Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model

Abstract

Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of grassland ecosystems. The Medium Resolution Imaging Spectrometer (MERIS) is a promising sensor for measuring and monitoring grasslands due to its high spectral resolution, medium spatial resolution and a two- to three-day repeat cycle. However, thus far the multi-biome MERIS land products have limited consistency with in-situ measurements of leaf area index (LAI), while the multi-biome canopy chlorophyll content (CCC) has not been validated yet with in-situ data. This study proposes a single-biome approach to estimate grassland LAI (a surrogate of grass quantity) and leaf chlorophyll content (LCC) and CCC (surrogates of grass quality) using the inversion of the PROSAIL model and MERIS reflectance. Both multi-biome and single-biome approaches were validated using two-season in-situ data sets and the temporal consistency was analyzed using time-series of MERIS data. The single-biome approach showed a consistently better performance for estimating LAI (R2=0.70, root mean square error (RMSE)=1.02, normalized RMSE (NRMSE)=16%) and CCC (R2=0.61, RMSE=0.36, NRMSE=23%) compared with the multi-biome approach (LAI: R2=0.36, RMSE=1.77, NRMSE=28%; CCC: R2=0.47, RMSE=1.33, NRMSE=84%). However, both single-biome and multi-biome approaches failed to retrieve LCC. The multi-biome LAI was overestimated at lower LAI values (<2) and saturated at higher LAI values (≥4), and the multi-biome CCC was consistently overestimated through the whole data range. Similar temporal trajectories of grassland LAI and CCC estimates were observed using these two approaches, but the multi-biome trajectory consistently produced larger values than the single-biome trajectory. The spatio-temporal variation of grassland LAI and CCC estimated by the single-biome approach was shown to be closely associated with agricultural practices. Our results underline the potential of mapping grassland LAI and CCC using the PROSAIL model and MERIS satellite data.