Second Generation Global Imager Research Articles

Five year operation of a cooler Dewar assembly for infrared scanner on board GCOM-C

Global Change Observation Mission – Climate (GCOM-C) “Shikisai”, a satellite designed to observe global climate change, was launched from Tanegashima Space Center on December 23, 2017 by an H2A launch vehicle. The Second-generation GLobal Imager (SGLI) on GCOM-C is a multi-channel optical sensor for observing aerosols, vegetation, and temperatures. Through long-term monitoring, our understanding of climate change mechanisms will be improved. The infrared scanner (IRS) on SGLI has a Thermal InfraRed (TIR) detector requested to operate at 55 K. A Cooler Dewar Assembly (CDA) developed to keep the detector at 55 K is designed to minimize the heat load for the small cooler. The detector is supported on a thermal isolator made of Glass FRP and is thermally connected to the cooler by flexible thermal link. The Cooler Control Electronics (CCE) uses a heater to compensate heat load fluctuations, thereby maintaining temperature and stability. The heater power decreases gradually during five years, consequently decreasing the cooling power. Despite that cooler degradation, the detector temperature has been maintained at 55 ± 0.1 K for 5 years in orbit and has continued operating with 36 W power consumption. This paper describes the cooler Dewar Assembly and its five years of operation in orbit.

Observations of water optical properties during red tide outbreaks off southeast Hokkaido by GCOM-C/SGLI: implications for the development of red tide algorithms

ABSTRACT A severe red tide event, caused primarily by dinoflagellate Karenia selliformis, occurred during the autumn of 2021 in the waters off southeast Hokkaido and resulted in damage of $70 million to fishery industries. There is a high demand for early warning methods based on ocean colour observations to respond to future occurrences of red tides. We therefore used a quasi-analytical algorithm to compute the total absorption coefficient (a) from the Second Generation Global Imager (SGLI) remote sensing reflectance and assessed its potential to detect blooms of K. selliformis. We discovered that, within the same range of SGLI-retrieved chlorophyll-a concentrations, the a at a wavelength of 530 nm a(530) tended to be lower during K. selliformis blooms than during diatom blooms. The a(530) observed by the SGLI therefore showed promise as an optical property for detecting K. selliformis blooms. We discuss the reasons for selection of a(530) and the limitations of the current study’s bloom detection method.

Biomass Burning Plume from Simultaneous Observations of Polarization and Radiance at Different Viewing Directions with SGLI

The Earth Observation Satellite Global Change Observation Mission—Climate (GCOM)-C (SHIKISAI in Japanese), carrying a second-generation global imager (SGLI), was launched in 2017 by the Japan Aerospace Exploration Agency. The SGLI performs wide-swath multi-spectral measurements in 19 channels, from near-ultraviolet to thermal infrared (IR), including the red (674 nm; PL1 channel) and near-IR (869 nm; PL2 channel) polarization channels. This work aimed to demonstrate the advantages of SGLI, particularly the significance of simultaneous off-nadir polarized and nadir multi-spectral observations. The PL1 and PL2 channels were tilted at 45° for the off-nadir measurements, whereas the other channels took a straight downward view for the nadir measurements. As a result, the SGLI provided two-directional total radiance data at two wavelengths (674 and 869 nm) that were included in both off-nadir and nadir observations. Using these bidirectional data, an algorithm was applied to derive the altitude of the aerosol plume. Furthermore, because of the significance of the simultaneous observation of polarized and non-polarized light, the sensitivity difference between the radiance and polarized radiance was demonstrated. Severe wildfire events in Indonesia and California were considered as examples of specific applications. Herein, we present the results of our analysis of optically thick biomass-burning aerosol events. The results of the satellite-based analysis were compared with those of a chemical transport model. Exploring the SGLI’s unique capability and continuous 5-year global record paves the way for advanced data exploitation from future satellite missions as a number of multi-directional polarization sensors are programmed to fly in the late 2020s.

Aerosol Optical Properties of Extreme Global Wildfires and Estimated Radiative Forcing With GCOM‐C SGLI

AbstractTo understand the climate impact of the wildfires, it is essential to monitor the aerosol emissions from biomass burning and to estimate their optical properties and radiative forcing. This study analyzed wildfires in Brazil, Angola, Australia, California, Siberia, and South‐east Asia that occurred after 2018 using data obtained with the Second‐generation GLobal Imager (SGLI) onboard Global Change Observation Mission—Climate which is a polar‐orbit satellite launched by JAXA on 23 December 2017. We showed the SGLI‐based 4‐year variation of the aerosol optical properties, Aerosol Optical Thickness, Ångström Exponent (AE), and Single Scattering Albedo (SSA) for each region complemented with other satellite‐ and ground‐based data. From the SSA and AE relationship analyses, we confirmed that their optical properties depend on the relative humidity and type of burned vegetation, which is consistent with the previous studies. Moreover, we estimated the net incoming radiation at the top of the atmosphere during the intense fire periods to understand the impact of the direct effect of biomass burning aerosols on radiative forcing. The estimates show a significant negative radiative forcing (i.e., a cooling effect) over the ocean (−78 and −96 Wm−2in Australia and California, respectively). In contrast, small values are observed over land (∼−10 Wm−2for all six regions). Therefore, taking the regional characteristics of the optical properties and surface reflectance into account is necessary to estimate the effect on radiative forcing and future impacts of a short‐lived climate forcer from wildfires.

Evaluation of Polarization Observation Accuracy of SGLI VNR-PL Using In-Orbit Calibration Data

The Second Generation Global Imager (SGLI) on the Global Change Observation Mission—Climate (GCOM-C) “SHIKISAI” has polarization observation channels at wavelengths of red (673.5 nm—P1) and near infrared (868.5 nm—P2), and it is expected to extract information of aerosols on land with higher accuracy than conventional observation methods by utilizing the scattering by atmospheric particles obtained by polarization observation. The polarization observation of SGLI adopts a method to derive the Stokes parameters I, Q, and U by observing three polarized azimuths. In this paper, the polarization observation accuracy means the polarization degree accuracy and polarization azimuth accuracy, which can be expressed as a relative value of I, Q, and U, and does not include the accuracy of radiance. In order to evaluate the polarization observation accuracy of SGLI on orbit, variations of the Q/I and U/I have been investigated using three kinds of calibration data. The effects of calibration methods and aging have been successfully eliminated by comparing independent evaluations of three kinds of calibration data. As a result, it was concluded that the variations of Q/I and U/I are achieved within a slight variation range of ±0.07% at the P1 telescope and ±0.04% at the P2 telescope.

Comparison of Cloud Properties between SGLI Aboard GCOM-C Satellite and MODIS Aboard Terra Satellite

This study presents a comprehensive comparison of Level 2.0 cloud properties between a Second-generation Global Imager (SGLI) aboard the GCOM-C satellite and a Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, to better understand the qualities of cloud properties obtained from SGLI/GCOM-C launched on 23 December 2017. The cloud pixels identified as water phase by both satellite sensors are highly consistent to each other by more than 90%, although the consistency is only ~60% for ice phase cloud pixels. A comparison of cloud properties—cloud optical thickness (COT) and cloud particle effective radius (CER)—between these two satellite sensors reveals that water and ice cloud properties can have different degrees of agreement depending on underlying surface. The relative difference (RD) values of 22% (18%) and 37% (24%) for water cloud COT (CER) comparison over ocean and land surfaces and respective values of 35% (42%) and 35% (62%) for comparisons of ice cloud properties, and also other comparison metrics, suggest better agreements for water cloud properties than for ice cloud properties, and for ocean surface than for land surface. Though cloud properties differences between MODIS and SGLI can arise from inherent features of cloud retrieval algorithms, such as differences in ancillary data, surface reflectance, cloud droplet size distribution function, model for ice particle habit, etc., this study further identifies the important roles of cloud thickness and Sun and satellite positions for differences in cloud properties between SGLI and MODIS: the differences in cloud properties are found to increase for thinner clouds, higher solar zenith angle, and higher differences in viewing zenith and azimuth angles between these satellite sensors, and such differences are more distinct for water cloud properties than for ice cloud properties.

Atmospheric correction algorithm based on the interpolation of ultraviolet and shortwave infrared bands.

Traditional atmospheric correction algorithms of ocean color remote sensing are mostly based on the extrapolation of aerosol scattering radiance from a reference band (near infrared, shortwave infrared, or ultraviolet bands), which inevitably leads to the problem of extrapolation error amplification with the increase of extrapolation spectral distance. In this study, we propose a practical interpolation-based algorithm (named the UV-SWIR-AC algorithm) using three reference bands (one ultraviolet and two shortwave infrared bands) for turbid waters. According to 6SV radiative transfer simulations with 15 customized aerosol types, we establish a fitting function framework for the aerosol scattering radiance in the wavelength range of 322-1643 nm. We apply the UV-SWIR-AC algorithm to the real satellite ocean color data observed by the Second-Generation Global Imager aboard the Global Change Observation Mission-Climate (SGLI/GCOM-C) and compare the retrieved remote sensing reflectance with the in-situ data from the observation platform of Hangzhou Bay in the East China Sea and typical bays. The results show that the UV-SWIR-AC algorithm can achieve a better performance than the traditional, extrapolation-based algorithm in turbid waters. Moreover, in the typical regional analysis, this new algorithm also demonstrates a high applicability. The UV-SWIR-AC algorithm should be helpful to improve the atmospheric correction accuracy for next-generation ocean color missions (e.g., NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission and China's Haiyang-1E/F (HY-1E/F) mission) with wider spectral ranges from the ultraviolet to shortwave infrared wavelengths.

A Bispectral Approach for Destriping and Denoising the Sea Surface Temperature from SGLI Thermal Infrared Data

Abstract Stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. We developed a bispectral filter (BSF) to reduce the stripe noise. The BSF is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. The Second-Generation Global Imager (SGLI) is an optical sensor on board the Global Change Observation Mission–Climate (GCOM-C) satellite. We applied the BSF to SGLI data and validated the retrieved SSTs. The validation results demonstrate the effectiveness of BSF, which reduced stripe noise in the retrieved SGLI SSTs without blurring SST fronts. It also improved the accuracy of the SSTs by about 0.04 K (about 13%) in the robust standard deviation. Significance Statement This method reduces stripe noise and improves the accuracy of SST data with minimal compromise of spatial resolution. The method assumes the relationship between the brightness temperature and the brightness temperature difference in the split window based on the physical background of atmospheric radiative transfer. The physical background of the data provides an easy solution to a complex problem. Although destriping generally requires a complex algorithm, our approach is based on a simple Gaussian filter and is easy to implement.

Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China Sea

The coast of the East China Sea (ECS) is one of the regions most frequently affected by harmful algal blooms in China. Remote sensing monitoring could assist in understanding the mechanism of blooms and their associated environmental changes. Based on imagery from the Second-Generation Global Imager (SGLI) conducted by Global Change Observation Mission-Climate (GCOM-C) (Japan), the accuracy of satellite measurements was initially validated using matched pairs of satellite and ground data relating to the ECS. Additionally, using SGLI data from the coast of the ECS, we compared the applicability of three bloom extraction methods: spectral shape, red tide index, and algal bloom ratio. With an RMSE of less than 25%, satellite data at 490 nm, 565 nm, and 670 nm showed good consistency with locally measured remote sensing reflectance data. However, there was unexpected overestimation at 443 nm of SGLI data. By using a linear correction method, the RMSE at 443 nm was decreased from 27% to 17%. Based on the linear corrected SGLI data, the spectral shape at 490 nm was found to provide the most satisfactory results in separating bloom and non-bloom waters among the three bloom detection methods. In addition, the capability in harmful algae distinguished using SGLI data was discussed. Both of the Bloom Index method and the green-red Spectral Slope method were found to be applicable for phytoplankton classification using SGLI data. Overall, the SGLI data provided by GCOM-C are consistent with local data and can be used to identify bloom water bodies in the ECS, thereby providing new satellite data to support monitoring of bloom changes in the ECS.

Accuracy Assessment of Photochemical Reflectance Index (PRI) and Chlorophyll Carotenoid Index (CCI) Derived from GCOM-C/SGLI with In Situ Data

The photochemical reflectance index (PRI) and the chlorophyll carotenoid index (CCI) are carotenoid-sensitive vegetation indices, which can monitor vegetation’s photosynthetic activities. One unique satellite named “Global Change Observation Mission-Climate (GCOM-C)” is equipped with a sensor, “Second Generation Global Imager (SGLI)”, which has the potential to frequently and simultaneously observe PRI and CCI over a wide swath. However, the observation accuracy of PRI and CCI derived from GCOM-C/SGLI remains unclear in forests. Thus, we demonstrated their accuracy assessment by comparing them with in situ data. We collected in situ spectral irradiance data at four forest sites in Japan for three years. We statistically compared satellite PRI with in situ PRI, and satellite CCI with in situ CCI. From the obtained results, the satellite PRI showed poor agreement (the best: r=0.294 (p<0.05)) and the satellite CCI showed good agreement (the best: r=0.911 (p<0.001)). The greater agreement of satellite CCI is possibly because satellite CCI contained fewer outliers and satellite CCI was more resistant to small noise, compared to satellite PRI. Our results suggest that the satellite CCI is more suitable for practical use than the satellite PRI with the latest version (version 3) of GCOM-C/SGLI’s products.

Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data

Vegetation phenology not only describes the life cycle events of periodic plants during the growing season but also acts as an indicator of biological responses to climate change. Satellite monitoring of vegetation phenology can capture the spatial patterns of vegetation dynamics at global scales. However, the existing satellite products of global vegetation phenology still show uncertainties in estimating phenological metrices, especially for dormancy onset. The Second-Generation Global Imager (SGLI) onboard the satellite Global Change Observation Mission—Climate (GCOM-C) that launched in 2017 provides a new opportunity to improve the estimation of global vegetation phenology with a spatial resolution of 250 m. In this study, SGLI land surface reflectance data were employed to estimate the green-up and dormancy dates for different vegetation types based on a relative threshold method, in which a snow-free vegetation index (i.e., the normalized difference greenness index, NDGI) was adopted. The validation results show that there are significant agreements between the trajectories of the SGLI-based NDGI and the near-surface green color coordinate index (GCC) at the PhenoCam sites with different vegetation types. The SGLI-based estimation of the green-up dates slightly outperformed that of the existing MODIS and VIIRS phenology products, with an RMSE and R2 of 11.0 days and 0.71, respectively. In contrast, the estimation of the dormancy dates based on the SGLI data yielded much higher accuracies than the MODIS and VIIRS products, with an RMSE decreased from >23.8 days to 15.6 days, and R2 increased from <0.51 to 0.72. These results suggest that GCOM-C/SGLI data have the potential to generate improved monitoring of global vegetation phenology in the future.

Use of AERONET-OC for validation of SGLI/GCOM-C products in Ariake Sea, Japan

A station of AErosol RObotic NETwork Ocean Color (AERONET-OC) has been set on the Ariake Observation Tower of Saga University on April, 2018, for verification of the Second generation Global Imager (SGLI)/Global Change Observation Mission-Climate (GCOM-C). Remote sensing reflectance (Rrs) observed by the AERONET-OC was used for verification of SGLI. SGLI Version 1 data underestimated the shortwave Rrs and Rrs (380) and Rrs(412) were mostly negative, while the estimation was improved by Version 2 with the correction of Rrs(412) to be positive. It was indicated that absorptive aerosol was influenced to SGLI atmospheric correction and caused the underestimation of Rrs. Simple linear correction method to improve shortwave Rrs also worked well for specifically Version 1 data. Water constituents, chlorophyll-a (Chl-a), total suspended matter (TSM) and colored dissolved organic matter (CDOM) of the SGLI were also verified by the ship observation data. All constituents were improved from Version 1 to 2 with the correction of Rrs, although Version 2 underestimated Chl-a and CDOM. Simple regression algorithms were also examined with in situ as well as SGLI Rrs data, and it indicated that more sophisticated algorithms may be required. Time series of water constituents derived from AERONET-OC and SGLI data were compared to river discharge and spring–neap tidal cycle. The results indicated that the behavior, such as the increase of Chl-a after river discharge and interaction of Chl-a and TSM with the spring–neap tidal cycles were observed, although estimations of water constituents were not completely separated by the algorithms.

