Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 1, 2005
Publication Date: April 20, 2005
Citation: Doraiswamy, P.C., Sinclair, T.R., Hollinger, S., Stern, A.J., Akhmedov, B., Prueger, J.H. 2005. Application of MODIS derived parameters for regional yield assessment. International Journal of Remote Sensing, Remote Sensing of Environment. 97(2):192-202.
Interpretive Summary: Accurate and timely assessment of crop yields is important to assess the economic impact of agricultural production on the world market. The National Agricultural Statistics Service (NASS) of USDA has the mandate to provide crop production estimates for the U.S. We have been working in cooperation with NASS for developing methods that use satellite imagery and crop models to assess crop progress and predict crop yields prior the harvest. Imagery from the new MODIS sensor onboard the Terra satellite offers an excellent opportunity for daily coverage at 250 m resolution. The objective of this research was to evaluate the potential use of MODIS imagery for operational use in assessing crop condition and predicting crop yields. A field study was conducted during the 2000 crop season in the predominantly corn and soybean area of McLean county, Illinois. Crop growth and development parameters were retrieved from imagery and used in a crop yield model to predict corn and soybean crop yields. The results of the simulations provided a spatial distribution of yields with the county. There was a good agreement between predicted yield and NASS county average reported yield.
Integration of operational satellites data with crop growth models allows assessing crop condition and yield at regional scale. Monitoring regional agricultural crop condition has traditionally been accomplished using NOAA AVHRR 1 km spatial resolution data. Daily coverage of MODIS imagery at 250 m. resolution from the Terra Satellite potentially offers an opportunity for operational assessment of the crop condition and yield in field and regional scale. The objective of this research was to evaluate the quality of the MODIS-250m resolution data for retrieval of crop biophysical parameters that could be integrated in crop yield simulation models.
A secondary objective was evaluating the potential use of MODIS-250 m resolution data for crop classification. A field study (24 fields) was conducted during the 2000 crop season in McLean county, Illinois, in the U.S. Midwest to evaluate the applicability of the MODIS 8-day, 250 m resolution composite imagery (version 4) for operational assessment of crop condition and yields. Ground-based canopy and leaf reflectance and leaf area index (LAI) measurements were used to calibrate a radiative transfer model and to develop a look up tables (LUT) between LAI and canopy reflectance. Using the MODIS surface reflectance data, the LUT was used to estimate LAI during the crop season. The seasonal trend of MODIS derived LAI was used for adjusting the LAI simulated by the climate-based crop yield model. Other intermediate products such as crop phonological events of crop growth and development were reset from the LAI seasonal profile. Simulations of corn and soybean yields were conducted at 1.6 x 1.6 km2 grids and the results integrated to the county level. The results were comparable to county yield reported by the USDA National Agricultural Statistics Service (NASS).