• Overview

    gsistcMISSION: The mission of the Modeling in Remote Sensing Technical Committee (MIRS TC) is to serve as a
    technical and professional forum for advancing the science of predicting remotely sensed observations from first principles theory.

    The MIRS TC addresses the technical space between basic electromagnetic theory and data collected by remote sensing instruments. It focuses on models and techniques used to take geometric, volumetric
    and material composition descriptions of a scene along with their EM (e.g., scattering, absorption, emission, optical BRDF, dielectric properties, etc.) attributes and then predict for a given remote sensing instrument the resulting observation.

    To join this Committee, use this form.

    Contact Information

    Prof. Jiancheng Shi, Chair
    Director: State Key Laboratory for Remote Sensing Science
    Institute of Remote Sensing and Digital Earth
    Beijing, China
    E-Mail: shijc@radi.ac.cn

    Prof. Joel Johnson, Co-Chair
    The Ohio State University
    Department of Electrical and Computer Engineering
    205 Dreese Laboratories
    2015 Neil Ave
    Columbus, OH 43210
    Phone: 614-292-1593
    FAX: 614-292-7297
    E-Mail: johnson@ece.osu.edu

    Dr. John Kerekes
    Rochester Institute of Technology
    54 Lomb Memorial Dr.
    Rochester, NY 14623
    Phone: 585-475-6996
    FAX: 585-475-5988
    E-Mail: kerekes@cis.rit.edu

  • Activities

    Call for Models

    If you have any models or codes that you wish to share with the community, please send an email to: webmaster@grss-ieee.org.


    JSTARS Special Issue

    A special issue of JSTARS was recently published with papers related to Modeling and Simulation of Remote Sensing Data.
    Read this issue

  • Members

    Current membership (as of July 2018):

    Last Name First Name Affiliation Country
    Ali Mustak ASTEC/ARSAC India
    Amorer H. Emanuel Universidade Estadual de Campinas – Instituto de Geociências Brazil
    Bamler Richard German Aerospace Center (DLR), Earth Observation Center (EOC), Remote Sensing Technology Institute Germany
    Benson Michael Univ of Michigan USA
    Berk Alexander Spectral Sciences, Inc USA
    Bhattacharya Avik Indian Institute of Technology Bombay India
    Bindlish Rajat NASA GSFC USA
    Bonafoni Stefania University of Perugia Italy
    Brown Scott Rochester Inst. Technology USA
    Burgin Mariko S NASA – Jet Propulsion Laboratory USA
    Cao Biao State Key Laboratory for Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences China
    Chen Haonan Colorado State University USA
    Chen Kunshan State Key Laboratory for Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences China
    Di Martino Gerardo University of Naples Federico II Italy
    Du Yang Zhejiang Univ. China
    Dutsenwai Hafsat Saleh Universiti Teknologi Malaysia Malaysia
    Gaikwad Sandeep Marathwada University Aurangabad India
    Gastellu-Etchegorry Jean Philippe CESBIO, CNES France
    Hajnsek Irena DLR-German Aerospace Center Germany
    Hallikainen Martti Aalto University Finland
    Hernandez Emanuel Amorer Inst. Geosciences, State Univ. of Campinas, UNICAMP Brazil
    Jiao Ziti Beijing Normal University China
    Jiang Lingmei Beijing Normal University China
    Johnson Joel T. Ohio State Univ. USA
    Kelly Richard University of Waterloo Canada
    Kerekes John Rochester Inst. Technology USA
    Kim Yonghyun Seoul National University South Korea
    Kleynhans Waldo Council for Scientific and Industrial Research South Africa
    Kumar Vineet Indian Institute of Technology Bombay India
    Kurum Mehmet Mississippi State University USA
    Lang Roger The George Washington University USA
    Lévesque Josée Valcartier Research Center Canada
    Liang Shunlin Univ. of Maryland USA
    Liu Qinhuo Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences China
    Lu Hui Tsinghua University China
    Mallenahalli Naresh Kumar National Remote Sensing Centre (ISRO) India
    Mandal Dipankar Indian Institute of Technology, Bombay India
    Moghaddam Mahta Univ of Southern California USA
    Monsivais-Huertero Alejandro Instituto Politecnico Nacional, Mexico Mexico
    Mu Xihan Beijing Normal University China
    Ni Wenjian Chinese Academy of Sciences China
    Padmanabhan Sharmila JPL USA
    Panditrao Satej NRSC India
    Pierce Leland The Univ. of Michigan USA
    Richtsmeier Steven Spectral Sciences, Inc. USA
    Rother Tom German Aerospace Center (DLR), Remote Sensing Technology Institute Germany
    Saha Arnab National Institute of Hydrology, Roorkee India
    Salam Abdul University of Nebraska-Lincoln USA
    Sarabandi Kamal The University of Michigan USA
    Schaepman Michael E. Univ. of Zurich Switzerland
    Shi Jianchen State Key Laboratory for Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences China
    Song Jinling Beijing Normal University China
    Sundberg Robert Spectral Sciences, Inc. USA
    Trautmann Thomas German Aerospace Center (DLR), Remote Sensing Technology Institute Germany
    Tsang Leung The University of Michigan USA
    Upadhyay Ashish Indian Institute of Public Health – Gandhinagar India
    van den Bosch Jeannette Air Force Research Laboratory USA
    Xu Xiaolan JPL USA
    Yan Guangjian State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University China
    Yin Tiangang Singapore-MIT Alliance for Research and Technology Singapore
    3/15/2018 5:38:02 Mustak Ali ASTEC/ARSAC mustakali25@gmail.com
  • Models

    Links to some models we have found that involve modeling of remotely-sensed data using physics-based modeling of environments on the Earth.
    If you know of models that should be included here, please email: webmaster@grss-ieee.org

    • PolSARPro
      The Polarimetric SAR Data Processing and Educational Tool aims to facilitate the accessibility and exploitation of multi-polarized SAR datasets.
      The combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL, has been used for about sixteen years to study plant canopy spectral and directional reflectance in the solar domain. PROSAIL has also been used to develop new methods for retrieval of vegetation biophysical properties. It links the spectral variation of canopy reflectance, which is mainly related to leaf biochemical contents, with its directional variation, which is primarily related to canopy architecture and soil/vegetation contrast. This link is key to simultaneous estimation of canopy biophysical/structural variables for applications in agriculture, plant physiology, and ecology at different scales. PROSAIL has become one of the most popular radiative transfer tools due to its ease of use, general robustness, and consistent validation by lab/field/space experiments over the years.
    • Models from the Chinese Academy of Sciences