The CERES measured shortwave and longwave fluxes rely on the cloud properties derived using the coincident observations from the accompanying high-resolution MODIS and VIIRS imagers. The calibration consistency is required between MODIS and VIIRS radiances to ensure that the CERES provided cloud property retrievals are temporally consistent. This paper presents multiple approaches of cross-calibrating the spectrally comparable reflective solar bands (RSB) of Aqua-MODIS and NPP- VIIRS, and estimates the radiometric biases for individual band pair. The inter-comparison is performed between the Aqua-MODIS collection 6.1 level 1B and NPP-VIIRS Land PEATE V1 datasets. Radiometric biases up to 3% were estimated bet een the MODIS and VIIRS radiances for visible bands

Bhatt, Rajendra

Doelling, David

Gopalan, Arun

Haney, Conor O.

Scarino, Benjamin

English

NASA Technical Reports Server

CROSS-CALIBRATION OF AQUA-MODIS AND NPP-VIIRS REFLECTIVE SOLAR BANDS 
FOR A SEAMLESS RECORD OF CERES CLOUD AND FLUX PROPERTIES 
 
Rajendra Bhatta, David Doellingb, Benjamin Scarinoa, Conor Haneya, Arun Gopalana 
 
aScience Systems and Applications, Inc., Hampton, VA 23666  
bNASA Langley Research Center, Hampton, VA 23666  
 
ABSTRACT 
 
The CERES measured shortwave and longwave fluxes rely 
on the cloud properties derived using the coincident 
observations from the accompanying high-resolution 
MODIS and VIIRS imagers. The calibration consistency is 
required between MODIS and VIIRS radiances to ensure 
that the CERES provided cloud property retrievals are 
temporally consistent. This paper presents multiple 
approaches of cross-calibrating the spectrally comparable 
reflective solar bands (RSB) of Aqua-MODIS and NPP-
VIIRS, and estimates the radiometric biases for individual 
band pair. The inter-comparison is performed between the 
Aqua-MODIS collection 6.1 level 1B and NPP-VIIRS Land 
PEATE V1 datasets. Radiometric biases up to 3% were 
estimated between the MODIS and VIIRS radiances for 
visible bands. 
 
Index Terms— Cross-calibration, VIIRS, MODIS, 
DCC, CERES, SBAF 
 
1. INTRODUCTION 
 
The Clouds and the Earth’s Radiant Energy System 
(CERES) instruments on board the Terra, Aqua, and S-NPP 
satellites have been providing global surface and top-of-
atmosphere (TOA) radiative fluxes for over one and a half 
decade to help scientists to understand the role of clouds in 
the Earth’s energy balance and improve climate prediction 
models [1]. The climate quality of the CERES data is 
maintained using a state-of-the-art on-board calibrator. All 
CERES instruments rely on simultaneous measurements 
from high-resolution on-board imagers, MODIS on Aqua 
and Terra, and VIIRS on NPP, for retrieving cloud 
properties, that determine scene type, which are needed to 
convert the observed CERES shortwave and longwave 
radiances into fluxes. For the reliability of such datasets, it is 
necessary to ensure that the imagers on different satellites 
are radiometrically consistent. Although MODIS and VIIRS 
are both equipped with a solar diffuser for their in-flight 
calibration, radiometric biases may still exist between them 
due to their finite signal-to-noise ratio, differences in the 
characterization of solar diffusers, instrument aging, etc.  
    Doelling et al [2] performed a detailed inter-consistency 
study of Aqua- and Terra-MODIS Collection 6 calibration 
utilizing multiple independent approaches, which included 
the use of near-simultaneous nadir overpasses (SNO) over 
the poles and invariant Earth targets, and found that the 
radiometric biases are minimal and within the specified 
calibration accuracy of both MODIS instruments. A similar 
inter-consistency analysis of MODIS and VIIRS imagers is 
required to ensure the radiometric consistency between the 
cloud retrievals from the two instruments. This paper reports 
radiometric biases between the matching spectral bands of 
Aqua-MODIS and NPP-VIIRS using three independent 
cross-calibration approaches. The MODIS and VIIRS 
radiance data used in this inter-comparison study are a 
reformatted subset of the MODIS Collection 6.1 level 1B 
product and the NPP VIIRS V1 dataset generated by the 
NASA Land Product Evaluation and Analysis Tool 
Elements (PEATE), respectively, for support of independent 
cloud retrievals for the CERES project. In all three methods, 
the spectral differences between the comparable MODIS 
and VIIRS channels are accounted for with scene-specific 
spectral band adjustment factors (SBAFs) based on 
hyperspectral TOA radiance measurements acquired from 
the SCanning Imaging Absorption SpectroMeter for 
Atmospheric CHartographY (SCIAMACHY) instrument as 
described by Scarino et al. [3]. 
 
