Modeling in Remote Sensing

Modeling in Remote Sensing (MIRS)


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 electromagnetic (e.g., scattering, absorption, emission, optical bidirectional reflectance distribution function, dielectric properties, etc.) attributes and then predict for a given remote sensing instrument the resulting observation.


The MIRS Technical Committee encourages participation from all its members. The committee organization includes the Chair and two Co-Chairs.

MIRS Technical Committee Chair

Nazzareno Pierdicca

Sapienza University of Rome Italy

MIRS Technical Committee Co-Chair

Jean Phillippe Gastellu-Etchegorry

MIRS Technical Committee Co-Chair

Dr. Rob Sundberg
Spectral Sciences Inc.

MIRS Technical Committee Co-Chair

Tianlin Wang
The Ohio State University

Working Group on GNSS-R


Dr. Davide Comite
Sapienza University of Rome


Dr. James Campbell
University of Southern California

Call for Models

If you have any models or codes that you wish to share with the community, please send an email to Sharmila Padmanabhan.

JSTARS Special Issue

A special issue of JSTARS was recently published with papers related to the modeling and simulation of remote sensing data. Read this issue


Links to some models 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 Sharmila Padmanabhan.

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 the 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,

Models from the Chinese Academy of Sciences

The Discrete Anisotropic Radiative Transfer Model: An efficient model for environmental studies from space. Satellite and airborne optical sensors are increasingly used by scientists, policymakers, 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.

Current membership (as of February 2021)


You can contact the Committee Chairs by email at

Membership in the MIRS Technical Committee is open to anyone interested in issues related to modeling for remote sensing. IEEE Geoscience and Remote Society membership is encouraged, but not required to join the MIRS Technical Committee. Join the MIRS Technical Committee!

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