Derivation of leaf area index for grassland within alpine upland using multi-temporal RapidEye data

Abstract

Biophysical parameters such as leaf area index (LAI) are key variables for vegetation monitoring and particularly important for modelling energy and matter fluxes in the biosphere. Therefore LAI has been derived from remote sensing data operationally based on data with a somewhat coarse spatial resolution. This study aims at deriving high-spatial resolution (6.5 m) multi-temporal LAI for grasslands based on RapidEye data by statistical regressions between vegetation indices (VIs) and field samplings. However, the suitability of those data for grassland LAI derivation has not been tested to date. Thus, the potential of RapidEye data in general and its red edge band in particular are investigated, as well as the robustness of the established relationships for different points in time.LAI was measured repeatedly over summer 2011 at about 30 different meadows in the Bavarian alpine upland using the LAI-2000 and correlated with VI values. The best relationships resulted from using the ratio vegetation index and red edge indices (NDVIrededge, rededge ratio index 1, and relative length) in non-linear models. Thus the indices based on the red edge channel improved regression modelling. The associated transfer functions achieved R2 values ranging from 0.57 to 0.85. The temporal transferability of those transfer functions to other dates was shown to be limited, with the root mean square errors (RMSEs) of several scenes exceeding one. However, when the LAI ranges are similar, a reliable transfer is possible: for example, the transfer of the regression function based on early autumn measurements showed RMSEs of only 0.77–0.95 for the other scenes except for the high-density stage in July, when the LAI reaches unprecedented maximal values. Also, the combination of multi-temporal training data shows no saturation of the selected indices and enables a satisfactory LAI mapping of different dates (RMSE = 0.59 – 1.02).