Shinji Matsumura

About

Shinji Matsumura is a leading figure in satellite-based atmospheric remote sensing, with a focused expertise in aerosol optical depth (AOD) retrieval over land. His work is central to improving the accuracy of greenhouse gas monitoring by addressing the critical challenge of cloud and aerosol interference. Matsumura’s major contributions include the development of two pioneering algorithms for the GOSAT TANSO-CAI sensor: a Modified Aerosol Free Vegetation Index (AFVI) algorithm and a Dark Target (DT) algorithm. These innovations enable precise AOD retrieval from the Cloud and Aerosol Imager, directly enhancing the quality of data from the Greenhouse Gases Observing Satellite (GOSAT). His most cited papers—"A Modified Aerosol Free Vegetation Index Algorithm..." (2016, 6 citations) and "A Dark Target Algorithm..." (2017, 5 citations)—are foundational references in the field, demonstrating a clear and growing impact. By refining how aerosols are detected and corrected over land, Matsumura’s work supports more accurate global carbon cycle studies and climate modeling, marking him as a key contributor to advancing satellite-based environmental observation.

Research Focus

Key Achievements

Top Papers

1. 1
   A Modified Aerosol Free Vegetation Index Algorithm for Aerosol Optical Depth Retrieval Using GOSAT TANSO-CAI Data

   6 citations · 2016
2. 2
   A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

   5 citations · 2017

Key Collaborators

Contact & Links