New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863–881 nm) and the H18 (745–751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.

[1]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[2]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

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

[4]  S. Prince A model of regional primary production for use with coarse resolution satellite data , 1991 .

[5]  Hui Qing Liu,et al.  A feedback based modification of the NDVI to minimize canopy background and atmospheric noise , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Ranga B. Myneni,et al.  The interpretation of spectral vegetation indexes , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[7]  J. Everitt,et al.  Estimating grassland phytomass production with near-infrared and mid-infrared spectral variables , 1989 .

[8]  J. C. Price,et al.  Estimating leaf area index from satellite data , 1993, IEEE Trans. Geosci. Remote. Sens..

[9]  Damiano Gianelle,et al.  Ecosystem carbon fluxes and canopy spectral reflectance of a mountain meadow , 2009 .

[10]  N. Gobron,et al.  On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption , 2009 .

[11]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

[12]  R. Myneni,et al.  The interpretation of spectral vegetation indexes , 1995 .

[13]  E. Hunt,et al.  Estimating near-infrared leaf reflectance from leaf structural characteristics. , 2001, American journal of botany.

[14]  Katja Klumpp,et al.  Determination of Aboveground Net Primary Productivity and Plant Traits in Grasslands with Near-Infrared Reflectance Spectroscopy , 2010, Ecosystems.

[15]  J. Chen Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

[16]  M. Schildhauer,et al.  Spectral Network (SpecNet)—What is it and why do we need it? , 2006 .

[17]  A. Gitelson Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation. , 2004, Journal of plant physiology.

[18]  A. Huete,et al.  Optical-Biophysical Relationships of Vegetation Spectra without Background Contamination , 2000 .

[19]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[20]  P. Sellers Canopy reflectance, photosynthesis and transpiration , 1985 .

[21]  Eva Boegh,et al.  Evaluating evapotranspiration rates and surface conditions using Landsat TM to estimate atmospheric resistance and surface resistance , 2002 .

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

[23]  C. Tucker Asymptotic nature of grass canopy spectral reflectance. , 1977, Applied optics.

[24]  Andrew E. Suyker,et al.  Synoptic Monitoring of Gross Primary Productivity of Maize Using Landsat Data , 2008, IEEE Geoscience and Remote Sensing Letters.

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

[26]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[27]  Bernard Pinty,et al.  Evaluation of the performance of various vegetation indices to retrieve vegetation cover from AVHRR data , 1994 .

[28]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[29]  Wenjiang Huang,et al.  Estimating winter wheat plant water content using red edge parameters , 2004 .

[30]  R. Lucas,et al.  A review of remote sensing technology in support of the Kyoto Protocol , 2003 .

[31]  Vescovo Loris,et al.  Mapping the green herbage ratio of grasslands using both aerial and satellite-derived spectral reflectance , 2006 .

[32]  Barron J. Orr,et al.  Spectral vegetation indices and uncertainty: insights from a user's perspective , 2006, IEEE Transactions on Geoscience and Remote Sensing.