The Global Imager (GLI) and the Advanced Microwave Scanning Radiometer (AMSR) are the core mission instruments developed by the National Space Development Agency of Japan (NASDA), currently the Japan Aerospace Exploration Agency (JAXA), onboard the Advanced Earth Observing Satellite-II (ADEOS-II, or Midori-II). The GLI instrument is a 36-channels visible and near-infrared radiometer and AMSR is a dual-polarized multi-frequency microwave radiometer. Although the ADEOS-II mission life was shorter than expected, they collected about 7-month's global Earth observation data with their advanced performances and provided valuable experiences to JAXA and science community. The Global Change Observation Mission (GCOM) will succeed the ADEOS-II mission and develop into the long-term observation with two medium sized satellites and multiple generations. The accumulated experiences in hardware development, retrieval algorithms, calibration and validation, and data utilization through the GLI and AMSR activities should be reflected in the GCOM program.
The GLI was a 36-channel near UV-visible-IR imager on board the ADEOS-II satellite. In spite of its short lifetime of only ten months due to solar paddle accident of the satellite, the GLI project made several significant contributions to the satellite remote sensing and climate studies, because of their useful products and related studies making use of its advanced functions as a satellite-borne imager. We like to overview the GLI mission regarding what we learned and what we lost from the satellite accident and to present several highlights gotten from the data analysis.
Various optical and microphysical properties of warm water-phase clouds as retrieved from the GLI and AMSR global datasets are presented. The results indicate that the retrieved effective particle radius at the cloud top was small (6 to 10μm) not only over continental and coastal ocean areas, but also over the North Pacific Ocean. The ADEOS-II GLI-AMSR coupled analysis first yielded the vertical structure of the effective particle radius over middle-to-high latitude areas, which was not covered by TRMM TMI-VIRS. The results also show that the effective particle radius at the cloud top is comparable to or larger than that in the middle to bottom layers in areas where the effective radius at the cloud top is small. These features are very similar to those at the Namibian, East Asian, Californian, and Peruvian regions, which are known as non-precipitation areas. In addition, comparisons between cloud properties retrieved from GLI and MODIS revealed that the GLI-derived effective particle radius was smaller than that derived from MODIS. The GLI-derived effective radius had a single mode in the histogram at 12μm, while the MODIS had a bi-modal structure at 10μm and 14μm as well as an inflection point at 18μm. One of the reasons for this difference in the retrieved effective radii is considered to be the difference in wavelength used for retrieving the cloud effective particle radius, where GLI and MODIS used 3.7μm and 2.1μm, respectively.
Using ADEOS-II/GLI aerosol and cloud products, downward and upward solar radiation at the surface and at the top of the atmosphere are estimated to study the Earth radiation budget. There is a good agreement in the main features of the global distribution of radiative fluxes as derived from GLI and from Terra/MODIS, yet, some differences can be noticed and need to be explained. In order to evaluate satellite-retrieved parameters that play a role in the Earth radiation budget, an observational network known as SKYNET has been established in Eastern Asia and it has already been operational during the ADEOS-II/GLI launch. Specifically, observations from the newly developed i-sky radiometer have been used for aerosol and cloud product evaluation. The aerosol products have been found to be in good agreement with observations while the cloud products need further evaluation.
A procedure for aerosol retrieval by combining data provided by POLDER (Polarization and Directionality of the Earth's Reflectances) and GLI (Global Imager) sensors mounted on the ADEOS-2 satellite (Advanced Earth Observing Satellite-2) is proposed. The POLDER sensor provides three channels of unique directional polarization measurements, and the GLI sensor provides high-resolution images over a wide range of wavelengths from near-UV to thermal infrared. It is known that POLDER polarization data are effective for aerosol retrieval over land, and the ratio of reflectances at 0.40 and 0.38μm from GLI can be used to distinguish between nonabsorbing and absorbing aerosols. Our algorithm is tested by detecting the plume from Siberian biomass burning in May 2003. The retrieved aerosol properties are compared with model simulations and ground-based AERONET (Aerosol Robotic Network) data. The results show that our proposed algorithm provides improved data on the aerosol optical thickness.
