Estimation of grassland biomass and nitrogen using MERIS data

This study aimed to investigate the potential of MERIS in estimating the quantity and quality of a grassland using various vegetation indices (NDVI, SAVI, TSAVI, REIP, MTCI and band depth analysis parameters) at a regional scale. Green biomass was best predicted by NBDI (normalised band depth index) and yielded a calibration R2 of 0.73 and a Root Mean Square Error (RMSE) of 136.2 g m−2 (using an independent validation dataset, n = 30) compared to a much higher RMSE obtained from soil adjusted vegetation index SAVI (444.6 g m−2). Nitrogen density was also best predicted by NBDI and yielded a calibration R2 of 0.51 and a RMSE of 4.2 g m−2 compared to a relatively higher RMSE obtained from MERIS terrestrial chlorophyll index MTCI (6.6 g m−2). For the estimation of nitrogen concentration (%), band depth analysis parameters showed poor R2 of 0.21 and the results of MTCI and REIP were statistically non-significant (P > 0.05). It is concluded that band depth analysis parameters consistently showed higher accuracy than vegetation indices, suggesting that band depth analysis parameters could be used to monitor grassland condition over time at regional scale.

► MERIS data have the potential for quantifying the biomass and nitrogen of a grassland ecosystem. ► Band depth analysis parameters are more accurate predictor than vegetation indices for grassland biomass and nitrogen density. ► The possibility exist to quantifying grassland ecosystem at regional scale using MERIS data. ► MERIS data can be used to monitor dynamics of a grassland ecosystem over a period of time.