SAR Radiometric Cross-Calibration Based on Multiple Pseudoinvariant Calibration Sites With Extensive Backscattering Coefficient Range by Yongsheng Zhou, Xiang Chen, Qiang Yin, Fei Ma, Fan Zhang

“…In addition, the backscattering coefficient's time-series root mean square error (RMSE) is leveraged to identify stable targets. …”
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Glacier surface melt monitoring using Sentinel-1 SAR backscattering coefficient and polarimetric decomposition features at Greenland ice sheet by Huimin Jiao, Gang Li, Zhuoqi Chen, Xiao Cheng

“…At five selected study sites in the Greenland ice sheet (GrIS) where automatic weather stations (AWS) are distributed, backscatter coefficient and polarimetric decomposition parameters of Sentinel-1 IW imagery are analyzed. …”
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The Optimal Choice of Semiconductor Converter to Increase Power and Efficiency in Betavoltaic Batteries with 3H, 63Ni, and 147Pm Beta Sources by D. Ghasemabadi, H. Zaki Dizaji, M Abdollahzade

“…Evaluation criteria include backscattering coefficient of beta particles from semiconductors, efficiency of electron-hole pairs generation, electronic specifications and properties, radiation damage threshold, radiation yield, stopping power and penetration of beta particles in semiconductors, physical characteristics, and temperature tolerance, accessibility, and fabrication were considered. …”
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Interferometric phase diagram generation method based on dot trace texture information and semantic segmentation of correlation coefficients by Xuelin Zhang, Zhaoxia Wang, Hui Zhang, Dongliang Xie

“…First, the InSAR images are sharpened according to the different backscattering characteristics of various pixel values, and DTTI is extracted to generate the Dot Trace Texture Map (DTTM). …”
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Landslide susceptibility evaluation considering the importance selection of influencing factors and soil moisture content by Zhongyu WANG, Sumin LI, Liwei YUAN, Weipeng LE

“…Soil moisture content factors were extracted using SAR satellite backscatter coefficients and DEM data, and 15 evaluation factors were ranked for importance using XGBoost and Lasso regression models. …”
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Validation of the vertical canopy cover profile products derived from GEDI over selected forest sites by Yu Li, Hongliang Fang, Yao Wang, Sijia Li, Tian Ma, Yunjia Wu, Hao Tang

“…The GEDI CC was improved at moderate CC values using a canopy-to-ground backscattering coefficient ratio (ρv/ρg) determined with the regression method. …”
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Full cycle rice growth monitoring with dual-pol SAR data and interpretable deep learning by Ji Ge, Hong Zhang, Lu Xu, Wenjiang Huang, Jingling Jiang, Mingyang Song, Zihuan Guo, Chao Wang

“…Initially, radar vegetation indices, polarimetric decomposition parameters, and backscattering coefficients characterize rice growth in multiple dimensions. …”
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Estimation of Daylily Leaf Area Index by Synergy Multispectral and Radar Remote-Sensing Data Based on Machine-Learning Algorithm by Minhuan Hu, Jingshu Wang, Peng Yang, Ping Li, Peng He, Rutian Bi

“…The RFR importance score screened the top five important features, including vegetation indices land surface water index (LSWI), generalized difference vegetation index (GDVI), normalized difference yellowness index (NDYI), and backscatter coefficients VV and VH. Vegetation index features characterized canopy moisture and the color of daylilies, and the backscatter coefficient reflected dielectric properties and geometric structure. …”
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Optimizing the Radiative Transfer Model Using Deep Neural Networks for NISAR Soil Moisture Retrieval by Xiaodong Huang, Lorenzo Giuliano Papale, Marco Lavalle, Fabio Del Frate, Heresh Fattahi, Steven K. Chan, Rowena B. Lohman, Xiaolan Xu, Yunjin Kim

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A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR Data by Mingyang Song, Lu Xu, Nanhuanuowa Zhu, Zihuan Guo, Haoxuan Duan, Jiahua Teng, Xia Lei, Lijun Zuo

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X-band Repeat-pass Coherence at Short Temporal Baselines for Crop Monitoring by Arturo Villarroya-Carpio, Juan M. Lopez-Sanchez

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A Polarization-Power-Maximum-Based 3-D Imaging Method for Ku-Band UAV-Borne Fully Polarimetric Array InSAR by Shujie Song, Xiaolan Qiu, Songtao Shangguan, Yitong Luo, Ziya Li, Zhe Zhang, Hang Li

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The Influences of Meteorological Factors on Mapping Forest Stock Volume With Sentinel-1 Images by Xunwei Li, Jiangping Long, Hui Lin, Tingchen Zhang, Huanna Zheng

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Oil Spill Detection in PolSAR Imagery Using Composite Scattering Power Entropy and Multiscale Hybrid Feature Fusion Network by Deliang Xiang, Yu Lu, Dongdong Guan, Guannan Li, Jianda Cheng, Bangjie Li

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Sample-Adjusted Threshold Calculation (SATC) for Optimizing Flood Mapping From a Trustworthy and Explainable Perspective by Zhijun Jiao, Biyan Chen, Zhimei Zhang, Lixin Wu

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EVALUATION OF THE OBJECTIVES POLARIZATION DISCRIMINATION AGAINST PASSIVE HINDRANCES WITH THE DEVICE, BASED ON CONSIDERATION OF THE SPATIAL VARIATION OF THE FARADAY ROTATION ANGLE I... by Volodymyr I. Mirnenko, Evgen A.Yufa A. Yufa, Maxim N. Zhuravskij

“…In order to determine the operability of the device under various conditions and to evaluate its efficiency, the signal-to-noise ratio at the output of the device is calculated in the case where the backscattering matrix of the target and the matrix of the interference element (for example, dipole, angle reflector, etc.) are not diagonal, m .e. when a cross-polarization component appears when the signal from the target or from the interference element is reflected.…”
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Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data by Thanabalan Palanisamy, Vidhya R

“…The retrieval of backscattering coefficient values (σ ̊) from SAR is the common principle factor for soil moisture estimation. …”
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Enhanced U-Net for Underwater Laser Range-Gated Image Restoration: Boosting Underwater Target Recognition by Peng Liu, Shuaibao Chen, Wei He, Jue Wang, Liangpei Chen, Yuguang Tan, Dong Luo, Wei Chen, Guohua Jiao

“…To address these issues, Underwater Laser Range-Gated Imaging has emerged as a promising solution. By selectively capturing photons within a controlled temporal gate, this technique effectively suppresses backscattering noise-enhancing image clarity, contrast, and detection range. …”
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Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) by Gordan Mimić, Amit Kumar Mishra, Miljana Marković, Branislav Živaljević, Dejan Pavlović, Oskar Marko

“…Data from the Sentinel-1 satellite were used in the study. Time series of backscattering coefficients for vertical–horizontal (VH) and vertical–vertical (VV) polarisations, both from ascending and descending orbits, were collected from Google Earth Engine. …”
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Dust Aerosol Classification in Northwest China Using CALIPSO Data and an Enhanced 1D U-Net Network by Xin Gong, Delong Xiu, Xiaoling Sun, Ruizhao Zhang, Jiandong Mao, Hu Zhao, Zhimin Rao

“…Using CALIPSO Level 1B and Level 2 Vertical Feature Mask (VFM) data from 2015 to 2020, the model processed backscatter coefficients, polarization characteristics, and color ratios at 532 nm and 1064 nm to classify aerosol types. …”
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