This paper introduces algorithm development strategies for the ADEOS-II (Midori-II) GLI atmospheric mission, and presents the flow chart and the principle of each algorithm. During the GLI science mission, which operated between 1996 and 2008, the principal investigators and co-investigators of the atmospheric discipline were developing and improving algorithms which distinguish cloudy from clear pixels on GLI images, retrieve cloud and aerosol properties, retrieve the amount of precipitable water, and estimate the radiation budget from GLI multispectral radiance data in collaboration with the GAIT team managed by JAXA EORC. Despite the short lifetime of the GLI project, important scientific results in the atmospheric discipline were obtained from GLI multispectral imaging data by using the algorithms we developed. This legacy will be applied in future Japanese Earth observing missions in next decade.
Retrieval algorithms of cloud, water vapor, and aerosol, were developed for ADEOS-II/GLI dataset. The retrieval algorithm was applied to ADEOS-II GLI data, using oxygen A-band (763nm) for cloud geometrical properties such as cloud top and bottom heights. As a result, a global map of the heights was obtained as a preliminary one. Columnar amount of water vapor was also retrieved using near infrared bands (1150nm) over land in particular, which is possibly complementary to the water vapor amount retrieved with microwave radiometer over ocean. Monthly global maps of columnar water vapor amount were obtained together from ADEOS-II/GLI and ADEOS-II/AMSR. Further, Columnar amount of yellow sand (Kosa aerosol), which is one of the UV-absorbing aerosols, was retrieved using near ultraviolet bands (380nm) with 1 km spatial resolution over land. The retrieved aerosol property was compared to a ground-based lidar observation inland China and it was found that the result was consistent with each other. Although our algorithms seem to work well, these results are still preliminary, and detailed validation studies are necessary in furture.
Advanced Earth Observation Satellite-II (ADEOS-II) was launched on December 14, 2002, and it operated from April to October 2003. One of the core sensors of ADEOS-II is Global Imager (GLI) which measured ocean color. Ocean color measurement is important because phytoplankton biomass (chlorophyll-a) is one of the most important parameter for estimating ocean primary production and associated carbon flux in the ocean. Cooperation study between JAXA and Nagasaki University was started from 2000. The study covered 1) collection of in situ data for calibration and validation of ocean color products of GLI and MODIS, 2) development of primary production algorithm, 3) showing usefulness of ocean color data for red tide monitoring, 4) showing usefulness of ocean color data for coastal environmental monitoring, and 5) establishing international collaborations. This study supported the product accuracy of GLI and MODIS, and supported the necessity of 250m ocean color data of Second-generation GLI (SGLI) on Global Change Observation Mission-Climate (GCOM-C). Furthermore, application of ocean color data was explored.
Empirical bio-optical algorithms developed for GLI are described. These algorithms can be used to retrieve in-water properties from GLI normalized water leaving radiances in temperate Case 1 waters. The OC4 Chl-a algorithm uses the maximum band ratio switching procedure for bands that do not saturate over the ocean. GLI is the only ocean color sensor that has ultra-violet (UV) bands and we developed an experimental red tide UV index for the early detection of dinoflagellate blooms. The red tide index uses the increased absorption of UV light by mycosporine-like amino-acids (MAAs). A special version of the Chl-a algorithm was developed for the Southern Ocean Case 1 waters. The standard algorithms cannot be applied to turbid near-shore waters, e.g. off Korea and Hong Kong where bio-optical characteristics deviate drastically from Case 1 characteristics. Developing new and improved algorithms for Case 2 waters is a major challenge in bio-optical oceanography.
A general function field methodology for estimating ocean color variables from space is applied to the retrieval of spectral marine reflectance from Global Imager (GLI) data. The top-of-atmosphere GLI reflectance vectors, after correction for molecular effects, are considered as explanatory variables conditioned by the angular geometry. The inverse problem, therefore, is viewed as a collection of similar inverse problems, continuously indexed by the angular variables. The solution is in the form of a field of nonlinear regression models over the set of permitted values for the angular variables. The selected models, for reasons of approximation theory, are fields of shifted ridge functions. The fields constructed on synthetic GLI data for Case 1 waters are robust to noise, they handle well situations of weakly and strongly absorbing aerosols, and the retrievals are accurate in both oligotrophic and productive waters. In the presence of 1% noise, the RMS error is 0.0006 (4.2%) at 380 nm, 0.0003 (2.8%) at 460nm, and 0.0001 (1.5%) at 545nm, i.e., well within the acceptable limits for quantitative biology applications. The theoretical results, and the possible extensions, show the potential of the function field methodology for operational estimation of marine reflectance from GLI data, even in the near ultraviolet.
