CONAE Microwave Radiometer (MWR) Counts to Tb Algorithm and On-Orbit Validation

Zoubair Ghazi1,Student Member IEEE, Andrea Santos-Garcia1,Student Member IEEE, María Marta Jacob2 and Linwood Jones1, Life Fellow IEEE

1Central Florida Remote Sensing Lab, Department of EECS, University of Central Florida 2CONAE – Comisión Nacional de Actividades Espaciales

Abstract— A joint international science mission

(Aquarius/SAC-D) between NASA and the Argentine Space Agency (Comisión Nacional de Actividades Espaciales, CONAE) was launched on June 10, 2011. The Microwave Radiometer (MWR), flown on board of SAC-D, complements the prime sensor, Aquarius L-band radiometer/scatterometer, by providing simultaneous spatially collocated environmental measurements such as ocean surface wind speed, oceanic rain rate, integrated atmospheric water vapor, and sea ice concentration, which aid in retrieving accurate Sea Surface Salinity (SSS). This paper presents the brightness temperature algorithm and on orbit validation for the newest version (V6.0), which includes a radiometer system non-linearity correction. Post launch analysis was conducted to perform antenna pattern correction and other radiometric biases correction using inter-satellite radiometric comparison with the US Navy’s WindSat radiometer during a 6 month period of MWR on-orbit measurements.

Index Terms— Non-linearity, Dicke microwave radiometer, MWR, Aquarius.

I. INTRODUCTION The goal of the Aquarius/SAC-D joint NASA/CONAE

earth science mission is to understand the oceanic hydrological cycle, by measuring the sea surface salinity (SSS). The prime remote sensor is the L-band Aquarius microwave radiometer/scatterometer – a pushbroom system that produces three antenna foot print in a cross-track direction over a 380 km swath (Fig.1).

Also, the CONAE Microwave Radiometer (MWR) is designed to make brightness temperature (Tb) measurements within Aquarius swath with a higher spatial resolution. MWR consists of three separate radiometer receivers, one operating at 23.8 GHz (K band) H-pol, and two operating at 36.5 GHz (Ka band) H- & V-pol. Each MWR receiver is connected to a set of eight feed-horns arranged in two rows. The off-set parabolic reflected antenna produces 8 footprints on the ground in two conical arcs (Fig.1). The closest four to the sub-satellite are the even beams with Earth Incident Angle (EIA) ~52º, and the farthest are the even beams (EIA~58º). For each beam, the scene Tb is sampled using a 1x8 antenna switch matrix feeding a single time shared receiver.

II. DICKE MICROWAVE RADIOMETER The MWR is a 3-state Dicke radiometer (Fig. 2), which

calibrates using two internal noise sources: 1) a blackbody

waveguide termination at ambient physical temperature and 2) an active noise diode “hot” blackbody source. The Dicke switch is used to alternately sample the noise collected from the antenna (Dicke switch in antenna position), the antenna plus noise (Dicke switch in antenna position and noise diode is on), and the waveguide termination in a matched load (Dicke switch in reference load position). The output voltage of these 3 radiometer states are converted into digital counts (Rad_counts).

Fig. 1. MWR and Aquarius measurement geometry.

Fig.2 MWR radiometer with two internal calibration.

207978-1-4799-4644-0/14/$31.00 ©2014 IEEE Microrad 2014

For an ideal Dicke radiometer, the radiometer transfer function, that relates Rad_counts to the radiometer input (at the antenna port of the Dicke switch), is linear (Fig.3). The slope of the linear regression represents the receiver power gain, which is independent of the input power. Thus, by incorporating the receiver noise temperature into the offset term, the 3-Dicke radiometer state are:

Ca = Gain * Tin + offset (1) Cn = Gain * (Tin + Tn) + offset (2) Co = Gain * To + offset (3)

where Ca is the radiometer digital output “antenna counts”, Cn is the “antenna plus noise” counts, Co is the “reference load” counts, To is the physical temperature of the reference load, and Tn is the injected noise diode temperature (constant and known). By subtracting (1) from (2), we derive the gain as:

Gain = ( Cn - Ca)/ Tn (4)

where Cn - Ca is the noise diode deflection (deflection counts), which should be a constant and independent of the scene brightness temperature. Using (4) and subtracting (3) from (1), yields the antenna brightness temperature:

Tant = [(Ca – Co)/( Cn – Ca)]* Tn + To (5)

Fig.3 Typical Dicke radiometer transfer function.

