Study on the Remote Sensing Estimation Models for Aboveground Biomass of Corn Based on ETM/SPOT5 Data

To accurately obtain the agricultural crops biomass information is of significance for the estimation of yield according to the growth conditions. This paper applied four vegetation indices with ETM/SPOT5 data for Mapo Town of Weishan County of Shandong province as an example which located at the junction of two drainage areas of Yellow River and Huai River. Combined with the field survey, the study analyzed the relationship between vegetation indices and aboveground biomass of corn, and established three regression models (linear, exponential, and power functions). The results showed that cropland biomass could be well simulated by linear, exponential, and power functions, and nonlinear functions was better than linear function, and exponential function performed best. Four vegetation indices (RVI, NDVI, RDVI and SAVI) had significant positive correlation with cornland biomass, and were suitable for the successful quantification of aboveground biomass of corn. NDVI function had behaved most efficient with aboveground biomass (R-2=0.867), and the estimation models based on SPOTS data were more effective than that of ETM data.