In this commentary, we describe our activities on Sea Surface Temperature derivation from GLI onboard ADEOS-II and the other sensors onboard Japanese R&D satellites. A new product of ADEOS-II SST using GLI and AMSR has been realized as the merged SST using multi-platform sensors, which is also reported.
This paper describes the algorithm used to produce a global land cover product using data sets from the second Advanced Earth Observing Satellite/Global Imager (ADEOS-II/GLI). Although ADEOS-II was in operation for only a short period, we were able to obtain data sets for about 8 months, from March to October 2003. The aim of our classification system is to produce useful information for monitoring changes in vegetation cover conditions caused by climate changes. To achieve this aim, we determined classification conditions using only the GLI data sets, and constructed a land cover classification system for monitoring truly unique vegetation responses. We started with seven classes based on the International Geosphere-Biosphere Program, and expanded this to 23 classes. Each classification condition was determined using the universal pattern decomposition method (UPDM) coefficients and a modified vegetation index based on the UPDM that was obtained by applying the UPDM to GLI data. Comparisons between information from the FLUXNET Project site and our global land classification system (NWUGLC) at a regional level showed many areas where classes agreed, such as deciduous forest, cropland, and savanna ; exceptions were evergreen forest and grassland. One possible reason for the disagreement may be the difference in class definitions between the FLUXNET site and NWUGLC. ADEOS-II/GLI data sets do not contain information relating to surface objects ; therefore our system could not distinguish between forest and flat fields covered with thick vegetation. The next JAXA project (second-generation GLI) will provide more useful data that will help in producing accurate global land cover data for the study of global environmental changes.
High-accuracy estimation of the net primary production (NPP) of vegetation is important in the study of the carbon cycle and the biotic response to climatic warming. This study estimated the NPP of vegetation using Advanced Earth Observing Satellite-II/Global Imager (GLI) data with a modified vegetation index based on the universal pattern decomposition method. The NPP was estimated using GLI 250-m data sets and ground observations of air temperature and solar radiation. The results agreed with the NPP calculation from forest survey data gathered in Nara, Japan, within the limit of estimation error. The annual NPP was estimated using v210 global mosaic data, air temperature data from the European Centre for Medium-range Weather Forecasts, and GLI photosynthetically active radiation (PAR) data. The result was compared to the NPP calculated by the light-use efficiency-based method using the normalized difference vegetation index (NDVI). The NPP for the latter was less than in our results for areas near the equator. This difference may be due to the NDVI saturation for dense vegetation. Using the GLI PAR data, the global annual NPP was estimated at 60.8±15.8PgCyr-1. This value is similar to that reported by the Intergovernmental Panel on Climate Change (59.9 and 62.6PgCyr-1)31) and the Moderate-resolution Imaging Spectroradiometer group (56.04 PgCyr-1)32).
The algorithm principles, calibration and validation (Cal/Val) experiments, and retrieval results of ADEOS-II/GLI snow/ice products are reported. The GLI snow/ice products are snow surface temperature, two types of snow grain sizes for topmost and shallow snow layers, and mass fraction of snow impurities. The snow grain size and snow impurities as satellite standard products are unique, but very important because snow surface albedo essentially depends on those parameters. From the analyses of GLI snow/ice products, the new findings on spatial distributions of clean snow, drastic spatial and seasonal evolution of snow grain size, and the possibility of detecting spatial distributions of vertical inhomogeneity in the top several centimeters of snow cover in the northern hemisphere were obtained. These snow parameters are expected to be used an indicator of climate change by long-period monitoring. From the Cal/Val experiments in Alaska and eastern Hokkaido, Japan from 2001 to 2005 using MODIS and GLI data, it was found that snow surface temperature and grain size for shallow layer agreed well with in-situ measured values, while the accuracies of mass fractions of snow impurities and grain size at topmost layer were not so good compared with those for the former two products. The Cal/Val experiments also revealed some scientific results which are spectral or broadband snow albedos depending on snow grain size and snow impurities, relationship between snow surface temperature and snow grain size, and spectral and directional features of emissivity depending on snow types.
The Advanced Microwave Scanning Radiometer (AMSR) manufactured by the Japan Aerospace Exploration Agency (JAXA) is a successor of MSR aboard the Marine Observation Satellite-1 (MOS-1) launched in 1987. Two sensors of AMSR were manufactured : AMSR aboard the Advanced Earth Observing Satellite-II (ADEOS-II) of JAXA launched in December 2002, and AMSR-E aboard the AQUA of the National Aeronautics and Space Administration (NASA) launched in May 2002. Global observations by AMSR and AMSR-E with a high spatial resolution and with six frequencies from 6 to 89GHz exceed those by previous sensors. In particular, a continuous observation by AMSR-E since the launch date provides a valuable data for monitoring of the climate change of the Earth.
