Hyperspectral chlorophyll indices sensitivity analysis to soil backgrounds in agrirultural aplications using field, Probe-1 and Hyperion data

This paper focuses on the evaluation and comparison of the sensitivity of several chlorophyll indices to bare soils optical property variations. To achieve our goal, field spectroradiometric measurements were used as well as hyperspectral data acquired with the Probe-1 airborne and Hyperion EO-1 satellite sensors. The field-based reflectance measurements were acquired above 90 bare soil plots with various optical properties and selected from different agricultural lands. Probe-1 and Hyperion data were spectrally and radiometrically calibrated as well as atmospherically corrected. After these pre-processing steps, sixty spectra of different bare soils with various optical properties were extracted from each dataset for use in the analysis. The obtained results show an excellent agreement between the accuracies estimated from field, airborne and satellite data. Independently from the data source and from the bare soil background, CARI, MCARI and TCARI indices are basically not sensitive to changes in soil optical properties with an RMSE less than 1% and will permit a better estimation of chlorophyll content in sparse crop cover environment.

[1]  Paul Budkewitsch,et al.  Vicarious calibration of airborne hyperspectral sensors in operational environments , 2001 .

[2]  K. Staenz,et al.  Retrieval of surface reflectance from hyperspectral Data using a look-up table approach , 1997 .

[3]  Moon S. Kim,et al.  The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (A par) , 1994 .

[4]  A. Gitelson,et al.  Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm , 1996 .

[5]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

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

[7]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

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

[9]  George Alan Blackburn,et al.  Relationships between Spectral Reflectance and Pigment Concentrations in Stacks of Deciduous Broadleaves , 1999 .

[10]  Karl Staenz,et al.  A Comparison of Hyperspectral Chlorophyll Indices for Wheat Crop Chlorophyll Content Estimation Using Laboratory Reflectance Measurements , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[11]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[12]  G. A. Blackburn,et al.  Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves , 1998 .

[13]  Robert A. Neville,et al.  Preprocessing of EO-1 Hyperion data , 2006 .

[14]  N. Oppelt,et al.  Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data , 2004 .

[15]  K. Staenz,et al.  Potential of Hyperion EO-1 hyperspectral data for wheat crop chlorophyll content estimation , 2008 .

[16]  Heather McNairn,et al.  Estimation of Crop Cover and Chlorophyll from Hyperspectral Remote Sensing , 2001 .

[17]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[18]  R. P. Gauthier,et al.  Vicarious Calibration of Hyperspectral Sensors in Operational Environments , 2001 .

[19]  J. A. Schell,et al.  Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor] , 1973 .

[20]  N. Oppelt,et al.  The chlorophyll content of maize (zea mays) derived with the Airborne Imaging Spectrometer AVIS , 2001 .

[21]  J. Peñuelas,et al.  The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .