The NSF-supported Biocomplexity project at Barrow, Alaska, is a large scale hydrological manipulation experiment investigating the effect of soil moisture variation on carbon balance. A robotic tramline system was used to collect hyperspectral reflectance data in the vis-nearIR range using a portable spectrometer onboard the cart for the 2007 and 2008 growing seasons. This study investigates if hyperspectral reflectance data can be used to estimate the water table depth on the experimental basin.

Results/Conclusions

Two methods were used to estimate the water table depth, the water band index (WBI = R900/ R970), and the 970 nm reflectance band. WBI and 970 nm reflectance were used to develop a regression model for the estimation of water table depth the flooded, drained and control treatment areas. Our analysis shows a strong logarithmic relationship between the 970nm reflectance band and the water table depth (r²=0.83), consistent with the Beer-Lambert Law.   However, the water band index (WBI) was not able to predict the water table depth successfully (r² = 0.1). This finding indicates the potential for remote sensing to scale up water table depth measurements for shallow water bodies to the landscape level in an arctic tundra landscape.