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Optical remote sensing imagery can serve as a crucial data source in numerous fields. However, the susceptibility to weather conditions (like clouds and rain) can render the cloud contamination, leading to the significant influence on optical images. The image quality can be required to restore the spectral information in cloud-contaminated regions. In this study, the spectral information was reconstructed in the cloud-contaminated regions using an object-oriented approach. Synthetic aperture radar (SAR) data was integrated with the radar signals and land cover information, in order to serve as the constraints for the reconstruction. The distance matrix was calculated on the SAR images, and then transplant onto the optical images for the replacement. The pixel-level matching accuracy was enhanced to maximize the retention of spectral information from the original images. A solid foundation was provided for the accurate extraction of subsequent land surface information. The performance was also evaluated to verify the improved model. The comparative experiments were conducted with the traditional pixel replacement, weighted linear regression, and curvature-driven. Visual inspection was carried out over various scenes. The results revealed that the superior quality was achieved in the imagery reconstruction in the thick cloud areas and cloud shadows. Particularly, the consistency was maintained among various land cover pixels in the reconstructed areas by the object-oriented method. The boundaries and areas were accurately delineated in the various types of fine-grained land cover. Quantitative analysis was conducted to validate using some metrics, such as correlation coefficient, root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The spectral characteristic curves of the pixels were reconstructed to closely match those of the reference images, with the correlation coefficients exceeding 0.99. Notably, significant improvement was observed in the spectral curve recovery of vegetation pixels with the enhanced relative to the rest. The radar vegetation indices were optimized to employ an object-oriented matching mechanism during pixel reconstruction. The traditional pixel replacement model was optimized effectively. Furthermore, the band reflectance was closely resembled that of the reference images. Among the five bands of cloud-contaminated images after reconstruction, the highest correlation coefficients were observed with Band 2 and Band 3 reaching 0.925 and 0.922, respectively, and RMSE values of 0.009 and 0.007, respectively. The reconstructed images shared the minimal quality loss and high similarity to the reference images, with the highest PSNR and SSIM values across all bands. Specifically, the structural similarity values of the reconstructed Band 2 and Band 3 images were 0.902 and 0.910, respectively, with the PSNR values reaching 31.978 and 33.173, respectively. The superior image quality was obtained similar to reference images, which was consistence with spectral characteristics. The experimental results were used to the qualitatively and quantitatively validated the effectiveness of the reconstruction. The object-oriented approach can be expected to reconstruct the spectral information in the cloud-contaminated regions. The remotely sensed images were consistently produced with the higher spectral reconstruction accuracy in the different types of land cover and spectral bands. However, the replacing pixel spectral information was relied primarily on from single-image data. Some limitations was matched the effectiveness between cloudy and cloud-free regions, when reconstructing the large-scale, thick cloud areas in single remote sensing images. Additionally, the global searching can fail to the optimal reconstruction for the locally contaminated cloud pixels. Therefore, further investigation is required to determine the optimal local search range for the pixel matching using different extents of cloud contamination. [ABSTRACT FROM AUTHOR]

According to the remote sensing monitoring method of the ground deformation before mine landslide, the discrimination basis of the reliability of different orbit SAR data sets was proposed, and the inducing factors of landslide were analyzed. Taking the slope of the Anqian Yabaling open-pit mine in Anshan city as an example, using 44 Sentinel-1 radar images(two groups of ascending orbits and one group of descending orbit), the time series InSAR method was used to analyze the surface movement characteristics of the slope 6 months before the landslide occurred on November 25, 2019. The results show that the monitoring results obtained by different orbit data sets in the same studied area are different. The position of the top and rear edge of the landslide area showed an unusually strong acceleration within a period of time(approximately 45 days) before the landslide. The research results can provide new ideas for early identification of landslide hazard in mining areas using InSAR technology in the future. [ABSTRACT FROM AUTHOR]

As one important part of post-earthquake loss assessment, building damage detection has important significance for understanding the disaster in time, implementing the emergency response, and post-disaster reconstructing. In recent years the development of the multi-platform remote sensing and the high-resolution remote sensing technology have provided opportunities to building damage detection with high accuracy. In this paper， firstly，the basic building damage characteristics and the concrete classification criteria are introduced. Then，the common damage detection methods at home and abroad based on the remote sensing technology are concluded，including the single-temporal image detection of the post-earthquake, the change detection between the pre-earthquake and the post-earthquake images，and the damage detection based on the multi-sources data. Limitations and drawbacks of these methods are proposed. Finally, combining with some cutting-edge technologies in remote sensing domain，the latest research progress of building damage detection based on the high-resolution remote sensing technology，synthetic aperture radar technology，light detection and ranging technology 9 and oblique aerial photography technology are summarized in detail. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has made great strides in the last few decades, which enables the long-term consistent observations of many variables of the terrestrial water cycle and thus advances the understanding of the terrestrial water cycle. This paper reviews the principles of remote sensing in retrieving key variables of the terrestrial water cycle, illustrates the progress of satellite remote sen-sing in hydrological applications, and discusses the future direction. Although most of hydrological states and fluxes variables are observable by remote sensing, closing terrestrial water budget with the remote sensing products is still an open question, suggesting more efforts are needed to improve the hydrological consistency of the remote sensing products. In the future, efforts should be made to develop new generation sensors and platforms for the consistent remote sensing products with finer spatiotemporal resolution. At the same time, efforts should be devoted to the evaluation of remote sen-sing products of the terrestrial water cycle through carrying out integrated field experiments. [ABSTRACT FROM AUTHOR]