Submitted to: Complete Book
Publication Type: Book / Chapter
Publication Acceptance Date: 2/10/2000
Publication Date: 6/2/2000
Citation: Interpretive Summary: In this study, we have demonstrated the use of a crop simulation model (EPIC) together with remote sensing data (Landsat TM) for monitoring crop growth and yields of spring wheat for three counties in Southeastern North Dakota. Model simulation calibrated with remotely sensed data obtained during the growing season predicted spring wheat yields with a high degree of accuracy, to within one bushel per acre of the USDA/NASS reported yields. The final adjustment to the crop model using remotely sensed satellite data took place about midway between the time of flowering and crop maturity. This is the optimum period of crop development when the combined models can provide a good assessment of the potential yields. Although only two satellite images were used to calibrate the model, this provided sufficient data for a successful calibration. The availability of cloud-free satellite data during a critical window of data acquisition is necessary to achieve an optimum calibration of the crop model. The three optimum calibration periods occur during the early vegetative phase, flowering and senescence. However, only two effective Landsat TM overpass dates are usually available during the crop growing season. This research has demonstrated two ways of improving crop yield assessments: 1) Landsat TM data can provide an effective means to calibrate the climate-based crop growth model (EPIC) during the crop growing season when the satellite data is available at optimum times. 2) The models can generate crop yield predictions at the soil association level that can be aggregated to provide county yields.
Technical Abstract: Monitoring crop condition and production estimates at the state and county level is of great interest to the U.S. Department of Agriculture. The National Agricultural Statistical Service (NASS) of the U.S. Department of Agriculture conducts field interviews with sampled farm operators and crop cuttings to obtain crop yield estimates at regional and state levels. NASS needs supplemental spatial data that provides timely information on crop condition and potential yields. In this research, the crop model EPIC (Erosion Productivity Impact Calculator) was adapted for simulations at regional scales. Satellite remotely sensed data provides a real time assessment of the magnitude and variation of crop condition parameters and this study investigates the use of these parameters as an input to a crop growth model. This investigation was conducted in the semi-arid region of North Dakota in the southeastern part of the state. The primary objective was to evaluate a method of integrating Landsat TM satellite data in a cro growth model to simulate spring wheat yields at the sub-county level. The input parameters derived from remotely sensed data provided spatial integrity, as well as a real-time calibration of model simulated parameters during the season to ensure that the modeled and observed conditions agree. A radiative transfer model (SAIL) provided the link between the satellite data and crop model. The model parameters were simulated at the satellite pixel level in a geographic information system, which was the platform for aggregating yields at local and regional scales. The simulation was run for each soil type within the county and the results integrated to provide county yields. The model simulated yields were similar to reported county averages and the farm level yields at selected NASS survey sites.