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The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud. [ABSTRACT FROM AUTHOR]

The matrix inversion method (MIM) is an effective algorithm for estimating water constituent concentrations in case II waters. To apply this method, appropriate and accurate specific inherent optical properties (SIOPs) for each constituent in water are essential. However, many routine observations of lake water quality do not in fact provide SIOPs, thus limiting the application of the MIM. In this paper, an alternative MIM method based on linear matrix inversion theory was proposed to relax the requirement of SIOPs measurement. For this, so-called ESIOPs (Estimated SIOPs) were first derived by an unusual application of MIM based on adequate calibration samples; then the water constituent concentrations for the whole study area were retrieved by the standard application of MIM based on the derived ESIOPs. For each calibration sample, measurement of the reflectance spectrum and corresponding water constituent concentrations, which can be obtained from periodical satellite data and routine field surveys, is required. The performance of the proposed method was evaluated using the simulation data from Hydrolight and three MEdium Resolution Imaging Spectrometer Instrument (MERIS) images. The results showed that this method yielded satisfactory estimations of the water constituent concentrations for the noise-contaminated simulation data sets. For MERIS data in our study area (Lake Kasumigaura, Japan), the average bias (mean normalized bias or MNB) and relative random uncertainty (normalized root mean square error, or NRMS) were in the range of -11.2% to 3.4% and 4.8% to 29.7% for each water constituent concentration. These findings imply that the algorithm proposed in this study is theoretically reasonable and practically applicable. [ABSTRACT FROM AUTHOR]

In our previous regression model for estimating forest biomass, it was shown that non-Gaussian amplitude fluctuations in high-resolution polarimetric synthetic aperture radar (SAR) data of coniferous forests can accurately be described by the K-distribution and that the order parameter of the K-distribution can be useful in estimating the tree biomass of coniferous forests from L-band cross-polarization amplitude images in a wider range than the conventional method using the radar cross section alone. The result was based on the analysis of the "ground-truth" biomass data of 19 forest stands and airborne polarimetric interferometric SAR L-band data over the Tomakomai forests in Hokkaido, Japan. From this relation, an empirical regression model was developed to estimate forest biomass from SAR data. In this paper, we report the results on further analyses of this regression model. The validity of the K-distribution is first reconfirmed using the Akaike information criterion, followed by the description on the accuracy of the model. To examine model accuracy, we carried out further field measurements on 22 forest stands in 2005, and the ground survey was made in 2006 to find out the causes of several anomalous data. Based on a comparison of the model-based biomass and the ground-truth data, the accuracy of the model was found to be approximately 86%. The regression model was then updated for practical application in estimating the biomass of the Hokkaido forests by including the ground-truth data of all 41 forest stands. [ABSTRACT FROM AUTHOR]

Wind retrieval from Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) using an L-band model function in coastal regions is evaluated. It is known that JERS-1 SAR has excessive ambiguities. This paper also gives a quantitative evaluation of excessive ambiguities in coastal scenes of JERS-1 SAR. First, focusing on the cases where wind blows from the shore in Sagami Bay, we investigate phenomena of wind speed increase with offshore distance using European Remote sensing Satellite-1 (ERS-1) SAR-derived wind speeds. The relation between wind speed and offshore distance is well formulated, which indicates the transition of the atmospheric boundary layer from land to sea surface. Wind speeds derived from JERS-1 SAR should be overestimated due to the excessive ambiguity. Then, for observation time of each JERS-1 SAR capturing the cases that wind blows from the shore in Sagami Bay, the expected wind speed growth profile is derived from the wind speed growth formula and an in situ wind observation of Hiratsuka Experiment Station. We convert the wind-speed profile into the sigma-0 profile by an L-band model function. Finally, the profiles of JERS-1 SAR-observed and the estimated sigma-0 are compared, and the excessive ambiguity is estimated as the difference between them. As a result, the dynamic range of first azimuth ambiguity is as large as that of the wind-relating signal from the ocean surface. Moreover, higher order azimuth ambiguities and range ambiguity also may have a significant impact on near-shore wind retrieval. Ambiguities, Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR), L-band model function, synthetic aperture radar (SAR) wind retrieval. [ABSTRACT FROM AUTHOR]