Abstract
Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and play a significant role in the global cycles of carbon, nitrogen and water. The purpose of this study is to use field-based and satellite remote-sensing-based methods to assess leaf nitrogen pools in five diverse European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy. REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse canopy radiative transfer modelling system which has shown proficiency for regional mapping of leaf area index (LAI) and leaf chlorophyll (CHLl) using remote sensing data. In this study, high spatial resolution (10–20 m) remote sensing images acquired from the multispectral sensors aboard the SPOT (Satellite For Observation of Earth) satellites were used to assess the capability of REGFLEC for mapping spatial variations in LAI, CHLland the relation to leaf nitrogen (Nl) data in five diverse European agricultural landscapes. REGFLEC is based on physical laws and includes an automatic model parameterization scheme which makes the tool independent of field data for model calibration. In this study, REGFLEC performance was evaluated using LAI measurements and non-destructive measurements (using a SPAD meter) of leaf-scale CHLl and Nl concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHLl and Nl and five spectral vegetation indices (the Normalized Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green chlorophyll index) were used to assess field data coherence and to serve as a comparison basis for assessing REGFLEC model performance. The field measurements showed strong vertical CHLl gradient profiles in 26% of fields which affected REGFLEC performance as well as the relationships between spectral vegetation indices (SVIs) and field measurements. When the range of surface types increased, the REGFLEC results were in better agreement with field data than the empirical SVI regression models. Selecting only homogeneous canopies with uniform CHLl distributions as reference data for evaluation, REGFLEC was able to explain 69% of LAI observations (rmse = 0.76), 46% of measured canopy chlorophyll contents (rmse = 719 mg m−2) and 51% of measured canopy nitrogen contents (rmse = 2.7 g m−2). Better results were obtained for individual landscapes, except for Italy, where REGFLEC performed poorly due to a lack of dense vegetation canopies at the time of satellite recording. Presence of vegetation is needed to parameterize the REGFLEC model. Combining REGFLEC- and SVI-based model results to minimize errors for a "snap-shot" assessment of total leaf nitrogen pools in the five landscapes, results varied from 0.6 to 4.0 t km−2. Differences in leaf nitrogen pools between landscapes are attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. In order to facilitate a substantial assessment of variations in Nl pools and their relation to landscape based nitrogen and carbon cycling processes, time series of satellite data are needed. The upcoming Sentinel-2 satellite mission will provide new multiple narrowband data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHLl and Nl.
Highlights
Nutrient availability is highly variable and related to land use, farming systems, soil type and topography (Duretz et al, 2011) as well as the atmospheric deposition of ammonia and nitrogen oxides (Churkina et al, 2010)
Because the bulk of leaf nitrogen is associated with Rubisco, leaf nitrogen is considered a critical determinant of the maximum Rubisco capacity in photosynthesis modelling (e.g. Farquhar et al, 1980; dePury and Farquhar, 1997; Boegh et al, 2002; and Kattge et al, 2009), and it plays an important role for the NH3 exchange between vegetation and the atmosphere (Mattson et al, 2009; Massad et al, 2010), which is an important component of the nitrogen (N) cycle and closely coupled to the carbon cycle
The seasonality appeared quite similar in Denmark and Poland, with the maximal Normalized Difference Vegetation index (NDVI) around 1 June 2008 corresponding to the timing of the SPOT image acquisitions
Summary
Nutrient availability is highly variable and related to land use, farming systems, soil type and topography (Duretz et al, 2011) as well as the atmospheric deposition of ammonia and nitrogen oxides (Churkina et al, 2010). Despite the excessive use of nitrogen fertilizers in many European croplands (Eurostat, 2012), water and nutrient resource availability is responsible for large inter-plant-species spatial variation in photosynthetic capacity and carbon exchange rates (Moors et al, 2010). This causes the carbon balance of fields to either be a source or a sink (Ciais et al, 2010). Due to the characteristic spectral signature of leaf pigments and their N contents, remote sensing of leaf chlorophyll (CHLl) and leaf nitrogen (Nl) is feasible (e.g. Blackburn, 1998; Broge and LeBlanc, , 2000; Boegh et al, 2002; Hansen and Schjoerring, 2002; Sims and Gamon, 2002; Gitelson et al, 2005; Zhao et al, 2005b; Houborg and Boegh, 2008; Houborg et al, 2009; Dash et al, 2010; Main et al, 2011; and Peng and Gitelson, 2012), and it has been found that such variables can be used as measures of the light-use efficiency (Houborg et al, 2011; Peng and Gitelson, 2012) and the maximum Rubisco capacity (Boegh et al, 2002) in photosynthesis modelling
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