The all-weather, day-and-night imaging capability of synthetic aperture radar (SAR) has become essential for flood monitoring. We have developed and implemented a fully automatic flood detection algorithm using SAR data from Advanced Land Observing Satellite-2 (ALOS-2). The algorithm rapidly processes ALOS-2 and ancillary data (flood simulation, hazard map, and other geographical information) and provides damage information less than an hour after the input data are entered. The algorithm has been operational since 2022 for disaster response users and is still being improved for better accuracy and robustness. During the two years of operation, our algorithm has responded to 15 flood events, demonstrated superior performance, and provided information used in disaster response activities.

This paper summarizes an operational calibration result of the Advanced Land Observing Satellites-2 (ALOS-2, nicknamed "DAICHI-2"). ALOS-2, the successor to the Phased Array-type L-band Synthetic Aperture Radar (PALSAR) onboarded ALOS "DAICHI" from 2006 to 2011, was launched in 2014 and has been operating smoothly beyond its design mission life of 7 years; the PALSAR-2 onboard ALOS-2 has shown its maximum contribution in practical applications by the basic observations of global land areas with time-series high resolution, and in emergency observations during natural disasters. The observation capability of PALSAR-2 has been improved over PALSAR e.g., a finer spatial resolution of 1 x 3m by the Spotlight observing mode, a wider swath width of 490 km by the ScanSAR mode, and a right-and-left looking function. To take full advantage of these capabilities, sensor calibration is extremely important. This paper reports the latest operational calibration result of PALSAR-2 during more than 9 years of its mission operation period.

This paper evaluates the digital surface model (DSM) generated from satellite stereo imageries that are simulated by the technology of 3-dimensional computer graphics (3D/CG), as one of the preliminary simulations for the future mission in the Japan Aerospace Exploration Agency (JAXA). By using the 3D/CG instead of real satellite imageries, arbitrary conditions of stereo observation, such as the combinations of viewing angles, can be simulated with fewer limitations. Here we validate the effects of the B/H differences in the DSM generations as a first experiment of this approach. The height accuracies of the DSMs, which are derived from simulated stereo imageries of different B/Hs, are compared quantitatively. The result showed that height errors in the city area rapidly increase as the B/Hs are enlarged due to the effect of occlusions around the buildings.

Ionospheric total electron content (TEC) can significantly affect the range geometric accuracy of spaceborne L-band synthetic aperture radar (SAR) data. This study aims to estimate the ionospheric TEC from single spaceborne L-band SAR data based on the concept of the split-spectrum method. We estimated the SAR-based TEC from the range misregistration between lower- and higher-range spectral split-band amplitude images acquired using the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2). A comparison of the SAR-based TEC with the TEC observed by the Global Navigation Satellite System showed a linear relationship with a slope of 0.76 after correcting for offsets in the estimated value. This slope seemingly represents the ionospheric TEC ratio below the ALOS-2 orbit. Correcting the ionospheric range delay in PALSAR-2 data with the SAR-based TEC estimates improved the slant range accuracy from 2.67 m to 0.77 m.

Operational deforestation detection and forest early warning is one hot topic in synthetic aperture radar (SAR) Earth observation (EO) today. Especially in tropical regions where persistent cloud coverage is incapacitating optical sensors during large parts of the year, active microwave is regarded as the most promising technology to combat illegal and unsustainable deforestation activities. The key aspect for a true EWS is the timely detections of fresh and ongoing deforestation activities in a matter of days. allowing authorities to act on a given warning in meaningful fashion, e.g., inspecting detected sites, detain deforesters and pretend the remaining forest from further destruction. The development of JAXA’s next-generation algorithm (NGA) for deforestation detection builds upon the important breakthroughs in long-term pantropical forest monitoring made by the ALOS-2 mission. A field survey conducted in the Brazilian Amazon verified the unparalleled performance of the NGA to reliably detect ongoing deforestation.

Forest monitoring is a prime objective for orbital synthetic aperture radar (SAR) missions. Especially in tropical regions where persistent cloud coverage incapacitates optical satellite observations for large parts of the year, radar remote sensing provides a unique tool for consistent all-year round data acquisition. In contrast to high-frequencies SARs at C- and X-band, L-band radar constitutes a powerful tool to obtain direct information from the full forest strata ranging from the top canopy all the way down to the forest ground. However, despite important advantages, SAR faces various challenges when it comes to potential error sources. As radar backscattering from distributed natural forest targets is profoundly complicated in nature, ill-defined signal fluctuations often limited the reliability of models and applications. To overcome these challenges by improved understanding of the spatial-temporal fluctuations in forest backscatter, coherent, multitemporal and polarimetric observations covering all kinds of environment conditions are required. Nearing 9 years in orbit since its launch in 2014, ALOS-2 has acquired the world’s first long-term pantropical L-band SAR archive fulfilling exactly these requirements.

The south coast of Java is located in an active subduction zone where the Indo-Australian plate is beneath the Eurasian plate with a velocity of 7,0 cm/year. The phenomena lead to the area front of disaster, especially earthquakes and tsunami. Some areas along the coast of JAVA are being pressed (subsidence) or lifted due to the tectonic activity of the plate. Also, the activity can result in the creation of areas that have the potential to experience ground movement. However, information about it with scientific evidence is still inadequate. Therefore it is necessary to observe the effects of tectonic activity in southern Java on land surface changes along the coastline and estimate the potential epicenter of earthquakes due to this activity. In this study, we observed land deformation along the southern coast of Java to determine which areas experienced significant deformation. Based on the result, we can estimate the future earthquake's potential epicenter and the land movement area. Also, we analyzed the duration required since the last observation of earthquake events or land movement in the area. The land deformation observation uses satellite radar SAR data of ALOS-2 PALSAR-2 provided by JAXA. The type of SAR data is a stripmap with a 3 x 8-meter resolution. The time acquisition varied for each area observed from 2017 to 2020. The covered area of studies is spread from West to East (Pandagelang, Lebak, Sukabumi, Cianjur, Garut, Pangandaran, Banjar, Cilacap, Kebumen, Purworejo, and Bantul).

This paper evaluates the digital surface model (DSM) derived from high-resolution optical satellite imageries, as one of the preliminary simulations for the future mission of the Japan Aerospace Exploration Agency (JAXA). The DSMs are generated from the imagery pairs of different satellites, i.e., the WorldView-2 and the SkySat, as the simulation of the stereo observation with the constellation of satellites that have different sensor specifications. The results are compared with the ones generated from the conventional imagery pairs of the same satellites. For the preliminary study of environmental monitoring using DSMs generated from satellite imageries, two DSMs of different observing dates on the isolated volcanic island are generated from SkySat stereo imagery pairs. The analysis of the height differences between these two DSMs revealed that it has the potential to detect detailed temporal changes, such as smoke rising from volcanic clacks.

The objective of this study was to develop a 10-meter resolution land cover classification map using Sentinel-1 and Sentinel-2 satellite imagery along with the Dynamic World data set. A Gradient Boosted Decision Tree (GBDT) classification was employed with reference samples. To reduce the salt-and-pepper effect, a decision fusion approach was applied to the classification probabilities obtained from the GBDT and the CNN-based classification from Dynamic World. The model’s accuracy increased when CNN-based classification probabilities were used as inputs of the GBDT classification, while the salt-and-pepper effect was reduced when a decision fusion was applied to those two probabilities. This study suggests that the traditional land cover classification approach with reference sampling can still be effective when integrating with CNN-based classification probabilities for rapid land cover classification mapping.

Japanese local governments are increasingly utilizing forest cloud systems, and we have developed practical logging detection methodology using ALOS-2/PALSAR-2 data to improve the efficiency of forest management based on these systems. In the study in Ibaraki Prefecture, we established a method to improve both user's and producer's accuracies, and quantitatively compared the characteristics of logging detection by SAR and optical sensors. Currently, the logging information is being used in the Ibaraki Prefecture's forest cloud system.

Japan Aerospace Exploration Agency (JAXA) Earth Observation Research Center (EORC) has produced a high-resolution land-use and land-cover classification map for Japan indicating the average cover in 2018–2020. The classification accuracy was improved by using a combination of Sentinel-2 and ALOS-2/PALSAR-2 data. We developed a convolutional neural network-based algorithm specialized for pattern recognition in multispectral and time-series feature spaces, and achieved an overall accuracy of 88.85% in 12 classifications. This is the most accurate high-resolution land-cover map ever produced by JAXA. This highly accurate product is being used for the calculation of SDGs global indicators such as 15.4.2 “Mountain Green Cover Index”.

Ionospheric path delay (IPD) correction methods for creating an ortho-rectification product of L-band SAR were examined using the Phased Array type L-band Synthetic Aperture Rader-2 (PALSAR-2). Slant range correction values for the IPD were estimated using two methods. First one is coregistration between a radar geometry simulated image created using digital surface model (DSM) and a SAR amplitude image. Second one is using a total electron content (TEC) map. The geometric accuracy of the ortho products created using each correction was evaluated. The TEC map correction improved the geometric accuracy, while the coregistration one decreased. The individual slant-range shifts calculated by the co-registration showed a relationship with forest canopy height. The reason why the error increased in the co-registration correction is inferred that a false slant range shift is created due to the height difference between the scattering center point of the L-band SAR and the DSM in the forest, which results in adding an error to the slant-range correction value.

Nearing 8 years in orbit, ALOS-2/PALSAR-2 has acquired the world's first long-term pantropical L-band SAR forest monitoring data archive. The unprecedented dual-polarimetric time-series data allow to revolutionize our knowledge of the complex spatial-temporal processes governing the backscattering behaviors in tropical forests from local to global scale. The paper discusses ALOS-2's tremendous contributions to fundamental SAR forest research, forest cover mapping, biomass estimation, and deforestation monitoring, from better forest/non-forest maps and improved forest type classifications to all-new detailed flooded forest maps and groundbreaking insights on the rainfall impacts on L-band SAR imaging shedding light on the often overlooked relation between precipitation and forest backscatter. Based on these pioneering achievements a novel AI-supported false alarm suppression method was developed for the JICA-JAXA forest early warning system in the tropics (JJ-FAST) enabling the first operational fully automatic near real-time deforestation detection service with overall accuracies above 80% in the Amazon basin.

This study conducted simultaneous experiments using a flood experiment field that can reproduce the conditions of flooded buildings, along with satellite monitoring using the L-band synthetic aperture radar (SAR) aboard the Advanced Land Observing Satellite-2 (ALOS-2). Through the experiments, we investigated the relationship between the multi-look number of interferometric processing, flooded building detection using interferometric coherence, and floodwater depth. For a better interpretation of the experiment results, we also performed a theoretical coherence simulation. Our results revealed that 1) coherence statistically depends on the multi-look number, 2) coherence-based change detection method can classify a flooded building with 6 cm or less water depth, and 3) there is no clear correlation between coherence degradation and floodwater depth. These findings will be useful for robust flood-area estimation of actual urban flooding events.

Soil water content in agricultural land is a major factor for forecasting drought, estimating crop yields, and grasping methane emissions. The needs of spatiotemporal soil moisture data are increased recently. However, spatially seamless soil moisture estimations are challenging due to the lack of ground truth and the small spatial representativeness of point-based in-situ measurement. In contrast, microwave imagery has an advantage for soil moisture monitoring with high sensitivity to dielectric constant of land surface. Japan Aerospace Exploration Agency (JAXA) provides stable accuracy and high resolution at 6.25m spatial resolution microwave images with L-band observation using PALSAR-2 sensor of ALOS-2 since 2014. The objective of this study is volumetric soil water content estimation using dielectric constant derived by Dubois algorithm with PALSAR-2. Then we converted the dielectric constant result into soil moisture using Topp model. As the result, the average of soil water content in Ibaraki field was estimated as 28% by using PALSAR-2. When it was compared with in-situ, the RMSE was 5.2%. Further spatial and quantitative comparisons have shown.

