1. Introduction
Aerosols and greenhouse gases (GHGs) such as carbon dioxide (CO
2) and methane (CH
4) are important factors in understanding climate change caused by anthropogenic activities [
1] since the preindustrial period. Extensive satellite observations have been carried out to understand the global distribution of these particles and their changes. In particular, to reduce the uncertainty of CO
2 amounts retrieved from satellite remote sensing, it is very important to have simultaneous, accurate aerosol information. To retrieve aerosol properties from satellites, there have been numerous studies based on the look-up table (LUT) approach [
2,
3,
4,
5,
6] and optimal estimation theory [
7,
8,
9].
To measure CO
2 and CH
4 concentrations, the Greenhouse Gases Observing Satellite (GOSAT) was launched on 23 January 2009 into sun-synchronous orbit at an altitude of 666 km, at a local time of approximately 13:00 of descending node, and an inclination angle of 98° [
10,
11]. Thermal And Near-infrared Sensor for carbon Observation (TANSO), which is the observation instrument on board GOSAT, is composed of two subunits: a Fourier Transform Spectrometer (FTS) to measure GHGs and a Cloud and Aerosol Imager (CAI) to provide cloud and aerosol information simultaneously with FTS measurements [
12]. The TANSO-CAI is a push-broom imager with one channel at ultraviolet (UV) band 1 (0.37–0.39 μm), two channels at visible (VIS)-near infrared (NIR) bands 2 (0.664–0.684 μm) and 3 (0.86–0.88 μm), and one channel at short-wave infrared band 4 (1.56–1.65 μm), as shown in
Table 1 [
10,
12].
On 2 July 2014, the Orbiting Carbon Observatory-2 (OCO-2) was launched to provide global space-based observations of atmospheric CO
2 [
13]. In the CO
2 retrieval algorithm, based on OCO-2 observations, aerosol properties were assumed by simulating data with the forward radiative transfer model and the aerosol information was used to combine different subtypes from the referred average aerosol types [
14]. For OCO-2, aerosol optical properties are selected from two dominant and scaled to match from five aerosol types, e.g., dust, smoke, sea salt, sulfate aerosol, organic carbon, and black carbon, in the Modern Era Retrospective-Analysis for Research and Applications model [
13].
In contrast, GOSAT has the TANSO-CAI instrument to obtain data on clouds and aerosols, which has a unique UV wavelength band at 0.38 μm. Despite these advantages, the aerosol properties from TANSO-CAI have not been used for the CO2 gas amounts retrieval from TANSO-FTS because of several limitations (e.g., the derivation of accurate surface reflectance, cloud masking, irregular radiometric degradation, different swath and spatial resolution between bands 1, 2, 3 and 4) in retrieving aerosol properties operationally from TANSO-CAI measurements.
In general, accurate surface reflectance is an essential element in the aerosol retrieval process from satellite observations [
6,
15,
16]. However, GOSAT observes the entire globe over a three-day period, which makes it difficult to retrieve accurate surface reflectance data because of discontinuous observations. Several studies have attempted to overcome this limitation by calculating surface reflectance, but these still have limited operational use [
10,
12,
17,
18]. Among these studies, Fukuda et al. [
17] retrieved aerosol product from the UV wavelength at 0.38 μm with a new surface reflectance correction technique using TANSO-CAI measurements instead of the minimum reflectance method. Aerosol optical depths (AODs) retrieved by applying the new surface reflectance correction technique yielded improved results compared to the minimum reflectance method, but their point by point comparison still showed large deviations. Japan Aerospace Exploration Agency’s GOSAT project team is also developing an aerosol algorithm using multi-wavelength and multi-pixel information from TANSO-CAI measurements [
19]. Currently, aerosol properties for the CO
2 retrieval algorithm utilize aerosol products from the Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS), instead of TANSO-CAI observations [
20,
21]. Because the observations of CO
2 and CH
4 from TANSO-FTS measurements have been provided only for clear sky conditions, the final product’s coverages are only 7% of the average output of the total measurement scenes [
20].
In addition, TANSO-FTS and CAI have suffered from continuous radiometric degradation at each band. In particular, during the first two years after launch [
22,
23], the TANSO-CAI spectra at bands 1 and 4 showed degradation of approximately 20% from the pre-flight test. To date, there has been no study on aerosol retrieval that considers the quantitative radiometric degradation of TANSO-CAI spectra. We adopted annual or seasonal radiometric degradation factors (RDFs) to improve algorithm accuracy, which is expected to improve the accuracy and data coverage of CO
2 retrievals from TANSO-FTS [
24].
