Global Change Observation Mission-Climate Research Articles

Estimation of Evapotranspiration in South Eastern Afghanistan Using the GCOM-C Algorithm on the Basis of Landsat Satellite Imagery

This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ETindex estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ETindex algorithm was adopted to estimate the monthly ETa for the period from November 2016 to October 2017 using a series of Landsat 8, Thermal Infrared Sensor (TIRS) Band 10 satellite imagery. The estimation accuracy was evaluated by comparing the results with other estimates of ETa, namely the mapping evapotranspiration with the internalized calibration (METRIC) model, the MODIS Global Evapotranspiration Project (MOD16), the surface energy balance system (SEBS) tools, and with the crop evapotranspiration under standard conditions (ETc) as estimated by the FAO-56 procedure. The evaluation was made for irrigated wheat, maize, rice, and orchards and for non-irrigated bare soil land. The comparison of ETa values showed good correlation among the GCOM-C, METRIC, and FAO-56, while the MOD16 and SEBS showed significantly lower values of ETa. The agreement with the METRIC ETa implies that the simple GCOM-C algorithm successfully estimated the ETa in the region and that the precision was similar to that of the METRIC. This study provides the first high-quality evapotranspiration data with the spatial resolution of Landsat Band 10 data for the southeastern part of Afghanistan. The estimation procedure is straightforward, and its results are anticipated to enhance the understanding of regional hydrology.

Novel methods for monitoring low chlorophyll-a concentrations in the large, oligotrophic Lake Malawi/Nyasa/Niassa

The use of remote sensing for monitoring chlorophyll-a (chla) and modelling eutrophication has advanced over the last decades. Although the application of the technology has proven successful in ocean ecosystems, there is a need to monitor chla concentrations in large, nutrient-poor inland water bodies. The main objective of this study was to explore the utility of publicly available remotely sensed Sentinel-2 (S2) imagery to quantify chla concentrations in the nutrient-deficient Lake Malawi/Niassa/Nyasa (LMNN). A secondary objective was to compare the S2 derived chla with the Global Change Observation Mission–Climate (GCOM-C) chla product that provides uninterrupted data throughout the year. In situ chla data (n = 76) from upper, middle and lower sections of LMNN served as a reference to produce remote sensing-based quantification. The line-height approach method built on color index, was applied for chla concentrations below 0.25 mg/m3. Moderate Resolution Imaging Spectroradiometer 3-band Ocean Color (MODIS-OC3) – was adopted when chla concentration exceeded 0.35 mg/m3. The MODIS-OC3 algorithm had generic model coefficients that were calibrated for each in situ sample by using GCOM-C Level 3 chla product. A weighted sum of the two algorithms was applied for chla concentrations that fell between 0.25 and 0.35 mg/m3. The above methods were then applied to the S2 data to estimate chla at each pixel. S2 showed a promising accuracy in distinguishing chla levels (MSE = 0.18) although the chla range in the lake was relatively narrow, particularly using the locally calibrated coefficients of the OC3 algorithm. Chla distribution maps produced from the S2 data revealed limited spatial variation across the LMNN with higher concentrations identified in the coastal areas. S2-derived chla and GCOM-C chla comparison showed fairly good similarity between the two datasets (MSE = 0.205). Accepting this similarity, monthly chla dynamics of the lake was profiled using the temporally reliable GCOM-C data that showed oligotrophic conditions (1.7 mg/m3 to 3.2 mg/m3) in most parts of the lake throughout the year. The study's findings advance the potential for both remote sensing approaches to provide vital information at the required spatial and temporal resolution for evidence-based policymaking and proactive environmental management in an otherwise very data deficient region.

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.

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.

The development of a global LAI and FAPAR product using GCOM-C/SGLI data

The Japan Aerospace Exploration Agency (JAXA) launched the Global Change Observation Mission - Climate (GCOM-C) satellite on December 23rd, 2017. As a part of the standard products of JAXA’s GCOM-C satellite, we developed the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product. In comparison to other global LAI and FAPAR products by the National Aeronautics and Space Administration (NASA) and the Copernicus Global Land Service (CGLS), our products have the following advantageous features: high spatial and temporal resolution (250 m and daily); the retrieval algorithm (look-up-table) is based on “the Forest Light Environmental Simulator” (FLiES) which is a full 3-D canopy radiative transfer model as well as a vegetation landscape model and a global forest landscape map created for this study. The algorithm and ancillary data sets are completely independent of the existing products that are more or less influenced by the MODIS LAI/FAPAR, providing an alternate and independent source of LAI and FAPAR. The dataset has been created since April 2018 and is freely available on JAXA’s G-Portal site (https://gportal.jaxa.jp/gpr). As a result of accuracy assessment with in-situ data, the Root Mean Square Errors (RMSEs) for total LAI and FAPAR were 0.776 and 0.112, respectively. We also compared our LAI and FAPAR estimates with three existing global datasets (PROBA-V/OLCI, MODIS and VIIRS product). The spatial distribution of LAI and FAPAR showed different characteristics among the datasets although their temporal trends were similar. The differences in estimation results among datasets due to differences in the retrieval algorithms, base map (or land cover data) and input data need to be evaluated in the future.