The “Advanced Optical Satellite” (ALOS-3, nicknamed “DAICHI-3”) is the next high-resolution optical mission as a successor of the Advanced Land Observing Satellite (ALOS) in Japan Aerospace Exploration Agency (JAXA), and will be launched in early 2022. The mission objectives of ALOS-3 are (1) to contribute safe and secure social including provision for natural disasters, and (2) to create and update geospatial information. The “wide-swath and high-resolution optical imager” is designed to be achieved the missions, which consists of the panchromatic band with 0.8 m ground sampling distance (GSD) and multispectral six bands with 3.2 m GSD, and the observation swath width is 70 km at nadir. This paper introduces the updated ALOS-3 calibration and validation preparation status at JAXA. As the target accuracy of ALOS-3 standard product, the absolute geometric accuracy is 1.25 m in horizontal and 2.5 m in vertical with GCPs (1 sigma), and the absolute radiometric accuracy is +/ - 10 % for the multispectral band.

The “Advanced Land Observing Satellite-3 (ALOS-3)” is a follow-on satellite of the optical sensors onboard the Advanced Land Observing Satellite (ALOS) in the series of land observing satellites launched by Japan Aerospace Exploration Agency (JAXA). It equips with panchromatic and six-band multispectral optical sensors that have ground resolutions of 0.8 m and 3.2 m respectively while keeping the wide swath of 70 km that is the same as their predecessors onboard the ALOS. The satellite can collect stereo imageries by using a function of large body-pointing up to 60 degrees from the nadir. The digital surface models (DSMs) are generated from stereo imageries as one of the high-level products. This paper reports on plans for the geometric calibration and its derived DSM processing for the ALOS-3 optical imageries.

Land cover classification mapping is a process to depict terrestrial surfaces by pre-defined thematic classes across space and is often implemented by a supervised classification approach with satellite and/or aero images. However, due to labor-intensive and time-consuming tasks to identify land cover class from those images manually, it is challenging to build richquantity and highquality reference data sets for the mapping. To capture land covers on the ground, we developed a deep learning model to estimate land covers from street-level photos. A transfer learning from the pre-trained DenseNet was applied, and this model yields an overall accuracy of 0.91. This model contributes to the subsequent study of building a semi-automatic reference database from geo-tagged street-level photos.

The COVID-19 pandemic has had substantial impacts on the Earth system and socioeconomic activities. Restrictions aimed at reducing the spread of COVID-19 by limiting human interaction have led to significant reductions in air pollution and CO2emissions, improvement in water quality, changes in agricultural output, and changes in economic activity for certain industries such as airlines and shipping, among others [1]–[5]. Those economic impact assessment related information are made available on the trilateral COVID-19 Earth Observing Dashboard (https://eodashboard.org) [6]. The presented use cases in economic activities make full use of the combined satellites fleet of NASA, ESA and JAXA as well as the expertise of the Earth Observation community.

The COVID-19 pandemic has brought difficulties to our daily lives and caused various changes in socio-economic activities and the natural environment. To monitor and record the changes in socio-economic activities and the environment before and during the COVID-19 pandemic, JAXA has collaborated with NASA and ESA in developing a trilateral dashboard to provide socio-economic activities and natural environmental change information derived from earth observation data. JAXA also has analyzed Earth observation data, for example, ALOS-2, GCOM-C, GOSAT and GOSAT-2 and opened at a special website “JAXA for Earth on COVID-19” in the categories of airports, industry, shipping, agriculture, greenhouse gasses and water quality.

This study investigated the geometric errors in L-band synthetic aperture radar (SAR) caused by the effects of the ionosphere and troposphere based on observational evidence derived from the Phased Array type L-band SAR-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2) data, which were obtained by calibration and validation works, and external numerical data. First, the ionospheric path delay (IPD) and the tropospheric path delay (TPD) on PALSAR-2 data were estimated. The results revealed that the zenith IPD ranges from approximately 1 m to 12 m, according to the observation period, due to the solar activity cycle. In contrast, the zenith TPD ranges from 2.30 m to 2.75 m depending on the seasonal variation. Next, the slant-range error of PALSAR-2 with the slant IPD and the slant TPD were compared to investigate the relationships between them. These relationships indicated a strong correlation between the slant-range error of L-band SAR and the slant IPD, whereas the correlation with the slant TPD was not confirmed. This result suggests that the effect of the ionosphere could be the significant error factor in the L-band SAR geometric accuracy. Finally, the effective geometric accuracy of PALSAR-2 was simulated by removing the IPD and TPD. As a result, the standard deviation of the slant-range error was reduced from 2.709 m to 1.206 m, and a bias of −5.288 m remained.

The relationship between hurricane sea surface wind speed and L-band$\sigma^{0}$is investigated to enable wind estimation under extreme wind condition based on four the Phased Array type L-band Synthetic Aperture Rader-2 (PALSAR-2) hurricane images and wind speed simultaneously measured by National Oceanic and Atmospheric Administration (NOAA)'s airborne Stepped Frequency Microwave Radiometer (SFMR). It was confirmed that HV$\sigma^{0}$has a little dependence on the incidence angle and tends to increase without saturation at least up to 55 m/s and an L-band HV geophysical model function (GMF) is constructed using match-ups between HV$\sigma^{0}$and SFMR wind speed. A comparison between the PALSAR-2-derived and SFMR-measured wind speeds reveals a −0.2m/s bias and 4.1m/s root mean square error (RMSE). The calculated wind speed structure shows the asymmetry wind speed radii which is almost consistent with the best track data.

Forest observation is one prime objective for current and future L-band SAR missions. Despite the tremendous achievements made in the last 3 decades, the reliability of derived products, e.g., forest cover and biomass maps, is still largely hampered by ill-defined backscatter fluctuations. To better understand the rain-induced noise-like variations in tropical forest backscatter timeseries, we demonstrate big data deep learning applications exploiting the unprecedented long-term data sets provided by ALOS-2/PALSAR-2 from 2016–2021. Robust fully automatic deforestation detection is achieved in an operational forest monitoring system by using a CNN-based false alarm suppression method. Using 400 TB of ScanSAR images together with 50 billion auxiliary rainfall data an automatic rainfall prediction and correction procedure for L-band SAR is proposed based on LSTM artificial neural network. First results show the approach's potential to reliably reduce rain related disturbances in L-band forest timeseries in an unsupervised fashion.

The all-weather, day-and-night imaging capability of synthetic aperture radar (SAR) has become essential for flood disaster monitoring. This study developed a SAR-based flood monitoring method that automatically estimates flood extent, floodwater depth, and their changes. The flood extent was estimated from a Bayesian probabilistic model using ALOS-2 SAR data (amplitude and coherence) and hydrodynamic simulation data provided by Today's Earth (TE). The floodwater depth was calculated using the SAR-derived flood extent and high-resolution digital elevation model (DEM). We examined our methods using ALOS-2 data for the 2015 flood in Japan.

The drained peatland is a typical anthropogenic disturbance for carbon dioxide emissions in tropical peatlands of Indonesia, decreasing the groundwater table and activating the decomposition of peat soil. Especially in Indonesia, drainages have purposes for converting it to oil palm plantation and it potentially induces more fire events in peatlands which have huge amount of organic soils. In this study we tried to detect drainages using PALSAR-2 microwave images at a 10 m of spatial resolution. High resolution microwave data enabled the detection of small drainage canals, which are second-order streams, not only large (first-order) drainage streams. As the result, more areas were classified as drainage canals in 2020 than the former version in 2007. The overall accuracy of the validation was 58.82% which showed Producer and User's accuracy were 55.35% and 99.8%, respectively.

In this paper we provide a brief review of the Earth observation requirements for a number key science applications for which spaceborne Synthetic Aperture Radar sensors can contribute with critical measurements. We outline the current state of the science and identify information gaps associated with each application, and subsequently, provide recommendations on how these gaps can be mitigated in the 2020's time-frame by coordination of current and already planned missions, and for the next decade, with a vision for a comprehensive constellation system that would address the outstanding scientific requirements.

The “Advanced Optical Satellite” (ALOS-3, nicknamed “DAICHI-3”) is the next high-resolution optical mission as a successor of the Advanced Land Observing Satellite (ALOS) in Japan Aerospace Exploration Agency (JAXA), and will be launched in Japanese Fiscal Year 2021. The mission objectives of ALOS-3 are (1) to contribute safe and secure social including provisions for natural disasters, and (2) to create and update geo-spatial information. The “wide-swath and high-resolution optical imager” is designed to be achieved the missions, which consists of the panchromatic band with 0.8 m ground sampling distance (GSD) and multispectral six bands with 3.2 m GSD, and the observation swath width is 70 km at nadir. This paper introduces the updated calibration and validation plan of ALOS-3 at JAXA. As the target accuracy of ALOS-3 standard product, the absolute geometric accuracy is 1.25 m in horizontal and 2.5 m in vertical with GCPs (1 sigma), and the absolute radiometric accuracy is +/−10 % for multispectral band.

The “Advanced Land Observing Satellite-3 (ALOS-3)” is a follow-on mission of the optical sensors onboard the Advanced Land Observing Satellite (ALOS) in the series of land observing satellites launched by Japan Aerospace Exploration Agency (JAXA). It equips with a panchromatic optical sensor that has a ground resolution of 0.8 m and a multispectral optical sensor that has six spectral bands at a ground resolution of 3.2 m, while keeping the wide swath of 70 km that is the same as the preceding optical sensors onboard the ALOS. The satellite will acquire stereo imageries by using a function of large body-pointing up to 60 degrees from nadir. The digital surface model (DSM) will be generated from the stereo imageries as one of the high-level products. This paper reports on an overview of the geometric calibration and its derived DSM generation for the ALOS-3 optical imageries.

JICA-JAXA Forest Early Warning System in the Tropics (JJ-FAST) monitors tropical forest in 77 countries by using PALSAR-2/ScanSAR time-series data. The deforestation detection algorithm used in JJ-FAST shows overall user's accuracies of 71.1%, but the user's accuracies was 53.3 % for Latin America. Four convolutional neural network models (CNN) have been developed and tested to improve deforestation detection accuracies in Peru and Brazil. By applying the suggested CNN models, averaged user's accuracies are improved from 45 % to 77 - 85 % in dry season, and from 34 % to 48 - 57 % in rainy season. Averaged relative F1 are improved from 0.6 to 0.67 - 0.75 in dry season, and from 0.49 to 0.52 - 0.63 for rainy season. It is shown that adding CNN operation improves the deforestation detection accuracies. Intermediate CNN outputs were visualized to gain insights about what features of the image does each layer learn for improving the accuracies.

A series of multi-mode ALOS-2 Synthetic Aperture Radar (SAR) images were used to examine surface change/deformation associated with the eruption of the Kilauea volcano in Hawaii on May 3, 2018. The first interferogram using ScanSAR InSAR images reveals large surface displacements in the Leilani Estates, the East Rift Zone, and the Kilauea Caldera. Stripmap polarimetric color-composite images show a spatial distribution of lava flow over Leilani Estates. Spotlight images captured the collapse of the Kilauea Caldera and the 3D displacement by offset tracking. ScanSAR time-series InSAR from September 2018 to December 2019 indicates a post-eruptive surface uplift in the East Rift Zone and the Kilauea Caldera.