To provide improved aerosol properties, this study developed a TANSO-CAI aerosol algorithm with a spatial resolution of 0.1° (approximately 10 km) over Northeast Asia. Although TANSO-CAI products have spatial resolutions of 0.5 km (bands 1, 2 and 3) and 1.5 km (band 4), the algorithm provides coarse resolution results (0.1° × 0.1°) to maintain signal quality and provide accurate aerosol products. To construct LUTs at 0.674 μm (band 2) and 0.870 μm (band 3), aerosol properties and types were compiled using extensive inversion products from Aerosol Robotic Network (AERONET) sun-photometer observations over Northeast Asia (100°–160°E, 10°–60°N). In this study, surface reflectance was determined from 23 path composites of Rayleigh- and gas-corrected reflectance to avoid errors caused by stripe noises. To distinguish aerosol absorptivity and select the aerosol model from the LUTs, a reflectance difference test between UV (band 1) and VIS (band 2) wavelengths, depending on AODs, was used. To remove the cloud contamination in aerosol retrievals, we adopted a concept that combined the normalized difference vegetation index (NDVI) and reflectance ratio at band 2 (0.674 μm) and band 3 (0.870 μm) for cloud mask. To mask turbid waters over ocean, a threshold value for the estimated surface reflectance at band 2 was also introduced.
The TANSO-CAI aerosol algorithm provided aerosol properties such as AOD, size information, and aerosol types from June 2009 to December 2013. In order to evaluate the retrieved AODs from TANSO-CAI measurements, we compared the results with AERONET sun-photometer observations and Aqua/MODerate resolution Imaging Sensor (MODIS) collection 6 (C6) Level 2 observations for the entire period. In the comparisons with AERONET, AOD values were time-averaged within ±15 min of TANSO-CAI overpass in and by a 3 × 3 pixel grid around the AERONET site. The collocation with MODIS was also done by time difference within ±15 min of TANSO-CAI overpass and by spatial grids of 3 × 3 pixels over ocean. Furthermore, the retrieved AODs with seasonal and annual RDFs were compared to confirm the impact on accuracy of different RDF temporal resolutions in 2009.
Many CO
2 retrieval algorithms have been limited to using inputs of aerosol information from simultaneous aerosol measurements on board the same platform. If more accurate aerosol properties were provided for the CO
2 retrieval algorithm, these could increase the data coverage of CO
2 retrieval and reduce errors induced by aerosol. Using more accurate aerosol properties from TANSO-CAI with the same geometry of TANSO-FTS can improve the accuracy and data coverage of the CO
2 retrieval algorithm using TANSO-FTS measurements [
25].
In
Section 2, the construction of LUTs and the TANSO-CAI aerosol algorithm are described. The results of the TANSO-CAI aerosol algorithm and comparisons with other aerosol products are presented in
Section 3. The summary and discussion of the results are presented in
Section 4.
4. Summary and Conclusions
An aerosol retrieval algorithm was developed for the TANSO-CAI on board the GOSAT. The TANSO-CAI algorithm can retrieve aerosol properties in the grid resolution of 0.1° (approximately 10 km) with the LUT approach. Here, we suggested a new method using NDVI and R(band 2)/R(band 3) for the cloud mask, and a threshold using surface reflectance (>0.101) at band 2, for the elimination of turbid water over ocean. The retrieval grid boxes and sun glint mask used in the MODIS aerosol algorithm [
6,
29] were also adopted to reduce the errors caused by bad reflectance data. To calculate the surface reflectance, the minimum reflectance composite method was used with a search window containing the same 23 paths before and after a target day, which is to avoid the stripes at each band. Reflectance difference tests between UV and VIS channels for absorptivity were also used to select aerosol types from LUT.
