Sarah Asam

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

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).

Mountain crop monitoring with multitemporal Sentinel-1 and Sentinel-2 imagery

An analysis of radar signal sensitivity to crop and soil conditions was conducted using a time series of Sentinel-1 C-band dual-polarized (VH-VV) SAR images acquired from October 2014 to September 2016 for different crops (meadows, pasture, orchard, vineyards) located in mountain areas. Together with Sentinel-1 images, corresponding Sentinel-2 images and ground measurements were exploited. Preliminary results showed that the cross-polarized VH backscattering coefficients were useful for monitoring crop phenology. In particularly for meadows the VH signal is well correlated with NDVI derived from both Sentinel-2 images and from ground observations.

Comparison of leaf area indices for grasslands within the Alpine upland based on multi-scale satellite data time series and radiation transfer modeling

In this study, the leaf area index (LAI) of grasslands in the Bavarian Alpine uplands has been derived using inverted radiation transfer modeling (RTM) on original as well as simulated remote sensing data time series. The spatial resolutions of the data sets range from 6.5 to 250 m. While the high resolution data are available for four points in the vegetation period, the medium resolution time series consist of weekly scenes. The aim is to investigate the performance of the inverse RTM when applied to satellite data of different spatial and temporal resolutions. Further, we determine the adequate resolutions of remote sensing data for LAI retrieval in a heterogeneous landscape. All results were validated using in situ measurements. While the algorithm proves to be generally applicable in this challenging landscape on different scales, retrieval accuracy increases with higher spatial resolution. Satellite images with a spatial resolution up to 20 m are identified as a good compromise between accurate results and spatial detail. The 250 m resolution LAI time series on the other hand provides valuable information on the phenology and sudden LAI reductions caused by harvest, which are not captured by the high spatial resolution time series with few scenes.

Comparison of leaf area index derived by statistical relationships and inverse radiation transport modeling using RapidEye data in the European alpine upland

Leaf Area Index (LAI) is a relevant input parameter for flux modeling of energy and matter in the biosphere. However, in a landscape such as the European alpine upland with small-scale land use patterns and high vegetation heterogeneity, existing global products are less suited and a high spatial resolution is required. Within this study two methods are compared to derive the LAI for grassland in the prealpine River Ammer catchment from high spatial resolution RapidEye data: the empirical approach based on regression functions, and the physical approach of inverted radiation transfer modeling (RTM). Established vegetation indices (VIs) as well as new ones incorporating RapidEye’s red edge band are calculated for four dates of the vegetation period 2011 and correlated with in situ LAI data. The statistical regressions between VIs and LAI of the different time steps show high correlations (R2 of 0.57 up to 0.85). However, the established regressions are scene specific and the method requires excessive field work. In the physical approach the RapidEye reflectances are used as input data to an inverted RTM (PROSAIL), which is parameterized with leaf and canopy properties collected in the field. The LAI derived by the RTM have a RMSE between 2.02 and 2.28 for the different dates. Both methods capture the general LAI pattern. However, due to the broad parameterization of the RTM used to cover the heterogeneous grassland conditions, resulting LAI values are generally higher than the statistically derived LAI values.