• 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

    Dr. Sharmila Padmanabhan
    Modeling in Remote Sensing Technical Committee Chair
    Pasadena, California, USA
    EMail: sharmila.padmanabhan@ieee.org

    Jean Phillippe Gastellu-Etchegorry
    Modeling in Remote Sensing Technical Committee Co-Chair
    Toulouse, France
    EMail: jean-philippe.gastellu@iut-tlse3.fr

    Dr. Rob Sundberg
    Spectral Sciences Inc.
    Burlington, Massachusetts, USA
    EMail: rob@spectral.com

  • 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 February 2020):

    Last Name First Name Affiliation Country
    Sunita Amity university, noida Inida
    Adams Ian S. NASA Goddard Space Flight Center USA
    Ahmad Touseef Space Applications Centre Ahmedabad India
    Aksoy Mustafa University at Albany, State University of New York USA
    Al-Khaldi Mohammad ElectroScience Laboratory, Department of Electrical and Computer Engineering, The Ohio State University USA
    Ali Mustak ASTEC/ARSAC India
    Alkliaffan AlAnoud Arabian Gulf University Bahrain
    Almendra Laura
    Amorer H. Emanuel Universidade Estadual de Campinas – Instituto de Geociências Brazil
    Awuviri Alexander RFMICROTEK Ghana
    Balakhder Ahmed ElectroScience Laboratory, The Ohio State University United States
    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
    Boopathi Nithyaprij IITB-MonaXXX Belarus
    Brown Scott Rochester Inst. Technology USA
    Buono Andrea
    Burgin Mariko S NASA – Jet Propulsion Laboratory USA
    Campbell James University of Southern California United States of America
    Cao Biao State Key Laboratory for Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences China
    Carlier Simon Aalto University Finland
    Chakichi Swastika Sikkim Institute India
    Chandrasekar Colorado State University USA
    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
    Comite Davide Sapienza University of Rome Italia
    Comite Davide Sapienza University of Rome Italy
    Corcione Valeria
    De Rosnay Patricia
    Di Martino Gerardo University of Naples Federico II Italy
    Doctor Katharina NRESEARCH LAB Unknown
    Donnellan Andrea JPL USA
    Du Yang Zhejiang Univ. China
    Dutsenwai Hafsat Saleh Universiti Teknologi Malaysia Malaysia
    Ewe Hong Tat Universiti Tunku Abdul Rahman Malaysia
    Faroo Adnan
    Gaikwad Sandeep Marathwada University Aurangabad India
    Gastellu-Etchegorry Jean Philippe CESBIO, CNES France
    Guerriero Leila Tor Vergata University of Rome Italy
    Hajnsek Irena DLR-German Aerospace Center Germany
    Hallikainen Martti Aalto University Finland
    Hamzeh Saeid
    He Lei Chengdu University China
    He Wei Nanjing Agriculture University China
    Hernandez Emanuel Amorer Inst. Geosciences, State Univ. of Campinas, UNICAMP Brazil
    Huang Miaofen Guangdon University China
    Jagdhuber Thomas German Aerospace Center Germany
    Jain Mawal IIT-Rorkee India
    Jiang Lingmei Beijing Normal University China
    Jiao Ziti Beijing Normal University China
    Jiny Yuwei UESTC China
    Johnson Joel T. Ohio State Univ. USA
    Jordi Cortes Andrei
    Kanwal Shamsa
    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
    Kurum Mehmet Mississippi State University USA
    LI Weiqiang Institute of Space Sciences (ICE, CSIC) Spain
    Lan Yang Xidian University China
    Lang Roger The George Washington University USA
    Li Aija Beijing University of mining and technology China
    Li Lele Ocean University of China China
    Liang Shunlin Univ. of Maryland USA
    Liew Soo Chin National Univ. of Singapore Singapore
    Liu Qinhuo Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences China
    Lombardini Fabrizio University of Pisa Italy
    Lopez Carlos
    Loria Eric The Ohio State University United States
    Lu Hui Tsinghua University China
    Lu Yim Nanjing Agriculture University China
    Lévesque Josée Valcartier Research Center Canada
    Malenovsky Zbynek University of Tasmania Czech Republic
    Mallenahalli Naresh Kumar National Remote Sensing Centre (ISRO) India
    Mandal Dipankar Indian Institute of Technology, Bombay India
    Massetti Andrea
    Menan Vineithe University of Alabama USA
    Meng Zhiguao Italian University Italy
    Miao Yuanjing National Space Science Center, Chinese Academy of Sciences China
