Identification of Sun Glint Contamination in GMI Measurements Over the Global Ocean

This paper utilizes the model regression difference method to identify sun glint contamination on Global Precipitation Measurement Microwave Imager (GMI) data over the ocean based on observations from 2015 to 2016. The spatial distribution characteristics and the critical angles of the sun glint flags are analyzed in depth. It is found that the GMI measurements with horizontal and vertical polarizations at 10.65 GHz over the ocean are sometimes contaminated by the solar radiation reflected by the sea surface. The sun glint contamination has also been detected over high reflected land surface. The intensity and locations of the contamination are related to the sun glint angle. Only those GMI field of views with smaller sun glint angles are easily contaminated. The closer the sun glint angle is to 0°, the stronger the magnitude of the contamination. The GMI observations at other channels are not contaminated mainly because sun glint is most pronounced at 10 GHz. There are too strong constraints and tossing out of too many useful data in current GMI sun glint algorithms. The suggested critical angles of the sun glint flags for 10.65GHz is 20° to reduce false flagging. By applying the model regression difference method, the error in brightness temperature caused by sun glint can be corrected. The Tropical Rainfall Measuring Mission Microwave Imager (TMI) observations at 10.65 GHz are also contaminated by the reflected solar radiation from the ocean, and the intensity and locations of the contamination are similar to those of the GMI.

[1]  C. Cox.  Statistics of the sea surface derived from sun glitter , 1954 .

[2]  Frank Wentz,et al.  Post-launch calibration of the TRMM microwave imager , 2001, IEEE Trans. Geosci. Remote. Sens..

[3]  C. Kummerow,et al.  The Tropical Rainfall Measuring Mission (TRMM) Sensor Package , 1998 .

[4]  G. Petty,et al.  Field-of-view characteristics and resolution matching for the Global Precipitation Measurement (GPM) Microwave Imager (GMI) , 2016 .

[5]  Li Li,et al.  WindSat radio-frequency interference signature and its identification over land and ocean , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Wenying Su,et al.  Observations of reflectance distribution around sunglint from a coastal ocean platform. , 2002, Applied optics.

[7]  David A. Newell,et al.  The Global Precipitation Measurement (GPM) Microwave Imager (GMI): Instrument Overview and Early On-Orbit Performance , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Ye Hong,et al.  Special Sensor Microwave Imager Sounder (SSMIS) Radiometric Calibration Anomalies—Part I: Identification and Characterization , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Paul A. Hwang,et al.  The dependence of sea surface slope on atmospheric stability and swell conditions , 1988 .

[10]  Bertrand Chapron,et al.  Modeling Sun Glitter at L-Band for Sea Surface Salinity Remote Sensing With SMOS , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[11]  C. T. Swift,et al.  Microwave radiometer measurements of the Cape Cod Canal , 1974 .

[12]  Edward J. Kim,et al.  The NAFE'05/CoSMOS Data Set: Toward SMOS Soil Moisture Retrieval, Downscaling, and Assimilation , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Fuzhong Weng,et al.  WindSat Radio-Frequency Interference Signature and Its Identification Over Greenland and Antarctic , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Jin Wu.  Slope and Curvature Distributions of Wind-Disturbed Water Surface , 1971 .

[15]  M. Leroy,et al.  Surface bidirectional reflectance distribution function observed at global scale by POLDER/ADEOS , 1998 .

[16]  Michael D. King,et al.  Airborne Spectral Measurements of Ocean Directional Reflectance , 2013 .

[17]  Peter Bauer,et al.  Solar Biases in Microwave Imager Observations Assimilated at ECMWF , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Fuzhong Weng,et al.  Detection of Radio-Frequency Interference Signal Over Land From FY-3B Microwave Radiation Imager (MWRI) , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Keiji Imaoka,et al.  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 , 2003, IEEE Trans. Geosci. Remote. Sens..

[20]  James P. Hollinger,et al.  SSM/I instrument evaluation , 1990 .

[21]  W. Munk,et al.  Measurement of the Roughness of the Sea Surface from Photographs of the Sun’s Glitter , 1954 .

[22]  David W. Draper,et al.  Terrestrial and space-based RFI observed by the GPM microwave imager (GMI) within NTIA semi-protected passive earth exploration bands at 10.65 and 18.7 GHz , 2016, 2016 Radio Frequency Interference (RFI).

[23]  W. Linwood Jones,et al.  The WindSat spaceborne polarimetric microwave radiometer: sensor description and early orbit performance , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Rasmus Fensholt,et al.  Remote Sensing , 2008, Encyclopedia of GIS.

[25]  F. J. Wentz.  The forward scattering of microwave solar radiation from a water surface , 1978 .

[26]  David A. Newell,et al.  An assessment of radio frequency interference using the GPM Microwave Imager , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[27]  Jun Yang,et al.  An Overview of a New Chinese Weather Satellite FY-3A , 2009 .

[28]  Xiaolei Zou,et al.  An Empirical Model for Television Frequency Interference Correction of AMSR2 Data Over Ocean Near the U.S. and Europe , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Jing M. Chen,et al.  Simulation and SMAP Observation of Sun-Glint Over the Land Surface at the L-Band , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[30]  N. Grody.  Classification of snow cover and precipitation using the special sensor microwave imager , 1991 .