One of the prime objectives for orbital civilian SARs is forest observation. Though tremendous effort has been devoted to improving applications like biomass estimation and deforestation detection, the reliability has been impeded by insufficient knowledge about spatial-temporal variabilities of forest backscatter. In the tropics, these backscatter fluctuations are largely controlled by the rainfall regime. This paper investigates the impact of rainfall on L-band forest observation using 300 TB pan-tropical ALOS-2 ScanSAR timeseries with 1.5 billion TRMM based GSMaP precipitation datasets. The results show that the correlation between forest gamma naught and antecedent rainfall can exceed R=0.9 in many areas outside the rainforest. In the rainforest strong negative correlation below R=-0.8 can occur in the severely flooded forest. We assess the impact of rainfall on deforestation detection accuracies and discuss a new classification scheme for tropical forests using statistics of seasonal backscattering behaviors together with response to precipitation.

JICA and JAXA have operated a tropical forest early warning system (JJ-FAST) since 2016 and ALOS-2 L-band SAR system (PALSAR-2/ScanSAR) is used for forest monitoring and deforestation detection. There are many factors to cause commission errors in SAR image and ground moisture is one of them. To reduce such commission errors, seasonal change analysis is effective. In this study, a time-series analysis method using Savitzky-Golay (SG) filtering was developed. The method of speckle noise filtering by frost and 2-step thresholding and time-series analysis using SG was good method. The accuracy of forest change detection was improved from 19% of the threshold method to 80%.

In this study, we developed an automatic flood detection method using ALOS-2 and ancillary data. Our method can be automatically processed and provide easy-to-understand flood information rapidly for users working on-site disaster response. A Bayesian probabilistic model was used for integrating ancillary data (quasi-realtime flood simulation) and multiple SAR-derived images (amplitude and coherence). We also used a land-use/land-cover map to improve the flood classification result. We examined our methods using ALOS-2 data in the 2015 flood in Japan and obtained an accurate flood map in good agreement with reference data.

Over the past several years Analysis Ready Data has been a major focus for the Committee on Earth Observation Satellites (CEOS). The CEOS Analysis Ready Data concept within the context of Land started in 2015 and has been led by the CEOS Land Surface Imaging Virtual Constellation (LSI-VC) community. LSI-VC has developed the definition that CEOS uses for ARD for Land and the overall framework [1]. The CEOS ARD definition identifies that the major benefit of ARD lies in enabling a broad community of non-remote sensing experts to better use CEOS data; and in increasing the interoperability of products through space and time as a foundation for `Big Data' analyses, future data architectures and the application of AI (artificial intelligence and machine learning) methods. LSI-VC also took the responsibility to coordinate the development of the Product Family Specifications (PFS). An important step in the CARD specification approach is that it is not prescriptive with regard to the data processing approach. This recognises that there are generally multiple approaches and that these will evolve through time. Four PFS have been endorsed by the CEOS LSI-VC community during 2019/2020. These endorsements represent the conclusion of over two years of work since the agreement of the CEOS ARD definition & framework at CEOS Plenary in 2016. The endorsed specifications contain inputs from experts around the world and it demonstrates the resolution of various points of view. It also shows the emerging acceptance of the concept of Analysis Ready Data and the value proposition of the CEOS ARD. As the increase of available EO data and processing platforms offer an unprecedented choice for the users, the need for harmonized data is growing [2]. Thus, over the past few years, there has been an increased interest from the private sector in the CEOS ARD initiative as well, and the expectation is that the availability of CEOS ARD datasets from both institutional and private data providers will increase in the coming years. To address the CEOS ARD long-term view and to ensure a consensus between players is achieved, the CEOS ARD strategy was developed in 2019. The strategy recognises that the private industry has different roles to play within the CEOS ARD initiative such as data users, data providers, data hosts or as providers of processing chains to produce data that meet CEOS ARD specifications. The strategy is also focused on the need, prioritization and continuity of CEOS ARD PFS development including the development of ARD specifications for other thematic areas such as ocean and atmosphere. This paper intends to give an update on the current CEOS ARD for Land accomplishments, and what activities are envisaged for the near future.

JJ-FAST is the SAR-satellite based early deforestation monitoring system, and deforestation information obtained by using PALSAR-2/ScanSAR data are released on the JJ-FAST web site, 3 to 4 days after the observation. ScanSAR/50m spatial resolution data is currently used for the deforestation detection, and the minimum detection size is 3ha. In this paper, ScanSAR/25m spatial resolution data is used for the deforestation detection, and the detection accuracies are compared with the one obtained with the 50m spatial resolution data for four test sites. The minimum deforestation detection size is set to be 1ha in this time. The results show that the 25m spatial resolution data always shows higher detection accuracies. The results obtained in Cambodia test site show that user's accuracies are 21.4 % higher than the case of 50m resolution, and number of correctly detected polygon is 2.3 times more than the one for the 50m resolution case. It is indicated that the 25m spatial resolution ScanSAR data improves the detection accuracies with minimum detection size of 1 ha.

This study investigated a method of flood detection in built-up areas by L-band synthetic aperture radar (SAR) images, using interferometric phase statistics instead of the widely used coherence. Simulation and actual SAR data analysis showed that phase statistics are more accurate than coherence is, especially when there are many point-like scatterers. Accuracy was further improved by using an interferometric phase filter. The flood detection accuracies obtained by the phase statistics were κ = 0.535 (0.304 by coherence) in the 2015 flood in Joso and κ = 0.679 (0.609 by coherence) in the 2018 flood in Mabi. These results showed that coherence should be used with great care with point targets and that the phase-only statistics can be more robust in such cases. The proposed method is expected to apply to other types of disasters where coherence has been used previously.

We use L-band Synthetic Aperture Radar data to reveal crustal deformation associated with the eruption of Sierra Negra volcano, Galápagos Island, on June 26, 2018. Intensity analysis revealed the area of lava flowing out from the northside of the mountain. Interferograms resulted in large surface displacement in the crater as well as the northwestern slope of the summit. Using the offset fields from ascending and descending orbit data, we calculated 3D deformation, showing about 8 m subsidence in the crater. The amount of the deformation was greater than that in the previous eruptions, which implies the volume of magma accumulation and flowing out can be larger than that in the past events.

We investigated L-band cross-polarized normalized radar cross section (NRCS) dependence on ocean surface wind for wind speed retrieval. Because it has been reported that the L-band HV NRCS significantly changes due to the Faraday rotation (FR), analysis was conducted on observed NRCS with less influence of FR. About 50,000 match-ups, each consisting of the L-band NRCS, incidence angles, wind speeds and wind directions, were collected from the Phased- Array L-band Synthetic Aperture Radar-2 (PALSAR-2) and scatterometer wind vectors. Based on the match-ups, the L- band HH NRCS dependence on incidence angle and wind vector is modeled for 25 to 40° incidence angles. The derived relation indicates that the wind sensitivity of the L-band HV NRCS is almost equivalent to that of the HH one. Wind speeds are then estimated from the match-ups, based on the derived model function. A comparison with the reference scatterometer winds reveals a 0.37m/s bias and 2.9m/s root mean square (RMS) error.

In order to reduce speckle noise effects in forest cover change analysis, two types of SAR processing approach have been proposed so far. One is a type of object-based segmentation approaches and another is pixel-based processing approaches using speckle noise filtering such as Lee and Frost filter. This paper proposes a methodology of the pixel-based algorithm for forest change detection using PALSAR-2 ScanSAR data. Frost filter is applied to reduce speckle noise and normalization filter is implemented to reduce ground moisture effects. In case of Peru and Paraguay, the user's accuracy of developed algorithm was almost same as those of JJ-FAST version-2 while the producer's accuracy was 3 times better than JJ-FAST latest version (version-2).

In Earth Observation, Analysis Ready Data (ARD) are commonly referred to as satellite data that have been processed to a minimum set of requirements and organised into a form that allows immediate analysis with a minimum of additional user effort and that facilitates interoperability through both time and space with other datasets. The Committee on Earth Observation Satellites (CEOS) is currently developing a set of ARD specifications for both optical and Synthetic Aperture Radar (SAR) satellite data under the so called CARD4L (CEOS Analysis Ready Data for Land) framework [1], [2].The Japan Aerospace Exploration Agency (JAXA) generates global SAR mosaic on an annual basis from data acquired by the JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR-2 missions. The mosaic products have been subject to full geometric and radiometric corrections and organised into a format that can be considered ARD. In order for the mosaic products to obtain CARD4L certification, JAXA has undertaken a product alignment assessment of what additional processing steps or ancillary information that would be required for the mosaic products also to be compliant with the CARD4L specifications for Normalised Radar Backscatter. The data products, alignment process and outcome of the assessment are described below.

Analysis Ready Data (ARD) products are enabling users to get first hand satellite data that are `ready to use' for a wide range of applications, including time-series analysis and the way forward to multi-sensor interoperability. The Committee on Earth Observation Satellites (CEOS) is leading the CEOS Analysis Ready Data for Land (CARD4L) initiative. The rationale behind this initiative is to enable users to access products that have been already processed to a certain level. This will allow users to carry out immediate analysis on the data and to address a variety of applications without the extra time and cost that is associated with the data pre-processing steps. The aim of the proposed paper is to give an overview on the work already carried out by the CEOS community on the CARD4L framework, to introduce the processes and steps that Earth Observation (EO) data providers should undertake to have a CARD4L level product as well as the planned steps that CEOS LSIVC will be undertaking to accomplish the CARD4L initiative.

The "Advanced Optical Satellite" (nicknamed "ALOS-3") is the next high-resolution optical mission as a successor of the Advanced Land Observing Satellite (ALOS) in Japan Aerospace Exploration Agency (JAXA), and now conducting the Critical Design Review (CDR) phase. The mission objecttives of ALOS-3 are (1) to contribute safe and secure social including provisions for natural disasters, and (2) to create and update geo-spatial information. The "wide-swath and high-resolution optical imager" is designed to be achieved the missions, which consists of the panchromatic band with 0.8 m ground sampling distance (GSD) and multi-spectral six bands with 3.2 m GSD, and the observation swath width is 70 km at nadir. In this study, mission overview, and planned- and expected-products of ALOS-3 are introduced.

The "Advanced Optical Satellite" is a follow-on satellite of the optical sensors onboard the Advanced Land Observing Satellite (ALOS) and is called "ALOS-3" in the series of land observing satellites launched by Japan Aerospace Exploration Agency (JAXA). It will achieve approximately three times higher ground resolution of 0.8 m while keeping the wide swath of 70 km as compared with its preceding Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the ALOS. Though the sensor does not equip with multiple sensor-units which achieve the full-time along-track stereo observation like the PRISM, it will be able to acquire stereo imageries by using a large body-pointing capability within 60 degrees from nadir. The digital surface model (DSM) is expected to be processed from the stereo imageries as one of the high-level products in the ALOS-3 mission. This paper reports on a pre-launch simulation of the DSM generation from the ALOS-3 stereo imageries.

JJ-FAST is the first SAR-based global and early deforestation monitoring system, and started the operation from November 2016. In this paper, improved deforestation detection algorithm were suggested for using JJ-FAST system. One of the improvement is to increase maximum number of data used from 15 to 20 to suppress error detection occurred in mountain area and swamp forest. The other improvement is to adopt 0.5 dB lower threshold level to increase number of deforestation detection. Suggested algorithm were evaluated by using optical satellite data (GLAD), obtained in one of the active deforestation spots in Peru, Brazil, Cambodia, and Mozambique. User's accuracies increases by 6.7%, and number of correctly detected deforestation polygon make it 1.3 times as large.

The advent of satellite constellations has been rapidly increasing the acquisition frequency of Earth observation images, enabling continuous updates of land cover maps on a global scale with supervised learning. Since annotation of land cover semantic classes is expensive with a field survey, the number of labeled samples and the update frequency are limited. To compensate for the lack of ground-truth labels, we propose to substitute automatically labeled ground-shot images for human annotated data. In our methodology, unlabeled georeferenced ground-shot images are classified with a Convolutional Neural Network (CNN), which is trained by a set of ground-shot images annotated by humans in advance. We propose a new method that automatically grades human annotation to address noisy labels used for training the CNN. The outcomes of ground-shot image classification are used as labeled samples for pixel-wise land cover classification of multispectral satellite imagery. The resulted land cover classification map resembles the results supervised by human-annotated samples. The experimental results show that our methodology has the potential to associate satellite imagery with ground-shot imagery.

