Detection of radio-frequency interference signals from AMSR-E data over the United States with snow cover

2015 ◽  
Vol 10 (2) ◽  
pp. 195-204 ◽  
Author(s):  
Chengcheng Feng ◽  
Xiaolei Zou ◽  
Juan Zhao
Keyword(s):  
United States ◽  
Radio Frequency ◽  
Snow Cover ◽  
The United States ◽  
Radio Frequency Interference ◽  
Interference Signals

scholarly journals Detection and Analysis of C-Band Radio Frequency Interference in AMSR2 Data over Land

2019 ◽  
Vol 11 (10) ◽  
pp. 1228 ◽  
Author(s):  
Ying Wu ◽  
Bo Qian ◽  
Yansong Bao ◽  
Meixin Li ◽  
George P. Petropoulos ◽  
...  
Keyword(s):  
United States ◽  
Radio Frequency ◽  
Detection Method ◽  
Principal Component ◽  
The United States ◽  
Temperature Data ◽  
Radio Frequency Interference ◽  
The Earth ◽  
Pca Method ◽  
Contaminated Area

A simplified generalized radio frequency interference (RFI) detection method and principal component analysis (PCA) method are utilized to detect and attribute the sources of C-band RFI in AMSR2 L1 brightness temperature data over land during 1–16 July 2017. The results show that the consistency between the two methods provides confidence that RFI may be reliably detected using either of the methods, and the only difference is that the scope of the RFI-contaminated area identified by the former algorithm is larger in some areas than that using the latter method. Strong RFI signals at 6.925 GHz are mainly distributed in the United States, Japan, India, Brazil, and some parts of Europe; meanwhile, RFI signals at 7.3 GHz are mainly distributed in Latin America, Asia, Southern Europe, and Africa. However, no obvious 7.3 GHz RFI appears in the United States or India, indicating that the 7.3 GHz channels mitigate the effects of the C-band RFI in these regions. The RFI signals whose position does not vary with the Earth azimuth of the observations generally come from stable, continuous sources of active ground-based microwave radiation, while the RFI signals which are observed only in some directions on a kind of scanning orbit (ascending/descending) mostly arise from reflected geostationary satellite signals.

Trends in Snow Cover and Related Quantities at Weather Stations in the Conterminous United States

2015 ◽  
Vol 28 (19) ◽  
pp. 7518-7528 ◽  
Author(s):  
Noah Knowles
Keyword(s):  
United States ◽  
Correlation Analysis ◽  
Snow Cover ◽  
Canonical Correlation Analysis ◽  
Great Plains ◽  
Canonical Correlation ◽  
Snow Depth ◽  
The United States ◽  
Eastern Half ◽  
Precipitation Trends

Abstract Trend tests, linear regression, and canonical correlation analysis were used to quantify changes in National Weather Service Cooperative Observer (COOP) snow depth data and derived quantities, precipitation, snowfall, and temperature over the study period 1950–2010. Despite widespread warming, historical trends in snowfall and snow depth are generally mixed owing to competing influences of trends in precipitation. Trends toward later snow-cover onset in the western half of the conterminous United States and earlier onset in the eastern half and a widespread trend toward earlier final meltoff of snow cover combined to produce trends toward shorter snow seasons in the eastern half of the United States and in the west and longer snow seasons in the Great Plains and southern Rockies. The annual total number of days with snow cover exhibited a widespread decline. Monthly trend patterns show the dominant influence of temperature trends on occurrence of snow cover in the warmer snow-season months and a combination of temperature and precipitation trends in the colder months. A canonical correlation analysis indicated that most trends presented here took hold in the 1970s, consistent with the temporal pattern of global warming during the study period.

scholarly journals Multiclass Radio Frequency Interference Detection and Suppression for SAR Based on the Single Shot MultiBox Detector

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4034 ◽  
Author(s):  
Junfei Yu ◽  
Jingwen Li ◽  
Bing Sun ◽  
Jie Chen ◽  
Chunsheng Li
Keyword(s):  
Radio Frequency ◽  
Pulse Compression ◽  
Radio Frequency Interference ◽  
Single Shot ◽  
Separation Problem ◽  
Underdetermined Blind Source Separation ◽  
Time Frequency ◽  
Interference Signals ◽  
Novel Method ◽  
Short Time

