Abstract
Radio-frequency interference (RFI) affects greatly the quality of the data and retrieval products from space-borne microwave radiometry. Analysis of the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Aqua satellite observations reveals very strong and widespread RFI contaminations on the C- and X-band data. Fortunately, the strong and moderate RFI signals can be easily identified using an index on observed brightness temperature spectrum. It is the weak RFI that is difficult to be separated from the nature surface emission. In this study, a new algorithm is proposed for RFI detection and correction. The simulated brightness temperature is used as a background signal (B) and a departure of the observation from the background (O-B) is utilized for detection of RFI. It is found that the O-B departure can result from either a natural event (e.g., precipitation or flooding) or an RFI signal. A separation between the nature event and RFI is further realized based on the scattering index (SI). A positive SI index and low brightness temperatures at high frequencies indicate precipitation. In the RFI correction, a relationship between AMSR-E measurements at 10.65 GHz and those at 18.7 or 6.925 GHz is first developed using the AMSR-E training data sets under RFI-free conditions. Contamination of AMSR-E measurements at 10.65 GHz is then predicted from the RFI-free measurements at 18.7 or 6.925 GHz using this relationship. It is shown that AMSR-E measurements with the RFI-correction algorithm have better agreement with simulations in a variety of surface conditions.
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Chauhan, N. S., S. Miller, and P. Ardanuy, 2003: Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach. Int. J. Remote Sens., 24(22), 4599–4622.
Gasiewski, A. J., M. Klein, A. Yevgrafov, et al., 2002: Interference mitigation in passive microwave radiometry. IEEE Geosci. Remote Sens. Symp. (IGARSS), 24–28.
Kawanishi, T., T. Sezai, Y. Ito, et al., 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. Remote Sens., 41(2), 184–194.
Kelly, R. E., A. T. Chang, L. Tsang, et al., 2003: A prototype AMSR-E global snow area and snow depth algorithm. IEEE Trans. Geosci. Remote Sens., 41(2), 230–242.
Li, L., E. G. Njoku, E. Im, et al., 2004: A preliminary survey of radio-frequency interference over the U.S. in Aqua AMSR-E data. IEEE Trans. Geosci. Remote Sens., 42(2), 380–390.
Njoku, E. G., andL. Li, 1999: Retrieval of land surface parameters using passive microwave measurements at 6–18 GHz. IEEE Trans. Geosci. Remote Sens., 37(1), 79–93.
—, W. J. Wilson, S. H. Yueh, et al., 2000: A largeantenna microwave radiometer-scatterometer concept for ocean salinity and soil moisture sensing. IEEE Trans. Geosci. Remote Sens., 38(6), 2645–2655.
—, T. J. Jackson, V. Lakshmi, et al., 2003: Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens., 41(2), 215–229.
—, T. Chan, W. Crosson, et al., 2004: Evaluation of the AMSR-E data calibration over land. Int. J. Remote Sens., 30/31, 19–38.
—, P. Ashcroft, T. K. Chan, and L. Li, 2005: Global survey and statistics of radio-frequency interference in AMSR-E land observations. IEEE Trans. Geosci. Remote Sens., 43(5), 938–947.
Owe, M., R. D. Jeu, and J. Walker, 2001: A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Trans. Geosci. Remote Sens., 39(8), 1643–1654.
Paloscia, S., G. Macelloni, E. Santi, et al., 2001: A multifrequency algorithm for the retrieval of soil moisture on a large scale using microwave data from SMMR and SSM/I satellites. IEEE Trans. Geosci. Remote Sens., 39(8), 1655–1661.
Ruf, C. S., S. M. Gross, and S. Misra, 2006: RFI detection and mitigation for microwave radiometry with an agile digital detector. IEEE Trans. Geosci. Remote Sens., 44(3), 694–706.
Weng, F. Z., B. H. Yan, and N. C. Grody, 2001: A microwave land emissivity model. J. Geophys. Res., 106(D17), 20115–20123, doi: 10.1029/2001JD900019.
Wilheit, T., C. D. Kummerow, and R. Ferraro, 2003: Rainfall algorithms for AMSR-E. IEEE Trans. Geosci. Remote Sens., 41(2), 204–214.
Yan, B. H., and F. Z. Weng, 2008: Application of AMSRE measurements for tropical cyclone predictions. Part I: Retrieval of sea surface temperature and wind speed. Adv. Atmos. Sci., 25(2), 227–245.
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Supported by the National Key Basic Research and Development (973) Program of China (2010CB951600), National Natural Science Foundation of China (40875015, 40875016, and 40975019), Special Fund for University Doctoral Students of China (20060300002), Chinese Academy of Meteorological Sciences “Application of Meteorological Data in GRAPES-3DVar” Program, and NOAA/NESDIS/Center for Satellite Applications and Research (STAR) CalVal Program.
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Wu, Y., Weng, F. Detection and correction of AMSR-E radio-frequency interference. Acta Meteorol Sin 25, 669–681 (2011). https://doi.org/10.1007/s13351-011-0510-0
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DOI: https://doi.org/10.1007/s13351-011-0510-0