Using vegetation health indices and partial least squares method for estimation of corn yield

Abstract

In this paper the possibility of predicting corn yield by the use of real time acquired satellite data from the Advance Very High Resolution Radiometer (AVHRR) sensor and Partial Least Squares (PLS) method was investigated. To test the methodology in practice, 23 years (1982–2004) of AVHRR data together with official corn yield statistics of Haskell County, Kansas, USA, were used for model development and validation. The AVHRR reflectances in the visible and thermal bands, extracted from the National Oceanic and Atmospheric Administration (NOAA)/NESDIS archive, were transformed into Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)). The PLS method was used to construct a model relating corn yield anomaly with VH indices. Independent model verification showed that the error of corn yield prediction in Haskell County, Kansas, USA, was less than 6%. For several reasons this approach could have excellent potential as a commercial application: satellite data are inexpensive, easily available and yield estimates can be known two to three months before harvest has been completed.

Acknowledgements

This study was supported and monitored by National Oceanic and Atmospheric Administration (NOAA) under (Grant or Contract) Number NA17AE162. The statements contained within the manuscript/research article are not the opinions of the funding agency or the US government, but reflect the authors' opinions.