Makiko Hashimoto
Japan Aerospace Exploration Agency, Matsushiro Seismological Observatory
Papers
5
Total Citations
192
H-Index
5
About
Makiko Hashimoto is a remote sensing scientist whose research centers on atmospheric aerosol characterization, satellite algorithm development, and radiative transfer modeling. Her work has made significant contributions to the field of aerosol optical property retrieval, particularly from spaceborne and ground-based instruments over both ocean and land surfaces. Hashimoto's most influential contribution is her development of a neural network-based radiative transfer algorithm for the GOSAT-2/CAI-2 satellite, which enables fast yet flexible retrieval of aerosol optical properties over water — a technically demanding achievement that has garnered 66 citations since its 2020 publication. Complementing this, her multi-wavelength and multi-pixel retrieval frameworks, published in 2017 and 2019, introduced innovative approaches that exploit spatial inhomogeneity in surface reflectance to improve urban and oceanic aerosol estimates, collectively accumulating over 60 citations. Her 2016 comparative study of SKYNET and AERONET aerosol single scattering albedo measurements — cited 44 times — has become an important reference for understanding systematic differences between two globally used ground-based networks. Hashimoto has also demonstrated expertise in simultaneous ocean color and aerosol retrieval, reflecting a sophisticated understanding of coupled atmosphere-ocean radiative systems that continues to advance satellite-based environmental monitoring.
Research Focus
Key Achievements
Top Papers
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