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

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)

References

[1] Ashcroft P, Wentz F (2000). Algorithm theoretical basis document, AMSR Level 2A algorithm. RSS Tech. Report 121599B-1

[2] Ellingson S W, Johnson J T (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

[3] Imaoka K, Sezai T, Takeshima T, Kawanishi T, Shibata A (2002). Instrument characteristics and calibration of AMSR and AMSR-E. Geoscience and Remote Sensing Symposium, 1: 18–20

[4] Kawanishi T, Sezai T, Ito Y, Imaoka K, Takeshima T, Ishido Y, Shibata A, Miura M, Inahata H, Spencer R W (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

[5] Kelly R E, Chang A T, Tsang L, Foster J L (2003). A prototype AMSR-E global snow area and snow depth algorithm. IEEE Trans Geosci Rem Sens, 41(2): 230–242

[6] Lacava T, Coviello I, Faruolo M, Mazzeo G, Pergola N, Tramutoli V (2013). A multitemporal Investigation of AMSR-E C-band radio-frequency interference. IEEE Trans Geosci Rem Sens, 51(4): 2007–2015

[7] Li L, Gaiser P W, Bettenhausen M, Johnston W (2006). WindSat radio-frequency interference signature and its identification over land and ocean. IEEE Trans Geosci Rem Sens, 44(3): 530–539

[8] Li L, Njoku E G, Im E, Chang P, Germain K S (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

[9] McKague D, Puckett J J, Ruf C (2010). Characterization of K-band radio frequency interference from AMSR-E, WindSat and SSM/I. In: Proc. IGARSS: 2492–2494

[10] Njoku E G, Ashcroft P, Chan T K, Li L (2005). Global survey and statistics of radio-frequency interference in AMSR-E land observations. IEEE Trans Geosci Rem Sens, 43(5): 938–947

[11] Njoku E G, Jackson T J, Lakshmi V, Chan T K, Nghiem S V (2003). Soil moisture retrieval from AMSR-E. IEEE Trans Geosci Remote Sens, 41(2): 215–229

[12] Njoku E G, Koike T, Jackson T, Paloscia S (2000). Retrieval of soil moisture from AMSR data. In: Pampaloni P, Paloscia S, eds. Microwave Radiometry for Remote Sensing of the Earth’s Surface and Atmosphere. Utrecht: VSP, 525–533

[13] Njoku E G, Li L (1999). Retrieval of land surface parameters using passive microwave measurements at 6‒18 GHz. IEEE Trans Geosci Rem Sens, 37(1): 79–93

[14] Owe M, De Jeu R, Walker J (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

[15] Parkinson C L, Chahine M T, Kummerow C D, Alomonson V V S (2003). Aqua: an earth-observing satellite mission to examine water and other climate variables. IEEE Trans Geosci Rem Sens, 41: 172–183

[16] Shibata A, Imaoka K, Koike T (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

[17] Wilheit T, Kummerow C D, Ferraro R (2003). Rainfall algorithms for AMSR-E. IEEE Trans Geosci Remote Sens, 41(2): 204–214

[18] Wu Y, Weng F Z (2011). Detection and correction of AMSR-E radio-frequency interference (RFI). Acta Meteor Sinica, 25(5): 669–681

[19] Zhao J, Zou X L, Weng F Z (2013). WindSat radio-frequency interference signature and its identification over Greenland and Antarctic. IEEE Trans Geosci Remote Sens, 51(9): 1–10

[20] Zou X L, Zhao J, Weng F Z, Qin Z K (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

Acknowledgements

This work was supported jointly by China Special Fund for Meteorological Research in the Public Interest (No. GYHY201406008), the Research innovation Program for college graduates of Jiangsu Province (CXLX13_483), and the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.

RIGHTS & PERMISSIONS

2015 Higher Education Press and Springer-Verlag Berlin Heidelberg