Ice shelves have a key role to assess mass balance in Antarctic Ice Sheet and its contribution to global sea-level rise. On July 12 2017, a large iceberg separated from Larsen-C Ice Shelf in Antarctic Peninsula, which lost greater than 12% of total ice mass. We use ALOS-2/PALSAR-2 ScanSAR images to capture the overall picture of this event and to understand what happens to Larsen-C. ALOS-2 images reveal that the iceberg has been gradually moved toward sea, sometimes affected by sea ice motion. The ALOS and ALOS-2 images show that the ice front has been advancing by ~ 8 km in the last 10 years. The interferometric coherence analysis reveals that there are some cracks on the ice shelf despite not seen in the intensity images. The distribution of the cracks derived from the coherence can be useful to assess the instability of Larsen-C.

The relationship between ocean wind vectors and L-band normalized radar cross sections (NRCS) is examined to modify the L-band geophysical model function (GMF) using the Phased-Array L-band Synthetic Aperture Radar 2 (PALSAR-2) and scatterometer wind data. Since the PALSAR-2 has a function of dual polarized ScanSAR, the sensitivity of L-band HV on wind fields is examined. Though the HV signal shows wind speed and incidence angle dependencies, they are too noisy to be used for wind detection. In order to seek the possible cause, the L-band NRCS is compared with Faraday rotation (FR) angle, indicating a clear positive relationship between the HV NRCSs and with the FR angle. The HH/HV ratio from the PALSAR-2 observation is compared with theoretical ones calculated from FR angle. The observation and theory correspond in terms of the tendency that the HH/HV decreases with increasing the FR angle and its sensitivity decreases with incidence angle. On the other hand, the decreasing ratio of the observation exceeds the theoretical one resulting in increasing discrepancy with the FR angle. Since the HH NRCS is not significantly affected by FR, the L-band HH GMF is modified in order to cover the wider incidence angle ranges from 17° to 50°. Based on the derived GMF, wind speeds are estimated and compared with the buoy-measured winds. The comparison shows the -0.75 m/s bias and 2.07 m/s root mean square (rms) error.

For many land monitoring applications using remote sensing, lack of data is no longer an issue, as it may have been in the past. Programs, such as Copernicus by the European Commission and the Landsat Missions by the United States Geological Survey, have adopted systematic acquisition strategies, and distribute vast amounts of satellite data under open licenses. In parallel, storage and computing capability have evolved to make it cost-effective and practical to process and analyze these data at various scales. Data architecture solutions, such as the Open Data Cube (ODC) and the Copernicus Data and Information Access Services (DIAS), are providing frameworks that make [scientific] analysis much simpler and straightforward. However, enabling non-expert users without the expertise and/or computation resources to pre-process and store low-level data products in order to exploit these capabilities, has proven more challenging. The Committee on Earth Observation Satellites (CEOS1) is working to address this challenge through the CEOS Analysis Ready Data for Land (CARD4L) initiative [1]. CARD4 L is foreseen to enable users to access satellite data products that are `ready to use' for a wide range of land applications. Moreover, CARD4L aims to enable non-expert users access to products that have been processed `far enough' to be suitable for immediate analysis for a range of applications, while ensuring they are not too specific to only be used for particular topics or areas. CARD4L will be an important enabler of the Open Data Cube (ODC) initiative [2]. Through CARD4L, users will be able to easily locate products that are suitable for ingestion into Data Cubes [3], and will have confidence that these different CARD4 L products will limit as far as possible barriers to interoperability.

The Advanced Land Observing Satellite-2 (ALOS-2) was successfully launched on 24th May, 2014, carrying the state-of-the-art L-band synthetic aperture radar, PALSAR-2. The satellite was developed and is stably operated by Japan Aerospace Exploration Agency (JAXA). As of January 2018, the satellite bus system and PALSAR-2 system are working very well. The PALSAR-2 data were acquired based on a systematic observation strategy to achieve consistent data acquisitions in time and space. JAXA also respond to the emergency observation requests for disasters. The observation data are distributed to both scientific and commercial users and utilized for disaster monitoring, environmental monitoring, and many other fields of applications.

Remote sensing has established as key technology for monitoring of environmental degradation such as forest clearing. One of the state-of-the-art microwave EO systems for forest monitoring is Japan's L-band ALOS-2/PALSAR-2 which provides outstanding means for observing tropical forests due its cloud and canopy penetration capability. However, the complexity of the physical backscattering properties of forests and the associated spatial and temporal variabilities, render straightforward change detection methods based on simple thresholding rather inaccurate with high false alarm rates. In this paper, we develop a framework to alleviate problems caused by forest backscatter variability. We define three essential elements, namely “structures of density”, “speed of change”, and “expansion patterns” which are obtained by differential computing between two repeat-pass PALSAR-2 images. To improve both the detection and assessing of deforestation, a “deforestation behavior pattern” is sought through temporal machine learning mechanism of the three proposed elements. Our results indicate that the use of “structure of density” can introduce a more robust performance for detecting deforestation. Meanwhile, “speed of change” and “expansion pattern” are capable to provide additional information with respect to the drivers of deforestation and the land-use change.

The “Advanced Optical Satellite” (nicknamed “ALOS-3”) is the next high-resolution optical mission as a successor of the Advanced Land Observing Satellite (ALOS) in Japan Aerospace Exploration Agency (JAXA), and now conducting the Critical Design Review (CDR) phase. The mission objectives of ALOS-3 are (1) to contribute safe and secure social including provisions for natural disasters, and (2) to create and update geo-spatial information. The “wide-swath and high-resolution optical imager” is designed to be achieved the missions, which consists of the panchromatic band with 0.8 m ground sampling distance (GSD) and multi-spectral six bands with 3.2 m GSD, and the observation swath width is 70 km at nadir. In addition, the satellite has the body pointing capability within 60 deg. from nadir that will contribute to an emergency observation. In this study, the simulated images generation is introduced to investigate the feasibility of the missions as a part of pre- flight phase study.

In 2016 we first completed the global data processing of digital surface models (DSMs) by using the whole archives of stereo images derived from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS). The dataset has unprecedented 5 m grid spacing utilizing the optical stereo images in 2.5 m resolution while the accuracy was confirmed at 5 m (rms) in vertical and also at 5 m (rms) in horizontal. One year later from the first release of the dataset we updated it with additional calibrations of PRISM interior parameters to eliminate slight systematic height errors which appeared along track. In this paper we present additional updates of the dataset with applying the semiglobal matching (SGM) for the DSM generation as well as corrections of mask data for sea areas.

A preliminary version of the semi-automatic deforestation detection algorithm was developed for the JJ-FAST system, which monitors deforestation and forest changes in tropical regions for approximately every 1.5 months and covers 77 countries. PALSAR-2/ScanSAR HH, HV, HH and HV ratio were used to detect various deforestation stages. Multitemporal data were used to suppress the effect of seasonality and rainfall. Moreover, the deforestation accuracies for the active deforestation spots in Brazil and Peru were estimated. The user's accuracies achieved as 80.5% in the dry season, but these decreased to 11.1% in the rainy season.

This paper reports the polarimetric characteristics of temporarily coherent radio frequency interference (RFI) observed by ALOS-2 PALSAR-2. The RFI has high interferometric coherence as if it is a backscattered signal from the ground. However, its radio frequency band and the Doppler frequency are different from the backscattered signal. The RFI, namely, intermittently transmitted wideband (ITWB) RFI, effects both interferometric and polarimetric analysis, because it has high temporal interferometric and polarimetric coherency. The detection and removal scheme for the ITWV RFI is also discussed in this paper.

In 2016, the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2”) observed various disaster affected areas. Japan Aerospace Exploration Agency (JAXA) operated the emergency observation more than hundred times in the year. The Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard ALOS-2 contributed for detecting the disaster affected area, ground deformation and flood affected area. Especially for the ground deformation and damaged area detection caused by the devastating earthquakes in 2016, e.g., Kumamoto earthquakes in Japan and Kaikoura earthquake in New Zealand, researchers provided variable analytical results from ALOS-2 observation data. In this paper, some examples of the emergency observation results are presented.

A new global digital surface model (DSM) dataset using the archived data of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS) has been completed on March 2016 by Japan Aerospace Exploration Agency (JAXA) collaborating with NTT DATA Corp. and Remote Sensing Technology Center, Japan. This project calls “ALOS World 3D” (AW3D), and the dataset consists of the global DSM with 0.15 arcsec. pixel spacing (approx. 5 m mesh) and ortho-rectified PRISM images. JAXA is also processed the global DSM dataset with 1 arcsec. spacing (approx. 30 m mesh) calls “ALOS World 3D 30 m mesh” (AW3D30) based on the AW3D DSM, and released it free of charge on May 2016 as version 1. A weakness of the AW3D30 DSM is remaining void pixels due to cloud covers. This paper describes the AW3D30 partially updates by complementing void pixels using other global digital elevation models, and reports a validation result.

In March, 2016 we first completed the global data processing of digital surface models (DSMs) by using the whole archives of stereo images derived from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS). The dataset has unprecedented 5 m grid spacing utilizing the original stereo images in 2.5 m resolution while its target accuracy was set to 5 m (rms) in vertical and also 5 m (rms) in horizontal. After the first global validation was completed, we detected slight systematic relative height errors in the DSM dataset. This paper reports on the dataset updates which eliminate the errors with an additional calibration of the PRISM physical sensor model.

One of the main missions of the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2” ) is the disaster monitoring. Japan Aerospace Exploration Agency (JAXA) has operated the emergency observation more than hundred times in 2015. Not only the most important event in 2015, the Mw 7.8 Gorkha earthquake on April 25, the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard ALOS-2 observed various floods, volcano eruptions and earthquakes. In this paper, we present some emergency observation results which were impossible to be performed by the previous ALOS. That is, automatically burst aligned ScanSAR to ScanSAR interferometry and, left / right looking for increasing acquisition opportunity.

One of the fundamental issues in the generation of digital surface models (DSMs) from optical stereo imageries is the noise reduction on the low correlation areas. We apply the non-local means filtering to the DSM derived from Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard Advanced Land Observing Satellite (ALOS) with modifying the original algorithm so that it can appropriately remove the noise while preserving the original resolutions/profiles of the terrain. The results of the filtering are evaluated quantitatively with a reference DSM derived from an airborne LiDAR instrument, as well as qualitatively with visual inspections. The proposed algorithm shows the ability to improve global DSM datasets generated from ALOS PRISM.

The Japan Aerospace Exploration Agency is generating the global digital elevation/surface model (DEM/DSM) and ortho-rectified image (ORI) using the archived data of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS, nicknamed “Daichi”), which was operated from 2006 to 2011. The overview and the processing status of the global DSM/ORI dataset generation project called “ALOS World 3D” (AW3D) is introduced in this paper. It is necessary to consider data processing strategies, since the processing capabilities of the level 1 standard product and AW3D products must be developed both hardware and software to achieve the project aims. The processing strategies and an initial validation of generated DSM dataset are described. As an initial validation results, 4.10 m of height accuracy as root mean square error was confirmed. The generation and distribution of a lower resolution AW3D DSM dataset is also introduced.