III. MWR NON-LINEARITY CHARACTERIZATION (V5.0S) For a linear radiometer, the system gain should be

independent of the scene brightness temperature. However, under typical on-orbit condition, it is expected that the gain will vary systematically over an orbit period because of the cyclic change of the receiver physical temperature. Usually the receiver gain is inversely propositional to the receiver physical temperature Tphy , which creates an apparent change in gain between the northern and southern hemispheres (see Fig. 4).

However, based upon on-orbit data analysis (using the MWR counts-to-Tb algorithm, version 5.0S), it was discovered that all three receivers exhibited an anomalous behavior that was correlated with the antenna scene Tb. The noise diode deflection (proportional to gain) changed abruptly at Ocean/Land boundaries, where high Tb contrast exists. This is illustrated in the global image of the noise deflection counts

(Fig.4) for the 36.5 GHz H-pol channel, where the colors represent the noise deflection counts. Note that the noise deflection counts changed not only with the change of receiver physical temperature (northern/southern hemispheres) but also with the change of the scene brightness temperature.

Fig.4 Image of observed noise deflection (Cn - Ca) for 36.5GHz H-pol. Warm colors indicate higher than average noise deflection and cool colors indicate

less than average counts.

From Fig.4, it was concluded that the radiometer system was non-linear because of the abrupt noise diode deflection changes with the scene Tb. To characterize this non-linearity, the time variable gain (due to orbital physical temperature changes) was removed, and all the counts were normalized using the following equation:

])/([)}/({* _

irecoio

recooxnormx TTC

TTCmeanCC

i (6)

where Cx_norm stands for gain normalized Rad_counts (antenna, antenna + noise, and reference load), and "i" is the instantaneous value. An example of gain normalization is illustrated in Fig. 5, where the noise deflection counts was cross-correlated with the radiometer input Tb in a scatter diagram, where the colors represent the scene Tb. Figure 5a (before gain normalization) shows that counts are quite variable because of the time-variable gain. After the counts were gain normalized (Fig. 5b), the scatter diagram show a linear trend. Note that the noise diode deflection monotonically

(a) (b) Fig.5 Noise diode deflection vs Tin for MWR 37H, a) before gain

normalization, b) after gain normalization.

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decreases with the increase of the radiometer input brightness temperature Tin, which means that the system is non-linear.

IV. MWR NON-LINEARITY CORRECTION The Central Florida Remote Sensing Lab (CFRSL), in

collaboration with CONAE, has developed a new version of the radiometric counts to Tb algorithm (V6.0) for MWR. This algorithm includes the radiometer non-linearity correction.

To establish a radiometer transfer function that relates Rad_counts to the antenna input Tb, seven orbits of MWR measurements were analyzed. This analysis includes Cold Sky Calibration (CSC), ocean, and land observations, to provide a wide dynamic range of brightness temperatures. After performing gain normalization to Rad_counts, a quadratic regression was applied, to derive the following radiometer transfer function equation for one orbit (Fig. 6):

Rad_counts = 7.719*10-4(Tin)2 + 16.61(Tin) + 3272.9 (8)

Fig.6 MWR Radiometer Transfer Function for 37V V5.0S (constant gain) for

one orbit.

After the above procedure was repeated for seven CSC orbits separately, and the quadratic coefficients from the quadratic regressions were averaged. The instantaneous counts linearization equation for 37GHz V-pol was then derived as:

Cx_linear = Cx - (-7.4677e-004)*Tin2 (9)

where Cx_linear represents linearized Rad_counts (antenna, antenna + noise, reference load). Figure 7a and 7b show the global image of the noise diode deflection before and after the non-linearity correction, respectively. The colors represent the noise diode deflection, and the dynamic range of color-bar is 60 counts for both plots. It can be concluded from Fig. 7b that the noise deflection doesn’t change with the radiometric contrast at land/water boundaries, which illustrates that the radiometer non-linearity was corrected.

Another example of the non-linearity correction is shown in Fig.8, where we performed the scatter diagram between the radiometer input Tb and: a) noise diode deflection using non-

linear Rad_counts, b) noise diode deflection using linearized Rad_counts. As illustrated in Fig. 7a, the slope of the linear regression is -0.36, and the noise diode deflection (V5.0S) is monotonic decreasing with increasing radiometer input brightness temperature Tin. After non-linearity correction (Fig. 7b), the slope was reduced to 0.01, and the noise diode deflection counts (V6.0) become independent of scene Tb. Note the colors represent scene brightness Tb.

