scholarly journals Year-long, broad-band, microwave backscatter observations of an Alpine Meadow over the Tibetan Plateau with a ground-based scatterometer

2020 ◽  
Author(s):  
Jan G. Hofste ◽  
Rogier van der Velde ◽  
Jun Wen ◽  
Xin Wang ◽  
Zuoliang Wang ◽  
...  

Abstract. A ground-based scatterometer was installed on an alpine meadow over the Tibetan Plateau to study the soil moisture and -temperature dynamics of the top soil layer and air-soil interface during the period August 2017–August 2018. The deployed system measured the amplitude and phase of the ground surface radar return at hourly and half-hourly intervals over 1–10 GHz in the four linear polarization combinations (vv, hh, hv, vh). In this paper we describe the developed scatterometer system, gathered datasets, retrieval method for the backscattering coefficient (σ0), and results of (σ0) for co-polarization. The system was installed on a 5 m high tower and designed using only commercially available components: a Vector Network Analyser (VNA), four coaxial cables, and two dual polarization broadband gain horn antennas at a fixed position and orientation. We provide a detailed description on how to retrieve the co-polarized backscattering coefficients σ0vv & σ0hh for this specific scatterometer design. To account for the particular effects caused by wide antenna radiation patterns (G) at lower frequencies, σ0 was calculated using the narrow-beam approximation combined with a mapping the function G2/R4 over the ground surface. (R is the distance between antennas and the infinitesimal patches of ground surface.) This approach allowed for a proper derivation of footprint positions and -areas, and incidence angle ranges. The frequency averaging technique was used to reduce the effects of fading on the σ0 uncertainty. Absolute calibration of the scatterometer was achieved with measured backscatter from a rectangular metal plate as reference target. In the retrieved time-series of σ0vv & σ0hh for S-band (2.5–3.0 GHz), C-band (4.5–5.0 GHz), and X-band (9.0–10.0 GHz) we observed characteristic changes or features that can be attributed to seasonal or diurnal changes in the soil. For example a fully frozen top soil, diurnal freeze-thaw changes in the top soil, emerging vegetation in spring, and drying of soil. Our preliminary analysis on the collected σ0 time-series data set demonstrates that it contains valuable information on water- and energy exchange directly below the air-soil interface. Information which is difficult to quantify, at that particular position, with in-situ measurements techniques alone. Availability of backscattering data for multiple frequency bands allows for studying scattering effects at different depths within the soil and vegetation canopy during the spring and summer periods. Hence further investigation of this scatterometer data set provides an opportunity to gain new insights in hydro-meteorological processes, such as freezing and thawing, and how these can be monitored with multi-frequency scatterometer observations. The data set is available via https://doi.org/10.17026/dans-zc5-skyg (Hofste and Su, 2020). The effects of fading, calibration, and system stability on the uncertainty in σ0 are estimated to vary from ± 1.3 dB for X-band with vv-polarization to ± 2.7 dB for S-band with hh-polarization through the campaign. The low angular resolution of the antennas result in additional σ0 uncertainty, one that is more difficult to quantify. Estimations point out that it probably will not exceed ± 2 dB with C-band. Despite these uncertainties, we believe that the strength of our approach lies in the capability of measuring σ0 dynamics over a broad frequency range, 1–10 GHz, with high temporal resolution over a full-year period.

2021 ◽  
Vol 13 (6) ◽  
pp. 2819-2856 ◽  
Author(s):  
Jan G. Hofste ◽  
Rogier van der Velde ◽  
Jun Wen ◽  
Xin Wang ◽  
Zuoliang Wang ◽  
...  

