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Introduction
Published in Yoshio Yamaguchi, Polarimetric SAR Imaging, 2020
In this book, we deal with microwave SAR. In the microwave frequency region, frequencies are further divided into some bands as shown in Table 1.1. Each band has special frequency characteristics for targets with respect to target size and material constants having electrical properties. It is known the C-band, which is suited for crop monitoring, and the L-band is suitable for forest, land cover, disaster monitoring, etc. The lower frequency can propagate with low attenuation and penetrate the medium deeper inside. The P-band wave can penetrate not only forests but also dry ground, snow, ice, and the soil surface at several centimeters. The higher frequency above the X-band scatters at the surface of objects and does not penetrate objects. The scattering nature at higher frequency becomes close to those of optical waves. Therefore, these bands are suited for high-resolution imaging of terrain.
Fixed and Mobile Antennas for Satellite Communications
Published in Lal Chand Godara, Handbook of Antennas in Wireless Communications, 2018
Marek E. Bialkowski, Nemai C. Karmakar, Paul W. Davis, Hyok J. Song
The frequencies that are mostly used at present time cover the range between 1.5 and 18 GHz. The L-band is least affected by rain, because rain attenuation is negligible at this frequency range. However, the ionosphere introduces a source of significant link degradation. Because of ionospheric scintillation, which results in the signal splitting into direct and refracted paths, signals combine with random phase causing signal fade. The ionosphere also rotates linear polarization. To reduce the effect of rotation, L-band links use circular polarization. S-band suffers less from ionospheric effects than L-band. However, it suffers slightly higher atmospheric attenuation as a result of rain. C-band exhibits a modest amount of fading from rain and ionospheric scintillation. It offers much larger bandwidth than L- or S-band. As a result, C-band remains the most heavily developed and used segment of the satellite spectrum. The only drawback of C-band is the requirement for relatively large size earth station antennas, with diameters between 1 and 3 m as a norm. The most significant advantage of C-band is its immunity against rain attenuation. This explains why this band is mainly used in tropical regions, which feature an abundance of heavy rains.
Satellite communications
Published in Matthew N. O. Sadiku, Optical and Wireless Communications, 2018
Satellite services are classified into 17 categories:5 fixed, intersatellite, mobile, land mobile, maritime mobile, aeronautical mobile, broadcasting, earth exploration, space research, meteorological, space operation, amateur, radiodetermination, radionavigation, maritime radionavigation, and standard frequency and time signal. The Ku band is presently used for broadcasting services and also for certain fixed satellite services. The C band is exclusively for fixed satellite services, and no broadcasting is allowed. The L band is employed by mobile satellite services and navigation systems.
Improvement of microwave emissivity parameterization of frozen Arctic soils using roughness measurements derived from photogrammetry
Published in International Journal of Digital Earth, 2021
J. Meloche, A. Royer, A. Langlois, N. Rutter, V. Sasseville
Most research on natural soil reflectivity focuses on soil moisture retrievals at L-band (reviewed by Wigneron et al. 2017) or higher frequencies (Njoku et al. 2003). Soil permittivity values are key parameters that allow retrieval of soil moisture using soil emissivity models such as those developed by Zhang et al. (2010) and Mironov et al. (2017). However, active and passive microwave dielectric sensitivity to soil moisture is strongly reduced by surface roughness that must be known or derived in the retrieval processing. During Arctic winter, surface parametrization is more difficult due to the presence of snow cover, and significant lingering uncertainties remain, specifically regarding required soil characteristics that are challenging to quantify in the Arctic.
Spatial interpolation based on previously-observed behavior: a framework for interpolating spaceborne GNSS-R data from CYGNSS
Published in Journal of Spatial Science, 2023
Compared to the ocean, the roughness of the land surface does not vary so dramatically over time, with the exception perhaps of wind-roughened inland waters. However, the dielectric constant of natural land surfaces does change, and in many places it changes quite rapidly. At L-band, the dielectric constant of the land surface is primarily a function of the amount of water on the surface, be it in the form of inundation or in near-surface (0–5 cm) soil moisture (Hallikainen et al. 1985). Although not designed for land surface remote sensing, since the launch of CYGNSS there has been a flurry of activity in the GNSS-R community to assess the potential of the data to retrieve variables related to terrestrial hydrology like near-surface soil moisture (Chew and Small 2018, Kim and Lakshmi 2018, Al-Khaldi et al. 2019, Clarizia et al. 2019, Senyurek et al. 2020), inundation extent (Chew et al. 2018, Ruf et al. 2018, Morris et al. 2019, Chew and Small 2020b), and vegetation biomass or water content (Carreno-Luengo et al. 2020). So far, it appears that the CYGNSS observations of surface reflectivity () are highly sensitive to small surface water features less than 100 m wide, making this new satellite technique a boon for surface hydrologists. Although the precise spatial resolution of the CYGNSS observations over land has yet to be determined, the smallest theoretical footprint for smooth reflecting surfaces is approximately the size of the first Fresnel zone, which for a low Earth orbiting satellite like CYGNSS is approximately 0.5 km2 (± a few hundred sq. meters depending on incidence angle) (Katzberg and Garrison 1996). The software onboard CYGNSS actually integrates observations recorded over a period of 0.5 s, which leads to an along-track smearing of the data such that the smallest spatial resolution is approximately 3.5 km along-track and 0.5 km across-track, or 5.5 km2.
Parameterization of the freeze/thaw discriminant function algorithm using dense in-situ observation network data
Published in International Journal of Digital Earth, 2019
Pingkai Wang, Tianjie Zhao, Jiancheng Shi, Tongxi Hu, Alexandre Roy, Yubao Qiu, Hui Lu
In recent years, several FT algorithms based on passive microwave radiation characteristics were developed. It includes dual-indices (Zuerndorfer and England 1992; Zhang, Armstrong, and Smith 2003), new dual-indices (Han et al. 2015), decision tree (Jin, Li, and Che 2009; Jin et al. 2015) and discriminant function algorithm (DFA) (Zhao et al. 2011). There are also FT algorithms based on active microwave information relying on the time change detection. It mainly includes three algorithms which are seasonal threshold method (Rignot and Way 1994; Way et al. 1997; Entekhabi et al. 2004; Gamon et al. 2004), threshold window method (Frolking et al. 1999; Kimball et al. 2001; Kimball, McDonald, et al. 2004; Kimball and Mcdonald 2004; Rawlins et al. 2005) and edge detection method (Canny 1986). Besides, in recent years there has been a great interest in L-band technology such as Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture and Active Passive (SMAP) (Entekhabi et al. 2010; Kerr et al. 2012) due to its sensitivity to soil water phase transition (Rautiainen et al. 2012). Due to the high revisit period of time and many new types of passive microwave sensors, passive microwave remote sensing has huge potential for large-scale FT detection. The DFA was developed based on Advanced Microwave Scanning Radiometer for EOS (AMSR-E) passive microwave imagery (Zhao et al. 2011), where the concepts of ‘quasi-temperature’ and ‘quasi-emissivity’ were used as two criteria to discriminate the FT state. The authors developed the algorithm based on the observed brightness temperature (Tb) from a trunk-mounted microwave radiometer and data simulated by physical models. In another study, the DFA was used to obtain global near-surface FT state validated using the World Meteorological Organization (WMO) weather stations data, Global Land Data Assimilation Systems (GLDAS) modelled soil temperature and the International Soil Moisture Network (ISMN) soil temperature (Hu, Zhao, Shi, Wang, et al. 2016). Despite this progress, the DFA still lacks sufficient calibration and validation on a global scale and as yet does not consider the differences between ascending orbit and descending orbit.