TY - JOUR
T1 - Spectral unmixing of vegetation, soil and dry carbon cover in arid regions
T2 - Comparing multispectral and hyperspectral observations
AU - Asner, G. P.
AU - Heidebrecht, K. B.
N1 - Funding Information:
We would like to tnhk B.aSawtelle, C. Cody, and B. Nolen for their inavleuabl asssaitenin thce Ž eld studies pesreed innthstpipra. Wee would also like to tnhk a two anosnreviewyersmof the moanuut ansd Ac. Krieliiapfor providinrng editorial giduna. Wce aleso tnhk theapersonnel from Joaa Ldron-ntm Eegcroical Rlesearcohg prra,othemUSDg A Jornada Experimental Rane ogYc,aned tehNSA AVISIR Program. This work was supported by NASA NIP grant NAG8957and-0NA AS New Millenium (EO-1) Proam grangt rNCC540-t8o G. Asner.
PY - 2002
Y1 - 2002
N2 - Remote measurements of the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil are critical to understanding climate and land-use controls over the functional properties of arid and semi-arid ecosystems. Spectral mixture analysis is a method employed to estimate PV, NPV and bare soil extent from multispectral and hyperspectral imagery. To date, no studies have systematically compared multispectral and hyperspectral sampling schemes for quantifying PV, NPV and bare soil covers using spectral mixture models. We tested the accuracy and precision of spectral mixture analysis in arid shrubland and grassland sites of the Chihuahuan Desert, New Mexico, USA using the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). A general, probabilistic spectral mixture model, Auto-MCU, was developed that allows for automated sub-pixel cover analysis using any number or combination of optical wavelength samples. The model was tested with five different hyperspectral sampling schemes available from the AVIRIS data as well as with data convolved to Landsat TM, Terra MODIS, and Terra ASTER optical channels. Full-range (0.4-2.5 μm) sampling strategies using the most common hyperspectral or multispectral channels consistently over-estimated bare soil extent and under-estimated PV cover in our shrubland and grassland sites. This was due to bright soil reflectance relative to PV reflectance in visible, near-IR, and shortwave-IR channels. However, by utilizing the shortwave-IR 2 region (SWIR2; 2.0-2.3 μm) with a procedure that normalizes all reflectance values to 2.03 μm, the sub-pixel fractional covers of PV, NPV and bare soil constituents were accurately estimated. AVIRIS is one of the few sensors that can provide the spectral coverage and signal-to-noise ratio in the SWIR2 to carry out this particular analysis. ASTER, with its 5-channel SWIR2 sampling, provides some means for isolating bare soil fractional cover within image pixels, but additional studies are needed to verify the results.
AB - Remote measurements of the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil are critical to understanding climate and land-use controls over the functional properties of arid and semi-arid ecosystems. Spectral mixture analysis is a method employed to estimate PV, NPV and bare soil extent from multispectral and hyperspectral imagery. To date, no studies have systematically compared multispectral and hyperspectral sampling schemes for quantifying PV, NPV and bare soil covers using spectral mixture models. We tested the accuracy and precision of spectral mixture analysis in arid shrubland and grassland sites of the Chihuahuan Desert, New Mexico, USA using the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). A general, probabilistic spectral mixture model, Auto-MCU, was developed that allows for automated sub-pixel cover analysis using any number or combination of optical wavelength samples. The model was tested with five different hyperspectral sampling schemes available from the AVIRIS data as well as with data convolved to Landsat TM, Terra MODIS, and Terra ASTER optical channels. Full-range (0.4-2.5 μm) sampling strategies using the most common hyperspectral or multispectral channels consistently over-estimated bare soil extent and under-estimated PV cover in our shrubland and grassland sites. This was due to bright soil reflectance relative to PV reflectance in visible, near-IR, and shortwave-IR channels. However, by utilizing the shortwave-IR 2 region (SWIR2; 2.0-2.3 μm) with a procedure that normalizes all reflectance values to 2.03 μm, the sub-pixel fractional covers of PV, NPV and bare soil constituents were accurately estimated. AVIRIS is one of the few sensors that can provide the spectral coverage and signal-to-noise ratio in the SWIR2 to carry out this particular analysis. ASTER, with its 5-channel SWIR2 sampling, provides some means for isolating bare soil fractional cover within image pixels, but additional studies are needed to verify the results.
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U2 - 10.1080/01431160110115960
DO - 10.1080/01431160110115960
M3 - Article
AN - SCOPUS:0036378759
SN - 0143-1161
VL - 23
SP - 3939
EP - 3958
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
IS - 19
ER -