Panchromatic Remote-sensing Instrument for Stereo Mapping, one of onboard sensors carried by Advanced Land Observing Satellite, was designed to generate worldwide topographic data with its optical stereoscopic observation. It has an exclusive ability to perform a triplet stereo observation along its track, and collected its derived images all over the world during the mission life of the satellite from 2006 through 2011. We have started a new project which generates global elevation datasets with the image archives since last year. The data is processed in unprecedented 5m grid spacing utilizing the original triplet stereo images in 2.5m resolution. As the number of processed data is growing steadily, a trend of actual data qualities became apparent more. This paper reports on up-to-date results of the quality assessments for the data products as well as some algorithm updates for the processing software.

We tested the performance of the stereo observations of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS) in the mapping of forest aboveground biomass (AGB) in Japan. Digital canopy height models (DCHMs), which are differences between PRISM digital surface models and surveying-based digital terrain models (DTMs), were compared to in situ AGB measurements of several forest types (number of stands: 28; average stand size: 0.54 ha; stand size range: 0.25-3.00 ha). DCHM values exhibited a significant correlation with AGB (r = 0.66-0.87; five different DCHMs), and the root-mean-square error and bias of the regression model evaluated by the leave-one-out cross-validation were 37.2-57.8t/ha(22.1%-32.6%) and-0.11-1.89 t/ha, respectively. There was no saturation in the relationship between DCHM and AGB (AGB range: 19-332 t/ha). The correlations between DCHM and mean canopy height (r = 0.17-0.52) and between DCHM and Lorey's height (r = 0.26-0.66) were weaker than the correlation between DCHM and AGB. The PRISM AGB distribution estimated by the regression model was consistent with a tree density map produced from aerial photos. Comparison to Phased Array-type L-band Synthetic Aperture Radar (PALSAR) data showed that the PRISM DCHMs can estimate high AGB over the saturation level of PALSAR backscattering coefficient, i.e., 100-200 t/ha. The results described here demonstrate that the PRISM DCHMs are capable of wall-to-wall AGB estimation at 50-m resolution. This approach will be useful for improving the performance of satellite-based AGB estimation when an accurate DTM is available.

Panchromatic Remote-sensing Instrument for Stereo Mapping, one of onboard sensors carried by Advanced Land Observing Satellite, was designed to generate worldwide topographic data with its optical stereoscopic observation. The sensor had observed huge amount of stereo images all over the world during the mission life of the satellite from 2006 through 2011. To utilize all these archive data we proceed to generate new global elevation datasets, named `ALOS World 3D', which have finer ground resolution and higher accuracy than the existing ones e.g., Shuttle Radar Topography Mission data. This paper briefly reports on latest algorithms for the production of the global datasets with preliminary results of its early operations. The accuracies and error characteristics of the datasets are analyzed and discussed by the comparison with reference data on various fields.

This paper describes a new land use and land cover (LULC) classification method for classifying multi-temporal high-resolution satellite data in large areas. The classification method uses combined value of both reflectance of a pixel and its observation date as an input data, and calculates its probability distribution among all LULC classes via Bayesian inference based on a generative model estimated by kernel density estimation. This method can be easily applied to multi-temporal data to exploit phenological change information of vegetation, even if available multi-temporal data have a seasonal bias. In this paper, we conducted the classification over the entire land mass of Japan, using the multi-temporal data observed by the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) aboard the ALOS, and we evaluated its accuracy in comparison to conventional methods.

Panchromatic Remote-sensing Instrument for Stereo Mapping 2 (PRISM-2), which will be used for the next high spatial resolution global land observation mission, will extend the capabilities of earlier Advanced Land Observing Satellite (ALOS) missions. PRISM-2 will be able to collect wide-swath (50 km) and high-resolution (0.8 m) images with high geo-location accuracy without ground control points. It will acquire stereo pair images from two telescopes for stereo mapping and precise Digital Surface Models (DSMs). This paper introduces the mission requirement for the next high spatial resolution global land observation mission and a conceptual design for PRISM-2.

Preservation of the spectral characteristics in multispectral images is important in the development of pansharpening methods because it affects the accuracy of subsequent applications, such as visual interpretation, land cover classification, and change detection. The combinations of the spectral properties (observation wavelength and width of spectral bands) of multispectral and panchromatic images affect both the spatial and spectral quality of pansharpened images. Therefore, the clarification of the relations between spectral bands and quality of pansharpened image is important for improving our understanding of pansharpening methods, and for developing better schemes for image fusion. The objective of this study is to investigate the effect of bandwidth of panchromatic image on the spectral quality of pansharpened multispectral image by the simulation using hyperspectral data. Fifty-four panchromatic images with shifting bandwidths were generated using airborne hyperspectral data by averaging of different band combinations. Four bands of multispectral images were generated similarly from hyperspectral data, and spatially degraded by a factor of 6 in order to simulate low-resolution images. The pansharpened multispectral images were produced from these multispectral and panchromatic data by means of 5 pansharpening methods. The results were evaluated by the comparison with the non-degraded multispectral bands both qualitatively by visual assessment, and quantitatively by statistical index ERGAS. Quality of near infrared band was worse than visible bands, mainly due to the spectral position of panchromatic band in visible wave region. It was, however, gradually improved as longer bandwidth of panchromatic band that was expanded into near infrared region. Gram-Schmidt Spectral Sharpening generated somewhat better quality among the method from the view point of ERGAS. It was concluded that wider spectral bandwidth resulted in balanced quality of images in all bands, though the appearances of images are rather dissimilar to the original.

In this study, we examine the applicability of digital surface model (DSM) generated from ALOS PRISM data for mapping forest above ground biomass (AGB). We obtained the digital canopy height model (DCHM) by calculating the differences between the PRISM DSM and surveying-based digital elevation model (DEM), and investigated the relationship between the DCHM values and field measured mean tree height and AGB in Northern Hokkaido, Japan. The results show a strong linear relationship between the field measured AGB and PRISM-DCHM (R = 0.872, n = 25). The RMSE and bias of the regression model, which were evaluated by the leave-one-out cross validation, are 38.1 t/ha (22 %) and -0.2 t/ha, respectively. Saturation at high AGB is not shown. We also demonstrate the wall-to-wall AGB mapping by using the regression model and the PRISM DCHM image. A pan-sharpen image generated from PRISM and AVNIR-2 images is utilized for forest cover mapping. The PRISM-based AGB values are generally reasonable except for high mountain areas. The relationship between the PRISM AGB and backscattering coefficient measured by L-band synthetic aperture radar (PALSAR) is consistent with the regression models discovered by the previous researches.

The special and temporal analysis is a primitive approach in the remote sensing field. The Advanced Land Observing Satellite (ALOS, nicknamed `Daichi') has been operated very well from 2006 to 2011, and mainly observed global land region with fine resolutions by three sensors i.e. an L-band Synthetic Aperture Radar called PALSAR and two optical instruments called PRISM and AVNIR-2. They have been captured actual Earth's surface in to this era. As the ALOS follow-on mission, ALOS-2 is succeeded to the Radar mission of ALOS and carried on the state-of-the-art L-band SAR called PALSAR-2, and will be launched in JFY2013. On another front, the ALOS follow-on optical mission has not been approved by the government. This study describes to generate the simulated images and their evaluations for the next land observation satellite. In JAXA, the optical imager that has capabilities of a finer resolution, wide observation swath width, and stereo function are expected as the PRISM follow-on instrument tentatively called PRISM-2. To identify hardware specifications of PRISM-2 as well as utilization capability of acquired data, simulated images were generated with various parameters using the airborne line scanner, and the image quality, interpretation capacity, and the height estimation accuracy by stereo pair images were evaluated.

Japan Aerospace Exploration Agency creates the “JAXA High Resolution Land-Use and Land-Cover Map (JHR LULC Map)” in Japan. The data was acquired by the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2), onboard the Advanced Land Observation Satellite. Ortho-rectification, atmospheric and slope correction were applied to the AVNIR-2 data. The number of the input data for the classification is 1,876 scenes which were acquired from April 28, 2006 to April 15, 2011. The classification categories are 1) water, 2) urban, 3) paddy, 4) crop, 5) grass, 6) deciduous forest, 7) evergreen forest, 8) bare land, and 9) snow and ice. Pixel spacing of the JHR LULC Map is 0.000278° and 0.000333° for the latitude and longitude, respectively. The temporal classification accuracy was found to be 89.3%, which was calculated using the validated accuracy of the classification multiplied by the surface ratio in each category by the Ministry of Internal Affairs and Communications.

Panchromatic Remote-sensing Instrument for Stereo Mapping, one of onboard sensors carried by Advanced Land Observing Satellite, was designed to generate worldwide topographic data with its stereoscopic, high resolution observation. The global digital surface models are expected to be generated by utilizing the archives of the stereo images observed during more than five years of the satellite mission. The operation to generate the digital surface model should be full-automatic so that we can process a huge amount of archived stereo images consisting of more than one million scenes on global land areas. One of the most challenging tasks in the full-automatic process is the masking/discarding of outlier areas covered with water, snow, cloud, etc. This paper briefly reports on the preliminary algorithms of the full-automatic process, including the masking/discarding of outlier areas, to generate the global digital surface models.

The recognition of concepts that we human beings are able to locate within satellite imagery requires analysis based on the particular context using knowledge. In this paper, we present a supervised pixel-based classification approach toward utilization of the classification results in knowledgebased satellite data interpretation system. The proposed approach is based upon a generative model, which is able to output the classification results with their probabilities and subsequently utilize them in detailed analysis. The experiment of classification was performed to demonstrate characteristics of the approach.

The Advanced Land Observing Satellite (ALOS, nicknamed `Daichi') was launched on January 2006, and has operated very well. However, its operation moved to the low load mode (LLM) since April 22, 2011, and was subsequently followed by an official termination on May 12, 2011. Notwithstanding, approx. 6,500,000 scenes of archived data covering the entire globe are available to users. Therefore the calibration result is still important information for users. This paper describes calibration results of two optical instruments called PRISM and AVNIR-2 onboard ALOS are summarized during whole mission period. The previous result has been published in 2009, which was explained an overview of calibration and validation plan, and their results at that time. As the updated calibration results, we confirmed 6.1 m (root mean square error, RMSE) for PRISM nadir-looking radiometer and 21.9 m (RMSE) for AVNIR-2 0 deg. pointing of absolute geometric accuracies, and no significant degradation on radiometric accuracy.

This paper describes comparison of digital elevation data derived from synthetic aperture radar (SAR) and stereo optical imagery used to understand temporal changes of tropical forest and to estimate tree height in tropical regions. Tree height information is important for forest management and global carbon cycle studies. An intereferometric SAR (InSAR) potentially unhampered by tropical cloud cover. InSAR heights depend on the microwave frequency: low frequency P-band penetrates to the forest floor and ground-volume scattering yields a height close to terrain height, high frequency X-band displays only shallow canopy penetration, and may yield heights several metres below the canopy surface. The exact behaviours depend on the nature of both terrain and forest. A stereo-optical sensor may be used to derive digital elevation data using photogrammetry. Several recent satellite-based stereo-optical instruments operate with fine spatial resolution however optical imagery is affected by clouds. The forest height derived from optical images is essentially the top of the canopy. This research investigates whether it is possible to understand forest changes in tropical regions by comparing digital elevations from dual-frequency, single-pass airborne InSAR with satellite-based stereo-optical data in Papua New Guinea. The results indicate the possibility of forest change and tree height estimation using this combination of data.

Japan is one of the main forest countries in the developed countries. For management and administration of the forest, amount of cost and work is needed to be observed vast woodland with regularity. Since the earth observation satellites can repeatedly observe wider area at one time is significant, they are suitable for observing the boundless natural environment. It is important for forest management and forest industry to measure forest area and height of trees. In this study, tree height was calculated using digital surface model (DSM) generated by the image taken by the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS), which was launched by the Japan Aerospace Exploration Agency (JAXA) on January 24, 2006. This paper reports procedure of calculating trees height using the DSM generated by the PRISM and its validation.