Observation data by Advanced Microwave Scanning Radiometer for EOS (AMSR-E) on board the U.S. earth observation satellite Aqua are used for Numerical Weather Prediction (NWP) models of Japan Meteorological Agency, namely MesoScale Model (MSM) and Global Spectral Model (GSM). The AMSR-E data have been assimilated in the Meso-Analysis (MA) since November 2004 and in the Global-Analysis (GA) since May 2006. This paper overviews some technical issues related the assimilation of AMSR-E data for MA and GA, respectively. For MA, 1) an efficient retrieval algorithm for precipitable water and rain rate, 2) a bias correction between the observations by similar instruments and between the observation and the model, 3) a four dimensional variational (4D-Var) scheme, 4) data thinning and smoothing scheme and 5) appropriate setting of observation error are key issues for successful assimilation of the AMSR-E data in operation. A pre-operational Observation System Experiment (OSE) shows the AMSR-E precipitable-water and rain-rate data improve the forecast in a heavy rain event. On the other hand, for GA, 1) a 4D-Var scheme, 2) a direct assimilation of AMSR-E radiance based on the effective radiative transfer scheme named RTTOV-7, and 3) a Variational Bias Correction (Var-BC) are key issues. A pre-operational OSE shows the improvement of typhoon-track forecast and precipitation distribution over the Indian Ocean. The accuracy of typhoon-track forecast has been improving recently by the improvement of the NWP models and the associated analysis system as well as the introduction of new observation data including microwave imager data. The observation network of the microwave radiometers by the Global Precipitation Measurement (GPM) mission will be constructed in the near future. Another promising data are provided by ground-based Global Positioning System (GPS) precipitable-water data for MA and GPS occultation temperature and water vapor data for GA.
This review paper serves for two purposes : describing the algorithm currently used by JAXA to produce precipitation product from AMSR/AMSR-E data and discussing two most important challenges facing passive microwave precipitation algorithm development. The precipitation algorithm used in JAXA today combines emission and scattering signatures, includes a correction for beam-filling problem, and has been validated by surface radar-gauge network data and other satellite retrievals. Because we pieced together all the modifications to the original version of the algorithm in this paper, this article gives the most complete description to the current version of the algorithm, and therefore, can serve as a reference for the precipitation product users to cite. The discussion on the challenges, beam-filling and ice scattering, in passive microwave precipitation retrievals is aimed to more general audiences. While the footprint size of the recent satellite microwave sensors is finer than earlier ones, the beam-filling problem seems still to be one of the most important error sources in current retrieval algorithms. The ice scattering problem is important for retrieving snowfall and precipitation over land since the primary signature for these applications is scattering signal.
Simulation of the brightness temperatures at frequencies of AMSR and AMSR-E radiometers and algorithms for retrieval of the integrated atmospheric parameters, sea surface temperature and sea surface wind are described. The developed algorithms were used to process Aqua and ADEOS-II microwave measurements over the ocean. Efficiency of satellite microwave sensing in combination with ancillary remote and in situ data is demonstrated by analysis of diurnal warming in the Okhotsk Sea and the various marine weather systems. AMSR/AMSR-E data were applied for detection of a warm core of the tropical cyclones and estimation of their central pressure, for investigation of structure and parameters of intense mesoscale vortices and mesoscale convective cells and rolls.
For two spaceborne passive microwave radiometers : Advanced Microwave Scanning Radiometer (AMSR) aboard the JAXA/ADEOS-II and AMSR-E aboard the NASA/AQUA, algorithms of retrieving sea surface temperature (SST) and sea surface wind speed (SSW) have been developed. Retrieved SST from AMSR-E was compared with buoy SST, and root mean square (rms) of SST difference was 0.561°C for AMSR-E using one year data in 2003, and it was 0.633°C for AMSR using seven month data in 2003. Retrieved SSW was also compared with buoy SSW, and rms of the difference was 1.013 and 0.942m/s, respectively in the same period.
