Geostatistical scaling of canopy water content in a California salt marsh

Remote sensing data are typically collected at a scale which is larger in both grain and extent than traditional ecological measurements. To compare with remotely sensed data on a one-to-one basis, field measurements frequently must be rescaled to match the grain of image data. Once a one-to-one correspondence is established, it may be possible to extrapolate site based relationships over a wider extent. This paper presents a methodology for rescaling the grain of ecological field data to match the grain of remotely sensed data and gives an example of the method in verification of remote sensing estimates of canopy water content in a tidal salt marsh. We measured canopy water content at 169 points on a semi-regular grid in the Petaluma Marsh, CA. A variogram describing the spatial correlation structure of the canopy water content was calculated and modeled. Ordinary kriging estimates of the canopy water content were calculated over blocks corresponding to image pixels acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). A water content index was determined from the reflectance data by calculating the area of a water absorption feature at 970 nm. A regression developed between the blocks and the pixels at the site was extrapolated over the image to obtain an estimate of canopy water content for the entire marsh. The patterns of canopy water content at the site and landscape levels suggest that different processes are important for determining patterns of canopy water content at different spatial extents. The errors involved in the rescaling procedures and the remote sensing interpretation are discussed.