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United States Department of Agriculture

Agricultural Research Service

Title: Application of Noaa Avhrr Data for Spring Wheat Yield Assessment

Authors
item Doraiswamy, Paul
item Moulin, Sophie - INRA/UNITE CLIMAT
item Cook, Paul - NASS
item Stern, Alan

Submitted to: Photogrammetric Engineering and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 6, 2003
Publication Date: January 6, 2003
Citation: Doraiswamy, P.C., S. Moulin, P.W. Cook and A. Stern. 2003. Application of NOAA AVHRR data for spring wheat yield assessment. Photogrammetric Engineering and Remote Sensing. 69(6):665-674.

Interpretive Summary: Accurate and timely information on crop yields is important because of the economic impact of agricultural production on the world market. Current USDA's National Agricultural Statistical Service procedure is expensive and time consuming. There is a need for a faster and possibly more economical alternative methodology for collecting crop condition and yield estimations to supplement current USDA assessments at the County and State level. Crop simulation models using climate-station data are the traditional methods for estimation/prediction of yields and the results are extrapolated to regional scales. Earlier studies where only NOAA AVHRR satellite imagery was used to estimate or predict crop yields suggested that this approach requires location-specific regression equations to predict yield. This project investigated the feasibility of integrating remotely sensed crop parameters in a crop model to improve spatial assessment of crop yields at regional scales. A four-year analysis of spring wheat yields in North Dakota was conducted with remote sensing data to adjust planting date and crop development characteristics of the model. The results suggest that using remote sensing data to adjust the model simulations improved the production estimates and was consistent for the study period. The regression analysis of simulated and report production at the county levels were between 0.8 and 0.96 for the study.

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 obtains crop cuttings to make 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 parameters retrieved from satellite imagery in a crop growth model to simulate spring wheat yields at the sub-county and county levels. 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 in a geographic information system grid, which was the platform for aggregating yields at local and regional scales. A model calibration was performed to initialize the model parameters. This calibration was performed using Landsat data over three south-east counties in North Dakota. The model was then used to simulate crop yields for the state of North Dakota with inputs derived from NOAA AVHRR data. The calibration and the state level simulations are compared with spring wheat yields reported by NASS objective yield surveys.

Last Modified: 11/28/2014
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