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.

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.

Early validation study of the photochemical reflectance index (PRI) and the normalized difference vegetation index (NDVI) derived from the GCOM-C satellite in Mongolian grasslands

ABSTRACT The Global Change Observation Mission-Climate (GCOM-C), launched in 2017, has suitable bands matching the photochemical reflectance index (PRI) definition. It also has the bands for the normalized difference vegetation index (NDVI). The PRI has a unique capability to detect plant stress caused by excessive light and drought. However, no moderate-resolution satellites had suitable bands for the PRI, requiring two narrow bands in green light in the definition. In this study, we conducted the early validation study of PRI and NDVI derived from the GCOM-C satellite and demonstrated those accuracies and characteristics in Mongolian grassland. The Mongolian Steppes (dry grasslands) are widely distributed on the plateau and therefore suitable for satellite validation. It is particularly suitable for the PRI validation because Mongolian grasslands have water stress due to the small amount of precipitation in summer. Therefore, we carried out field campaigns at three study sites in Mongolia. In this study, we found the seasonal pattern of PRI suggesting the potential to detect the water stress of vegetation, which is essential information for informed management of the grasslands. However, the correlation between the satellite-derived PRI and the in-situ PRI was negative because of the dependence of GCOM-C PRI on the soil moisture at sparse vegetation. For the accuracy assessment of PRI, which depends on rapidly changing light and soil moisture in a day, more exact synchronization of in-situ and satellite observation is required. On the other hand, we found that the NDVI derived from GCOM-C was highly accurate: The correlation coefficient (R) between the satellite-derived NDVI and the in-situ NDVI was 0.988 (RMSE=0.052). GCOM-C NDVI has enough similarities with MODIS NDVI in terms of accuracy, spatial resolution, and frequency. For example, we demonstrated that GCOM-C NDVI could detect the phenology with the same or better accuracy than MODIS NDVI. We also demonstrated their difference: the soil moisture dependence in sparse vegetation. The less dependency of GCOM-C NDVI on the soil moisture leads to a better classification of vegetation and non-vegetation in the sparse grassland than MODIS NDVI.

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.

Investigation of ice cloud modeling capabilities for the irregularly shaped Voronoi ice scattering models in climate simulations

Abstract. Both weather–climate models and ice cloud remote sensing applications need to obtain effective ice crystal scattering (ICS) properties and the parameterization scheme. An irregularly shaped Voronoi ICS model has been suggested to be effective in remote sensing applications for several satellite programs, e.g., Himawari-8, GCOM-C (Global Change Observation Mission–Climate) and EarthCARE (Earth Cloud Aerosol and Radiation Explorer). As continuation work of Letu et al. (2016), an ice cloud optical property parameterization scheme (Voronoi scheme) of the Voronoi ICS model is employed in the Community Integrated Earth System Model (CIESM) to simulate the optical and radiative properties of ice clouds. We utilized the single-scattering properties (extinction efficiency, single-scattering albedo and asymmetry factor) of the Voronoi model from the ultraviolet to the infrared, combined with 14 408 particle size distributions obtained from aircraft measurements to complete the Voronoi scheme. The Voronoi scheme and existing schemes (Fu, Mitchell, Yi and Baum-yang05) are applied to the CIESM to simulate 10-year global cloud radiative effects during 2001–2010. Simulated globally averaged cloud radiative forcings at the top of the atmosphere (TOA) for Voronoi and the other four existing schemes are compared to the Clouds and the Earth's Radiant Energy System Energy Balanced and Filled (EBAF) product. The results show that the differences in shortwave and longwave globally averaged cloud radiative forcing at the TOA between the Voronoi scheme simulations and EBAF products are 1.1 % and 1.4 %, which are lower than those of the other four schemes. Particularly for regions (from 30∘ S to 30∘ N) where ice clouds occur frequently, the Voronoi scheme provides the closest match with EBAF products compared with the other four existing schemes. The results in this study fully demonstrated the effectiveness of the Voronoi ICS model in the simulation of the radiative properties of ice clouds in the climate model.

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.

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.

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.

A quasi-physical sea surface temperature method for the split-window data from the Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission-Climate (GCOM-C) satellite

This paper describes a quasi-physical method (the Q-method) for determining the sea surface temperatures (SSTs). The Q-method is a coefficient-based technique developed for processing the multiband infrared (IR) data of the geostationary Himawari-8 satellite. We applied the Q-method to the split-window data from the Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission-Climate (GCOM-C) satellite. A comparison of the determined SGLI SSTs and buoy data shows a bias with a robust standard deviation of −0.097 K and 0.28 K in the daytime and −0.18 K and 0.28 K at night, respectively. Meanwhile, high biases of nearly −0.5 K were calculated for SSTs at and around 305 K. A residuals analysis suggests that the high negative bias is caused by insufficient information on the atmospheric correction brought by split-window data. This paper discusses the physical and mathematical background of the Q-method and compares it with another coefficient-based physical scheme.