Global Change Observation Mission 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.

Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2

Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets.

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.

Brief communication: Identification of tundra topsoil frozen/thawed state from SMAP and GCOM-W1 radiometer measurements using the spectral gradient method

Abstract. From 2015 to 2020, using the spectral gradient radiometric method, the possibility of the frozen/thawed (FT) state identification of tundra soil was investigated based on Soil Moisture Active Passive (SMAP) and Global Change Observation Mission – Water Satellite 1 (GCOM-W1) satellite observations of 10 test sites located in the Arctic regions of Canada, Finland, Russia, and the USA. It is shown that the spectral gradients of brightness temperature and reflectivity (measured in the frequency range from 1.4 to 36.5 GHz with horizontal polarization, a determination coefficient from 0.775 to 0.834, a root-mean-square error from 6.6 to 10.7 d and a bias from −3.4 to +6.5 d) make it possible to identify the FT state of the tundra topsoil. The spectral gradient method has a higher accuracy with respect to the identification of the FT state of tundra soils than single-frequency methods based on the calculation of polarization index.

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.

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.

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.

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.

Air–Sea Interface Parameters and Heat Flux from Neural Network and Advanced Microwave Scanning Radiometer Observations

We present a new approach, based on a multi-parameter back-propagation neural network (BPNN) model, to simultaneously retrieve sea surface wind speed, sea surface temperature, near-surface air temperature, and dewpoint temperature over the global oceans from the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission 1st-Water (GCOM-W1). The model is trained and validated with the collocations of AMSR2 multi-channel (6.9–36.5 GHz) brightness temperatures, under both clear and cloudy conditions, and National Data Buoy Center (NDBC) and Tropical Atmosphere Ocean Project (TAO) buoy measurements along with ECMWF ERA5 reanalysis data. The root-mean-square (rms) errors of BPNN-retrieved sea surface wind speed, sea surface temperature, near-surface air temperature, and dewpoint temperature are 1.13 m/s, 1.02 °C, 1.20 °C, and 1.57 °C, respectively. The first three retrieved geophysical parameters and the estimated relative humidity from near-surface air temperature and dewpoint temperature are used to compute the sensible heat flux (SHF) and latent heat flux (LHF), using an improved bulk flux parametrization. The rms errors of the estimated SHF and LHF from BPNN-derived air–sea interface variables, and those from buoy and reanalysis data, are 18.13 W/m2 and 39.56 W/m2. We also compare SHF and LHF estimates with the Yongxing air–sea flux tower measurements in the northern South China Sea. The estimated SHF and LHF in summer and autumn periods are closer to observations than in winter and spring. The proposed method has potential to derive instantaneous air–sea interface atmospheric and oceanic parameters as well as surface sensible and latent heat fluxes from AMSR2 along-track wide swath observations.

Evaluation of SMOS, SMAP, AMSR2 and FY-3C soil moisture products over China

Microwave remote sensing can provide long-term near-surface soil moisture data on regional and global scales. Conducting standardized authenticity tests is critical to the effective use of observed data products in models, data assimilation, and various terminal scenarios. Global Land Data Assimilation System (GLDAS) soil moisture data were used as a reference for comparative analysis, and triple collocation analysis was used to validate data from four mainstream passive microwave remote sensing soil moisture products: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active and Passive (SMAP), Global Change Observation Mission–Water using the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Fengyun-3C (FY-3C). The effects of topography, land cover, and meteorological factors on the accuracy of soil moisture observation data were determined. The results show that SMAP had the best overall performance and AMSR2 the worst. Passive microwave detection technology can accurately capture soil moisture data in areas at high altitude with uniform terrain, particularly if the underlying surface is soil, and in areas with low average temperatures and little precipitation, such as the Qinghai–Tibet Plateau. FY-3C performed in the middle of the group and was relatively optimal in northeast China but showed poor data integrity. Variation in accuracy between products, together with other factors identified in the study, provides a baseline reference for the improvement of the retrieval algorithm, and the research results provide a quantitative basis for developing better use of passive microwave soil moisture products.

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.

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.