    Miao Hongli Ocean University of China China
    Migliaccio Maurizio Universita di Napoli Parthenope Italy
    Mofokeng Olga University of Free State, RSA South Africa
    Moghaddam Mahta Univ of Southern California USA
    Monsivais-Huertero Alejandro Instituto Politecnico Nacional, Mexico Mexico
    Mu Xihan Beijing Normal University China
    Murugan Deepak
    Nguyen Thanh Huy Université Laval Canada
    Ni Wenjian Chinese Academy of Sciences China
    Nouha Mezned University of Tunis ElManar Tunisia
    Olioso Albert INRIA France
    Oliveira Julianne Unicamp Brazil
    Padmanabhan Sharmila JPL USA
    Panditrao Satej NRSC India
    Pepe Giorgio High School Rummo Unknown
    Perez Lluis
    Pierce Leland The Univ. of Michigan USA
    Qin Han Lin
    Ramachandran Nareen
    Ratha Debanshu India Institute of Technology, Bombay India
    Ribó Serni Institute of Space Sciences (ICE,CSIC-IEEC) Spain
    Riccio Daniele University of Napoli Federico II Italy
    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
    Singh Sarvesh UNSH Australia
    Singh Gulab IIT Bombay India
    Singh Dharmandra IIT Rorkee India
    Smolander Tuomo
    Song Jinling Beijing Normal University China
    Stiefel Avi ISI Israel
    Sun Weng Shanghai Martitime University China
    Sundberg Robert Spectral Sciences, Inc. USA
    Tadoyu Takeo JAXA Japan
    Tan Wenjie Peking University China
    Tessari Giulia
    Tomi Atsushi Baruch College USA
    Tong linging University of Elec. Scienc.and Tech China
    Trautmann Thomas German Aerospace Center (DLR), Remote Sensing Technology Institute Germany
    Tsang Leung The University of Michigan USA
    Ullo Silvia L University of Sannio Italy
    Upadhyay Ashish Indian Institute of Public Health – Gandhinagar India
    Vicent-Servera Jorge Magellium Spain
    Wagner Otto
    Wang Tianlin University of Michigan USA
    Wang Shanshan Ocean University of China China
    Werner Charles GammaRemoteSensing Switzerland
    Wijesundara Shanka The Ohio State University United States
    Xu Xiaolan JPL USA
    Xu Xingou National Space Science Center, Chinese Academy of Sciences China
    Yan Guangjian State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University China
    Yang Siq Peking University China
    Yin Tiangang Singapore-MIT Alliance for Research and Technology Singapore
    Yu Wentas Radi Cas China
    Zhang Jing Capital Normal University China
    Zhao Feng Beihang University China
    Zhiguo Pang Chinese Inst. Of Water Resources China
    Zhin Xiaoling Nanjing Agriculture University China
    Zoppetti Claudia UNISI Italy
    bouzekri abdelhafid university of constantine Algeria
    kushwaha sunni kanta prasad Indian Institute of Technology – Roorkee India
    van den Bosch Jeannette Air Force Research Laboratory USA
  • 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.
    • Toolbox for Land Surface Temperature retrieval from Landsat 5, 7, and 8
      An ArcGis toolbox with 49 individual models was generated in the ModelBuilder for automated land surface temperature (LST) retrieval using different retrieval algorithms and land surface emissivity (LSE) models. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA), and Split Window Algorithm (SWA) are implemented as LST retrieval methods to process data of Landsat missions (Landsat 5, 7 and 8). Different Normalized Difference Vegetation Index (NDVI)-based LSE models are used. The toolbox consists of three main parts with reference to the three Landsat missions, and each mission was categorized considering different LSE models for LST retrieval methods. Furthermore, if users have their own LSE image, they can also use this toolbox by selecting the external LSE model for each Landsat mission. The full explanation of the Toolbox can be found in the open access paper:
      Sekertekin, A.; Bonafoni, S., “Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation.” Remote Sens. 2020, 12, 294, https://doi.org/10.3390/rs12020294
    • Models from the Chinese Academy of Sciences
    • DART
      The Discrete Anisotropic Radiative Transfer Model: An efficient model for environmental studies from space.
      Satellite and airborne optical sensors are increasingly used by scientists, policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification.