Advances in remote sensing of vegetation function and traits

Abstract Remote sensing of vegetation function and traits has advanced significantly over the past half-century in the capacity to retrieve useful plant biochemical, physiological and structural quantities across a range of spatial and temporal scales. However, the translation of remote sensing signals into meaningful descriptors of vegetation function and traits is still associated with large uncertainties due to complex interactions between leaf, canopy, and atmospheric mediums, and significant challenges in the treatment of confounding factors in spectrum-trait relations. This editorial provides (1) a background on major advances in the remote sensing of vegetation, (2) a detailed timeline and description of relevant historical and planned satellite missions, and (3) an outline of remaining challenges, upcoming opportunities and key research objectives to be tackled. The introduction sets the stage for thirteen Special Issue papers here that focus on novel approaches for exploiting current and future advancements in remote sensor technologies. The described enhancements in spectral, spatial and temporal resolution and radiometric performance provide exciting opportunities to significantly advance the ability to accurately monitor and model the state and function of vegetation canopies at multiple scales on a timely basis.

[1]  J. Dungan,et al.  Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. , 1990, Tree physiology.

[2]  Markus Reichstein,et al.  The imprint of plants on ecosystem functioning: A data-driven approach , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[3]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[4]  W. Verhoef Light scattering by leaf layers with application to canopy reflectance modeling: The Scattering by Arbitrarily Inclined Leaves (SAIL) model , 1984 .

[5]  Raul Zurita-Milla,et al.  Visualizing the ill-posedness of the inversion of a canopy radiative transfer model: A case study for Sentinel-2 , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[6]  J. Townshend,et al.  African Land-Cover Classification Using Satellite Data , 1985, Science.

[7]  Frédéric Baret,et al.  Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands , 1992 .

[8]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.

[9]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[10]  Matthew F. McCabe,et al.  Leaf chlorophyll constraint on model simulated gross primary productivity in agricultural systems , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Ruben Van De Kerchove,et al.  Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Alicia Palacios-Orueta,et al.  Ecosystem functional assessment based on the "optical type" concept and self-similarity patterns: An application using MODIS-NDVI time series autocorrelation , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[13]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[14]  P. Curran Remote sensing of foliar chemistry , 1989 .

[15]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[16]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[17]  Clement Atzberger,et al.  Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[18]  E. B. Knipling Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation , 1970 .

[19]  Takahiro Endo,et al.  A new 500-m resolution map of canopy height for Amazon forest using spaceborne LiDAR and cloud-free MODIS imagery , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[20]  J. Colwell Vegetation canopy reflectance , 1974 .

[21]  Mac McKee,et al.  Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Charles K. Gatebe,et al.  Observing system simulations for small satellite formations estimating bidirectional reflectance , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[23]  Wout Verhoef,et al.  A model for chlorophyll fluorescence and photosynthesis at leaf scale , 2009 .

[24]  Yuri Knyazikhin,et al.  Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem , 2003 .

[25]  Raymond F. Kokaly,et al.  Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[26]  Lammert Kooistra,et al.  An evaluation of remote sensing derived soil pH and average spring groundwater table for ecological assessments , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[27]  Jan Pisek,et al.  Spectral reflectance patterns and seasonal dynamics of common understory types in three mature hemi-boreal forests , 2015, Int. J. Appl. Earth Obs. Geoinformation.