Wind speed observed by the Advanced Microwave Scanning Radiometer (AMSR) on the Advanced Earth Observing Satellite-II (ADEOS-II) was evaluated using data from global offshore ocean buoys and the SeaWinds scatterometer. The wind speeds contained in the latest AMSR wind product (version 4) exhibit good agreement with buoy data, with a root-mean-square (rms) difference of 1.2ms-1. The systematic bias observed in the earlier versions has been eliminated by algorithm refinements. Intercomparison of the wind speeds observed globally by SeaWinds and AMSR on the same orbits also shows good agreements. The global wind speed histogram of the latest AMSR wind product (version 4) is closer to those of SeaWinds and ECMWF wind data than those of the earlier versions.
We have constructed gridded high resolution global surface wind products from satellite scatterometer Qscat/ SeaWinds (QSW) and radiometer Aqua/AMSR-E. Intercomparison of the two products in the North Pacific reveal significant difference ; the AMSR-E winds are weaker than the QSW winds with a maximum difference of 0.5 to 0.6 m s-1 in the western portion of the westerly region during the winter season. These products are validated by comparing them with moored buoy measurements ; the Kuroshio Extension Observatory (KEO) buoy in the Kuroshio Extension (KE) and the Tropical Atmosphere and Ocean (TAO) buoys in the tropical Pacific regions. Comparisons reveal that there are little differences between satellite-derived and in-situ wind speeds, while the QSW product has a smaller root-mean-square difference (RMSD) from and a higher correlation with in-situ wind speeds, indicating higher reliability. Spatial correlations between surface wind speed and sea surface temperature (SST) anomaly fields by TRMM Microwave Imager (TMI) reveal a significant positive correlation between wind-speed and SST anomaly in the KE region, suggesting an ocean-atmosphere interaction that may have some dependency on oceanic conditions, such as the existence of warm eddies.
Wind speed and Latent heat flux derived by Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (AMSR-E) on Aqua are validated using the tropical and the mid-latitude Pacific surface buoys. Obtaining the wind speed and reducing the Relative Wind Direction effect (RWD effect) according to Konda et al.(2006), the root mean square of the error of the wind speed at the mid-latitude buoys is reduced to 1.6ms-1, which is slightly worse than that validated by using Tropical Atmosphere Ocean project (TAO) data in the tropics. The validation shows that the mean error and its tendency are almost same as that of AMSR-E standard product. The combined use of the wind speed and the other AMSR-E products provides the instantaneous latent heat flux at every observation cells. We show that ambiguity of the estimation of the latent heat flux is caused by traditional way of computation from the boundary layer parameters, each of which is measured by different sun-synchronized satellites. The ambiguity caused by the time-lagged measurement of them is found to amount to -1.3±44.3Wm-2. The simultaneous measurement of boundary layer parameters can avoid it and make it possible to directly evaluate the satellite-derived latent heat flux by in situ observation.
Much of what we know about the large scale characteristics of the sea ice cover has been inferred from results of analysis of passive microwave data, the latest of which comes from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). AMSR-E has many advantages including higher resolution, wider spectral range and wider swath than historical data. This paper presents a review of techniques for sea ice concentration including the latest enhancements on the Bootstrap Algorithm for use with AMSR-E data. Validation studies confirms improvements in the accuracy after the enhancements depending on surface and atmospheric conditions. The improvements are also most apparent in winter along the coastal regions in the Antarctic where thin and young ice with little or no snow cover are abundant and the surface temperature of the ice can be much colder than the average. Improved characterization of the marginal ice zone leading to more accurate assessment of the ice edge is demonstrated. This in turn enables more accurate estimates of ice extent and ice area that are used to study seasonal and interannual variability of and trends in the sea ice cover. The AMSR-E extents and areas were used to evaluate and enhance historical passive microwave data for more accurate assessment of long term trends in the ice cover that may be associated with climate change. The AMSR-E data set is now regarded as the current baseline for the characterization of the sea ice cover because it provides the most accurate and comprehensive ice concentration data available.
The U.S. AMSR-E Science Team uses the enhanced NASA Team (NT2) sea ice concentration algorithm to calculate the standard Arctic and Antarctic sea ice concentration products. The NT2 algorithm significantly reduces the problem of a low ice concentration bias associated with surface effects apparent in sea ice retrievals from areas of deep snow using the original NASA Team (NT) algorithm. This enhancement is achieved through the use of the AMSR-E 89GHz channels. The NT2 accommodates ice temperature variability through the use of radiance ratios as in the original NT algorithm and has the added advantage of providing weather-corrected sea ice concentrations through the utilization of a forward atmospheric radiative transfer model. This paper gives a brief summary of the concept of the algorithm and provides details of its implementation as part of the routine U.S. AMSR-E Science Team data product generation.
