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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]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using the wind profile radar and microwave radiometer observation data of the Shanghai World Expo Station on July 15 and July 17 of 2017, and August 18 of 2017, which are non-precipitating periods, different remote sensing methods to retrieve boundary layer height were compared, and the conclusions were obtained as follows: 1) The traditional gradient method and the wavelet method that inverse based on SNR have been improved, and the improvement effect is good. The improved gradient method is closer to the SNR gradient, but the nighttime error is larger, and the improved wavelet method is simpler, and the height curve is smoother. 2) The Holzworth method that highly depends on surface temperature is only suitable for the daytime. 3) The humidity gradient method mainly depends on the selection of critical values, and the daily variation of inversion height is not obvious. [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]

Wheat lodging is one of the most popular agriculture disaster. It has a great influence on the yield formation of wheat and causes huge loss every year. However, available means for monitoring crop lodging in large area is very limited. The potential capability of radar remote sensing for monitoring wheat lodging was explored in this study. After the backscattering coefficient of wheat field parcels were extracted at different polarization mode, radar backscattering behavior of typical lodging wheat parcels and the one of typical normal wheat parcels was dynamically compared at different growth stages and at different polarization mode, based on 5 multi-temporal Radarsat-2 images that covered an entire wheat growth cycle from sowing to near harvest. Results showed that there were distinct differences between lodging and normal wheat parcels in all polarization channels. When compared to normal wheat, the backscattering coefficient at HH (H, horizontal polarization) polarization decreased remarkably, while the one at VV (V, vertical polarization) polarization increased and the one at HV polarization increased slightly for lodging wheat. It was also found that polarimetric feature from synthetic aperture radar (SAR) data was very sensitive to wheat lodging and the sensibility was caused by unique structural characteristics of wheat vegetation and inherent polarization characteristics of SAR observation. Then a method based on radar polarimetric index, which took the full advantage of this sensibility, was put forward to monitoring wheat lodging. And the method was validated by in-situ data collected in 28 wheat parcels in Shangkuli Farmland in Inner Mongolia, China, at heading and filling stages of spring wheat. The result revealed that the polarimetric index, especially based on the ratio of dual-polarization backscattering coefficient (HH/VV and HH/HV), had excellent performance for distinguishing lodging from normal wheat parcels: all the 11 lodging wheat parcels with different severity were detected successfully from 28 parcels. In addition, the results were compared with the ones by single polarization channel data. It showed that the polarimetric index method had good anti-inference ability such as resisting influence of wheat growth difference, and can better reflect the intrinsic feature of lodging. It should be mentioned that the monitoring result may be influenced by harvest situation and vegetation water content etc. Moreover, while optical remote sensing relied on its spectral features to monitor crop lodging, radar remote sensing utilized polarimetric features to monitor crop lodging since SAR observation had advantage in reflecting the structural variation of lodging. Therefore, radar remote sensing have great potential for crop lodging monitoring, and the study presented a simple and effective method for monitoring wheat lodging in large area. [ABSTRACT FROM AUTHOR]

In order to precisely calculate the clutter power of the radar seeker, this paper presents a new method of judging the ground clutter integration region of radar seeker. According to the geometric relationship between the iso-range lines and iso-Doppler, the shapes of integration regions, which are decided by distance and velocity gate under various sphere coordinates, can be partitioned. This method can precisely locate the ground clutter integration region under specified range gate and velocity gate of the radar seeker. The detailed instruction and analysis are given for each of these regions. Finally, the simulation proves the feasibility of the presented method. [ABSTRACT FROM AUTHOR]

With the development of wideband radar technology, the narrowband ambiguity function (NAF), which is based on the narrowband echo model, is not effective to analyze the signal process issue of moving target echo. To solve this problem, the wideband ambiguity function (WAF) of stepped frequency (SP) waveform is proposed based on wideband echo model. The property of wideband ambiguity function was studied, from which we could prove that NAF is just an approximate to WAF under low velocity condition. Making use of WAF match point characteristic, a target detection method based on the amplitude of match point was advanced. Simulation results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Studies the measuring distance of wind profiling radar and the rules of change of structure coefficient for troposphere atmosphere turbulence(Cn²) for different seasons in China. According to the test records of wind profiler in recent years, analyzed and discussed experimentation data of north China and the principles of wind profiler compostion were introduced. Wind profiler radar equations and formulas of maximum measuring distance estimation were also derived. It is concluded that Cn² changes in different areas is the main reason for wind profiler measuring distance estimation errors. The test data proves the undulation of C̄n². with seasons is not very obvious and smaller than that of the boundary-layer atmosphere. Furthermore, the loss of signal processing is not the ignored factor for wind profiler measuring distance estimation. [ABSTRACT FROM AUTHOR]

Intermittent clutter produced by flyers such as birds is a severe interference for wind detecting. For resolving the problem of clutter rejection, a method based on wavelet coefficients replacement is suggested. According to the clutter characteristics of the clutter bursting and short duration gained by the echoes analyzing, a novel method for the clutter recognition based on power consistency checking of different spectral integration signals in the wavelet transform domain, clutter discriminant function and clutter suppression method based on counterpart scale wavelet coefficients replacement are proposed. By the data measured from field testing, high efficiency of clutter rejection is verified. [ABSTRACT FROM AUTHOR]