Relationship between the Meris Vegetation Indices and Crop Yield for the State of South Dakota, Usa

Remotely sensed data can be used to estimate crop type, amount and condition and such estimates, made at key points in the growing season can be used to predict the eventual crop yield. The launch of the Medium Resolution Imaging Spectrometer (MERIS) on the Envisat satellite offers other approaches to the remote sensing of crop yield and these are explored in this paper. This sensor is now providing, through the European Space Agency, two operational ‘land products’ of the globe in every three days. The MERIS Global Vegetation Index (MGVI) product is related causally to leaf area index (LAI) and the MERIS Terrestrial Chlorophyll Index (MTCI) product is related causally to chlorophyll content (a combination of LAI and leaf chlorophyll concentration). Six approaches to the remote sensing of crop yield were evaluated using county level crop yield data for South Dakota, USA. For both MGVI and MTCI (i) maximum point in the growing season, (ii) area under the growing season curve up to this maxima and (iii) area under the whole of this curve were found to correlate strongly with crop yield. The strongest correlations with crop yields were observed for approach (ii) and (iii) using either MGVI or MTCI. The strongest of all correlations (R2=0.85) was between crop yield and the area under the whole growing season curve for MTCI as this captured most variability in crop condition over time. The ready availability of the MTCI, its direct relationship with key crop biophysical and biochemical variables and its strong correlation with crop yield suggest its suitability for the estimation of crop yield at regional scales.