Sea ice concentrations based on AMSR-E 89GHz data are unprecedented in combining data timeliness (about 6 hours after overflight), horizontal resolution (about 5km) and daily global coverage. Here the geoloclation of the AMSR-E Level 1 data (required to use due to the time constraints) is corrected and the sea ice concentrations are validated. The geolocation adjusts the cone angle and scan angle of AMSR-E's conical scanning scheme based on the comparisons of the jump of the AMSR-E brightness temperature at the global coastlines with a global landmask. The average residual error increasing from 250m for the 89GHz channels to 1425m in the 6GHz channels. The ice concentrations are based on the ARTIST (Arctic Radiation and Turbulence Interaction STudy) Sea Ice (ASI) retrieval algorithm which is an enhancement of the Svendsen 85GHz algorithm. Here we review the results of four types of comparisons of the ASI/AMSR-E ice concentrations, namely with (1) Arctic ship based bridge observations of RV Polarstern, (2) optical images of the multispectral imager ETM+ operating on Landsat-7, (3) Envisat and Radarsat-1 SAR images and (4) two other AMSR-E sea ice concentration algorithms (Bootstrap and NASA Team 2) which use the 19/37GHz channels. In spite of the different sensor types, wavelengths and interaction principles of the electromagnetic radiation the four comparisons yield a rather consistent picture. On average the ASI ice concentrations range between those from Landsat and SAR. Both the bias intervals (-2.9...2.6%) and the rms errors are slightly higher than those of the NT2 algorithm, applied to the same scenes. In the hemispherical (Arctic and Antarctic) comparisons of the ASI results with the widely used NASA Team 2 and Bootstrap concentrations, the biases do not exceed 2%, the rms error ranges between 7 and 11% ice concentration.
There is a lack of data on the state of the atmosphere and the surface in polar regions because of too few direct observations, which makes remote sensing methods especially important in these regions. We present two new methods for retrieving several surface and atmospheric parameters over the Arctic from radiances measured by AMSR-E (Advanced Microwave Scanning Radiometer for EOS) on the satellite Aqua. The first method retrieves the emissivities of sea ice at AMSR-E frequencies from AMSR-E radiances with the help of meteorological reanalysis data. This is valuable since the sea ice emissivity at this range is not well known. Mean emissivities thus retrieved for two representative regions (first-year ice and multiyear ice) are needed by the second method which simultaneously retrieves atmospheric and surface parameters from AMSR-E radiances. The parameters are : total water vapor, cloud liquid water path, surface wind speed, surface temperature of sea ice and ocean, sea ice concentration, and multiyear ice fraction. Both methods yield reasonable results.
Sea ice drift vectors extracted from satellite passive microwave sensors data are widely used today. However, the validation of which has not been done much so far. In this study, the sea ice drift vectors automatically extracted from pairs of the satellite passive microwave sensor AMSR-E images acquired in one or two days' interval were validated by comparing with sea ice drift vectors manually extracted from pairs of cloudless MODIS images acquired simultaneously with AMSR-E images. The cross correlation algorithm was used for automatically extracting sea ice drift vectors from passive microwave sensor images. Total of six pairs of cloudless MODIS images were selected for the validation. The six pairs of images consist of three from the Sea of Okhotsk, two from the Bering Sea, and one from the Antarctic Ocean. In order to evaluate the sensor differences, the sea ice drift vectors were also extracted automatically from a pair of SSM/I images. The sea ice drift vector extraction accuracy of AMSR-E images was 96% in the best case and was 55% in the worst case under the permissible error range of 6km. This result suggests the usefulness of AMSR-E for sea ice drift monitoring. As for the polarization difference, the accuracies of H polarization were higher than V polarization in some cases, but were reverse in other cases. By comparing MODIS images with simultaneously collected passive microwave sensor images, it has become clear that the changes of sea ice distribution, including deformation and condition change of sea ice, sometimes reduce and sometimes increase the advantage of H polarization against V polarization. The accuracy of extracted vectors from AMSR-E images was higher than that of SSM/I reflecting the IFOV difference of the two sensors. However, since there were four hours observation time difference between SSM/I and the other two sensors, the accuracy of the SSM/I should be noted as a reference.
