Effect of vegetation density and vegetation conditions on the spectral backscattering in the visible and the near infrared

The work presented in this paper investigates the sensitivity of the hyperspectral remotely sensed data to the vegetation density under different soil moisture conditions. The research testbed comprised four corn plots with 4 different densities, one grass plot, and one bare soil plot. For this purpose, the hyperspectral data were recorded simultaneously as the field measurements, which included soil moisture and temperature, soil characterization (gravimetric soil moisture, bulk density, surface roughness), and vegetation measurements (biomass; plant height; leaf orientations, length, thickness; dielectric constant of stalks and leaves; stalk diameter and height). The findings of this study showed that physical and physiological aspects, as well as the structure of the vegetation, have noticeable effects on its spectral response. The results showed distinct spectral response among the different vegetation densities, thus biomass. They also showed that hyperspectral data are effective in detecting soil moisture variability and discriminating among vegetation densities and conditions. The hyperspectral data were in agreement with the ground data and discriminated among small variations in soil moisture and vegetation densities and conditions. This study also showed that the variation in the spectral variability from different vegetation densities becomes negligible when the vegetation leaves cover completely the ground surface.

[1]  Jan G. P. W. Clevers,et al.  A simplified approach for yield prediction of sugar beet based on optical remote sensing data , 1997 .

[2]  Teferi D. Tsegaye,et al.  Precision agriculture with hyperspectral remotely sensed data, GIS, and GPS technology: a step toward environmentally responsible farming , 1998, Remote Sensing.

[3]  Ghassem R. Asrar,et al.  Assessing grassland biophysical characteristics from spectral measurements , 1986 .

[4]  C. Wiegand,et al.  Comparison of two models for simulating the soil-vegetation composite reflectance of a developing cotton canopy , 1990 .

[5]  Roger N. Clark,et al.  Initial vegetation species and senescience/stress indicator mapping in the San Luis Valley, Colorado using imaging spectrometer data , 1995 .

[6]  M. Owe,et al.  Microwave vegetation optical depth and inverse modelling of soil emissivity using Nimbus/SMMR satellite observations , 1994 .

[7]  Rosamaria Salvatori,et al.  Mediterranean vegetation analysis by multi-temporal satellite sensor data , 1997 .

[8]  R. M. Hoffer,et al.  Biomass estimation on grazed and ungrazed rangelands using spectral indices , 1998 .

[9]  T. Jackson,et al.  Use of active and passive microwave remote sensing for soil moisture estimation through corn , 1996 .

[10]  Curtis E. Woodcock,et al.  The effect of spatial resolution on the ability to monitor the status of agricultural lands , 1997 .

[11]  A. Huete.  Soil‐Dependent Spectral Response in a Developing Plant Canopy1 , 1987 .

[12]  H. Poilvé,et al.  Hyperspectral Imaging and Stress Mapping in Agriculture , 1998 .

[13]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[14]  C. A. Laymon,et al.  Huntsville '96: An experiment in ground-based microwave remote sensing of soil moisture , 1999 .

[15]  P. J. Curran,et al.  Airborne MSS data to estimate GLAI , 1987 .

[16]  Michael Jasinski,et al.  Estimation of subpixel vegetation density of natural regions using satellite multispectral imagery , 1996, IEEE Trans. Geosci. Remote. Sens..

[17]  P. J. Curran,et al.  The importance of measurement error for certain procedures in remote sensing at optical wavelengths , 1986 .

[18]  T. Schmugge,et al.  Vegetation effects on the microwave emission of soils , 1991 .

[19]  Thomas J. Jackson,et al.  Attenuation of soil microwave emission by corn and soybeans at 1.4 and 5 GHz , 1990 .

[20]  Hamza Erol,et al.  A multispectral classification algorithm for classifying parcels in an agricultural region , 1996 .

[21]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .