Inter-calibration of infrared bands of FY-3C MERSI ...

Inter-calibration of infrared bands of FY-3C MERSI and VIRR using hyperspectral sensor CrIS and IASI Hanlie Xu, Na Xu*, and Xiuqing Hu Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, Beijing 100081, China ABSTRACT An inter-calibration method of infrared channels of FY-3C MERSI and VIRR using NPP/CrIS and Metop/IASI as the hyperspectral reference sensor is introduced. Based on FY-3C SNO collocated samples with CrIS and IASI, on early orbit, we analyze the calibration biases of infrared bands of MERSI and VIRR. The results show that the brightness temperatures (BT) from the MERSI observation and CrIS have a good consistency, and the BT biases present an approximately normal distribution and the mean BT bias is about -0.18K with standard deviation of 0.83K. When the scene BT is lower than 250 K, the result of MERSI is higher than that of CrIS, while the result of MERSI is lower at the more than 250K scene. The BT from VIRR shows significant systematic bias with respect to CrIS and the mean BT bias is about -0.65 K (channel 4) and -0.72 K (channel 5) at 250K scene with standard deviation of 0.15 K and 0.12 K, respectively. Long term monitoring analysis demonstrates the above biases are stable in the early 6 months. The inter-calibration results using different hyperspectral sensors IASI and CrIS indicate the MERSI/VIRR biases with respect to two reference sensors have a good consistency and this further verifies the reliability of the method. It provides significant information to further correct the calibration biases of MERSI and VIRR. Keywords: inter-calibration, infrared band, hyperspectral sensors

1. INTRODUCTION Meteorological satellites have become an irreplaceable weather and ocean observing tool in China and all over the world. In resents years, the meteorological satellites play more and more important roles in meteorological, environmental and disaster monitor, especially the effects on weather and climate, hydrology and agriculture should not be ignored. FY-3 series meteorological satellites are the second generation of polar orbit meteorological satellites, and the first 2 satellites of FY-3 series were launched on May 27th, 2008 and November 5th, 2010. Both of them are sun-synchronous orbit while FY-3A is operated in morning orbit with a local equator-crossing time of 10:30 A.M. in descending node and FY-3B in an afternoon orbit with an equator-crossing time of

*

Corresponding author: [email protected]; phone 86-10-68406704

Earth Observing Missions and Sensors: Development, Implementation, and Characterization III, edited by Xiaoxiong Xiong, Haruhisa Shimoda, Proc. of SPIE Vol. 9264, 92640B © 2014 SPIE · CCC code: 0277-786X/14/$18 · doi: 10.1117/12.2069581 Proc. of SPIE Vol. 9264 92640B-1

1:30 P.M. in ascending node. FY-3C which was launched on September 23rd, 2013 is the latest satellite in FY-3 series and the first operational satellite in FY-3 series. As a latest satellite of FY-3 series and the first operational satellite, the operation and accuracy of calibration and products are important issues of concern. Meteorological satellites are usually used for daily weather monitoring, so the accurate calibration of sensors allows for retrievals of atmospheric and environmental parameters. The calibration establishes a relationship between the digital counts and the physical radiance. Besides, meteorological satellites have proven to be useful in long-term regional and global environmental and climatological monitoring. Thus, the ability to compare the observations from instruments on board different satellites has become more and more important. The Medium Resolution Spectral Imager (MERSI) and Visible Infrared Radiometer (VIRR) are two major multispectral imaging instruments on board FY-3C satellite in China[1]. The CrIS was launched on 28 October 2011, which is a Fourier transform spectrometer (FTS) on board the Suomi National Polar-Orbiting Operational nvironmental Satellite System Preparatory Project satellite (S-NPP). S-NPP is the first in a series of next-generation U.S. weather satellites of the Joint Polar Satellite System (JPSS)[2][3]. In this paper, we examine the calibration accuracy of the two sensors (MERSI and VIRR) using inter-calibration method in which hyperspectral sensor CrIS as reference sensor and using another hyperspectral sensor IASI to further test the inter-calibration results between FY-3C/MERSI-CrIS and FY-3C/VIRR-CrIS.

2. METHODOLOGY The LEO-LEO simultaneous nadir overpass (SNO) inter-calibration method could compare two instruments’ measurements which cover the similar spectral regions using their near simultaneous observations in the same location[4][5]. After a series of thresholds such as time difference threshold, angle threshold and so on, two instruments’ SNO measurements are also need for data transformation which change observations of the two instruments comparable, and achieving calibration of the target sensor with reference sensor[6][7]. This process includes the following steps: (1) Data collection: According to the prediction of satellite nadir tracks, orbit cross points of the two satellites are selected when their nadir overpass the same target within 600 seconds. (2) Observation collocation: The spatial and temporal collocated pixel pairs with similar view geometries are essential to inter-calibration. There are some collocation criteria that should be used to obtain the SNO samples. The time difference of observations is limited to less than 5 minutes. View geometry collocation ensures a similar atmospheric optical path that is dependent on the satellite zenith and azimuth angles. In this work, the cosine of the satellite viewing zenith angle is used for geometry collocation, and the criterion threshold is defined as |cos(FY_zen)/cos(REF_zen)-1| < 5%. The nearby collocated pixels are simultaneously observing the same target, such that the solar angle differences can be ignored. Regarding to the large

Proc. of SPIE Vol. 9264 92640B-2

difference in spatial resolution, MERSI and VIRR measurements with high spatial resolutions are averaged and degraded to match the large instantaneous field of view (IFOV) pixels of CrIS and IASI with lower resolutions (approximately 14 and 12 km at nadir, respectively). Observations from these matched footprints are supposed to be consistent, such that spatially homogeneous footprints are selected. In this work, the relative standard deviation of MERSI and VIRR pixels is used as a homogeneous scene criterion, i.e., (StdRadFY/MeanRadFY) < 0.5%. (3) Data transformation: The raw observations of Hyperspectral measurements should convolved with SRFs to derive simulated radiances corresponding to target sensor channels. The spectrum of IASI is continuous while the spectrum of CrIS is divided into three sections and there are obviously gap between three sections. The spectrums of CrIS and IASI could fully cover the spectrums of MERSI’s 5 channel and VIRR’s 4 and 5 channel. So in this paper, we choose the 5 channel of MERSI and the 4 and 5 channel of VIRR as target sensors’ target channels to calibration. (4) Radiance comparison: Calibration assessments are generally based on BT. Collocated radiances are converted to BT using pre-created lookup tables corresponding to MERSI and VIRR channels. We always consider the relationship between simulated BT of hyperspectral reference sensors and the target sensors is linear fitting, and calibration bias of MERSI and VIRR is assessed using reference sensors simulated BT as criteria.

3. RESULTS 3.1 MERSI The result of the inter-calibration between FY-3C/MERSI’s fifth channel and NPP/CrIS in March 2014 is shown in Figure 1. Through further controlling the quality of the matching points, there exists 96185 matching points between MERSI and CrIS in March 2014. It can be seen from Figure 1a that, the matching result between MERSI’s fifth channel and CrIS has a good BT consistency. Figure 1b shows the distribution of the BT bias of MERSI and CrIS, with the BT of the reference instrument CrIS. From the figure, it can be seen that, in March 2014, MERSI and CrIS’s BT biases (regardless of the individual) distribute between ±5 K, and average -0.13K, and has the standard deviation 0.64 K；It shows the tendency that the BT bias is decreasing with the increasing BT of the reference instrument. Figure 1c shows the distribution feature of the BT biases of MERSI and CrIS. The BT biases of them show an obvious normal distribution, and more than 85% of the BT biases distribute between ±1 K. It further shows that, using NPP/CrIS as the reference


instrument, the BT of MERSI’s infrared channel and the reference instrument has a high consistency; using CrIS as the radiation reference, the BT of the black body in MERSI’s fifth channel does not have systematic bias. In order to verify this result, Figure 2 gives out the cross-check results of FY-3C/MERSI during March 2014, in which IASI is used as the reference instrument. ri 15000

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Figure 2 Similar to Figure 1, but it is the result of the inter-calibration between FY-3C/MERSI and METOP/IASI.


In order to explicitly show the BT bias of MERSI and CrIS (while the BT being set to be arbitrary value), here (Figure 3) further statistic the mean and standard deviation values of BT biases in every 1K BT intervals. It can be seen that, except the high temperature side(more than 279 K), MERSI’s BT biases are all between ±1 K; The BT biases decrease obviously during 222K and 278K; The BT biases fluctuate relatively great with the changing temperature while it is in the low temperature side (less than 222K) and high temperature side (more than 279 K), and there has not obvious decreasing tredency; When it is 250 K, the BT bias is -0.18 K, and the standard deviation is 0.83 K; When it is 243 K, the BT bias reaches the minimum value, which is about 0 K; When it is more than 243K, the BT of MERSI would less than that of CrIS; When it is less than 243 K, the BT of MERSI becomes higher. The result further shows that, in the sense of statistics, the BT of MERSI is higher while it is in the low temperature side, however, it is lower while it is in the high temperature side.

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Figure 3 The distribution feature of the BT biases between FY-3C/MERSIde’s fifth channel and NPP/CrIS in March 2014. It shows the average deviation (star point) and standard deviation (error bar) in every 1K BT intervals. Figure 4 further shows the day-by-day long sequence result of BT bias between MERSI’s infrared channel and CrIS (during March 2014 and June 2014) when it is 250K. It can be seen that, during March 2014 and June 2014, all the BT biases of MERSI are negative. It means that, when it is 250K, the BT of MERSI is lower than that of CrIS. In the early to mid March 2014, the BT of MERSI shows increasing tendency, which means the BT bias of MERSI is reducing. In the late March 2014, the BT bias stabilizes under -0.25K, and until June the BT bias and standard deviation


