Authors: Arthur Rosales*,
Topics: Agricultural Geography
Keywords: remote sensing, MODIS, NDVI, agriculture, crop production, yield, linear regression,
Session Type: Paper
Start / End Time: 1:10 PM / 2:50 PM
Room: Tyler, Marriott, Mezzanine Level
Presentation File: No File Uploaded
The USDA National Agricultural Statistics Service (NASS) creates crop yield forecasts throughout the growing season using qualitative survey data and objective biophysical data regarding certain major commodity crops. In response to interest in improving timeliness and spatial detail, NASS has begun studying the use of remote sensing techniques to create national and state-level crop yield forecasts, as well as county-level yield estimates after the growing season has ended. Using cropland masks and data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra Satellite, NASS has standardized a remote sensing process for creating yield indications for corn and soybeans. There is now a ten year time series of coinciding satellite data, yield estimates, and land cover masks for these crops in the conterminous United States. The objective of this research is to use these data to explore spatial and temporal patterns in U.S. corn yields. Multispectral satellite data are used to calculate Normalized Difference Vegetation Index (NDVI), which is an indication of plant biomass. The functional relationship between corn yields and annual accumulated NDVI values are modeled and used to create yield maps for every year that corn crop masks are available. Corn yield patterns will be explored as they relate to crop migration, crop planting frequency, localized weather anomalies, and overall national production over time.