Abstract. A ground-based scatterometer was installed on an alpine meadow over the Tibetan Plateau to study the soil moisture and temperature dynamics of the top soil layer and air–soil interface during the period August 2017–August 2018. The deployed system measured the amplitude and phase of the ground surface radar return at hourly and half-hourly intervals over 1–10 GHz in the four linear polarization combinations (vv, hh, hv, vh). In this paper we describe the developed scatterometer system, gathered datasets, retrieval method for the backscattering coefficient (σ0), and results of σ0. The system was installed on a 5 m high tower and designed using only commercially available components: a vector network analyser (VNA), four coaxial cables, and two dual-polarization broad-band gain horn antennas at a fixed position and orientation. We provide a detailed description on how to retrieve the backscattering coefficients for all four linear polarization combinations σpq0, where p is the received and q the transmitted polarization (v or h), for this specific scatterometer design. To account for the particular effects caused by wide antenna radiation patterns (G) at lower frequencies, σ0 was calculated using the narrow-beam approximation combined with a mapping of the function G2/R4 over the ground surface. (R is the distance between antennas and the infinitesimal patches of ground surface.) This approach allowed for a proper derivation of footprint positions and areas, as well as incidence angle ranges. The frequency averaging technique was used to reduce the effects of fading on the σpq0 uncertainty. Absolute calibration of the scatterometer was achieved with measurements of a rectangular metal plate and rotated dihedral metal reflectors as reference targets. In the retrieved time series of σpq0 for L-band (1.5–1.75 GHz), S-band (2.5–3.0 GHz), C-band (4.5–5.0 GHz), and X-band (9.0–10.0 GHz), we observed characteristic changes or features that can be attributed to seasonal or diurnal changes in the soil: for example a fully frozen top soil, diurnal freeze–thaw changes in the top soil, emerging vegetation in spring, and drying of soil. Our preliminary analysis of the collected σpq0 time-series dataset demonstrates that it contains valuable information on water and energy exchange directly below the air–soil interface – information which is difficult to quantify, at that particular position, with in situ measurement techniques alone. Availability of backscattering data for multiple frequency bands (raw radar return and retrieved σpq0) allows for studying scattering effects at different depths within the soil and vegetation canopy during the spring and summer periods. Hence further investigation of this scatterometer dataset provides an opportunity to gain new insights in hydrometeorological processes, such as freezing and thawing, and how these can be monitored with multi-frequency scatterometer observations. The dataset is available via https://doi.org/10.17026/dans-zfb-qegy (Hofste et al., 2021). Software code for processing the data and retrieving σpq0 via the method presented in this paper can be found under https://doi.org/10.17026/dans-xyf-fmkk (Hofste, 2021).


2020 ◽  
Author(s):  
Robert Zinke ◽  
Gilles Peltzer ◽  
Eric Fielding ◽  
Simran Sangha ◽  
David Bekaert ◽  
...  

<p>We quantify deformation patterns resulting from tectonic motions and surface processes across the central Tibetan Plateau (29–45ºN, 83–92ºE) since late 2014 using ascending and descending passes of the Sentinel-1A and -1B radar satellites. The broad spatial extent of these data (> 10<sup>6</sup> km<sup>2</sup>), fine spatial resolution (originally 90 m pixels, resampled to 270 m pixels), and high rate of temporal sampling (12–24-day orbit repeat time) offer unprecedented resolution of surface deformation in space and time. To process such an extensive data set – including more than 100 dates and 300 interferograms per track thus far – we leverage the Advanced Rapid Imaging and Analysis (ARIA) standardized interferometric synthetic aperture radar (InSAR) products and toolbox. We construct time series of surface deformation constrained from our Sentinel-1 interferograms using the small baseline subset approach implemented by the Miami InSAR time series software in Python (MintPy). Our preliminary results from three Sentinel-1 orbits (two descending and one ascending; each comprising 10 frames along track) allow us to quantify deformation in the satellite lines of sight. Combinations of ascending and descending track measurements are used to approximate east-west and vertical ground velocities. The resulting velocity fields will provide a more complete and accurate picture of interseismic strain accumulation rates across active faults in the region such as the Altyn Tagh and Kunlun faults, and allow us to study surface processes such as permafrost active layer dynamics and isostatic adjustment due to lake level changes in unparalleled scope and detail.</p>


2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Wei Wan ◽  
Di Long ◽  
Yang Hong ◽  
Yingzhao Ma ◽  
Yuan Yuan ◽  
...  

