A challenge for high spatial, spectral, and temporal resolution data fusion

Abstract

The Remote Sensing Technologies Center (RSTC) at Mississippi State University (MSU) is investigating the application of high-resolution imagery in the areas of precision agriculture, forest resource management, and environmental assessments of transportation systems. For precision agriculture, the focus of the research is to develop the expertise to utilize this imagery to facilitate management decisions at the farm level. Decision making at the farm level requires high spatial resolution imagery (on the order of 1 meter or less) in order to develop prescriptions for nutrient supplements, integrated pest management (insects, weeds), irrigation, and to serve as a harvesting aid. This degree of analysis of the plant and soil also requires that higher spectral resolution be available to ascertain the nuances in the target vegetation's reflectance and to be able to relate this to specific agricultural problems. In addition, decisions at the farm level must be made within a few days of onset of a problem. Therefore, revisit frequencies for precision agriculture are on the order of 10-14 days. The requirement for high spatial, spectral, and temporal resolution imagery creates special image processing and analysis challenges. These include-data management, geometric and radiometric corrections, ground control points and georeferencing, and data analysis via such approaches as data fusion. The purpose of this paper is twofold. First, to illustrate the diversity of high spatial, spectral, and temporal resolution imagery required for precision agriculture applications. Second, to offer the opportunity to other data fusion researchers to join the author of this paper in developing new data fusion techniques for high-resolution imagery.