Panchromatic Remote-sensing Instrument for Stereo Mapping, one of onboard sensors carried by Advanced Land Observing Satellite, was designed to generate worldwide topographic data with its high resolution and stereoscopic observation. More than six thousand scenes of digital surface model were processed so far from its images in Japan Aerospace Exploration Agency. The areas of processed scenes cover all Japan land areas and some foreign sites for specific research projects. This paper reports on the latest status of the digital surface model scene archives in all Japan land areas and a mosaicking process which combines them into 1°×1° geographic latitude/longitude tiles. The absolute and relative accuracies of mosaicked digital surface model tiles are validated with ground control points and a variety of height reference data including digital surface models produced by airborne light detection and ranging instruments.

Simultaneous field data collections and Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (PALSAR) full polarimetry observations were performed in Ulaanbaatar (Mongolia) and Alaska (USA). Permafrost is present at the Alaska test sites. Backscattering copolarization ( σco-pol0) values derived from the PALSAR data were compared with those calculated using the integrated equation method (IEM) model, a popular theoretical model describing surface scattering. PALSAR data taken in Ulaanbaatar matched the IEM model results to within a few decibels, whereas data taken in Alaska were 5 to 7 dB lower than those calculated using the IEM model. On the other hand, the σcross-pol0(σVH0) components estimated from the Oh model were well matched to the PALSAR data in both Ulaanbaatar and Alaska. Moisture levels of the sphagnum moss layer in Alaska were estimated to be about 10% while moisture levels of the underlying organic and mineral layers were 25% to 79%; the moisture values of the organic and mineral layers were factored into the IEM and Oh models. When surface moisture levels of 10% were assumed for Alaska ground conditions, the σco-pol0values calculated using the IEM model and those derived from the PALSAR data were well matched. From these observations, we conclude that the sphagnum moss layer, which is a seasonally unfrozen layer that occurs above permafrost, plays an important role in radar backscattering processes in permafrost regions and is a main contributor to the σco-pol0backscattering component; the underlying organic and mineral layers contribute mainly to the σcross-pol0backscattering component. A two-layer model was applied to the data from a test site in Alaska; the model described the co- and cross-polarization backscatter (σ0) derived from PALSAR data with off-nadir angles of 21.5° and 34.3°.

This paper proposes a new framework for automatic interpretation, especially for change interpretation, in order to implement insightful and versatile interpretation like manual interpretation. The framework considers remote sensing data analysis as a knowledge information processing. For handling large amount of knowledge on computers consistently, the framework employs double-layered knowledge structure; ontology and heuristic. The ontology is a conceptual dictionary about remote sensing domain, and heuristic is experimental knowledge containing uncertainty. Additionally, this paper introduces the method for constructing Bayesian network using knowledge, and inferring target objects and changes using it. An experiment of change detection is conducted to demonstrate our framework.

The objective of this study is to develop new glacial lake inventory to understand existing condition using the optical imageries of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) onboard the Advanced Land Observing Satellite (ALOS, nicknamed "Daichi"). Glacial lakes can be caused outburst flood when natural dams terminated by moraines are broken, and it represents serious hazard damage in downstream regions. Problems of glacial lake outburst floods (GLOFs) are that anyone does not know when it will occur and how much damage causes by it. To contribute these problems, new glacial lake inventory is currently developing in the Bhutan Himalayan using ALOS imageries. This paper describes procedure of the inventory development including image processing that introduces pan-sharpened and ortho-rectified images by PRISM and AVNIR-2, glacial lakes extraction, and its validation.

The Rational Polynomial Coefficients (RPC) is being widely used for representing the physical sensor models of satellite optical images. The Remote Sensing Technology Center of Japan (RESTEC) has already released the RPC data sets for Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) onboard Advanced Land Observing Satellite (ALOS) with respect to their physical sensor models calibrated by Japan Aerospace Exploration Agency's (JAXA's) Earth Observation Research Center (EORC). Though the RPC data of PRISM/AVNIR-2 have been widely used in public users since the release, the research on them has not been published. This paper reports on the processing method and accuracy assessment results of the RPC data for PRISM/AVNIR-2. Their adjustment models to ground control points are presented with some experimental results as well as the triangulation accuracies and DSM generations for PRISM.

Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) carried by Advanced Land Observing Satellite (ALOS) was designed to generate worldwide topographic data with its high resolution and stereoscopic observation. According to this aim, we developed the software of generating Digital Surface Model (DSM) data. Following the accumulations of PRISM stereoscopic observation data during over five year operations, the capabilities of the matching-algorithm for stereo images, as well as the characteristics of processed DSMs, were widely validated. This paper reports on the latest validation results, archiving status, and the mosaicking results of the DSM.

This paper suggests a new automatic change detection approach using image-object-based contextual inference. The approach first detects changed areas, and then infers what happened there. The inference process uses knowledge based on a change detection process performed by humans. It enables flexible and intuitive description of the change detection process. In this paper, we describe the results of case studies on automatic change detection using the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) onboard the Advanced Land Observing Satellite (ALOS, nicknamed “Daichi”).

Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) carried at Advanced Land Observing Satellite (ALOS) was designed to generate worldwide topographic data in respects of its high resolution and stereoscopic land observation. Following accumulations of the on-orbit data since the launch, the characteristics of its sensor geometry and the capabilities to generate the Digital Surface Model (DSM) have been widely validated in Japan Aerospace Exploration Agency. This paper reports on the DSM processing status, over the four-year period of operations, including a mosaicking process and analysis of error characteristics. We especially focused on an occasional waving noise of the DSM caused by an attitude fluctuation of the satellite. Detailed analysis and correction methods on the waving noise are presented in this paper with inspection of satellite attitude data as well.

The Advanced Land Observing Satellite (ALOS) was developed for detailed observation of the Earth's surface and frequent monitoring of global environmental changes, using high-resolution optical (visible and near infrared push-broom) and active microwave sensors (L-band synthetic aperture radar). ALOS has four mission objectives: cartography, regional observations, disaster observations, and resource exploration. It has been operational since its launch in January 24, 2006, and is acquiring a large amount of land-surface data supported by the Ka-band intersatellite communication system that downlinks to ground receiving stations. A global systematic acquisition strategy is implemented for all three sensors to enable consistent data collection over all land areas on a repetitive basis. Through its three sensors, acquisition strategy, and communication infrastructure, the ALOS mission is aimed to contribute to monitoring water, carbon, and global climate change. In this paper, we describe ALOS and its contribution to global environmental monitoring.

Frozen ground is a sensitive indicator of how our home planet is changing. In the meantime, new spaceborne SAR systems have been launched, such as the polarimetric PALSAR sensor onboard ALOS in January 2006. In this paper, the relevance of L-band polarimetric SAR data for extracting information on frozen ground is presented. Dealing with ground assessments, the necessity for a validated Electromagnetic (EM) model is of importance. The adequation between Oh's po-larimetric EM model and PALSAR data is first studied over agricultural bare fields in Hokkaido, Japan. The assessment of residual liquid water can be realized by means of bare soil EM backscattering model. Over natural wildland area, an approach is proposed in order to tackle the effect of the vegetation or other irrelevant effects. The monitoring of permafrost active layer is performed over the ANWR, Alaska.

This paper describes the updated results of the PALSAR CALVAL activity conducted for last three years using the PALSAR calibration archives and the connected ground truth data. The results show that PALSAR has pretty good stabilities on the geometric and radiometric accuracies. Using PALSAR data, change detection on the forest in Indonesia and the polar regions (North Pole region, Antarctica region, and Greenland region) are in progress tracking the change of the sigma-naught and/or the phases.

The Advanced Land Observing Satellite (ALOS, nicknamed ¿Daichi¿) was successfully launched on January 24, 2006, and continues to work very well. In this paper, we introduce the updating calibration results and their time trends of the standard products for two optical instruments onboard ALOS, the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2). Geometric calibration is important in generating a precise digital surface model (DSM) by a stereo pair image of PRISM. Relative geometric calibration of both PRISM and AVNIR-2 are conducted by evaluating accuracies within one scene and evaluating sensor alignments as absolute calibration. For absolute geometric accuracies, we achieved 7.8 m for nadir-, 8.0 m for forward-, 18.7 m for backward-looking images of PRISM, and 114.2 m for AVNIR-2, respectively.

Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) carried by Advanced Land Observing Satellite (ALOS) was designed to generate worldwide topographic data in respects of its high resolution and stereoscopic observation. Japan Aerospace Exploration Agency's (JAXA's) Earth Observation Research Center (EORC) has widely assessed the on-orbit geometric performance of PRISM sensors since the launch in January 2006. This ongoing activity has generated various geometric performance data over three year on-orbit period of operations. EORC also performed the data analysis of digital surface model (DSM) derived from these geometric performance data. The detailed characteristics of triangulations for PRISM stereo-geometry were evaluated by ground control points (GCPs) and height accuracies of the DSM were also evaluated from the comparison with reference DSM data sets. DSMs are being routinely processed on a scene basis at EORC and mosaicked for global standardized datasets. The preliminary result of the mosaicking process was given with their accuracy analysis.

The Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) on-orbit radiometric performance has been improved through the comparison of cross-calibration and onboard lamp calibration. We proposed a new cross-calibration scheme which uses top-of-atmosphere reflectance functions of satellite zenith angle at temporally and spatially stable ground sites. Each function is made from Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution data over 16 days, which includes AVNIR-2 observation dates. The results showed that the radiances of AVNIR-2 bands 1 (463 nm), 2 (560 nm), and 3 (652 nm) agreed with the radiances of Aqua and Terra MODIS within 3% accuracy (standard deviation of 2%). AVNIR-2 band 4 (821 nm) had a difference of about 7% (AVNIR-2< MODIS) due to water-vapor absorption which could explain more than half of the 7%. Using many samples from this scheme, we found dependences of radiometric calibration errors within the field of view (FOV) and for different gain modes. The lamp calibration system onboard AVNIR-2 unveiled these dependences over the FOV and time. Furthermore, for the gain mode, consistent results could be retrieved using the cross-calibration scheme. The retrieved radiometric correction factors (over the FOV and gain modes) have been applied to the Japan Aerospace Exploration Agency AVNIR-2 processing scheme. The subsequent validation of the correction showed, for polar snow areas, an improved radiometric performance over the entire FOV.

This paper summarizes the results obtained from geometric and radiometric calibrations of the Phased-Array L-Band Synthetic Aperture Radar (PALSAR) on the Advanced Land Observing Satellite, which has been in space for three years. All of the imaging modes of the PALSAR, i.e., single, dual, and full polarimetric strip modes and scanning synthetic aperture radar (SCANSAR), were calibrated and validated using a total of 572 calibration points collected worldwide and distributed targets selected primarily from the Amazon forest. Through raw-data characterization, antenna-pattern estimation using the distributed target data, and polarimetric calibration using the Faraday rotation-free area in the Amazon, we performed the PALSAR radiometric and geometric calibrations and confirmed that the geometric accuracy of the strip mode is 9.7-m root mean square (rms), the geometric accuracy of SCANSAR is 70 m, and the radiometric accuracy is 0.76 dB from a corner-reflector analysis and 0.22 dB from the Amazon data analysis (standard deviation). Polarimetric calibration was successful, resulting in a VV/HH amplitude balance of 1.013 (0.0561 dB) with a standard deviation of 0.062 and a phase balance of 0.612deg with a standard deviation of 2.66deg .

The 17 papers in this special issue focus on the calibration and validation of ALOS sensors and their use for bio- and geophysical parameter retrievals.