2. METHODOLOGY 
 
2.1. ATO and DCC ray-matching 
 
Coincident, co-located, and co-angled radiance pairs for all 
comparable channels of Aqua-MODIS and NPP-VIIRS are 
acquired between 30 °N and 30 °S for all-sky tropical ocean 
(ATO) and deep convective clouds (DCC) scenes. For either 
of these ATO ray-matching or DCC ray-matching (ATO-
RM and DCC-RM) schemes, measurements from each 
instrument must be gathered within 15 minutes of one-
another, with less than 40° viewing zenith angle (VZA) and 
less than 40° solar zenith angle (SZA). Conditions where the 
relative azimuth angle (RAZ), with respect to either 
instrument, is less than 10° or greater than 170° are avoided. 
In all cases, inter-satellite VZA and SZA differences must 
be less than 3°, whereas the allowable RAZ difference 
varies from less than 3° for dark ocean scenes, less than 5° 
for mixed ocean/cloud and darker cloud scenes, and less 
than 10° for the brightest cloud scenes.  
     Both techniques have additional unique refinements to 
best suit their physical traits. ATO-RM focuses on capturing 
the entire range of Earth viewed radiances by combining 
clear-sky ocean, partly cloudy and overcast conditions. On 
https://ntrs.nasa.gov/search.jsp?R=20200002552 2020-05-24T04:49:39+00:00Z
the other hand, DCC-RM focuses on reducing the impact of 
the SBAF by selecting only spectrally uniform near 
Lambertian clouds, and therefore must employ more 
discriminating scene selection criteria. For ATO-RM, pixels 
from each instrument are averaged within a shared 50-km 
(at nadir) diameter, which constitutes one ray-matched 
radiance pair. The spatial standard deviation must be less 
than 50% of the average. In case of DCC-RM, a 30-km 
diameter is used, where the mean BT must be less than 
220K with a spatial standard deviation less than 20% of the 
average and a 11-µm BT standard deviation less than 7.5 K. 
These criteria allow for adequate distinction between the 
two methods with sufficient sampling, dynamic range, 
SBAF uncertainty, and statistical confidence. Fig. 1 shows 
the scatter plot of the ray-matched ATO radiance pairs 
acquired in July 2017 for Aqua-MODIS band 1 (0.65-µm) 
and NPP-VIIRS I1 (0.64-µm). A linear regression forced 
through zero is fitted to the data and the forced-slope is used 
as the cross-calibration ratio.  
 
  
Fig. 1. ATO-RM scatter plot of Aqua-MODIS band 1 and 
NPP-VIIRS I1 radiances from July 2017. 
 
