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Center for Remote Sensing

Center for Remote Sensing

About the Center for Remote Sensing

Originally established in 1982 as the Remote Sensing Applications Laboratory (RSAL) at the Agricultural and Biological Engineering Department of the University of Florida, the Center for Remote Sensing (CRS) was established in 1998 as an Institute of Food and Agricultural Sciences (IFAS) center by the late Dr. Sun-Fu Shih. The funding for the center was provided by the "Taiwan R.O.C. Fund" from the Chinese Taipei Committee, International Commission on Irrigation and Drainage, Taipei, R.O.C. The CRS plays a major role in helping to improve agricultural production and conserve natural resources in Florida, through research and application of remote sensing and related technologies.

  • To develop techniques for improving image processing and classification.
  • To develop models for improving satellite imagery use in studies involving water, environment, and agriculture.
  • To calibrate and evaluate the performance of different remote sensing sensors and their application in solving problems.
  • To develop models for the georeferencing of satellite imagery and its integration into a Geographical Information System (GIS).
  • Training programs involving remote sensing and GIS.

The research and teaching at the CRS involves applying state-of-the-art remote sensing, GIS, and GPS technologies to hydrology, agriculture, and natural resources. The research is highly interdisciplinary and collaborative involving experts from several disciplinary fields.

For example, remotely sensed observations in the Visible and Infrared region of the Electromagnetic Spectrum are being used to conduct land cover and hydrologic change analyses, to generate crop yield maps, to improve crop management, and to identify and monitor invasive species. The observations in the Microwave region are being used to understand the soil water dynamics, including evapotranspiration in cropping systems, to improve predictions from hydrological and crop growth models via data assimilation. We utilize remotely sensed observations from ground-based, airborne, or satellite-based sensors. We conduct intensive field campaigns and use the datasets to develop and test our models.


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