Contributers:
Dr. Stephen W. Searcy¹
Marcia Ruble¹

(1: Agricultural Engr. Dept., Texas A&M University, USA)

Crop models are able to predict the outcome of crops given the required input information. From a precision farming standpoint, crop models are valuable in aiding the management process during a growing season. A grower is prepared in advance when to expect certain physiological occurrences, reducing the amount of guesswork in managing a crop. Additionally, models serve as a surrogate for real experimentation. By altering the input parameters and implementing the model over and over, the user can identify the effect of individual input parameters. A drawback to these models, however,  is the compromise that occurs between input detail and accuracy of model results. As the accuracy of model results increases, the complexity and input requirements also increase. Most often the required information includes weather data, soil physical properties, and soil fertility data, each requiring multiple collection methods. Such information, to provide adequate detail for effective predictions, is typically feasible only on small research sites due to expensive and time-consuming collections. In order for crop models to be economical, more efficient methods for data collection is necessary.

Because of the ability to cover large areas in a short amount of time at a relatively low cost, remote sensing serves as a potential alternative to previous data collection methods. In the past, remotely sensed data has been used to identify correlation between crop and soil parameters and their spectral reflectance. Collection of the detailed data required by crop models to perform predictions and to update the model can be achieved through remote sensing if the correlation between spectral reflectance and parameters is present.

The objective of this project is to demonstrate the use of remotely sensed data in crop growth models. Imagery on two cotton research sites (Kingsville, TX and the Brazos Valley) are being analyzed to obtain biomass measurements. The resulting data will be incorporated into a cotton growth model to provide mid-season updates. Calibration of the model will occur accordingly, thereby improving the accuracy of the yield estimates provided by the model.