1. Introduction
The Moderate Resolution Imaging Spectroradiometer (MODIS) on the Earth Observing System (EOS) platform has 36 spectral bands to provide near-global observations every 2 days [
1]. Of the 36 spectral bands, 20 are reflective solar bands (RSB) with a spectral range of 0.41 to 2.1 μm. Within the RSB, bands 8–16 are optimized to observe ocean biological processes (
Table 1) and the rest of the RSB are designed for land and atmosphere applications but can also be used in ocean science application with reduced accuracy.
The MODIS is a key instrument aboard the Terra and Aqua Satellites. The two EOS spacecrafts, Terra and Aqua, were launched on 18 December 1999 and 4 May 2002, respectively, into sun-synchronous polar orbits at an altitude of 705 km. Terra is on a descending node with an equator crossing time of 10:30 AM (local time) and Aqua is on an ascending node with a 1:30 PM equator crossing time. Since they began operations, both instruments have continuously provided global earth observation data to study land, ocean, and atmospheric processes. However, due to the changing polarization sensitivities in MODIS Terra (MODIST) [
2], the Terra ocean products have been relying on Ocean Biology Processing Group (OBPG) crosscalibration to correct the temporal calibration biases [
3]. The crosscalibration uses MODIS Aqua (MODISA) global 7-day composites of water-leaving radiance as a truth field, combined with modeled atmospheric path radiance and surface contributions, to estimate the temporal change in gain and polarization as a function of time and scan angle.
Because of the MODIST-MODISA crosscalibration, the Terra ocean products are not considered an independent science data product and the accuracy of Terra products is dependent on the quality of Aqua products. Therefore, the calibration accuracy of MODISA is crucial, as the entire suite of MODIS ocean color products rely on the quality of Aqua products. Historically, the Aqua ocean color products are produced using the MODIS Calibration Support Team (MCST) radiometric calibration and are fine-tuned by OBPG crosscalibration [
4] and vicarious calibration [
5]. The Aqua ocean color products have been maintained at science quality until recent years [
6]. As it is getting increasingly challenging to calibrate the aging MODISA sensor, a decision was made by OBPG to develop an independent radiometric calibration specifically for the ocean color products.
The main challenge of calibrating remote-sensing ocean color products is its sensitivity to calibration uncertainties. In general, calibration errors are magnified by anywhere from 5 to 100 times in ocean color products [
7]. To produce meaningful climate data records, a long-term radiometric calibration stability of about 0.1% is often needed [
8]. To achieve this stringent requirement, there are several advantages for an independent, end-to-end calibration solution. First, because it is all “in house”, the radiometric calibration process can be efficiently integrated into the data production life cycle (
Figure 1). For each version of calibration look-up-table (LUT), corresponding vicarious coefficients and crosscalibration LUT [
4,
5] are regenerated and a global, life-of-mission test processing is performed to assess the impacts. The uncertainties in radiometric calibration are significantly amplified during ocean color product retrieval [
7]. This is further complicated by the subsequent changes in vicarious and crosscalibration in response to given changes made in radiometric calibration. Therefore, it is essential to integrate the testing of a radiometric calibration LUT into the data production life cycle in order to accurately evaluate a radiometric calibration LUT on ocean color products.
For each version of a calibration LUT, a global mission test reprocessing is performed that includes the first 4-day period of each month over the life of the mission. Each 4-day period is binned into a 9.2-km sinusoidal projection to form a 4-day mean global distribution of ocean color products for each month, which is then separated regionally (e.g., geographically and by water type) and trended with time. Temporal anomaly trends are also computed by subtracting the mean seasonal cycle from the global or regional time series. The ocean color products are reviewed by OBPG staff based on the known ocean biological processes to identify any product deficiency that could have originated from calibration issues. This process is vitally important to achieve the stringent radiometric calibration stability required to maintain ocean color products at science quality [
9]. Maintaining radiometric calibration stability is especially difficult for Aqua MODIS as its radiometric response has changed significantly during more than 17 years of operation [
10].