Abstract Long-term datasets of number and size of lakes over the Tibetan Plateau (TP) are among the most critical components for better understanding the interactions among the cryosphere, hydrosphere, and atmosphere at regional and global scales. Due to the harsh environment and the scarcity of data over the TP, data accumulation and sharing become more valuable for scientists worldwide to make new discoveries in this region. This paper, for the first time, presents a comprehensive and freely available data set of lakes’ status (name, location, shape, area, perimeter, etc.) over the TP region dating back to the 1960s, including three time series, i.e., the 1960s, 2005, and 2014, derived from ground survey (the 1960s) or high-spatial-resolution satellite images from the China-Brazil Earth Resources Satellite (CBERS) (2005) and China’s newly launched GaoFen-1 (GF-1, which means high-resolution images in Chinese) satellite (2014). The data set could provide scientists with useful information for revealing environmental changes and mechanisms over the TP region. Design Type(s) time series design • observation design • data integration objective Measurement Type(s) lake topography Technology Type(s) remote sensing Factor Type(s) Sample Characteristic(s) Tibetan Plateau • Qaidam Basin • Amu Darya • Brahmaputra River • River Ganges • Hexi District • Indus River • Mekong River • Salween River • Tarim Basin • Yangtze River • Yellow River • endorheic lake • exorheic lake Machine-accessible metadata file describing the reported data (ISA-Tab format)


AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.


2013 ◽  
Vol 56 ◽  
pp. 101-106 ◽  
Author(s):  
Jing Jiang ◽  
Ning Zong ◽  
Minghua Song ◽  
Peili Shi ◽  
Weiling Ma ◽  
...  

2007 ◽  
Vol 52 (1) ◽  
pp. 136-139 ◽  
Author(s):  
MeiXue Yang ◽  
TanDong Yao ◽  
XiaoHua Gou ◽  
Nozomu Hirose ◽  
Hide Yuki Fujii ◽  
...  

2021 ◽  
Vol 24 (3/4) ◽  
pp. 307
Author(s):  
Mingyuan Du ◽  
Yingnian Li ◽  
Fawei Zhang ◽  
Liang Zhao ◽  
Hongqin Li ◽  
...  

2021 ◽  
Author(s):  
Junyuan Fei ◽  
Jintao Liu

<p>Highly intermittent rivers are widespread on the Tibetan Plateau and deeply impact the ecological stability and social development downstream. Due to the highly intermittent rivers are small, seasonal variated and heavy cloud covered on the Tibetan Plateau, their distribution location is still unknown at catchment scale currently. To address these challenges, a new method is proposed for extracting the cumulative distribution location of highly intermittent river from Sentinel-1 time series in an alpine catchment on the Tibetan Plateau. The proposed method first determines the proper time scale of extracting highly intermittent river, based on which the statistical features are calculated to amplify the difference between land covers. Subsequently, the synoptic cumulative distribution location is extracted through Random Forest model using the statistical features above as explanatory variables. And the precise result is generated by combining the synoptic result with critical flow accumulation area.  The highly intermittent river segments are derived and assessed in an alpine catchment of Lhasa River Basin. The results show that the the intra-annual time scale is sufficient for highly intermittent river extraction. And the proposed method can extract highly intermittent river cumulative distribution locations with total precision of 0.62, distance error median of 64.03 m, outperforming other existing river extraction method.</p>


2016 ◽  
Vol 17 (6) ◽  
pp. 1626-1634 ◽  
Author(s):  
Caiyun Luo ◽  
Shiping Wang ◽  
Liang Zhao ◽  
Shixiao Xu ◽  
Burenbayin Xu ◽  
...  

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