Many microwave radiometer algorithms for the retrieval of soil moisture tend to overestimate moisture in very dry cases, partly due to volume scattering effects. This study reports the development of a physically-based soil moisture retrieval algorithm for passive microwave remote sensing. The algorithm is based on physically-based radiative transfer, which simulates the radiative transfer processes in soil by a 4-stream discrete ordinate method and the Henyey-Greenstein phase function. The multiple scattering effects of soil particles are calculated using the Dense Media Radiative Transfer Theory, and the surface roughness effects are simulated by the Advance Integral Equation method. The implementation of this algorithm consists of three steps : 1) forward model parameters optimization ; 2) lookup table generation ; and 3) lookup table reversion and soil moisture estimation. The algorithm was tested by retrieving soil moisture and temperature from AMSR-E Brightness Temperature data at a Coordinate Enhanced Observing Period reference site on the Mongolian Gobi. The retrieved soil moisture data was compared with in situ observations. The comparison shows that the performance of the new algorithm is satisfactory, with acceptable values of Standard Error of the Estimate and the square of the correlation coefficient. Moreover, the algorithm estimates soil physical temperature accurately.
Soil moisture is an important water cycle variable. The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) program was the first to implement a soil moisture algorithm development and validation program that would lead to global standard products. A key aspect of the program was the inclusion of multiple research algorithms with continuing evaluation. One approach is the single channel algorithm (SCA) that utilizes the AMSR-E channel with maximum sensitivity in combination with ancillary data on vegetation conditions. Over the course of the AMSR-E project, this approach has been developed and improved. A critical component of providing a standard soil moisture product is its validation. As part of the AMSR-E validation program networks of dedicated validation sites were developed. These networks provide estimates of the average soil moisture over watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Measurements have been made on a continuous basis since 2002. The NASA and JAXA standard soil moisture products were compared to the network observations, along with SCA. The results indicate that each algorithm has different performance statistics that depend upon the site and that there is much room for improvement in the algorithms adopted by JAXA and NASA. They also illustrate the potential pitfalls in using the products without caution.
During the summer of 2000, the monitoring of the water cycle using ground-based long-term monitoring data began to be used as ground truth for ADEOS-II (Advanced Earth Observing Satellite-II) /AQUA validation in the study area (160 km by 120 km) of the Mongolian Plateau. Since 2002, the AMSR-E (Advanced Microwave Scanning Radiometer for EOS) has successfully monitored the surface soil moisture on a global scale. In this study, we have attempted to validate the AMSR-E standard algorithm of JAXA using the JAXA standard product data (Ver. 5.0) of the AMSR-E soil moisture estimation and ground-based long-term monitoring data in the study area from 2002 to 2007. Although the standard product slightly overestimated the soil moisture, a good correlation was found between the AMSR-E soil moisture product and the ground-based soil moisture in Mongolia, and a reasonable matching of the change and distribution of soil moisture between them was found. The results suggest that the quality of the standard product of AMSR-E is good and basically useful for surface soil moisture monitoring over large areas of the steppe.
Soil moisture is an important component of the hydrology of land surfaces. Accurate monitoring of soil moisture is essential in understanding energy and water cycles and ecological system processes. Microwave remote sensing using satellites is an effective method for collecting global information on land surface hydrology. In this study, the soil moisture algorithm of Koike et al. was revised by focusing on the vegetation component, with the goal of improving the accuracy of the soil moisture product of the Advanced Microwave Scanning Radiometer for the Earth Observing System mounted on the satellite Aqua. The water content of vegetation affects the sensitivity of the microwave remote sensing of soil moisture. In the Koike algorithm, a semi-empirical vegetation model with the assumption of uniform vegetation coverage was used to evaluate the vegetation effects on the retrieval of soil moisture data. However, satellite microwave radiometer observations have large footprints of several tens of kilometers. There are few land surface regions in the world that are uniformly covered with vegetation at this scale. The results of ground-based experiments demonstrated that non-uniformities in the vegetation coverage have very large effects on horizontally polarized waves. We therefore created a global fractional vegetation coverage dataset from the data gathered by the Moderate Resolution Imaging Spectroradiometer, and attempted to incorporate this into the algorithm. In addition, model parameters in the semi-empirical vegetation model were replaced on the basis of a ground-based experiment. The results were verified by the comparison of estimated and measured data for three locations with differing vegetation coverage conditions. Compared with results estimated by the Japan Aerospace Exploration Agency standard product version 5 (created by the algorithm before the current revision), the results estimated by the revised algorithm showed a significant improvement in accuracy and reduction in the number of erroneous estimations.