The aerosol properties were retrieved over Northeast Asia from the TANSO-CAI algorithm, for the long period from June 2009 to December 2013. The retrieved AODs were compared with AERONET and MODIS products over land and ocean. This is the first study that compared both ground and satellite-based observations for a period of nearly five years to obtain the aerosol properties from the TANSO-CAI. Furthermore, comparisons of AODs between AERONET and TANSO-CAI showed improvement compared to previous studies [
17,
63]. The results of AERONET and TANSO-CAI showed a correlation coefficient of 0.739 ± 0.046, RMSE of 0.232 ± 0.047, and regression slope of 0.960 ± 0.083 for the entire period. In addition, the comparison of MODIS and TANSO-CAI over ocean showed improved agreement, with correlation coefficient of 0.830 ± 0.047, RMSE of 0.140 ± 0.019, and regression slope of 1.226 ± 0.063 for the entire period. The regression line slopes between TANSO-CAI and MODIS were higher by approximately 15–30% than the results over ocean, which showed a slope very close to 1. However, the comparison with MODIS and TANSO-CAI over land are still poorer (R = 0.667 ± 0.017, RMSE = 0.403 ± 0.105, slope = 0.771 ± 0.064) than those over ocean. We are continuously evaluating the AOD over land to improve the TANSO-CAI aerosol algorithm.
The frequency of CAI-AOD below 0.2 is approximately 30% of the total both after applying seasonal and annual RDFs. The contribution of the background marine AOD for clear days loading may be estimated to low over Northeast Asia. However, CAI-AODs show different tendency by season over ocean when AODs is lower than 0.2. In spring and summer, CAI-AODs below 0.2 show larger frequencies than AERONET and Aqua/MODIS. This means that the selected dark pixel for the surface reflectance is higher than that of Aqua/MODIS over ocean. In particular, it is difficult to determine a dark pixel in a poor composite in summer because of the included rainy season. In contrast, CAI-AODs below 0.2 show overestimated values compared to those of AERONET and Aqua/MODIS. Even though CAI-AODs below 0.4 show the similar frequency, it means that the TANSO-CAI aerosol algorithm is still not ideal for calculating the surface reflectance over ocean. When the comparisons were done for AODs above 0.2, the linear regression slope became closer to 1, showing some improvements.
The CAI-AODs tend to overestimate the AERONET values in all seasons except for when AOD is less than 0.2. The reasons for this are as follows. First, it can be explained by the difference in surface reflectance between TANSO-CAI and other measurements. As mentioned in
Section 2.6, the 30-day time window is reasonable when the composite method is considered for the calculation of surface reflectance. However, as GOSAT has a three-day revisit schedule, the TANSO-CAI aerosol algorithm extended the search window of the 23 paths instead of the previous 30-day. Therefore, the surface reflectance calculated from the TANSO-CAI aerosol algorithm is slightly lower than that obtained via Aqua/MODIS or other instruments. However, it is difficult to find clear pixels when a search window has fewer than 23 paths, resulting in a relatively high surface reflectance. Second, TANSO-CAI aerosol algorithm has cloud masking over land. TANSO-CAI has only four bands without the required IR wavelengths for the removal of clouds from TANSO-CAI measurements. It is also difficult to mask the cloud signals in the aerosol retrievals. Over ocean, we suggested the combined method with NDVI and R(band 2)/R(band 3); however, this method experiences difficulty in distinguishing between clouds and heavy dust, although it is very effective at removing thin clouds and cloud edges. However, this concept is not suitable for aerosol retrievals over land because the NDVI is affected by the rapid change of vegetation condition. Third, TANSO-CAI cannot correct reflectance because the irregular radiometric degradation reaches approximately 20%, which affects aerosol type selection from LUTs using a reflectance difference test between band 1 and 2.
The annual and seasonal RDFs derived from the comparison with Aqua/MODIS products were applied to improve the accuracy of the TANSO-CAI aerosol algorithm. According to Kuze et al. [
23,
33,
34], the TANSO-CAI spectra showed irregular radiometric degradation that was larger than other satellite observations through the VCC. During the two initial years, observed degradations reached approximately 20% at bands 1 and 4. As a result, we found that reflectance correction by more accurate RDFs improved the accuracy of retrieved surface reflectance and aerosol properties. In the future, with more accurate RDFs by month or season for the entire period, the accuracy of the retrieval can be improved.
Although the coverage of aerosol retrievals from TANSO-CAI was limited compared to other satellites such as geostationary satellites or polar-orbit satellites of daily global coverage, these aerosol products still provide valuable information for CO
2 retrieval using TANSO-FTS measurements. In fact, our aerosol properties at 0.76 μm have been used as inputs for the Yonsei Carbon retrieval (YCAR) algorithm instead of those by model or climatology. These have also improved the data coverage and accuracy of CO
2 retrieval from TANSO-FTS measurements [
25].