Fig.7 Global image of noise niode neflection for 37GHz, a) before non-

linearity correction, b) after non-linearity correction, colors.

Fig.8 Noise Diode Deflection for 37GHz for one-orbit. Left panel: before non-linearity correction, right panel: after non-linearity correction, colors represent

scene Tb

V. POST LAUNCH ANALYSIS To perform the Antenna Pattern Correction (APC) and

remove other radiometric biases, an on-orbit inter-satellite calibration (XCAL) was performed using the Naval Research Lab’s WindSat satellite radiometer. WindSat and MWR share many similarities including identical frequencies and similar EIA’s. Both sensors fly in similar sun-synchronous orbits, and ~ 60% of MWR data can be collocated with WindSat data within ±0.5 hrs. However, since MWR and WindSat have slightly different earth incident angles (EIA), the WindSat Tb’s were adjusted (using a theoretical radiative transfer model) that is expressed as:

WSadj = WSobs + (MWRsim – WSsim) (10)

where WSadj is adjusted WindSat Tb, WSobs is observed WindSat Tb, MWRsim is theoretical MWR Tb, and WSsim is the corresponding theoretical WS Tb.

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After selecting collocated clear sky ocean scenes for both instruments, a scatter diagram between observed MWR Tb and adjusted WS Tb was performed. Using an anchor point of the MWR observed CSC scene, a linear regression was performed to calculate the slope (~0.92) and the offset (~0.4) as shown in Fig. 9a. Finally, the APC corrected MWR Tb’s are compared with near-simultaneous collocated WindSat Tb’s in Fig. 9b, and results show excellent correlation.

XCAL validation of the previous version (V5.0S) and the new version (V6.0) algorithms was conducted for a time period between Sep 01, 2012 and Feb 28, 2013. The 5-day average double difference between MWR Tb’s observed and WSadj is expressed as:

DD = MWRobs – WSadj (11)

where DD means double difference. For example, the DD results for even beams are shown in Figs. 10a and 10b for 37GHz V-pol for V5.0S and V6.0, respectively. Note that y-axis has a dynamic range ± 5 K for all figures. From the DD time series, it is observed that V6.0 has significantly less bias than V5.0S.

Fig.9 Comparison of MWR V6.0 and WindSat for 37GHz V-pol for beam#1.

Left panel: before correction, right panel: after correction.

VI. CONCLUSION A new version (V6.0) of MWR counts-to-Tb algorithm

was introduced by CFRSL and CONAE in this paper. The radiometer system non-linearity was characterized and removed, which eliminated the effect of gain dependence on the input scene brightness temperature. On-orbit inter-satellite radiometric calibration (XCAL), between MWR and WindSat, have produced an antenna pattern correction and removed other radiometric biases. The time series of 5-day double difference Tb biases, for 2012 and 2013, is in progress to characterize the long term radiometric calibration stability.

VII. REFERENCES [1] Ghazi, Z.; Biswas, S.; Jones, L.; Hejazin, Y.; Jacob, M.M., "On-orbit

signal processing procedure for determining Microwave Radiometer non-linearity," Southeastcon, 2013 Proceedings of IEEE , vol., no., pp.1,5, 4-7 April 2013

[2] Santos-Garcia, A.; Biswas, S.; Jones, L., "Aquarius/SAC-D Microwave Radiometer brightness temperature validation," Oceans, 2012 , vol., no., pp.1,4, 14-19 Oct. 2012OCEANS.2012.6404830

[3] Fawwaz T. Ulaby, Richard K. Fung, and Adrian K. Fung; Microwave remote sensing: active andpassive.Volume I, Microwave Remote Sensing Fundamentals and Radiometry. Norwood, MA: Artech House, 1981.

[4] Biswas, Sayak K.; Jones, Linwood; Rocca, Daniel; Gallio, Juan-Cruz; , "Aquarius/SAC-D Microwave Radiometer (MWR): Instrument description & brightness temperature calibration," Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International , vol., no., pp.2956-2959, 22-27 July 2012.

[5] Biswas, Sayak K., "Brigthness temperature calibration of Aquarius/SAC-D microwave radiometer (MWR) " Dissertation for the degree of Doctor of Philosophy, UCF, Spring 2011.

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