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

doi:10.1007/s11707-015-0509-4

Front. Earth Sci.

2016, Vol. 10

Issue (2) : 195-204 https://doi.org/10.1007/s11707-015-0509-4

RESEARCH ARTICLE

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

Chengcheng FENG¹, Xiaolei ZOU²(

), Juan ZHAO³

¹. Center of Data Assimilation for Research and Application, Nanjing University of Information Science & Technology, Nanjing 210044, China
². Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740-3823, USA
³. China Meteorological Administration Training Centre, Beijing 100081, China

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Abstract

Radio Frequency Interference (RFI) causes severe contamination to passive and active microwave sensing observations and corresponding retrieval products. RFI signals should be detected and filtered before applying the microwave data to retrieval and data assimilation. It is difficult to detect RFI over land surfaces covered by snow because of the scattering effect of snow surface. The double principal component analysis (DPCA) method is adopted in this study, and its ability in identifying RFI signals in AMSR-E data over snow covered regions is investigated. Results show that the DPCA method can detect RFI signals effectively in spite of the impact of snow scattering, and the detected RFI signals persistent over time. Compared to other methods, such as PCA and normalized PCA, DPCA is more robust and suitable for operational application.

Keywords Radio Frequency Interference (RFI) AMSR-E double principal component analysis (DPCA)

Corresponding Author(s): Xiaolei ZOU

Just Accepted Date: 05 June 2015 Online First Date: 28 July 2015 Issue Date: 05 April 2016

Cite this article:

Chengcheng FENG,Xiaolei ZOU,Juan ZHAO. Detection of radio-frequency interference signals from AMSR-E data over the United States with snow cover[J]. Front. Earth Sci., 2016, 10(2): 195-204.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-015-0509-4
https://academic.hep.com.cn/fesci/EN/Y2016/V10/I2/195

1	P Ashcroft, F Wentz (2000). Algorithm theoretical basis document, AMSR Level 2A algorithm. RSS Tech. Report 121599B-1
2	S W Ellingson, J T Johnson (2006). A polarimetric survey of radio-frequency interference in C- and X-bands in the continental United States using WindSat radiometry. IEEE Trans Geosci Rem Sens, 44(3): 540–548 https://doi.org/10.1109/TGRS.2005.856131
3	K Imaoka, T Sezai, T Takeshima, T Kawanishi, A Shibata (2002). Instrument characteristics and calibration of AMSR and AMSR-E. Geoscience and Remote Sensing Symposium, 1: 18–20
4	T Kawanishi, T Sezai, Y Ito, K Imaoka, T Takeshima, Y Ishido, A Shibata, M Miura, H Inahata, R W Spencer (2003). The advanced microwave scanning radiometer for the earth observing system (AMSR-E), NASDA’s contribution to the EOS for global energy and water cycle studies. IEEE Trans Geosci Rem Sens, 41(2): 184–194 https://doi.org/10.1109/TGRS.2002.808331
5	R E Kelly, A T Chang, L Tsang, J L Foster (2003). A prototype AMSR-E global snow area and snow depth algorithm. IEEE Trans Geosci Rem Sens, 41(2): 230–242 https://doi.org/10.1109/TGRS.2003.809118
6	T Lacava, I Coviello, M Faruolo, G Mazzeo, N Pergola, V Tramutoli (2013). A multitemporal Investigation of AMSR-E C-band radio-frequency interference. IEEE Trans Geosci Rem Sens, 51(4): 2007–2015 https://doi.org/10.1109/TGRS.2012.2228487
7	L Li, P W Gaiser, M Bettenhausen, W Johnston (2006). WindSat radio-frequency interference signature and its identification over land and ocean. IEEE Trans Geosci Rem Sens, 44(3): 530–539 https://doi.org/10.1109/TGRS.2005.862503
8	L Li, E G Njoku, E Im, P Chang, K S Germain (2004). A preliminary survey of radio-frequency interference over the U.S. in Aqua AMSR-E data. IEEE Trans Geosci Rem Sens, 42(2): 380–390 https://doi.org/10.1109/TGRS.2003.817195
9	D McKague, J J Puckett, C Ruf (2010). Characterization of K-band radio frequency interference from AMSR-E, WindSat and SSM/I. In: Proc. IGARSS: 2492–2494
10	E G Njoku, P Ashcroft, T K Chan, L Li (2005). Global survey and statistics of radio-frequency interference in AMSR-E land observations. IEEE Trans Geosci Rem Sens, 43(5): 938–947 https://doi.org/10.1109/TGRS.2004.837507
11	E G Njoku, T J Jackson, V Lakshmi, T K Chan, S V Nghiem (2003). Soil moisture retrieval from AMSR-E. IEEE Trans Geosci Remote Sens, 41(2): 215–229 https://doi.org/10.1109/TGRS.2002.808243
12	E G Njoku, T Koike, T Jackson, S Paloscia (2000). Retrieval of soil moisture from AMSR data. In: P Pampaloni, S Paloscia, eds. Microwave Radiometry for Remote Sensing of the Earth’s Surface and Atmosphere. Utrecht: VSP, 525–533
13	E G Njoku, L Li (1999). Retrieval of land surface parameters using passive microwave measurements at 6‒18 GHz. IEEE Trans Geosci Rem Sens, 37(1): 79–93 https://doi.org/10.1109/36.739125
14	M Owe, R De Jeu, J Walker (2001). A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Trans Geosci Rem Sens, 39(8): 1643–1654 https://doi.org/10.1109/36.942542
15	C L Parkinson, M T Chahine, C D Kummerow, V V S Alomonson (2003). Aqua: an earth-observing satellite mission to examine water and other climate variables. IEEE Trans Geosci Rem Sens, 41: 172–183 https://doi.org/10.1109/TGRS.2003.811217
16	A Shibata, K Imaoka, T Koike (2003). AMSR/AMSR-E level 2 and 3 algorithm developments and data validation plans of NASDA. IEEE Trans Geosci Rem Sens, 41(2): 195–203 https://doi.org/10.1109/TGRS.2002.808320
17	T Wilheit, C D Kummerow, R Ferraro (2003). Rainfall algorithms for AMSR-E. IEEE Trans Geosci Remote Sens, 41(2): 204–214 https://doi.org/10.1109/TGRS.2002.808312
18	Y Wu, F Z Weng (2011). Detection and correction of AMSR-E radio-frequency interference (RFI). Acta Meteor Sinica, 25(5): 669–681 https://doi.org/10.1007/s13351-011-0510-0
19	J Zhao, X L Zou, F Z Weng (2013). WindSat radio-frequency interference signature and its identification over Greenland and Antarctic. IEEE Trans Geosci Remote Sens, 51(9): 1–10
20	X L Zou, J Zhao, F Z Weng, Z K Qin (2012). Detection of radio-frequency interference signal over land from FY-3B Microwave Radiation Imager (MWRI). IEEE Trans Geosci Rem Sens, 50(12): 4994–5003 https://doi.org/10.1109/TGRS.2012.2191792

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[2]	Sibo ZHANG,Li GUAN. Identifying AMSR-E radio-frequency interference over winter land[J]. Front. Earth Sci., 2015, 9(3): 437-448.
[3]	Jiangyuan ZENG,Zhen LI,Quan CHEN,Haiyun BI. A simplified physically-based algorithm for surface soil moisture retrieval using AMSR-E data[J]. Front. Earth Sci., 2014, 8(3): 427-438.

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