Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrialecosystems and the atmosphere, and they play a significant role in the global cycles ofcarbon, nitrogen and water. Remote sensing data from satellites can be used to estimate5 leaf area index (LAI), leaf chlorophyll (CHLl) and leaf nitrogen density (Nl). However,methods are often developed using plot scale data and not verified over extendedregions that represent a variety of soil spectral properties and canopy structures. Inthis paper, field measurements and high spatial resolution (10-20 m) remote sensingimages acquired from the HRG and HRVIR sensors aboard the SPOT satellites were10 used to assess the predictability of LAI, CHLl and Nl. Five spectral vegetation indices(SVIs) were used (the Normalized Difference Vegetation index, the Simple Ratio, theEnhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, andthe green Chlorophyll Index) together with the image-based inverse canopy radiativetransfer modelling system, REGFLEC (REGularized canopy reFLECtance). While the15 SVIs require field data for empirical model building, REGFLEC can be applied withoutcalibration. Field data measured in 93 fields within crop- and grasslands of five Europeanlandscapes showed strong vertical CHLl gradient profiles in 20% of fields. Thisaffected the predictability of SVIs and REGFLEC. However, selecting only homogeneouscanopies with uniform CHLl distributions as reference data for statistical evalu20ation, significant (p < 0.05) predictions were achieved for all landscapes, by all methods.The best performance was achieved by REGFLEC for LAI (r2 = 0.7; rmse=0.73),canopy chlorophyll content (r2 = 0.51; rmse=439mg m-2) and canopy nitrogen content(r2 = 0.53; rmse=2.21 g m-2). Predictabilities of SVIs and REGFLEC simulationsgenerally improved when constrained to single land use categories (wheat, maize, bar25ley, grass) across the European landscapes, reflecting sensitivity to canopy structures.Predictability further improved when constrained to local (10×10 km2) landscapes,thereby reflecting sensitivity to local environmental conditions. All methods showeddifferent predictabilities for land use categories and landscapes. Combining the bestmethods, LAI, canopy chlorophyll content (CHLc) and canopy nitrogen content (Nc) forthe five landscapes could be predicted with improved accuracy (LAI rmse=0.59; CHLcrmse=346 g m-2; Nc rmse=1.49 g m-2). Remote sensing-based results showed thatthe vegetation nitrogen pools of the five agricultural landscapes varied from 0.6 to5 4.0 tkm-2. Differences in nitrogen pools were attributed to seasonal variations, extentsof agricultural area, species variations, and spatial variations in nutrient availability. Informationon Nl and total Nc pools within the landscapes is important for the spatialevaluation of nitrogen and carbon cycling processes. The upcoming Sentinel-2 satellitemission will provide new multiple narrow-band data opportunities at high spatio10temporal resolution which are expected to further improve remote sensing predictabilitiesof LAI, CHLl and Nl.