Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Canopy nitrogen (N) concentration and content are linked to several vegetation processes and canopy N concentration is a state variable in global vegetation models with coupled carbon (C) and N cycles. While there is ample C data available to constrain the models, widespread N data are lacking. Remote sensing and vegetation indices have been used to detect canopy N concentration and canopy N content at the local scale in grasslands and forests. In this paper we conducted a regional case-study analysis investigating the relationship between the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) time series from ESA ENVISAT at 1 km spatial resolution and both canopy N concentration (%N) and canopy N content (g m −2 ) from a Mediterranean forests inventory in the region of Catalonia, NE of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20 km, 15 km, 10 km and 5 km) and at the initial higher spatial resolution of 1 km. The results at the higher spatial resolution yielded significant relationships between MTCI and both canopy N concentration and content, r 2  = 0.32 and r 2  = 0.17, respectively. We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N concentration was strongest for deciduous broadleaf and mixed plots ( r 2  = 0.25 and r 2  = 0.47, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees ( r 2  = 0.20). At the species level, canopy N concentration was strongly related to MTCI for European Beech plots ( r 2  = 0.71). These results present a new perspective on the application of MTCI time series for canopy N detection, ultimately leading towards the generation of canopy N maps that can be used to constrain global vegetation models. Keywords: vegetation index, MERIS, foliar nitrogen concentration, foliar nitrogen content, plant functional types, Mediterranean forest, remote sensing

[1]  S. Frolking,et al.  Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks , 2008, Proceedings of the National Academy of Sciences.

[2]  Valerie A. Thomas,et al.  Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales , 2014 .

[3]  Susan L. Ustin,et al.  Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices , 2014 .

[4]  Christopher B. Field,et al.  Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .

[5]  Peter B Reich,et al.  Key canopy traits drive forest productivity , 2012, Proceedings of the Royal Society B: Biological Sciences.

[6]  P. Reich,et al.  Different photosynthesis-nitrogen relations in deciduous hardwood and evergreen coniferous tree species , 1995, Oecologia.

[7]  Doreen S. Boyd,et al.  Phenology of vegetation in Southern England from Envisat MERIS terrestrial chlorophyll index (MTCI) data , 2011 .

[8]  Jan G. P. W. Clevers,et al.  Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[9]  Mary E. Martin,et al.  A plot-based method for rapid estimation of forest canopy chemistry , 2001 .

[10]  J. Peñuelas,et al.  Tree growth changes with climate and forest type are associated with relative allocation of nutrients, especially phosphorus, to leaves and wood , 2013 .

[11]  P. M. Hansena,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[12]  J. Peñuelas,et al.  The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. , 1994 .

[13]  Javier Pacheco-Labrador,et al.  Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[14]  John E. Erickson,et al.  Foliar morphology and canopy nitrogen as predictors of light-use efficiency in terrestrial vegetation , 2003 .

[15]  A. Arneth,et al.  Nitrogen controls plant canopy light-use efficiency in temperate and boreal ecosystems , 2008 .

[16]  S. Ollinger,et al.  Examining spectral reflectance features related to foliar nitrogen in forests: Implications for broad-scale nitrogen mapping , 2016 .

[17]  Philip Lewis,et al.  Hyperspectral remote sensing of foliar nitrogen content , 2012, Proceedings of the National Academy of Sciences.

[18]  J. Peñuelas,et al.  Factors affecting nutrient concentration and stoichiometry of forest trees in Catalonia (NE Spain) , 2011 .

[19]  Philip A. Townsend,et al.  Disentangling the contribution of biological and physical properties of leaves and canopies in imaging spectroscopy data , 2013, Proceedings of the National Academy of Sciences.

[20]  S. Ollinger,et al.  Net Primary Production and Canopy Nitrogen in a Temperate Forest Landscape: An Analysis Using Imaging Spectroscopy, Modeling and Field Data , 2005, Ecosystems.

[21]  J. Peñuelas,et al.  Trees increase their P:N ratio with size. , 2015, Global ecology and biogeography : a journal of macroecology.

[22]  Timo Pukkala,et al.  Characterization of forest fires in Catalonia (north-east Spain) , 2007, European Journal of Forest Research.

[23]  Giles M. Foody,et al.  Evaluation of Envisat MERIS Terrestrial Chlorophyll Index-Based Models for the Estimation of Terrestrial Gross Primary Productivity , 2012, IEEE Geoscience and Remote Sensing Letters.

[24]  Benjamin Smith,et al.  Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model , 2013 .

[25]  Andrew D. Friend,et al.  Carbon and nitrogen cycle dynamics in the O‐CN land surface model: 1. Model description, site‐scale evaluation, and sensitivity to parameter estimates , 2010 .

[26]  J. Retana,et al.  Forest management for adaptation to climate change in the Mediterranean basin: A synthesis of evidence , 2018 .

[27]  P. Reich,et al.  From tropics to tundra: global convergence in plant functioning. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Jadunandan Dash,et al.  The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation , 2010 .

[29]  S. G. Nelson,et al.  Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape , 2008, Sensors.

[30]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[31]  Marco Heurich,et al.  Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest , 2016, Remote. Sens..

[32]  Marie-Louise Smith,et al.  Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor , 2003, IEEE Trans. Geosci. Remote. Sens..

[33]  M. Cho,et al.  Assessing the effects of subtropical forest fragmentation on leaf nitrogen distribution using remote sensing data , 2013, Landscape Ecology.

[34]  A. Skidmore,et al.  Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species , 2005 .

[35]  K. Itten,et al.  Estimating foliar biochemistry from hyperspectral data in mixed forest canopy , 2008 .

[36]  X. Yao,et al.  Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance , 2011 .

[37]  Victor F. Rodriguez-Galiano,et al.  Characterising the Land Surface Phenology of Europe Using Decadal MERIS Data , 2015, Remote. Sens..

[38]  Paul J. Curran,et al.  Evaluation of the MERIS terrestrial chlorophyll index , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[39]  Andrew K. Skidmore,et al.  Estimation of grassland biomass and nitrogen using MERIS data , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[40]  J. R. Evans.  Photosynthesis and nitrogen relationships in leaves of C3 plants , 2004, Oecologia.

[41]  S. Ollinger,et al.  A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems , 2008 .

[42]  P. Reich,et al.  Generality of leaf trait relationships: a test across six biomes: Ecology , 1999 .

[43]  J. Shanahana,et al.  Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013 .

[44]  S. Ollinger,et al.  Foliar nitrogen in relation to plant traits and reflectance properties of New Hampshire forests , 2013 .

[45]  Anatoly A. Gitelson,et al.  Application of chlorophyll-related vegetation indices for remote estimation of maize productivity , 2011 .

[46]  Jadunandan Dash,et al.  A new approach for estimating northern peatland gross primary productivity using a satellite‐sensor‐derived chlorophyll index , 2011 .

[47]  Clement Atzberger,et al.  Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[48]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[49]  Giles M. Foody,et al.  Discriminating and mapping the C3 and C4 composition of grasslands in the northern Great Plains, USA , 2007, Ecol. Informatics.