Improving the analysis of Hyperion red-edge index from an agricultural area

The benefits of EO-1 data, and especially Hyperion hyperspectral data, are being studied at sites in the Coleambally Irrigation Area of Australia where a seasonal time series has been developed. Hyperion can provide effective measures of agricultural performance through the use of spectral indices if systematic and random noise is managed and such noise management methods have been established for Coleambally. Among the sources of noise specific to Hyperion is the spectral “smile” which affects the location of the red-edge -- an important index in agricultural assessment. We show how this phenomenon, which arises from the pushbroom technology of Hyperion, affects the data and discuss how its effects can be overcome to provide stable and accurate measures of the red-edge and related indices. HyMap airborne data are used to evaluate the results of the methods studied. This paper also shows how future pushbroom instruments should consider the wavelength sampling step in their design if it is intended to remove the “smile” effects by a systematic software processing.

[1]  J. Peñuelas,et al.  The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. , 1994 .

[2]  Tim R. McVicar,et al.  Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes , 2003, IEEE Trans. Geosci. Remote. Sens..

[3]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[4]  David L.B. Jupp,et al.  The Lake Frome field campaign in support of Hyperion instrument calibration and validation , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[5]  R. Jenssen,et al.  1 THE HYMAP TM AIRBORNE HYPERSPECTRAL SENSOR : THE SYSTEM , CALIBRATION AND PERFORMANCE , 1998 .

[6]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[7]  P. Jarecke,et al.  Overview of the Hyperion Imaging Spectrometer for the NASA EO-1 mission , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[8]  J. Dungan,et al.  The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration , 1991 .

[9]  S. Ungar,et al.  Overview of EO-1, the first 120 days , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

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

[11]  B. M. Brown.  The mathematical theory of linear systems , 1962 .

[12]  Paul E. Lewis,et al.  FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[13]  Zheng Qu,et al.  Atmospheric correction of Hyperion data and techniques for dynamic scene correction , 2002, IEEE International Geoscience and Remote Sensing Symposium.