梶原 康司

We conducted a high-resolution aeromagnetic survey using an autonomously driven uncrewed helicopter that flew as low as several tens of meters above the ground along precise flight tracks with 1 m accuracy. The geomagnetic total intensity was measured by a total intensity magnetometer suspended beneath the helicopter at a ~ 50 m or less flight spacing over the entire caldera of Mt. Mihara, located on Izu-Oshima Island, Japan. From the observed geomagnetic data, we estimated high-resolution subsurface magnetization intensity. A high average magnetization intensity of 13.5 A/m was obtained for the entire caldera. The distribution of the magnetization intensity was not only consistent with the results of conventional airborne surveys, but it also had a high spatial resolution of less than 100 m. Highly magnetized areas were observed along the NW–SE lines that intersected the summit pit crater, Crater A, which is consistent with the principal stress direction of Izu-Oshima Island. These highly magnetized areas might be solidified magma that did not reach the surface during past eruptions. A large and deep-rooted weakly magnetized area was found just outside of the NE side of the central cone, which corresponds to the location of Fissure B, and the conduit must have been demagnetized at the previous event. Other weakly magnetized areas were also observed at the N, E, and SW sides around the pit crater. These regions correspond to the location of fumaroles in the crater. The high-resolution subsurface magnetization imaged by the autonomous uncrewed helicopter will be helpful for the mitigation of future eruption damage by enabling the assessment of potential fissure eruption areas.

Abstract. This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research.

In situ accurate data sets of leaf area index (LAI), above-ground biomass (AGB), and fraction of absorbed photosynthetically active radiation (fAPAR) are indispensable to validate and improve ecological products obtained from satellites. In situ data for satellite validation must be created not from a single-point data but from areal data (such as multiple-points data) representing a satellite footprint. Using multiple-points data, the error of in situ data can be calculated statistically. The quantification of the error in the in situ data enables us to evaluate the discrepancy between the satellites' products and the in situ data as the error in the in situ data and the estimation error in the products separately. Besides, the accuracy of the in situ data is required to be much higher than the accuracy of the satellite products which was officially set. To obtain such in situ data, we have established observation sites for typical land cover types in East Asia, from temperate to cool ecosystems: deciduous needle-leaved forest (DNF), evergreen needle-leaved forest (ENF), deciduous broad-leaved forest (DBF), and grassland (GL). We conducted the observations in 500 m x 500 m areas, which is the footprint scale of the Global Change Observation Mission-Climate satellite. In this paper, the data of LAI, AGB, and fAPAR observed at DNF, DBF, and GL (i.e., except at ENF) are reported. These data are useful even for the validation of other satellite products, especially with higher spatial resolution. Also, the long-term tree census data from 2005 to 2018 at DNF are reported. The complete data set for this abstract published in the Data Paper section of the journal is available in electronic format in MetaCat in JaLTER at .

To validate and to improve ecological products obtained from satellites, such as a leaf area index (LAI), above-ground biomass (AGB), and a fraction of photosynthetically active radiation (fAPAR), in-situ accurate data are indispensable. They must be not a single point-data but an areal data representing the satellite footprint. Their accuracy needs to be much higher than the required accuracy for the satellite products. The quantitative assessment of their error is necessary for evaluating the satellite products' error from the discrepancy between the satellite products and the in-situ data. However, such data had not been available. In particular, there had been few data of LAI in a sparse evergreen needle-leaved forest, because of difficulty of accuracy control of in-situ observation in such a forest. To overcome the difficulty and to obtain the representative LAI, we made an allometric equation to estimate the leaf mass of Picea glehnii in northern Hokkaido. We report the allometric equations of leaf mass and AGB of P. glehnii, its leaf mass per area (LMA), its leaf life span, its leaf distribution, its crown shapes, its wood specific gravity, and tree locations. We also report LAI, AGB, and fAPAR within the 500 m x 500 m area, which is the footprint scale of the Global Change Observation Mission-Climate satellite, in a pure and sparse forest of P. glehnii in northern Hokkaido. These precise data are useful for validation of other satellite data, especially with higher spatial resolution, and forest structure modeling.

<p>In this paper, we present an algorithm for estimating terrestrial albedo for the product of Global Change Observation Mission-Climate (GCOM-C)/Second generation Global Imager (SGLI), that was launched in Dec. 2017 by Japan Aerospace Exploration Agency (JAXA), Japan. The algorithm is composed of spectral albedo estimation, narrowband-to-broadband albedo conversion and multi-regression model estimation so that only a single-day reflectance observation is available. In estimating spectral albedo, we derives coefficients of kernel-driven bidirectional reflectance distribution function (BRDF) model. The experiments by using in-situ data of bare soil and deciduous broadleaf forests show that the proposed method have potential to estimating albedos with acceptable accuracy of the root mean square of errors (RMSE) of 2.2×10^-2 and 4.3×10^-2.</p>