Quality assessment of Second-generation Global Imager (SGLI)-observed cloud properties using SKYNET surface observation data

Abstract. The Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite, launched on 23 December 2017, observes various geophysical parameters with the aim of better understanding the global climate system. As part of that aim, SGLI has great potential to unravel several uncertainties related to clouds by providing new cloud products along with several other atmospheric products related to cloud climatology, including aerosol products from polarization channels. However, very little is known about the quality of the SGLI cloud products. This study uses data about clouds and global irradiances observed from the Earth's surface using a sky radiometer and a pyranometer, respectively, to understand the quality of the two most fundamental cloud properties – cloud optical depth (COD) and cloud-particle effective radius (CER) – of both water and ice clouds. The SGLI-observed COD agrees well with values observed from the surface, although it agrees better for water clouds than for ice clouds, while the SGLI-observed CER exhibits poorer agreement than does the COD, with SGLI values being generally higher than the sky radiometer values. These comparisons between the SGLI and sky radiometer cloud properties are found to differ for different cloud types of both the water and ice cloud phases and different solar and satellite viewing angles by agreeing better for relatively uniform and flat cloud type and for relatively low solar zenith angle. Analyses of SGLI-observed reflectance functions and values calculated by assuming plane-parallel cloud layers suggest that SGLI-retrieved cloud properties can have biases in the solar and satellite viewing angles, similar to other satellite sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, it is found that the SGLI-observed cloud properties reproduce global irradiances quite satisfactorily for both water and ice clouds by resembling several important features of the COD comparison, such as better agreement for water clouds than for ice clouds and the tendency to underestimate (resp. overestimate) the COD in SGLI observations for optically thick (resp. thin) clouds.

System vicarious calibration of GCOM-C/SGLI visible and near-infrared channels

Vicarious calibration coefficients (kv) of Second-generation GLobal Imager (SGLI) for ocean color processing were derived using in-situ radiometric buoy measurements from the Marine Optical BuoY (MOBY) and the BOUée pour l'acquiSition d'une Série Optique à Long termE (BOUSSOLE). Two aerosol-model look up tables (LUTs) used in the GCOM-C aerosol retrieval algorithm (LUT-A) and in the previous version of ocean color atmospheric correction algorithm (LUT-B) were tested in the procedures to calculate kv and retrieve remote sensing reflectance (Rrs) and aerosol optical thickness (AOT). Bias of the processed Rrs compared to AERONET-OC Rrs was reduced by applying the determined kv (i.e., corrected SGLI radiance = original SGLI radiance/kv). LUT-A yielded smaller AOT bias compared to AERONET-OC AOT; on the other hand, LUT-B gave smaller Rrs noise due to gentle slope of the aerosol reflectance even though it caused AOT overestimation. When kv was derived by adjusting to the AOT measurements, kv was about 1.1 by LUT-A and 1.2 by LUT-B in the near-infrared (NIR) channel. However, the kv in the NIR channel was close to 1.0 when AOT and land surface reflectance measurements of Radiometric Calibration Network (RadCalNet) were used. The LUT-A with kv from MOBY and BOUSSOLE are currently adopted for the SGLI standard ocean color processing. Improvement is needed, however, to design an optimal LUT suitable for both aerosol and ocean color purposes.

