Most Mississippi crops are produced in the state's Delta region, which has extremely variable soils in fields. Producers know that production depends on soil properties like texture, which influences water infiltration and movement through the soil. Since water availability is the most important factor for crop growth, mapping soil texture would be an important technological advance. Data collection methods for soil property mapping require expensive and time-consuming manual soil sampling, with scarcely adequate spatial resolutions of about one sample per 2.5 acres. If remote-sensing data were to replace soil sampling, they would have to be superior in at least one but preferably all of the following characteristics: reliability, spatial resolution, cost, frequency, information content, and ease of use. Remote-sensing data are unlikely to be more reliable than actual sample data on soil texture, but remote sensing can provide higher resolution, frequency, and information content, while costing less and being at least as easy to use. In the future, digital maps of remotely sensed soil characteristics could be used for variable-rate application in the same way that maps of soil-sample data are today.

1. To develop relationships between soil physical texture (clay, silt, and sand content) and remote-sensing data.
2. To develop relationships between soil surface roughness and remote-sensing data.
3. To further develop and test a ground-based mobile platform equipped with DGPS for mapping numerous surface-layer soil physical properties with state-of-the-art sensors.
4. To further develop fundamental relationships between soil spectral properties and soil physical properties.

Spectral reflectance data will be collected on soil samples collected in fields already involved in precision-agriculture research. Soil texture will be determined by the hydrometer method and with a phase-doppler particle-size analyzer. Maps of soil texture and spectral data will be produced for the fields of interest.

A ground-based mobile sensor platform with DGPS will be further developed for mapping surface-layer soil characteristics. The platform will include state-of-the-art spectral-reflectance and image-analysis sensors and will be designed for mounting on a John Deere 4700 high-clearance spray tractor. This system 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, and an imaging system for surface roughness.

Instrument development for imaging and diffuse reflectance will take place as follows: (1) construct/integrate system components, (2) develop software for image acquisition and processing, (3) laboratory calibration and validation, (4) field testing and integration with GPS. After the sensor platform has been populated with sensors, it will be used frequently on the fields in the study. Multispectral and hyperspectral remote-sensing data will be collected on fields in bare-soil condition, and will be geo-referenced with ground-based maps of diffuse-reflectance, texture, and surface roughness developed with data from the sensor platform. Algorithms will be further developed to classify locations according to soil texture based on remote-sensing data. The neural network approach that has begun will be continued.