that the multispectral vegetation indexes based on the red-edge bands were more sensitive to the growth parameters, especially for the Sentinel-2 red-edge 2 band. (2) The constructed VIDRI indexes were more beneficial than the VIopt indexes in enhancing the estimation accuracy of corn growth parameters. Specifically, the nRMSEs of the four VIDRI indexes (NDVIDRI, NDVIre1DRI, NDVIre2DRI, and S2REPDRI) decreased to 19.64%, 18.11%, 15.00%, and 14.64% for plant height, and to 23.24%, 21.58%, 15.79%, and 15.91% for BBCH, indicating that even in cases of high vegetation coverage, the introduction of SAR DRI features can further improve the estimation accuracy of growth parameters. Our findings also demonstrated that the NDVIre2DRI and S2REPDRI indexes constructed using red-edge 2 band information of Sentinel-2 and SAR DRI features had more advantages in improving the estimation accuracy of corn growth parameters. [ABSTRACT FROM AUTHOR]

Pipelines are an important part of urban infrastructure development. As part of a virtual globe (VG), the high-efficiency and high-quality visualization of 3D large-scale and high-density urban pipelines is of great importance. This paper proposes a GPU-based pipeline ray casting method for the visualization of urban-scale pipelines in the framework of a VG. The method involves the initial partitioning of the pipeline data into tiles, based on the relationship between the pipeline layer scale and the discrete global grid system (DGGSs). The pipeline centerline in each tile is then segmented and encoded, and a coarser pipeline bounding volume is subsequently constructed using a geometry shader. Finally, the fine 3D pipeline is rendered using a pixel shader. The results of the experimental implementation of the proposed method show that it satisfies the requirements for the multiscale visualization of pipelines in a VG. Moreover, compared with the traditional polygon-based method, the method facilitates a 20% increase in rendering frame rate for the same pixel level accuracy display effect. It also enables the visualization of the thickness of the 3D pipeline without any obvious effect on the rendering efficiency. [ABSTRACT FROM AUTHOR]

Cross-calibration has the advantages of high precision, low resource requirements and simple implementation. It has been widely used in recent years. The four wide-field-of-view (WFV) cameras on-board Gaofen-1 satellite provide high spatial resolution and wide combined coverage (4×200km) without onboard calibration. In this paper, the four-band radiometric cross-calibration coefficients of WFV1 camera were obtained based on radiation and geometry matching taking Landsat 8 OLI (Operational Land Imager) sensor as reference. Scale Invariant Feature Transform (SIFT) feature detection method and distance and included angle weighting method were introduced to correct misregistration of WFV-OLI image pair. The radiative transfer model was used to eliminate difference between OLI sensor and WFV1 camera through the spectral match factor (SMF). The near-infrared band of WFV1 camera encompasses water vapor absorption bands, thus a Look Up Table (LUT) for SMF varies from water vapor amount is established to estimate the water vapor effects. The surface synchronization experiment was designed to verify the reliability of the cross-calibration coefficients, which seem to perform better than the official coefficients claimed by the China Centre for Resources Satellite Data and Application (CCRSDA). [ABSTRACT FROM AUTHOR]