In-situ observations on a moderate resolution scale for validation of the Global Change Observation Mission-Climate ecological products: The uncertainty quantification in ecological reference data

We report the ground validation activity for the terrestrial ecology products (leaf area index, above-ground biomass, and fraction of absorbed photosynthetically active radiation) of the Second-generation Global Imager (SGLI) on JAXA’s satellite named “Global Change Observation Mission-Climate.” We gave special attention to quantifying the uncertainty propagating from errors in the ecological reference data (ERD) obtained by the field work. Specifically, for optimal design and practical implementation of the field work with small uncertainty and small cost, we proposed: 1) a practical target which defined the accuracy threshold of ERD as a quarter of the satellite accuracy threshold, and 2) a calculation method of the uncertainty quantification of ERD by accounting for the uncertainty propagating from the empirical regression equations (such as allometry equations) and the statistical distribution of the population. As a result, we obtained ERD for GCOM-C/SGLI in various plant functional types (a deciduous needle-leaved forest, a deciduous broad-leaved forest, an evergreen needle-leaved forest, and dry and wet grassland) with sufficient quality, especially with a coverage area of 500 m × 500 m which can include a footprint of the sensor (250 m × 250 m) in any situation. We demonstrated: 1) the accuracy target was the key decision to make the practical calibration/validation work, 2) the regression uncertainty had a large impact, although little literature provided sufficient ancillary data about the regression equations necessary for quantification of the uncertainty, and 3) the optimal protocols of ERD observation can change depending on situations (plant functional types, phenology stages, type of products, accuracy targets, resources, and development of observation instruments and techniques); hence the choice should be made on the basis of quantification of the uncertainties.

Spatial-temporal habitat suitability for lemuru fish (Sardinella lemuru) using the Second-generation Global Imager (SGLI) and Maximum Entropy model in the Bali Strait, Indonesia

Lemuru fish (Sardinella lemuru), the most dominant fishery resource, has economic values for the fisherman fishing activities in the Bali Strait (between Jawa and Bali islands), Indonesia. Spatial and temporal prediction for the fishing location is essential information for effective fisheries management. The high spatial resolution of sea surface temperature (SST) and Chlorophyll-a (Chl-a) by the second-generation global imager (SGLI) on the global change observation mission (GCOM-C) satellite was employed for the input of the Maximum Entropy Model (MaxEnt) to predict the potential fishing area of lemuru fish in 2020. This study analyzed SST and Chl-a using the SGLI data and shows the variability of SST and Chl-a for lemuru fish-catching data. The MaxEnt model performance to predict the habitat suitability for lemuru fish in the Bali Strait has been shown in this study. As a result, the maximum average Chl-a estimated in August 2020 was around 1.62 mg m−3 and maximum SST in March 2020 around 28.12°C. The correlation between SST and Chl-a with total lemuru fish-catching were -0.209 and 0.375 for SST and Chl-a, respectively. The prediction of lemuru fishing areas using the MaxEnt model showed excellent model evaluations with a correlation value higher than 0.80.

Temperature‐Independent Cloud Phase Retrieval From Shortwave‐Infrared Measurement of GCOM‐C/SGLI With Comparison to CALIPSO

AbstractThe shortwave infrared (SWIR) channels commonly accommodated in satellite‐borne passive sensors contain information on cloud thermodynamic phase as well as cloud optical thickness (COT) and cloud effective radius (CER). This study develops algorithms for simultaneous retrieval of COT, CER, and cloud thermodynamic phase to estimate the fractional probability of the cloud phase as an alternative to discrete discrimination into liquid and ice typical of operational cloud retrievals. Two algorithms were developed and applied to the SWIR channels centered at 1.05, 1.63, and 2.21 μm of Second‐generation Global Imager (SGLI). The first algorithm retrieves COT, CER, and ice COT fractions relative to the total COT, which is a continuous quantity representing the partitioning into liquid and ice phases. The second represents the cloud phase partitioning in the form of differences between radiances observed and simulated under the assumptions of either liquid or ice clouds when retrieving COT and CER. The cloud phases from these algorithms agreed quantitatively with each other, and were able to characterize the phase occurrence on a global scale. The two types of cloud phase characterization were further compared against CALIPSO to find that the zonal‐mean occurrences of the cloud phase from the first algorithm were broadly consistent with those from CALIPSO, except for significantly smaller occurrences of supercooled water clouds in SGLI than in CALIPSO over middle‐to‐high latitude oceans. The cloud phase occurrences were also found to systematically vary with CER and cloud‐top temperature on a global scale in a manner significantly different between SGLI and CALIPSO.

