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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]

Grassland is one of the most widely distributed terrestrial ecosystems on Earth. It is critical to accurately estimate grassland biomass of the desert steppe, and to understand its dynamics changes in order to study the regional carbon cycle and the sustainable use of grassland resource. The integration of multisensory data provides the opportunity to explore the benefits of grassland biomass effective estimation via multiple data sources. Based on the field survey data, quadrature-polarization (qual-pol) RADARSAT-2 Synthetic Aperture Radar (SAR) C-band data was utilized to develop a biomass regression model and estimate the aboveground biomass (AGB) of the Caragana microphylla shrubbery in the desert steppe region in the northwest of China. The research area was located at Yangzhaizi Village in Ningxia Autonomous region. Grassland inventory was carried out in 45 randomly distributed plots (30 m × 30 m), and the data was used for either model development or validation. An allometric regression model was established to estimate its biomass for every Caragana microphylla shrub with CH (crown width multiple plant height) variable. The local allometric regression equation was applied to calculate AGB per plot. Furthermore, the correlation between the aboveground biomass of Caragana microphylla shrubbery and the radar backscatter coefficient was analyzed. The AGB regression model was developed by integrating field measurements of 25 sample plots with RADARSAT-2 backscatter remotely sensed data. The multiple stepwise regressions algorithm was applied to develop the AGB model and estimate the grassland above-ground biomass from RADARSAT-2 backscatter data. The developed model was validated by using 20 independent sample plots. Simultaneously, RADARSAT-2 images were fused with the optical HJ1B data by using a discrete wavelet transform for the land cover classification. The image classification based on the objects was performed by using the empirical-statistical machine learning techniques, such as a classification and regression trees (CART) algorithm. The overall accuracy and Kappa value of the proposed method was 90.2% and 0.88, respectively. It indicated that the proposed method performed well for the land use and land cover (LULC) classification. An AGB biomass distribution map was produced by RADARSAT-2 backscatter data in combination with the land cover classification image and AGB regression model. As a comparison, the AGB from RADARSAT-2 estimates were compared with the results from the HJ1B normalized difference vegetation index (NDVI) model. The result showed that there was a good quantitative relationship between the AGB from the microphylla shrubbery and the RADARSAT-2 radar backscatter coefficient. A good fit was found between AGB estimated by RADARSAT-2 and ground-measured biomass with a R² (coefficient of determination) and Root Mean-Square Error (RMSE) of 0.71 and 14.2 kg/hm² respectively. Its estimated accuracy was higher than that of the HJ1B NDVI model (R²=0.27, RMSE=20.58 kg/hm²). Consequently, the fusion of optical and radar data for the land cover classification could effectively improve the accuracy of the object recognition for the land cover classification and the estimation accuracy of AGB estimation. Radar remote sensing data could be used for quantitative studies on grassland structural parameters. Moreover, it demonstrated a high potential for monitoring indicators of grassland ecosystem by combining the optical with polarimetric SAR remote sensing imanges. [ABSTRACT FROM AUTHOR]