The Soil Moisture Experiments conducted in Iowa in the summer of 2002 (SMEX02) had many remote sensing instruments that were used to study the spatial and temporal variability of soil moisture. The sensors used in this paper (a subset of the suite of sensors) are the AQUA satellite-based AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System) and the aircraft-based PSR C/X (Polarimetric Scanning Radiometer). The SMEX02 design focused on the collection of near simultaneous brightness temperature observations from each of these instruments and in situ soil moisture measurements at field- and domain- scale. This methodology provided a basis for a quantitative analysis of the soil moisture remote sensing potential of each instrument using in situ comparisons and retrieved soil moisture estimates through the application of a radiative transfer model. To this end, the two sensors are compared with respect to their estimation of soil moisture.
An analysis on the capabilities of microwave radiometers in estimating soil moisture, snow cover and vegetation biomass was carried out on a global scale by using AMSR-E (Advanced Microwave Scanning Radiometer for EOS) data. The temporal trends of brightness temperature together with some microwave indexes, namely combinations of polarizations and frequencies, were taken into account over some test areas. In case of the estimate of soil moisture, the use of these indexes makes it possible eliminating deserts, dense vegetation and snow areas, as well as correcting for the effect of light vegetation. Afterwards, the inversion to retrieve soil moisture is performed by means of an Artificial Neural Network (ANN). Lastly, a technique based on a multi-sensor image fusion technique for enhancing the C-band spatial resolution is described here.
This paper describes the development of the current version of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) algorithm that is used to estimate snow depth for the Japan Aerospace Exploration Agency. The algorithm uses native resolution brightness temperature observations, except for the 89GHz channel which is resampled to the 36GHz footprint, with brightness temperature corrections made on the native measurements rather than using the aggregated brightness temperature observations. A shallow snow detector is developed using the 89GHz channels to detect shallow snow. Furthermore, algorithm retrievals are comprised of the sum of a forested component and a non-forested component with a dynamic estimation of snow depth related to snowpack evolution from selected polarization differences. When compared with up to 254 individual ground station measurements of snow depth, tests show that the new algorithm performs better than previous static parameterized versions both in overall terms and in terms of low to moderate fractional forest cover. For dense forest cover, the algorithm is similar in performance to the previous version. Bias improvements are also very encouraging, but further work is still required to improve the new algorithm's performance in overall error terms and for different fractional landcover mixtures.
In the past, many of the relationships between Arctic oscillations and snow cover in the Eurasian Continent were discussed assuming a decadal cycle. However, the possibility of an annual rotation of the relationship was not discussed. The target of our study is the investigation of an annual variation of snow quantity in the Northern Hemisphere. In 2007, we developed a snow depth retrieval algorithm for AMSR and AMSR-E. In this paper, that algorithm was adjusted to make it applicable to the Special Sensor Microwave Imager (SSM/I) data which have been collected continuously since 1987. Then, snow depth in the Northern Hemisphere by using our improved algorithm is estimated, and the interannual variation of the long-term distribution of snow depth in the Northern Hemisphere is discussed. Next, the relevance of our estimations of March snow depth and Arctic oscillation (AO) is confirmed. Then the estimation accuracy of snow depth as compared with a few in situ observations, concluding that its accuracy is comparatively good is discussed. Furthermore, the interannual fluctuation of the distribution of snow depth is discussed. Our result shows a tendency toward a converse fluctuation between the Siberia and the Alaska/Canada snow depths in January and February, which has been confirmed from 1995 to 1999.
This paper presents the history and the present structure of COSPAR (Committee on Space Research) and a few events of the Committee on the Peaceful Uses of Outer Space(COPUOS) in which the author has participated. COSPAR was established by ICSU as a reaction to the launch of Sputnik and supported by the United Nations (UN) and UNESCO in particular. The reason for this action was that there was a fear in the cold war era the space would be over politicized and its peaceful and scientific aspects would be neglected. Under the same reason the UN COPUOS was established. COSPAR has observer status at COPUOS. At the time of the COSPAR establishment a special privilege was given to two major space powers, USA and USSR. All the officers of COSPAR were nominated by the academies of these two countries. It took 34 years until the present democratic structure was completed. Since there are many experts of space science in COSPAR not a few of them have contributed to the activities of COPUOS. After the launch of Landsat remote sensing became one of the key subjects in both COSPAR and COPUOS. Finally the efforts of Japan, USA and France in developing earth observation satellites in early days are described.