Evaluation of Remote-Sensing Reflectance Products from Multiple Ocean Color Missions in Highly Turbid Water (Hangzhou Bay)

Validation of remote-sensing reflectance (Rrs) products is necessary for the quantitative application of ocean color satellite data. While validation of Rrs products has been performed in low to moderate turbidity waters, their performance in highly turbid water remains poorly known. Here, we used in situ Rrs data from Hangzhou Bay (HZB), one of the world’s most turbid estuaries, to evaluate agency-distributed Rrs products for multiple ocean color sensors, including the Geostationary Ocean Color Imager (GOCI), Chinese Ocean Color and Temperature Scanner aboard HaiYang-1C (COCTS/HY1C), Ocean and Land Color Instrument aboard Sentinel-3A and Sentinel-3B, respectively (OLCI/S3A and OLCI/S3B), Second-Generation Global Imager aboard Global Change Observation Mission-Climate (SGLI/GCOM-C), and Visible Infrared Imaging Radiometer Suite aboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP). Results showed that GOCI and SGLI/GCOM-C had almost no effective Rrs products in the HZB. Among the others four sensors (COCTS/HY1C, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP), VIIRS/SNPP obtained the largest correlation coefficient (R) with a value of 0.7, while OLCI/S3A obtained the best mean percentage differences (PD) with a value of −13.30%. The average absolute percentage difference (APD) values of the four remote sensors are close, all around 45%. In situ Rrs data from the AERONET-OC ARIAKE site were also used to evaluate the satellite-derived Rrs products in moderately turbid coastal water for comparison. Compared with the validation results at HZB, the performances of Rrs from GOCI, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP were much better at the ARIAKE site with the smallest R (0.77) and largest APD (35.38%) for GOCI, and the worst PD for these four sensors was only −13.15%, indicating that the satellite-retrieved Rrs exhibited better performance. In contrast, Rrs from COCTS/HY1C and SGLI/GCOM-C at ARIAKE site was still significantly underestimated, and the R values of the two satellites were not greater than 0.7, and the APD values were greater than 50%. Therefore, the performance of satellite Rrs products degrades significantly in highly turbid waters and needs to be improved for further retrieval of ocean color components.

Enhanced Simulation of an Asian Dust Storm by Assimilating GCOM-C Observations

Dust aerosols have great effects on global and regional climate systems. The Global Change Observation Mission-Climate (GCOM-C), also known as SHIKISAI, which was launched on 23 December 2017 by the Japan Aerospace Exploration Agency (JAXA), is a next-generation Earth observation satellite that is used for climate studies. The Second-Generation Global Imager (SGLI) aboard GCOM-C enables the retrieval of more precious global aerosols. Here, the first assimilation study of the aerosol optical thicknesses (AOTs) at 500 nm observed by this new satellite is performed to investigate a severe dust storm in spring over East Asia during 28–31 March 2018. The aerosol observation assimilation system is an integration of the four-dimensional local ensemble transform Kalman filter (4D-LETKF) and the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-Hydrostatic Icosahedral Atmospheric Model (NICAM). Through verification with the independent observations from the Aerosol Robotic Network (AERONET) and the Asian Dust and Aerosol Lidar Observation Network (AD-Net), the results demonstrate that the assimilation of the GCOM-C aerosol observations can significantly enhance Asian dust storm simulations. The dust characteristics over the regions without GCOM-C observations are better revealed from assimilating the adjacent observations within the localization length, suggesting the importance of the technical advances in observation and assimilation, which are helpful in clarifying the temporal–spatial structure of Asian dust and which could also improve the forecasting of dust storms, climate prediction models, and aerosol reanalysis.

ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE

Abstract. This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research.