2.2. DCC invariant target (DCC-IT) method 
 
DCCs are an excellent invariant Earth target for vicarious 
calibration of satellite reflective solar bands (RSBs) [4-7]. 
The DCC-IT is a large-ensemble statistical approach that 
collectively analyzes all identified DCC pixels on a monthly 
basis. DCC pixels are acquired globally over the tropics 
(±20° latitude) for all matching spectral channels of MODIS 
and VIIRS using the DCC identification criteria defined by 
Doelling et al. [4]. For visible and near-IR (VIS-NIR) 
(defined here as <1 µm) wavelengths, the DCC reflectance 
is mostly lambertian, and requires only small bidirectional 
reflectance distribution function (BRDF) corrections. Hu 
DCC BRDF is used for anisotropic corrections in VIS-NIR 
spectral channels [8]. The DCC reflectance in the shortwave 
infrared (SWIR) (defined here as >1 µm) bands is highly 
affected by the ice effective diameter that produces a large 
seasonal cycle with its magnitude depending on the 
wavelength [9]. Bhatt et al. [7] illustrated that a SWIR band 
DCC seasonal cycle repeats annually and can be 
characterized with a channel-specific seasonal BRDF. The 
application of the seasonal BRDF can reduce the seasonal 
variation in the SWIR band DCC time series by up to ~55% 
[7]. This study also incorporates channel-specific BRDFs to 
mitigate the temporal variation in the SWIR band DCC 
response of Aqua-MODIS and NPP-VIIRS. As confirmed 
by previous studies, the monthly probability density 
function (PDF) mode is more suitable to evaluate the sensor 
calibration stability in VIS-NIR wavelengths, whereas for 
SWIR bands the monthly mean DCC reflectance is 
preferable [4][7]. Because NPP-VIIRS and Aqua-MODIS 
are both in a 13:30 sun-synchronous orbit, they have 
consistent local time sampling of the DCC cycle, thereby 
avoiding any diurnal sampling differences of DCC 
properties. This allows a direct comparison of the monthly 
mode (for VIS-NIR bands) or mean (for SWIR bands) DCC 
response from the two instruments for computing the 
radiometric biases. 
 
2.3. PICS method 
 
The pseudo-invariant calibration site (PICS) method used in 
this paper follows the technique described by Bhatt et al. 
[6], where the radiometric characterization of the ground 
PICS is performed by constructing direction models (DMs) 
that define the near-nadir (VZA<10°) TOA radiances as a 
function of cosine of the solar zenith angle (SZA). The 
Libya-4 (28.55°N latitude and 23.40°E longitude) PICS is 
used in this study. The channel-specific DM is constructed 
using the Aqua-MODIS measurements acquired over Libya-
4 during 2012-2017. Because the observed Libya-4 TOA 
radiances are slightly darker in the forward scattering 
direction than in the backward scattering direction, the two 
scattering conditions were modeled independently as 
described in [6]. The reference DM is then used to predict 
the near-nadir TOA radiances for the matching VIIRS band. 
The ratio of the DM-predicted VIIRS radiance and the 
observed VIIRS radiance provides the calibration bias 
between VIIRS and MODIS.  
 
2.4. SBAF 
 
The spectral response functions (SRFs) of MODIS and 
VIIRS RSBs exhibit some differences. SBAFs are computed 
for individual calibration targets to account for the spectral 
band differences in cross-calibration. The computation of 
SBAF follows the approach of Scarino et al. [3], in which 
the target-specific hyper-spectral footprints measured by the 
SCIAMACHY instrument are convolved with the SRFs of 
the two sensors that are being cross-calibrated in order to 
estimate pseudo-imager radiance pairs for these sensors. The 
SBAF is then computed as the slope of regression of the 
pseudo-radiance pairs. The VIIRS radiances are then 
adjusted to equivalent MODIS radiances using the SBAF 
prior to cross-calibration. The SBAF regression for spectral 
correction between the MODIS band 1 and VIIRS M5 
channel for the PICS method using the Libya-4 target is 
shown in Fig. 2. The expected radiance bias due to the SRF 
differences between these two channels is 3.2% 
(SBAF=0.968) for Libya-4. 
 
 
Fig. 2. The SBAF regression of SCIAMACHY-based 
pseudo-radiances (Wm-2µm-1sr-1) for VIIRS M5 and Aqua-
MODIS B1 over the Libya-4 desert. 
 