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of MERSI keep stable. The result shows, by using CrIS as reference instrument, the calibration results of FY-3C/MERSI’s fifth channel are stable since March 2014. 1.5 1 0.5 0 -0.5 -1 -1.5 Mar.01 Mar.11

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Figure 4 The time sequences of the daily BT bias and standard deviation between FY-3C/MERSI’s 5 channel and NPP/CrIS, during March 2014 and June 2014 at 250 K. 3.2 VIRR According to the similar method, we use NPP/CrIS as the reference instrument, and cross-check the infrared channels (the fourth and fifth channel) of FY-3C/VIRR. The cross-check result between VIRR’s fourth/fifth channel and CrIS in March 2014 is shown in Figure 5 and 6, respectively. There are more than 26000 samples in the computation. It can be seen from the figure that, the BT of the black body in VIRR’s fourth and fifth channel is well consistent with the reference instrument CrIS, however there exists systematic bias between them. In March 2014, the average deviation between the fourth/fifth channel and CrIS is -0.74K/-0.82K，and the standard deviation is 0.49 K/0.52 K. The BT biases of VIRR’s two infrared channels are counted in each BT interval, and the counting result is shown in Figures 5c and 6c. The result further shows the existence of this kind of systematic bias. (s) 08Z

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Figure 6 Similar to Figure 5, but it is for the fifth channel of FY-3C/VIRR. Similar to the analysis for FY-3C/MERSI, Figure 7 shows the cross-check result of VIRR with IASI as the reference instrument. The result also shows the existence of systematic bias of VIRR. (b)

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Figure 7 Similar to Figure 2, but it is the cross-check result for the fourth and fifth channels of FY-3C/VIRR, respectively. The first three figures are for the fourth channel, and the last three figures are for the fifth channel. In the fourth and fifth channels of FY-3C/VIRR, the variation of BT bias with the BT is shown in Figure 8. For the fourth channel, when it is in the low temperature side (less than 220 K), the BT biases are obviously low (less than -1 K) and show the instability with the BT being changed; when it is more than 220 K, the variation of BT biases keeps stable (it is between -0.5 K and -1 K), and the BT biases would firstly increase and then decrease with the BT increasing; when it is between 250K and 260K, the BT bias reaches the maximum, and its absolute value reaches the minimum. For the fifth channel, when it is in the low temperature side (less then 220K), the BT biases also show the instability with the BT being changed; when BT is more than 220K, the BT biases would decrease with the BT increasing; when it is in the high temperature side, the BT bias reaches the minimum, and its absolute value reaches the maximum (more than -1K); when it is 250K, the BT bias in the fourth/fifth channel is -0.65 K/-0.72 K, and the standard deviation is 0.15 K/0.12K.


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Figure 8 Similar to Figure 3, but it is for the fourth and fifth channel of FY-3C/VIRR. The first figure is for the fourth channel and the last figure is for the fifth. During March 2014 and June 2014, when it is 250K, the day-by-day long sequence result of VIRR’s BT biases is shown in Figure 9. It can be seen that, in this period, the BT biases for the two channels are decreasing obviously; The BT bias for the fourth channel increases approximately from -0.6K to -1K during the early March and June; It is similar for the fifth channel.

DTBB(K, VIRR-CrIS, CH4) @250

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Figure 9 Similar to Figure 4, but for the fourth and fifth channel of FY-3C/VIRR. The first figure is for the fourth channel and the last figure is for the fifth.

4.

CONCLUSIONS

Using NPP/CrIS hyperspectral sensor as the reference instrument, we make cross-check of FY-3C’s two loadings (the infrared channels of MERSI and VIRR), and then test the cross-check result by employing another hyperspectral sensor IASI. Our conclusions are presented as follows. (1) The BT from the MERSI observation and CrIS have a good consistency, and the BT biases present an approximately normal distribution and the mean BT bias is about -0.18K with standard deviation of 0.83K. When the scene BT is lower than 250K, the result of MERSI is higher than that of CrIS, while the result of MERSI is lower at the more than 250K scene. (2) The BT from VIRR shows significant systematic bias with respect to CrIS and the mean BT bias is about -0.65K (channel 4) and -0.72K (channel 5) at 250K scene with standard deviation of 0.15K and 0.12K, respectively. Long term monitoring analyses demonstrate above biases are stable in the early 6 months. (3) From the long sequence result of BT biases of MERSI and VIRR at 250K, it can be seen that, MERSI’s infrared channel keeps stable since March 2013, and its BT bias changes very little; However, VIRR’s BT bias tends to decrease gradually, and its absolute value is gradually increasing. (4) No matter it is MERSI or VIRR, the cross-check results with CrIS both show that the BT bias is instable in the low temperature side, and the BT bias fluctuates relatively big with the reference BT; It is about more than 225K then the BT bias tends to be stable. This kind of phenomenon is more obvious in the fourth channel of VIRR.

ACKNOWLEDGEMENT The authors would like to acknowledge financial support from the projects 863 (2012AA120903-01) the Ministry of Science and Technology (MOST)) and GYHY20140611 for the financial support for this work.


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