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Abstract
During the 2001 EO-1 campaign in Argentina, two high spectral resolution image scenes of AVIRIS were acquired at two study sites in the Patagonia region of southern Argentina on 15 February 2001. A total of 70 LAI measurements were taken from different forest types in the same areas one month later, and some spectroradiometric measurements were also collected from the nearby highway and different forest stands in the areas. In this study, we compared the effectiveness of the three types of AVIRIS data used for estimating and mapping LAI. The three types of data correspond to AVIRIS original radiance (OR), corrected radiance (CR) and retrieved surface reflectance (SR). We first simulated the total at-sensor radiances using MODTRAN4, then used ground spectroradiometric measurements taken from different targets to improve the reflectances for each pixel on the image. The CR images were obtained by subtracting path radiance from the OR images. A 10-term LAI prediction model for each type of data was constructed to predict pixel-based LAI values. Finally, the pixel-based LAI value was sliced and mapped for all the three types of images. The results of mapping LAI using the three types of AVIRIS data (OR, CR and SR) indicate that mapping LAI by SR is the most realistic, followed by CR. The poorest result occurs when mapping LAI with OR data due to atmospheric effect. The SR data can lead to higher correlation with LAI in some bands and produce higher accuracy indices for the 10-term predictive model, although some indices from the test set for SR data have a somewhat lower correlation with LAI than those produced with OR data. Therefore, in general, it can be concluded that the retrieved surface reflectance data is more effective for mapping forest LAI compared to the other two types of data.
Acknowledgments
This research was partially supported by a NASA EO-1 science validation grant (NCC5-492) and field support provided by the forest plantation inventory section of the Secretariat of Agriculture, Livestock, Fisheries and Food of the government of Argentina. Assistance in field work by Mirta Rosa Larrieu, Gillsermo Defossé, Florencia Farías, and Maria Cristina Frugoni is appreciated.