Correlation between potato yield and MODIS-derived vegetation indices

Monitoring of crop growth and forecasting its yield well before harvest is very important for crop and food management. Remote sensing images are capable of identifying crop health, as well as predicting its yield. Vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR) calculated from remotely sensed data have been widely used to monitor crop growth and to predict crop yield. This study used 8 day TERRA MODIS reflectance data of 500m resolution for the years 2005 to 2006 to estimate the yield of potato in the Munshiganj area of Bangladesh. The satellite data has been validated using ground truth data from fields of 50 farmers. Regression models are developed between VIs and field level potato yield for six administrative units of Munshiganj District. The yield prediction equations have high coefficients of correlation (R 2) and are 0.84, 0.72 and 0.80 for the NDVI, LAI and fPAR, respectively. These equations were validated by using data from 2006 to 2007 seasons and found that an average error of estimation is about 15% for the study region. It can be concluded that VIs derived from remote sensing can be an effective tool for early estimation of potato yield.