An ocean color specific calibration LUT can be optimized to the spectral properties of ocean scenes. For example, the solar calibration for RSB are performed at 2 different solar diffuser (SD) radiance levels (with and without SD screen attenuation). Normally, the land bands are calibrated at high SD radiance and ocean bands are calibrated using low SD radiances to maximize the signal-to-noise ratio (SNR). Since the spectral radiance of the ocean is closer to the low SD radiances, the OBPG solar calibration computes all detectors gains at the low SD radiance levels. Because the MODIS calibration algorithm uses a linear gain, calibrating detector gains at radiance closer to ocean radiance will reduce the impact of nonlinearity in detector responses on ocean color products. This approach, however, will increase nonlinearity impacts on high radiance scenes.
In addition, some radiometric characteristics can be better refined in subsequent OBPG crosscalibration and vicarious calibration and the radiometric calibration strategy can be simplified to improve calibration stability. For example, a previous study showed that the response versus scan angle (RVS) of band 8 (412nm) can be better characterized by a 4th-order fit [
11]. However, our preliminary analysis showed that the fit residual reduction by the higher-order fit is insignificant. Compared to using the 2nd-order fit (same as in instrument prelaunch calibration), the 4th-order RVS fit is less stable temporally. Since the OBPG crosscalibration already used 4th-order polynomials to fine-tune the residual RVS uncertainties on ocean color products, using 2nd-order fit for RVS can improve temporal calibration stability while achieving similar RVS calibration accuracies on ocean color products.
The flow chart in
Figure 2 summarizes the steps used to generate the OBPG RSB LUT. First, we used the yaw maneuver data to characterize the solar diffuser stability monitor (SDSM)/SD sunscreen transmission functions (tau) and Bidirectional Reflectance Function (BRF) functions and to store them in an internal LUT. In each subsequent SD calibration event, the SDSM calibration computes the relative SD reflectivity (H factor) using SD door open data. After the H factor is determined, the SD calibration computes the detector gain (m1) from the detector response and the SD radiance estimate from H factor and solar incident radiance. The computed m1 is then adjusted by modulated relative spectral response (mRSR) impact on ocean color products based on the typical ocean spectral radiance [
12]. The mRSR impact is computed by optical degradation estimated by each detector’s radiometric gain (m1) and electronic gain determined by electronic self-calibration (Ecal).
To estimate temporal change in RVS, desert and lunar observations are used to track instrument response changes at various scan angles. At the desert site, the time series of calibrated instrument reflectance, after correcting the atmospheric contributions, are computed as desert trends, which are used to track the RVS change at the 16 repeated observation angles. The calibrated lunar irradiance is computed from the monthly lunar observation. The calibrated lunar irradiances are compared to RObotic Lunar Observatory (ROLO) model predictions to produce lunar trend, which is used to track the RVS at the lunar observation angle.
The raw m1 and RVS values are estimated at the temporal interval of the data collection frequency, i.e., solar, lunar, and desert calibration. A temporal smoothing is applied to generate the time series of m1 and RVS. The smoothed m1 and RVS time series are used to populate the RSB LUT at the predefined time intervals.
In this paper, we will describe the calibration methodology and procedures applied by the OBPG in producing the RSB calibration LUT for the R2018 ocean color reprocessing and forward stream processing. The paper will focus on describing the differences between OBPG MODISA calibration and the approach used in the MCST calibration. The principle mechanisms of MODIS RSB calibrations will not be described in detail, as they have been discussed extensively in past literatures during the 19+ years of MODIS operation.
6. Discussion
To demonstrate the impact of the calibration approach described in this paper, the ocean color products from the R2018 reprocessing are compared with the previous R2014 reprocessing. The only changes between these two reprocessing events were the updated instrument calibration approach and the updated vicarious calibration [
26].