Evapotranspiration estimation of Urmia Lake Basin using GCOM-C thermal imagery

Basin-scale monthly and annual evapotranspiration (ET) is estimated for Urmia Lake Basin by applying the Global Change Observation Mission for Climate (GCOM-C) global ETindex estimation algorithm to thermal imagery observed by the GCOM-C satellite. In total, 297 satellite images acquired during 2018-2019 were used in this study. ET estimation accuracy was examined for an area dominated by apple fields using traditional surface irrigation. The estimated ET was 15% lower than the standard crop ET, which was computed using a procedure suggested by the Food and Agriculture Organization of the United Nations on a monthly timescale, and was 8% lower on an annual timescale. Comparison of estimated ET with a satellite-based ET map derived by using the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) model showed a similar difference. The 8%–15% differences among the different sources of ET were small, given that a similar or wider range of uncertainty is frequently available even in ground-based ET measurements. Comparison between the estimated ET and the MODIS ET Product (MOD16) revealed a greater difference in the evaluated area of the apple fields. Given the climatic ET demands and the irrigation practices of the area, ET estimation accuracy is more likely to be higher using the dataset derived from this study than using MOD16. The GCOM-C satellite started routine surface observations in January 2018. Its contribution to agricultural water management, such as by estimating ET as presented in this study, will increase as the amount of historical data stored continues to accumulate.

Classification of the frozen/thawed surface state of Northern land areas based on SMAP and GCOM-W1 brightness temperature observations at 1.4 GHz and 6.9 GHz

ABSTRACT In this letter, the method created earlier by the authors and the information product SPL3FTP_E of the Soil Moisture Active Passive (SMAP) satellite for determining frozen/thawed state of soil surface on the example of test sites placed on North Slope of Alaska, U.S.A., Canada, Finland and Russian Federation were compared. As an indicator of the frozen/thawed state of soil surface, the polarization index calculated on the basis of the reflectivity of soils was proposed. The soil reflectivity was determined in the L-band based on the SMAP radiometric observations at a frequency of 1.4 GHz using the values of brightness temperatures measured by the Global Change Observation Mission – Water 1/Advanced Microwave Scanning Radiometer 2 (GCOM-W1/AMSR2) at a vertical polarization and a frequency of 6.9 GHz, as an estimate of the soil effective temperature. As a result, it was shown that the developed method makes it possible to increase accuracy of the frozen/thawed states determination of soil surface from 3% to 9% in relation to the SMAP data (SPL3FTP_E) for twelve Arctic test sites.

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.

RELATIONSHIP BETWEEN CHLOROPHYLL-A AND SEA SURFACE TEMPERATUR WITH Sardinella lemuru CATCHING IN BALI STRAIT

The Bali Strait is one of Indonesia's territorial waters that have high natural resource potential. The area is only about 2,500 km2 but has a high potential fishing ground. The Bali Strait has unique and dynamic waters that can cause fluctuations in fish production amount each year. The largest type of fish caught in the Bali Strait is lemuru (Sardinella lemuru), a fish found only in the Bali Strait. This fish plays a significant role in the economy of fishers in the Bali Strait. Each year the catch of lemuru has fluctuated, making fishing locations challenging to predict. Sea Surface Temperature (SST) and Chlorophyll-a (Chl-a) are oceanographic parameters that can affect the resources of the ocean. Oceanographic phenomena, such as upwelling, can also influence the condition of fish resources. Therefore, understanding the relationships between these factors is essential in practical fisheries management. Observation of oceanographic factors is very hard with the field observation due to time and cost limitations. The remote sensing technique is an efficient method to determine SST distributions and Chl-a concentrations using satellite imagery. This study analyzes SST and Chl-a concentration in the Bali Strait using the Global Change Observation Mission(GCOM-C) satellite and determines the correlation between Chl-a and SST with a total fish catch(lemuru) during 2019. The results showed the maximum average Chl-a concentration observed on August 1.62 mg/m3 and the lowest concentration observed on January 0.45 mg/m3, the maximum SST on March was 28.12° C, and on August (Dry season) with 22.40° C. The SST variable's influence provides a negative correlation (R = -0.209) with changes in lemuru catch, while the Chl-a parameter has a positive correlation (R = 0.375) with changes in the catch. The pattern of relationship between Chl-a with fish catching had a good relationship than the SST in 2019.Keywords: Bali Strait; Sardinella lemuru; SST; Chl-a.

Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed From CALIPSO: A Neural Network Approach

A neural network nonlinear regression algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS (AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurements of 2015 are also compared with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission–Water “Shizuku” (GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind–surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from our previous studies, as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.