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 terrestrial ecosystems and the atmosphere, and they play a significant role in the global cycles of carbon, nitrogen and water. In this study, high spatial resolution (10-20 m) remote sensing images acquired from the HRG and HRVIR sensors aboard the SPOT satellites were used to assess the applicability of remote sensing data to characterize spatial variability in vegetation traits in five selected landscapes representing crop- and grasslands over a large gradient of environmental conditions in Europe. Leaf Area Index (LAI) and non-destructive (using a SPADmeter) leaf scale chlorophyll (CHL) and nitrogen (N) concentrations were measured in 93 fields representing crop- and grasslands in Denmark, Poland, Scotland (United Kingdom), the Netherlands and Italy. The field measurements showed strong vertical leaf CHL profiles in 20 % of fields which affected the performance of the remote sensing based methods. The current study used homogeneous canopies with uniform CHL profile development as reference data to evaluate remote sensing estimations. The best results achieved over the large range of environmental conditions represented by the five landscapes used an automatic spatial regularization technique (REGFLEC) to parameterize an image-based atmospheric/leaf optics/canopy radiative transfer model. REGFLEC is applied without calibration. The use of simpler (fitted) spectral vegetation index (SVI) approaches also provided statistically significant results when calibrated against field data oft he five landscapes, however with lower accuracies. Assessing the remote sensing estimation capabilities for individual landscapes, it was found that REGFLEC performed poorly in Italy due to a lack of dense vegetation canopies at the time of satellite recording. Presence of dense fields is needed in order to parameterize the REGFLEC model. In contrast, the Simple Ratio (and other SVI's) was closely related to field measurements in Italy, thereby verifying field data quality as well as the practical utility of empirical SVI approaches which are however data-dependent methods. The results also indicated that the Enhanced Vegetation index-2 (EVI2) is a superior SVI in densely vegetated landscapes, and it was found that REGFLEC generally worked best in landscapes comprising a large range of vegetation covers (sparse to dense). Generally remote sensing capability improved when restricting the evaluation to separate land use categories distributed across the five landscapes, and it further improved when restricting the evaluation to local (10 x 10 km2) landscapes, thereby reflecting sensitivity to canopy structures and local environmental conditions (ie. soil background reflectance). Because REGFLEC did not work equally well in all landscapes, the assessment of differences in leaf N pools among the five landscapes werefinally based on the best performing methods (REGFLEC or SVI's). The assessed "snap-shot" leaf N pools of the five agricultural landscapes varied from 0.6 to 4.0 t km-2. Differences in leaf N pools between landscapes were attributed to seasonal variations, extents of agricultural area, species variations, and spatialvariations in nutrient availability. In order to facilitate a substantial assessment of variations in N pools and their relation to landscape based assessment of nitrogen and carbon cycling processes, satellite time series data are needed. The Sentinel-2 satellite mission will provide new multiple narrow-band data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHL and N. It is also recommended that future field studies are designed to investigate the possible effect of leaf CHL vertical profile variations on canopy reflectance and remote sensing based capabilities to assess LAI, CHL and N.