3. RESULTS 
 
Following the methods described above, the MODIS-to-
VIIRS radiance ratio is computed for each spectral band pair 
on a monthly basis and is trended over time. Fig. 3 shows 
the time series of MODIS-to-VIIRS radiance ratio for Aqua-
MODIS band 1 and two matching VIIRS channels, I1 and 
M5 (0.67-µm). No temporal trend is detected in any of these 
time series, which indicates an excellent radiometric 
stability of both MODIS and VIIRS datasets. The mean 
radiance bias after spectral corrections between VIIRS M5 
and MODIS band 1 is found to be 1.4%, 1.9%, 1.6%, and 
1.8% using the DCC-IT, DCC-RM, ATO-RM, and PICS 
methods, respectively. All four methods show that the 
VIIRS M5 channel is brighter than MODIS band 1. 
Similarly, the maximum calibration bias between VIIRS I1 
and Aqua-MODIS band 1 is found to be 0.5%. Calibration 
biases computed for the VIIRS M3 (0.47-µm), M4 (0.55-
µm), and M7 (0.86-µm) channels and their corresponding 
matching bands in Aqua-MODIS (band 3, 4, and 2, 
respectively) are listed in Table 1. Because MODIS band 2 
saturates for DCC targets, the DCC-IT and DCC-RM 
methods are inapplicable for this channel. The observed 
radiance biases between MODIS and VIIRS are within 3% 
for the band pairs listed in Table 1. Inter-comparison results 
for the remaining RSBs will be presented at the conference. 
 
 
Fig. 3. Cross-calibration radiance ratios computed for Aqua-
MODIS band 1, and VIIRS M5 (top) and I1 (bottom) bands 
using DCC-IT, DCC-RM, ATO-RM, and PICS approaches. 
 
Table 1. Cross-calibration biases between MODIS and 
VIIRS visible and near-infrared bands. 
Method B3/M3 B4/M4 B1/M5 B2/M7 B1/I1 
DCC-IT 0.985 0.982 0.986 NA 1.0 
DCC-RM 0.979 0.970 0.981 NA 0.995 
ATO-RM 0.992 0.972 0.984 0.995 0.997 
PICS 0.973 0.973 0.982 0.990 0.995 
 
4. CONCLUSIONS 
 
To achieve a consistent retrieval of cloud properties across 
the entire CERES record, it is important to ensure the 
radiometric consistency between the MODIS and VIIRS 
instruments. In this paper, we discussed three independent 
approaches establishing radiometric consistency between 
the Aqua-MODIS and NPP-VIIRS RSBs. The ATO-RM 
and DCC-RM methods utilize the coincident, co-located, 
and co-angled radiance pairs acquired by Aqua-MODIS and 
NPP-VIIRS within 15 minutes. The DCC-IT is a large-
ensemble statistical approach that relies on seasonal BRDFs 
to mitigate the large seasonal cycles in the SWIR band DCC 
response. The PICS method does not require simultaneous 
measurements from MODIS and VIIRS. It strictly relies on 
the radiometric stability of the ground PICS and the 
robustness of the DMs used to characterize the PICS TOA 
radiance. The mean radiometric bias computed for Aqua-
MODIS band 1 and VIIRS M5 from the multiple 
approaches is 1.7%, whereas it is less than 0.5% for Aqua-
MODIS band 1 and VIIRS I1 band pair. The calibration 
consistency of the MODIS and VIIRS visible and near-
infrared bands is found to be within 3% after accounting for 
the spectral differences. The radiometric biases for other 
RSB pairs of MODIS and VIIRS will be presented at the 
conference. 
5. ACKNOWLEDGEMENT 
 
The authors would like to thank the MODIS 
Characterization Support Team for providing the C6 to C6.1 
conversion coefficients for Aqua-MODIS RSBs. This work 
was supported by the National Aeronautics and Space 
Administration Earth (NASA) Science Enterprise Office 
through the CERES and Research Announcement (NRA) 
NNH15ZDA001N, Research Opportunities in Space and 
Earth Science (ROSES-2015), Program Element A.34: 
Satellite Calibration Interconsistency Studies. 
 
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Cross-Calibration of AQUA-MODIS and NPP-VIIRS Reflective Solar Bands for a Seamless Record of CERES Cloud and Flux Properties

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