Estimation of grassland biomass and nitrogen using MERIS data

Saleem Ullah*, Yali Si, Martin Schlerf, Andrew K. Skidmore, Muhammad Shafique, Irfan Akhtar Iqbal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)196-204
Number of pages9
JournalInternational Journal of Applied Earth Observation and Geoinformation
Publication statusPublished - 2012
Externally publishedYes


  • Quantifying biomass
  • Nitrogen concentration, and nitrogen density
  • Vegetation indices
  • Band depth analysis parameters


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