Radio frequency interference (RFI) is known to jam synthetic aperture radar (SAR) measurements, severely degrading the SAR imaging quality. The suppression of RFI in SAR echo signals is usually an underdetermined blind source separation problem. In this paper, we propose a novel method for multiclass RFI detection and suppression based on the single shot multibox detector (SSD). First, an echo-interference dataset is established by randomly combining the target signal with various types of RFI in a simulation, and the time–frequency form of the dataset is obtained by utilizing the short-time Fourier transform (STFT). Next, the time–frequency dataset acts as input data to train the SSD and obtain a network that is capable of detecting, identifying and estimating the interference. Finally, all of the interference signals are exactly reconstructed based on the prediction results of the SSD and mitigated by an adaptive filter. The proposed method can effectively increase the signal-to-interference-noise ratio (SINR) of RFI-contaminated SAR echoes and improve the peak sidelobe ratio (PSLR) after pulse compression. The simulated experimental results validate the effectiveness of the proposed method.

scholarly journals Shortwave Radiative Fluxes on Slopes

2016 ◽  
Vol 55 (7) ◽  
pp. 1513-1532
Author(s):  
Yingtao Ma ◽  
Rachel T. Pinker ◽  
Margaret M. Wonsick ◽  
Chuan Li ◽  
Laura M. Hinkelman
Keyword(s):  
United States ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Snow Water Equivalent ◽  
The United States ◽  
Spatial And Temporal Variability ◽  
Remotely Sensed Data ◽  
Radiative Fluxes ◽  
Attractive Option ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractSnow-covered mountain ranges are a major source of water supply for runoff and groundwater recharge. Snowmelt supplies as much as 75% of the surface water in basins of the western United States. Net radiative fluxes make up about 80% of the energy balance over snow-covered surfaces. Because of the large extent of snow cover and the scarcity of ground observations, use of remotely sensed data is an attractive option for estimating radiative fluxes. Most of the available methods have been applied to low-spatial-resolution satellite observations that do not capture the spatial variability of snow cover, clouds, or aerosols, all of which need to be accounted for to achieve accurate estimates of surface radiative fluxes. The objective of this study is to use high-spatial-resolution observations that are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive surface shortwave (0.2–4.0 μm) downward radiative fluxes in complex terrain, with attention on the effect of topography (e.g., shadowing or limited sky view) on the amount of radiation received. The developed method has been applied to several typical melt seasons (January–July during 2003, 2004, 2005, and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability of shortwave fluxes. Issues of scale in both the satellite and ground observations are also addressed to illuminate difficulties in the validation process of satellite-derived quantities. It is planned to apply the findings from this study to test improvements in estimation of snow water equivalent.

scholarly journals SnowCloudHydro—A New Framework for Forecasting Streamflow in Snowy, Data-Scarce Regions

2018 ◽  
Vol 10 (8) ◽  
pp. 1276 ◽  
Author(s):  
Eric A. Sproles ◽  
Ryan L. Crumley ◽  
Anne W. Nolin ◽  
Eugene Mar ◽  
Juan Ignacio Lopez Moreno
Keyword(s):  
United States ◽  
Cloud Computing ◽  
Snow Cover ◽  
User Experience ◽  
The United States ◽  
Google Earth ◽  
Northern Spain ◽  
Monthly Streamflow ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
John Day

We tested the efficacy and skill of SnowCloud, a prototype web-based, cloud-computing framework for snow mapping and hydrologic modeling. SnowCloud is the overarching framework that functions within the Google Earth Engine cloud-computing environment. SnowCloudMetrics is a sub-component of SnowCloud that provides users with spatially and temporally composited snow cover information in an easy-to-use format. SnowCloudHydro is a simple spreadsheet-based model that uses Snow Cover Frequency (SCF) output from SnowCloudMetrics as a key model input. In this application, SnowCloudMetrics rapidly converts NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover product (MOD10A1) into a monthly snow cover frequency for a user-specified watershed area. SnowCloudHydro uses SCF and prior monthly streamflow to forecast streamflow for the subsequent month. We tested the skill of SnowCloudHydro in three snow-dominated headwaters that represent a range of precipitation/snowmelt runoff categories: the Río Elqui in Northern Chile; the John Day River, in the Northwestern United States; and the Río Aragón in Northern Spain. The skill of the SnowCloudHydro model directly corresponded to snowpack contributions to streamflow. Watersheds with proportionately more snowmelt than rain provided better results (R2 values: 0.88, 0.52, and 0.22, respectively). To test the user experience of SnowCloud, we provided the tools and tutorials in English and Spanish to water resource managers in Chile, Spain, and the United States. Participants assessed their user experience, which was generally very positive. While these initial results focus on SnowCloud, they outline methods for developing cloud-based tools that can function effectively across cultures and languages. Our approach also addresses the primary challenges of science-based computing; human resource limitations, infrastructure costs, and expensive proprietary software. These challenges are particularly problematic in countries where scientific and computational resources are underdeveloped.

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