LANDSCAPE-BASED CROP MANAGEMENT FOR FOOD, FEED, AND BIOENERGY
Location: Cropping Systems and Water Quality Research
Title: Comparing LAI estimates of corn and soybean from vegetation indices of multi-resolution satellite images
Submitted to: Korean Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 7, 2012
Publication Date: December 1, 2012
Citation: Kim, S., Hong, S., Sudduth, K.A., Kim, Y., Lee, K. 2012. Comparing LAI estimates of corn and soybean from vegetation indices of multi-resolution satellite images. Korean Journal of Remote Sensing. 28(6):597-609.
Interpretive Summary: Variation in crop growth from place to place within a field can provide important information for precision agriculture management strategies. A standard approach is to determine the leaf area index (LAI), which indicates the amount of biomass produced by the plant. These LAI measurements can be made using instruments, but much time and effort is required to collect enough data to completely characterize spatial variability in a field. Satellite remote sensing data provides a more efficient approach, as a single image can cover an entire field. Prior research has shown that indices calculated from satellite remote sensing data are related to LAI, with the index most often used being the normalized difference vegetation index (NDVI). In this study, data from remote sensing satellites having different image sizes and resolutions were compared to corn and soybean LAI measured at multiple times over the growing season. We found that the NDVI data from the satellite with the highest resolution (IKONOS) was more strongly related to field-measured LAI than data from satellites with lower resolution (Landsat TM and MODIS). These results will provide useful information to researchers who need to obtain spatially dense information on within-field crop growth variation, for example as input to crop models.
Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than models using Landsat TM or MODIS NDVI. Expolinear models generally provided more accurate results than linear or exponential models.