The Advanced Land Observing Satellite [(ALOS); nicknamed ldquoDaichirdquo] was successfully launched on January 24, 2006, and it continues to work very well. This paper describes the calibrations of two optical instruments onboard ALOS, which are the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and Advanced Visible and Near-Infrared Radiometer type 2 (AVNIR-2). PRISM consists of three panchromatic radiometers and is used to derive a digital surface model (DSM) with high spatial resolution, which is an objective of the ALOS mission. AVNIR-2 has four radiometric bands from blue to near infrared, which are used for investigating regional environment issues such as land-use and land-cover classifications, and disaster monitoring. In this paper, we introduce the calibration and validation plan, methodologies, accuracy assessments of the standard products, image quality evaluations for PRISM and AVNIR-2, and their results. Geometric calibration is important in generating a precise DSM by a stereo pair image of PRISM. The relative geometric calibrations of both PRISM and AVNIR-2 are conducted by evaluating accuracies within one scene and evaluating sensor alignments as absolute calibration. For absolute geometric accuracies, we achieved 8.1 m for nadir-looking images and 9.3 m for forward- and backward-looking images of PRISM; we also achieved 114.2 m for AVNIR-2. PRISM is also radiometrically calibrated both relatively and absolutely. PRISM relative radiometric calibration is accomplished by using acquired images over homogeneous targets, such as oceans, deserts, ice, and snow areas, as well as nighttime observation. Simultaneously observed images with calibrated AVNIR-2 are used for absolute radiometric calibration by cross-calibration. The absolute radiometric accuracy of the PRISM nadir-looking radiometer is similar to that of AVNIR-2.

The Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) carried by the Advanced Land-Observing Satellite was designed to generate worldwide topographic data with its high-resolution and stereoscopic observation. For this objective, the on-orbit geometric performance of PRISM sensors has been widely assessed and calibrated since the launch in January 2006 as an activity of the calibration and validation team of the Japan Aerospace Exploration Agency's Earth Observation Research Center. This ongoing activity has generated various geometric performance data over two and a half years of on-orbit operation. A suite of geometric model parameters was calibrated to express the geometric characteristics of the PRISM sensors. These include static interior parameters (charge-coupled device camera parameters) and dynamic exterior parameters (orbit data, attitude data, and sensor alignment). The interior parameters were calibrated using test-field self-calibration with test sites of dense ground control points (GCPs). The exterior parameters were calibrated by adaptive orientation with test sites of worldwide GCPs. These parameters are correlated to the direct geolocation accuracy of the PRISM sensors and are monitored and validated to maintain performance. The performance of the digital surface model (DSM) derived from these geometric model parameters was analyzed. The detailed characteristics of the triangulation results were analyzed by GCPs, and the height accuracy was evaluated based on comparisons with high-accuracy high-resolution reference DSM data sets derived from Lidar and aerial photo matching of various terrain features.

A cross-calibration scheme which uses top-of-atmosphere (TOA) reflectance functions of satellite zenith angle at temporally and spatially stable ground sites is proposed. Each function is made from MODIS 500m-channel observations over 16 days which includes AVNIR-2 observation dates. The results showed that radiances of AVNIR-2 channels 1 (463 nm), 2 (560 nm), and 3 (652 nm) agreed to the radiances of Aqua and Terra MODIS channels 3 (466 nm), 4 (554 nm) and 1 (646 nm) respectively within 5% accuracy. AVNIR-2 channel-4 (821 nm) decreased more than 5% by water-vapor absorption in some cases. Using many samples from this scheme, we found possible gain dependencies on Field Of View (FOV) and gain-modes. Lamp calibration system onboard AVNIR-2 showed dependencies on FOV, time change of the FOV error, and gain-mode consistent with the cross-calibration results. After correction of the identified errors, inter-channel consistency of FOV in the polar-snow scenes was improved.

The Advanced Land Observing Satellite (ALOS, nicknamed "Daichi") was successfully launched on January 24, 2006 and it continuously working very well. This paper introduces the updated results of calibration for two optical sensors of ALOS i.e. the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2). PRISM consists of three panchromatic radiometers, and is using to derive a digital surface model (DSM) that is topography and terrain height information with high spatial resolution. Therefore, geometric calibration is important in generating a highly accurate DSM by stereo pair image of PRISM. The radiometric calibration is also important for PRISM as well as AVNIR-2. This paper describes the updated results of geometric and radiometric calibrations and image quality evaluation of PRISM and AVNIR-2 in the operational phase, which are including stability or time trend of accuracies during 2.5 years since launch the ALOS.

The algorithm for generating digital surface model (DSM) from PRISM was developed for Earth Observation Research Center in Japan Aerospace Exploration Agency (EORC/JAXA). The geometric model stability and accuracy of PRISM have been calibrated for the DSM generation. The interior parameters of PRISM geometry are CCD alignment models and calibrated by the self-calibrations with dense ground control points (GCPs). The exterior parameters of PRISM geometry consist of the orbit data measured by the onboard GPSR, the satellite attitude data measured by the onboard star tracker and sensor alignment models. Those exterior parameters have been calibrated with GCPs as well. The accuracies of DSM generated from those PRISM geometric models are presented with the results of the comparison with high accuracy and high resolution reference DSM data sets derived from LiDAR and aerial photo at the various terrain features.

This paper summarizes the geometric and radiometric calibration results of the PALSAR achieved during the ALOS initial calibration phase, which covers five months between May 16 2006 and October 23, 2006, and the half-year of the operational phase. All the PALSAR modes, FBS (fine beam single), FBD (Fine beam dual), SCANSAR, DSN (band limited SAR), and POL (Full polarimetry) were calibrated and validated using in-total 500 calibration points collected world widely and distributed target data from the Amazon. Through the characterization of the PALSAR, antenna pattern determination, and polarimetric calibration, we performed the adjustments of the PALSAR radiometric and geometric model installed on the SAR processor (SIGMA-SAR). Using the reference points, we finally confirmed that the geometric accuracy of the FBS, FBD, DSN, and POL modes is 9.3 m, that of SCANSAR is 70 m, and radiometric accuracy is 0.64 dB. Polarimetric calibration was successful that amplitude balance of VV/HH is 0.025 dB and the phase balance is 0.32 degrees.

The Advanced Land Observing Satellite (ALOS, the nickname is “Daichi”) was successfully launched on January 24th, 2006, and it continuously working very well. This paper introduces the updated results of calibration for two optical sensors of ALOS i.e., the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2). PRISM consists of three panchromatic radiometers, and is used to derive a Digital Surface Model (DSM) with high spatial resolution, which is also an objective of the ALOS mission. The geometric calibration is important in generating a highly accurate DSM by stereo pair image of PRISM. The radiometric calibration is also important for PRISM as well as AVNIR-2. This paper describes the results of geometric and radiometric calibrations for PRISM and AVNIR-2 in the operational phase after initial calibration phase, which are including methodology of analysis and experiment, update parameters related to the absolute accuracy, stability evaluations during one and half years after the launch, and accuracy assessment plan during mission life of the ALOS.

PRISM carried at ALOS satellite is expected to generate worldwide topographic data in respects of its high resolution and stereoscopic observation. The algorithms for generating Digital Surface Model (DSM) and Ortho-Rectified Image (ORI) have been developed for those objectives in Earth Observation Research Center / Japan Aerospace Exploration Agency (EORC / JAXA). During first one year following the successful ALOS launch, the capabilities of the algorithm have been widely tested. In this paper, the performance analysis intermediate results of DSM and corresponding ORI processing are described. First, the geometric model analysis of PRISM sensor is presented with the experimental results of the orientation processing. Then, the performance analysis of DSM and ORI generated with the PRISM geometric model is presented. The accuracy assessment results of generated DSM are presented from the comparison with high accuracy and high resolution reference DSM data sets of LiDAR DSM and Aerial Photo DSM. The accuracy assessment results of generated ORI are presented from the comparison with GCP.

This paper discusses an ambiguity resolution of polarimetric calibration parameter. Since ALOS was successfully launched on January 24, 2006, the utilization of polarimetric data observed by PALSAR is expected for various remote sensing applications. Thus, polarimetric calibration becomes important issue. However, there is a possibility that calibration algorithm has a 180deg sign ambiguity with respect to cross-polarized components of a calibrated scattering matrix. The reason is that two polarimetric calibration parameter sets are derived. On the other hand, we found that a 180deg phase shift of cross-polarized component occurred due to the change of an angle between a direction parallel to a building wall and a radar line of sight in azimuth plane. Thus, we examined that this scattering property was applied for selecting a correct polarimetric calibration parameter set. The experimental result shows that urban structures can be used for polarimetric calibration.

This paper describes the initial results of the PALSAR characterization using the data acquired in the commissioning phase for the five modes of FBS, FBD, DSN, SCANSAR and POL, and using the active radar calibrator deployed at the calibration site in Japan. This paper also describes the radiometric and geometric performances of the PALSAR images shortly. This paper will end up with the parameters which should be evaluated and determined within the calibration phase.

The advanced land observing satellite (ALOS, the nickname is "Daichi") was successfully launched on January 24th, 2006. This paper introduces the initial results of calibration and validation for two optical sensors of ALOS i.e., the panchromatic remote-sensing instrument for stereo mapping (PRISM) and the advanced visible and near infrared radiometer type-2 (AVNIR-2). PRISM consists of three panchromatic radiometers, and is used to derive a digital surface model (DSM) with high spatial resolution, which is also an objective of the ALOS mission. The geometric calibration is important in generating a highly accurate DSM by stereo pair image of PRISM. The radiometric calibration is also important for PRISM as well as AVNIR-2. The relative radiometric calibration will be done using acquired images over homogeneous targets such as ocean, deserts, ice and snow areas and the nighttime observation. The absolute radiometric calibration will be carried out by the cross calibration using with calibrated satellite images i.e., MODIS, ASTER etc. In this paper, results of the first images acquisition and initial analysis for calibration and validation are described.

The purpose of this letter is to present the results on the study of searching effective parameters that describe the relation between high-resolution synthetic aperture radar (SAR) images and forest parameters. The study is based on the non-Gaussian texture analysis of the polarimetric airborne Pi-SAR data over coniferous forests in Hokkaido, Japan. The radar cross section (RCS) in terms of a forest biomass is first analyzed. It is found that the$hboxL$-band RCS increases steadily with the biomass and saturates at approximately 40 tons/ha. These results are similar to the previous studies. The probability density function of the image amplitude is then investigated, and among Rayleigh, log-normal, Weibull, and$K$-distributions, the$K$-distribution is found to fit best to the$hboxL$-band data of all polarizations, although the Weibull distribution fits equally well. Further, the correlation between the tree biomass and the order parameter of the$K$-distribution in the cross-polarization images is found to be very high, and the order parameter increases consistently with the biomass to approximately 100 tons/ha, which is well beyond the saturation limit of the$hboxL$-band RCS. Thus, the order parameter of the$K$-distribution can be a promising new parameter to estimate the forest biomass from high-resolution polarimetric SAR data in a much wider range than the conventional RCS method.

Biophysical parameters and L-band polarimetry synthetic aperture radar observation data were taken for 59 test sites in Tomakomai national forest, which is located in the northern part of Japan. Correlations between the derived sigmaHH0, sigmaHV0, and sigmaVV0and the biophysical parameters are investigated and yield the following results. 1) The above-ground biomass-sigma0curves saturate above 50 tons/ha for sigmaVV0, 100 tons/ha for sigmaHH0, and over 100 tons/ha for sigmaHV0when all forest species are included in the curves. 2) The sigmaHH0-above-ground biomass curve for one forest species indicates a higher saturation level than that for the other forest species. Dependence on the forest species was absent for VV polarization and low for HV polarization. 3) A simple three-component scattering model indicates that volume scattering accounts for 80%-90% when the above-ground biomass exceeds 50 tons/ha. The surface-scattering components are up to ~20% for young stands, and the volume-scattering components are down to 70%. The origin of the dependency among the forest species was examined for the sigmaHH0-above-ground biomass. It is concluded that a possible cause of the dependency is the different characteristics of the stands rather than forest species

This paper describes the updated status of the PALSAR CAL/VAL and scientific activity of 2005, which is being conducted as a part of the JAXA's ALOS science program. The updated information includes 1) Detailed schedule of the initial calibration phase, 2) Distribution of the calibration targets, 3) Calibration strategy (antenna pattern, polarimetric calibration, absolute calibration, image quality evaluation, noise equivalent sigma-naught), 4) PALSAR data format update, and 5) Yaw steering mode and observation scenario for polarimetry.

This paper introduces the updated plans of calibration and validation for two optical sensors i.e., the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2), which are to fly on the Advanced Land Observing Satellite (ALOS). PRISM consists of three panchromatic radio- meters, and is used to derive a Digital Elevation Model (DEM) or a Digital Surface Model (DSM) with very high spatial resolution, which is also an objective of the ALOS mission. The geometric calibration is important in generating a highly accurate DEM by stereo pair images of PRISM. The radiometric calibration is also important for PRISM as well as AVNIR-2. The relative radiometric calibration will be done using acquired images over homogeneous targets such as the nighttime observation, ocean, deserts, and ice lands. The absolute radiometric calibration will be carried out by vicarious calibration and the cross calibration using with calibrated satellite images i.e., ASTER, IKONOS etc. In this paper, test simulations of calibration tools are described using current satellite images. Keywords-ALOS; PRISM; AVNIR-2; Calibration; Validation.

The Advanced Land Observing Satellite (ALOS) has been developed to contribute to the fields of mapping, precise land coverage observation, disaster monitoring, and resource surveying and will be launched in 2005. The Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) carried at ALOS satellite is expected to generate worldwide Digital Maps in respects of its high resolution and stereoscopic observation. The development and evaluation of the software for generating digital surface model (DSM), based on this aim of ALOS operation, is now undertaking for the purpose of equipping the system which producing DSM semi-routinely in the Earth Observation Research and Application Center (EORC). In this paper, the statuses of pre-launch simulations and DSM assessment plans are described. PRISM consists of three panchromatic radiometers for forward, nadir, and backward views and acquires each image in the same orbit at almost the same time. It enables to generate more precision DSM frequently than usual stereoscopic observations. For this specific stereo image configuration, the triplet images matching algorithm is applied for the DSM generation. To evaluate the algorithm, three line scanner (TLS) data is used as the PRISM simulation data. The DSM generation using the JERS-1 OPS stereo images is being performed as the operational simulation. The basic functions of the software such as bundle adjustment, DSM mosaic, mask operation, etc. are evaluated in those simulations. The accuracy assessment of generated DSM is planned to be performed using the LIDAR DSM data, SRTM data, GCP etc.

The Advanced Land Observing Satellite (ALOS) is scheduled for launch by the Japan Aerospace Exploration Agency (JAXA) in 2005, and it carries three remote sensing instruments: an L-band polarimetric Synthetic Aperture Radar (PALSAR), an along-track 2.5 metre panchromatic resolution stereo mapper (PRISM) and a 10-metre multi-spectral scanner (AVNIR-2). The successor of the JERS-1 satellite (1992-1998), ALOS not only provides enhanced sensor performance, but also feature an entirely new acquisition concept. Abandoning traditional, local-focused instrument operations, JAXA is implementing a comprehensive acquisition strategy, in which geographical region, sensor mode, acquisition timing and repetition frequency, are fixed in advance, to achieve spatially and temporally consistent, global coverage on a repetitive basis, at the same time as reducing programming and user conflicts.

This paper describes generation of the digital elevation model (DEM) using the L band repeat pass airborne polarimetric Interferometric SAR (Pi-SAR) system, which is jointly developed by Japan Aerospace Exploration Agency (JAXA) and National Institute for Communication Technology (NICT). An image pair acquired over the Tottori calibration site in 2000 had a baseline of less than hundred meters and was used for this interferometric data analysis. Although the quantitative evaluation has not been conducted, the result shows that the detailed feature of the topography in the target area was well obtained.

Proposes a new method to calibrate L band polarimetric SAR using the forest data. Model based covariance matrix containing the distortion matrices and the scattering components from the forest were solved using the Pi-SAR data over the Japan uniform forest of Tomakomai, Hokkaido. The result showed that the distortion matrices depend on the function of the incidence angle and the operation timings.

This paper introduces the updated plans for sensor calibration and product validation of the two optical sensors i.e., the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2), which are to fly on the Advanced Land Observing Satellite (ALOS). PRISM consists of three panchromatic radiometers, and is used to derive Digital Elevation Models (DEMs) with very high spatial resolution, which is also one of the objectives of the ALOS mission. The geometric calibration is important in generating a highly accurate DEM by using PRISM's triplet images. Highly accurate ground control points (GCPs) are necessary to calibrate the geometric accuracy and validate the generated DEM. The geometric calibration of AVNIR-2 can be done using the calibrated PRISM product. In this paper, we describe the current plans for calibrating and validating PRISM and AVNIR-2. In particular, preparation status of GCPs with evaluation items for geometric calibration, including expected problem effects regarding geometric accuracy, orbit and observation simulations to determine pointing accuracy, and preliminary investigation of nighttime GCP using current available satellite images

The Advanced Land Observing Satellite (ALOS) has been developed to contribute to the fields of mapping, precise land coverage observation, disaster monitoring, and resource surveying. The Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), carried on ALOS satellite is expected to generate worldwide Digital Maps in respects of its high resolution and stereoscopic observation. The development and evaluation of the software for generating Digital Elevation Model (DEM), based on this aim of ALOS operation, is now undertaking for the purpose of equipping the system which producing DEM semi-routinely in the Earth Observation Research and application Center (EORC). In this paper, the software's outline of functions, the algorithm and the current status of its development and evaluation are described. PRISM consists of three panchromatic radiometers for forward, nadir, and backward views and acquires each image in the same orbit at almost the same time. It enables to generate more precision DEM frequently than usual stereoscopic observations. For this specific stereo image configuration, the triplet images matching algorithm is applied for the DEM generation. To evaluate the algorithm, Three Line Scanner (TLS) data is used as the PRISM simulation data and the Lider DEM is used as the reference data. The absolute accuracy of generated DEM is estimated in this simulation

We examined relationship between forest parameters and /spl sigma//sup 0/ derived by airborne multi-polarization SAR (PiSAR). PiSAR observations and field measurements were simultaneously performed on November 2002 and August 2003. One data set was taken from stands with pure pine (Picea glehnii Masters) forest. More than 70% of the trees in biomass are Picea glehnii Masters in the data set with same stand age. The other data set was taken from the stands where mixture of species is proceeding in some degree. The former data set shows more tight correlations compared to the other and correlation coefficient reaches 0.98. We conclude that (1) the mixture of the species and stand age are two of the major reasons of data scattering between forest parameters and /spl sigma//sup 0/, and (2) the above ground biomass saturation level is about /spl sim/150 tons/ha for L-band /spl sigma//sub HH//sup 0/ and /spl sigma//sub HV//sup 0/ and /spl sim/70 tons/ha for /spl sigma//sub VV//sup 0/ in this akaezomatsu site, using a biomass expansion factor of 1.36 to convert from the trunk biomass to the above-ground biomass.

We used multi-sensor, multi-frequency and multi-polarization SAR data for biophysical parameter retrieval in plantation forests of Northern Japan. The statistical relationships with different biophysical parameters are quite robust for certain frequencies-polarization combination. A combination of different frequencies and polarizations facilitate the retrieval of these parameters with R 2 of 0.95 and rms error of 15.19 tons ha -1 .

The Kyoto & Carbon Initiative is an international collaboration initialised by NASDA in 2000 [1], set out to support dataand information needs raised by certain multinational environmental conventions and by global carbon cycle science, through provision of data products and high level information derived from ALOS, JERS-1 and ADEOS-II data. The conventions primarily in focus are the UNFCCC Kyoto Protocol and the Ramsar Convention on Wetlands, to which fineresolution, multi-scale information about the status and changes in forests and wetlands will be derived. There is apparent synergy with terrestrial carbon cycle science information needs, where improved spatial information about carbon pools, sources and sinks at local, regional to global scales are of high priority. Support to the UN Millennium Development goal on water access, as well as to the UN Convention to Combat Desertification, are

The Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM) sensor that will be carried on the Advanced Land Observing Satellite (ALOS) includes creation of digital elevation maps as one of its purposes. PRISM is capable of high spatial resolution and along-track stereoscopic observation. In order to provide software to create a Digital Elevation Model (DEM) semi-routinely at the Earth Observation Research Center (EORC), development of a DEM generation algorithm and software was carried out. The authors considered the basic function of DEM generation software, various processing algorithms for DEM generation were examined, and a trial production and evaluation which used test data was performed This paper reports the resulting processing method, and evaluation of results, of DEM generated from test data consisting of the nadir and forward images from JERS-1/OPS.

This paper presents the current calibration plan of the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2). Radiometric calibration methods based on internal calibration lamps and Earth-observed data are outlined. Several areas for surface radiometric reference are selected on the Asian and North American continents and in the Pacific Ocean. Vicarious calibration is planned in these areas. For geometric calibration, concerns about the pointing function of AVNIR-2 and the complementary method by the simultaneous use of Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) am outlined.

Several research products using the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) onboard Advanced Land Observing Satellite (ALOS) are planned to be produced in the Earth Observation Research Center, National Space Development Agency of Japan (NASDA EORC). The products are snow cover maps albedo maps, land cover maps, land cover classification maps, vegetation classification maps, and so on. These products are to be produced in combination with the Digital Elevation Model (DEM) derived from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), and to be mainly used for local to regional scale land applications. The outline and the methods to produce each product are introduced in this paper.

This paper introduces preliminary CAL/VAL of the Phased Array type of L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) in terms of the antenna pattern error distribution and the image correlation using the PALSAR parameters. More CAL VAL will be conducted to ensure the applicable potential of PALSAR to the Earth environmental monitoring, e.g., the disaster, forest (biomass), land surface classification, DEM generation capability, and so on.

Forest biomass is one of the key parameters that indicate temporal change of the global continental carbon volumes, and its global estimation is required as described in the Kyoto protocol. NASDA's ALOS science project strongly intends its estimation either solely by SAR or in conjunction with the other optical sensors and the ground truth data. There have been (or are being) developed SAR based methods to estimate the forest biomass, which utilize the relationship between sigma-zero and biomass quantity, those based on coherence of one day separated C band data and L band radar intensity, and those based on forest tree heights and biomass quantities, etc. For the third category, it is expected that Pol-In-SAR might relate the interferometric phase difference at different polarizations to the tree structures and heights, and two-frequency interferometry relates to the phase center difference to the tree height.

Develops an algorithm for estimating snow parameter distributions using synthetic aperture radar (SAR) images. The study area is a part of the Hokuriku District of Japan that is well known for having wet snow from the beginning of the winter season. We develop an algorithm to estimate snow density and roughness distributions using RADARSAT images acquired in February. Furthermore, we show the possibility of inferring snow depths from combined parameters estimated with an L-band SAR image.

Introduces the current plans for calibrating and validating the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) aboard the Advanced Land Observing Satellite (ALOS), which will be launched in 2004. PRISM will be used to derive a Digital Elevation Model (DEM) with very high spatial resolution. That also is one of the objectives of the ALOS mission. In order to do so, PRISM consists of three panchromatic radiometers for forward, nadir and backward views and acquires images in the same orbit and at almost the same time. More calibration and validation will be conducted to ensure high accuracy of the DEM.