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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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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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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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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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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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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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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Discussion of the spectral slope of the lidar ratio between 355 and 1064 nm from multiwavelength Raman lidar observations by M. Haarig, R. Engelmann, H. Baars, B. Gast, D. Althausen, A. Ansmann

“…It is a key parameter in aerosol typing and an essential quantity to derive the extinction coefficient from elastic backscatter lidars like the spaceborne Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) or ceilometer observations. …”
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Fluorescence spectra of atmospheric aerosols by J. Reichardt, F. Lauermann, O. Behrendt

“…The fluorescence maxima are below 500 <span class="inline-formula">nm</span>, and a linear decrease in the spectral backscatter coefficient can be seen at longer wavelengths; the spectral fluorescence capacity is low (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>&lt;</mo><mn mathvariant="normal">1</mn><mo>×</mo><msup><mn mathvariant="normal">10</mn><mrow><mo>-</mo><mn mathvariant="normal">6</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="fcbbb2805ab9534794f91444ae9ac33e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-5857-2025-ie00002.svg" width="52pt" height="14pt" src="acp-25-5857-2025-ie00002.png"/></svg:svg></span></span> <span class="inline-formula">nm⁻¹</span>). …”
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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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Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery by Reda Amer

“…The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. …”
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Integrating GIS and remote sensing for soil attributes mapping in degraded pastures of the Brazilian Cerrado by Rômullo Oliveira Louzada, Ivan Bergier, Édson Luis Bolfe, Jayme Garcia Arnal Barbedo

“…The variables include spectral bands, vegetation and soil indices, gray-level co-occurrence matrices (GLCM), backscatter coefficients, polarimetric decompositions, and topographic indices. …”
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