Identifying AMSR-E radio-frequency interference over winter land

Abstract

Satellite microwave emission mixed with signals from active sensors is referred to as radio-frequency interference (RFI). RFI affects greatly the quality of data and retrieval products from space-borne microwave radiometry. An accurate RFI detection will not only enhance geophysical retrievals over land but also provide evidence of the much-needed protection of the microwave frequency band for satellite remote sensing technologies. It is difficult to detect RFI from space-borne microwave radiometer data over winter land, because RFI signals are usually mixed with snow in mid-high latitudes. A modified principal component analysis (PCA) method is proposed in this paper for detecting microwave low frequency RFI signals. Only three original variables, one RFI index (sensitive to RFI signal) and two scattering indices (sensitive to snow scattering), are included in the vector for principal component analysis in this modified method instead of the nine or seven RFI index original variables used in a normal PCA algorithm. The principal component with higher correlation and contribution to the original RFI index is the RFI-related principal component. In the absence of a reliable validation data set of the “true” RFI, the consistency in the identified RFI distribution obtained from this method compared to other independent methods, such as the spectral difference method, the normalized PCA method, and the double PCA method, give confidence to the RFI signals’ identification over land. The simple and reliable modified PCA method could successfully detect RFI not only in summer but also in winter AMSR-E data.

Keywords

microwave remote sensing / radio-frequency interference (RFI) / the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) / principal component analysis (PCA)

References

[1] Chaurasia S, Thapliyal P K, Pal P K (2012). Application of a time-series-based methodology for soil moisture estimation from AMSR-E observations over India. IEEE Geosci Remote Sens Lett, 9(5): 818–821

[2] Ellingson S W, Johnson J T (2006). A polarimetric survey of radio-frequency interference in C- and X-Bands in the continental United S<?Pub Caret?>tates using windsat radiometry. IEEE Trans Geosci Rem Sens, 44(3): 540–548

[3] Grody N C (1991). Classification of snow cover and precipitation using the Special Sensor Microwave Imager. Journal of Geophysical Research: Atmospheres (1984–2012), 96(D4): 7423–7435

[4] Grody N C, Basist A N (1996). Global identification of snowcover using SSM/I measurements. IEEE Trans Geosci Rem Sens, 34(1): 237–249

[5] Guner B, Johnson J T, Niamsuwan N (2007). Time and frequency blanking for radio-frequency interference mitigation in microwave radiometry. IEEE Trans Geosci Rem Sens, 45(11): 3672–3679

[6] 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

[7] 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

[8] Kunzi K F, Fisher A D, Staelin D H, Waters J W (1976). Snow and ice surfaces measured by the Nimbus 5 Microwave Spectrometer. J Geophys Res, 81(27): 4965–4980

[9] Lattin J M, Carroll J D, Green P E (2003). Analyzing Multivariate Data. Beijing: China Machine Press, 83–123

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

[11] Li L, Njoku E G, Im E, Chang P S, 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

[12] Misra S, Ruf C S (2008). Detection of radio-frequency interference for the Aquarius Radiometer. IEEE Trans Geosci Rem Sens, 46(10): 3123–3128

[13] 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

[14] Njoku E G, Chan T K, Crosson W, Limaye A (2004). Evaluation of the AMSR-E Data Calibration over Land. Italian Journal of Remote Sensing, 30(31): 19–37

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

[16] 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

[17] Piepmeier J R, Mohammed P N, Knuble J J (2008). A double detector for RFI mitigation in microwave radiometers. IEEE Trans Geosci Rem Sens, 46(2): 458–465

[18] Qiu Y, Guo H, Shi J, Kang S, Wang J R, Lemmetyinen J, Jiang L (2010). Analysis between AMSR-E swath brightness temperature and ground snow depth data in winter time over Tibet Plateau, China. IEEE International Geoscience and Remote Sensing Symposium, 2367–2370

[19] Rothrock D A, Thomas D R, Thorndke A S (1988). Principal component analysis of satellite passive microwave data over sea ice. Journal of Geophysical Research: Oceans (1978–2012), 93(C3): 2321–2332

[20] Ruf C S, Gross S M, Misra S (2006). RFI detection and mitigation for microwave radiometry with an agile digital detector. IEEE Trans Geosci Rem Sens, 44(3): 694–706

[21] Weng F, Yan B, Grody N C (2001). A microwave land emissivity model. Journal of Geophysical Research: Atmospheres (1984–2012), 106(D17): 20115–20123

[22] Wentz F J, Gentemann C, Smith D, Chelton D (2000). Satellite measurements of sea surface temperature through clouds. Science, 288(5467): 847–850

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

[24] Yang H, Weng F (2011). Error sources in remote sensing of microwave land surface emissivity. IEEE Trans Geosci Rem Sens, 49(9): 3437–3442

[25] Yang H, Weng F, Lv L, Lu N, Liu G, Bai M, Qian Q, He J, Xu H (2011). The FengYun-3 microwave radiation imager on-orbit verification. IEEE Trans Geosci Rem Sens, 49(11): 4552–4560

[26] Zhao J, Zou X, Weng F (2013). WindSat radio-frequency interference signature and its identification over Greenland and Antarctic. IEEE Trans Geosci Rem Sens, 51(9): 4830–4839

[27] Zou X (2012). Introduction to microwave imager radiance observations from polar-orbiting meteorological satellites. Advances in Meteorological Science and Technology, 2(3): 45–50

[28] Zou X, Zhao J, Weng F, Qin Z (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 jointly supported by the National Natural Science Foundation of China (Grant No. 41175034) and Key University Science Research Project of Jiangsu Province (No. 13KJA170003).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg