The potential of the MERIS Terrestrial Chlorophyll Index for crop yield prediction

Abstract

Regional and global prediction of crop yield by remote sensing is of vital importance for food security in China. Vegetation indices (VIs) sensitive to leaf area index (LAI), such as Normalized Difference Vegetation Index (NDVI), have been widely used for crop yield prediction. However, chlorophyll content is the key component for crops to convert light energy to organics in the process of photosynthesis, and crop yield may be more accurately predicted by remote sensing models based on a spectral index sensitive to chlorophyll content, such as the medium resolution imaging spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI). In this study, we investigated the potential of MTCI for crop yield prediction. Firstly, the MTCI and the NDVI products in Henan Province, China, from 2003 to 2011 with a temporal resolution of half a month were calculated from the daily level-2 reduced resolution ENVISAT MERIS reflectance product (MER_RR_2P) using a Maximum Value Composite algorithm. Secondly, we established winter wheat prediction models based on MTCI and NDVI. Then, the accuracy of MTCI for winter wheat yield prediction was examined and compared to the NDVI through a leave-one-out cross-validation approach. The results show that (1) the correlation coefficient between yield and MTCI is significantly higher than that of models based on NDVI and the errors of models based on MTCI are lower than those of models based on NDVI except for milking stages. Moreover, the crop yield prediction model based on accumulated MTCI through the reviving stage to milking is most significantly correlated to crop yield with a coefficient of determination of 0.849 for MTCI; (2) the optimum phase for crop yield prediction based on MTCI is the heading stage, about 30 days earlier than that of NDVI (milking stage), which is another advantage of MTCI in crop yield prediction; and (3) the validation error of the crop yield model based on the accumulated MTCI is half of that based on the accumulated NDVI. The study indicates that MTCI is potentially a better VI for crop yield prediction compared to NDVI.

Acknowledgement

The authors are thankful to European Space Agency for providing medium resolution imaging spectrometer data and NASA for moderate resolution imaging spectroradiometer land cover data.