• J. Alex Thomasson (PI) - Agricultural & Biological Engineering, Mississippi State University
• S. D. Filip To (Co-PI) - Agricultural & Biological Engineering, Mississippi State University
• Michael S. Cox (Co-PI) - Plant & Soil Sciences, Mississippi State University
• William L. Kingery (Co-PI) - Plant & Soil Sciences, Mississippi State University
• Jagdish P. Singh (Co-PI) - DIAL
• David L. Monts (Co-PI) - DIAL
• Ping-Rey Jang (Co-PI) - DIAL

• Larry Oldham (Co-I) - Plant & Soil Sciences, Mississippi State University
• Yi Su (Co-I) - DIAL,
• Christopher B. Winstead (Co-I) - DIAL
• Andre Simpson (Postdoc) - Plant & Soil Sciences, Mississippi State University
• Ruixiu Sui (Postdoc) - Agricultural & Biological Engineering, Mississippi State University
• Jian Chen (Postdoc) - Agricultural & Biological Engineering, Mississippi State University
• Yushun Zhai (grad student) - Agricultural & Biological Engineering, Mississippi State University

1. Develop relationships between soil properties and remote-sensing data.
2. Build a ground-based mobile platform with DGPS for mapping numerous surface-layer soil characteristics with state-of-the-art sensors.
3. Develop relationships between soil spectral properties and soil physical and chemical properties.

Year 1. Spectrophotometers with sensitivity from 300-nm to 100mm will be used to collect reflectance data on soil samples collected in cotton fields. The location of each sample will be recorded with a DGPS receiver. Soil texture will be determined by the hydrometer method, soil mineralogy by x-ray diffraction, soil O.M. by loss on ignition, and soil moisture by gravimetric methods. Reflectance data will be related to the various soil characteristics. Also, geographic maps of the spectral data will be produced. Fundamental aspects of how soils reflect electromagnetic energy will be investigated.

Multispectral data will be collected on the fields at about the time soil samples are collected. In addition, Landsat TM data will be purchased. Remote-sensing data will be georeferenced with the maps of spectral data, and relationships will be investigated.

A ground-based mobile sensor platform with DGPS will be developed for mapping surface-layer soil characteristics. The platform will include state-of-the-art sensors and will be designed for mounting on a "highboy" type agricultural tractor. The advantage of such a system is that it can be used in fields throughout the season without significantly damaging the crop. The following sensors will be developed for inclusion on the platform: narrow-band diffuse-reflectance sensors, an imaging system for soil texture (re particle size), and an imaging system for surface roughness. Additionally, sensors applicable to crop conditions will be employed for use during the growing season: pyranometer for solar irradiance, infrared thermometer for canopy temperature, wind gauge to analyze leaf orientation, relative-humidity sensor, and a system to measure plant height.

Imaging systems will be developed to measure soil texture and surface roughness. It is expected that two systems will be used, one at high resolution for texture, and one at low resolution for surface-roughness.

Year 2. After the sensor platform has been developed, it will be used frequently on the fields in the study. Weather permitting, the platform will log data while being driven through the fields as often as necessary during the year. During this period, remote-sensing data will be collected on the fields as often as the schedule permits. Relationships will be investigated between data collected with remote-sensing and the sensor platform.

By linking image data to highly characterized in-situ systems, this project will aid the understanding of the relationship between remotely sensed soil reflectance data and actual soil properties. Spectral signatures of important soil properties can be used as a stand-alone product as in determining soil properties with little or no physical contact with the field, or they can be used in combination with remotely sensed crop images to remove confounding information. The results of this project will lend themselves to the commercialization of remote sensing for production of surface soil-property maps. We expect the sensor platform developed in this work to be a one-of-a-kind system worldwide, capable of being adapted for other remote-sensing research projects in agriculture.