Statistical derivation of fPAR and LAI for irrigated cotton and rice in arid Uzbekistan by combining multi-temporal RapidEye data and ground measurements

Abstract

Land surface biophysical parameters such as the fraction of photosynthetic active radiation (fPAR) and leaf area index (LAI) are keys for monitoring vegetation dynamics and in particular for biomass and carbon flux simulation. This study aimed at deriving accurate regression equations from the newly available RapidEye satellite sensor to be able to map regional fPAR and LAI which could be used as inputs for crop growth simulations. Therefore, multi-temporal geo- and atmospherically corrected RapidEye scenes were segmented to derive homogeneous patches within the experimental fields. Various vegetation indices (VI) were calculated for each patch focusing on indices that include RapidEye's red edge band and further correlated with in situ measured fPAR and LAI values of cotton and rice. Resulting coefficients of determination ranged from 0.55 to 0.95 depending on the indices analysed, object scale, crop type and regression function type. The general relationships between VI and fPAR were found to be linear. Nonlinear models gave a better fit for VI-LAI relation. VIs derived from the red edge channel did not prove to be generally superior to other VIs.