Figure 18 shows a comparison of the two reprocessing versions for the time series of spectral water-leaving reflectances (Rrs) and derived chlorophyll concentration, as derived from monthly averages for the globally distributed deepest ocean waters (>1000-m depth). We restrict our analysis to this deep-water subset as it allows for an assessment over the vast majority of the ocean surface while avoiding complex coastal regions with highly variable terrestrial influences. In
Figure 18a, the R2014 water-leaving reflectances (solid lines) for the shortest wavelengths (412 nm and 443 nm) show a strong downward trend after 2012, which is not associated with any known geophysical events. This late-mission drift in the blue was significantly reduced in the R2018 products. Similarly, the large seasonal variability in the R2014 chlorophyll product, especially after 2012, is much reduced in the R2018 products (
Figure 18b). Such variability will arise if the calibration in the blue is erroneously low, as the observed radiance has a strong seasonal cycle due to variations in solar geometry and associated atmospheric path radiance, and the correction for that variable atmospheric contribution to the TOA radiance will result in an underestimation of water-leaving reflectance in the blue. The chlorophyll concentration is derived from the ratio of blue to green water-leaving reflectances, such that a lower blue–green ratio implies a higher chlorophyll concentration, thus giving rise to seasonal peaks in the R2014 chlorophyll products. There is also an overall bias (increase) in the blue wavelengths between the R2014 and R2018 results, which is caused by an update to the vicarious calibration [
26] and which also contributes to reduced seasonal variability in the R2018 chlorophyll product over the full mission lifetime.
Another very useful subset of the oceans for assessment of product temporal stability is the mean over the clearest ocean gyres, far from terrestrial influences, where chlorophyll concentrations are very low (typically <0.1 mg m-3) and homogenous, and weak variations in phytoplankton abundance or physiology, which modulate the absorption of blue light much more than green light, are the only significant influences on the ocean water optical properties. Temporal anomalies of water-leaving reflectance for the green ocean channel (547 nm) and green land channel (555 nm) were computed by subtracting the mean seasonal cycle from the water-leaving reflectance time series as derived over oligotrophic waters. In the absence of any major geophysical events, we can expect this time series to be relatively stable over time. The temporal anomalies for R2014 (
Figure 19a,c) show a long-term trend and shorter-term variabilities that are larger than in the R2018 results (
Figure 19b,d). Also notable is that the R2014 results for the two green bands with very similar center wavelengths (
Figure 19a,c) are less consistent than the R2018 products (
Figure 19 b,d). These results indicate that the R2018 products are more stable and consistent relative to the R2014 products.
7. Conclusions
In this paper, we described the independently developed MODIS Aqua radiometric calibration used in OBPG R2018 ocean color products. The OBPG calibration is developed based on the general MODIS calibration principles with a focus on ocean color products. The in-house developed calibration process is integrated in the ocean-color-product production cycle which enables fast evaluation. Timely product evaluation facilitates accurate assessment of the combined changes in radiometric calibration and the subsequent OBPG vicarious and crosscalibration. The integrated calibration development process is key to achieving the high radiometric calibration accuracy required for ocean color science.
Because the calibration is developed specifically for OBPG ocean color products, it is optimized for OBPG ocean color processing. For example, the nominal detector gains are calibrated with low SD radiance to better approximate ocean surface radiance. Applying the OBPG calibration for high-radiance targets could result in biases due to detector response nonlinearity. Furthermore, the RVS is characterized by simple 2nd-order fits to improve temporal stability. Applying the OBPG crosscalibration is needed to obtain the optimal RVS characterization.
Using the OBPG-developed radiometric calibration, the R2018 ocean color products show improved quality over the previous products (R2014). The global trends of the R2018 products are more stable and do not have late-mission anomalous behaviors shown in the R2014 products. The R2018 products are more consistent across wavelengths than R2014 products. Compared to the R2014 coefficients, the OBPG crosscalibration in R2018 is more consistent over time, indicating a more stable RVS characterization in R2018. The improvement in R2018 ocean-color-product quality and crosscalibration stability are the results of improvements in R2018 radiometric calibration.