Deriving Water Content of Chaparral Vegetation from AVIRIS Data

Spectral imaging data acquired with Advanced Visible Infrared Imaging Spectrometer over Point Dume (Los Angeles County, CA, USA) were used to assess the ability of hyperspectral reflectance data to estimate canopy Relative Water Content (RWC) at the landscape level. The study was performed on 23 vegetation stands comprised of three characteristic chaparral plant communities, with contrasting phenological stages and canopy cover. Several estimates of water content based on the near-infrared (NIR; reflectance indices and water thickness derived from reflectance and radiance data) and shortwave infrared (SWIR) water absorption bands were compared to measurements of vegetation structure and water content made on the ground. The Water Index (WI) and Normalized Difference Water Index (NDWI), reflectance indices formulated from the NIR water absorption bands, were the best indicators of canopy RWC estimated from combining leaf relative water content with measures of canopy structure. A stepwise multiple regression revealed that canopy structure explained 36% and 41% of the variation in WI and NDWI, respectively. The explained variance in WI and NDWI increased to 44% and 48% when leaf relative water content was included in the model. By contrast, the inclusion of leaf relative water content did not contribute significantly to the explained variance in indices formulated using SWIR water absorption bands and in those based on water thickness. The relationship between WI and the canopy RWC significantly improved when only data from plots with green vegetation cover >70% were considered (r2=0.88, p<0.001). All the indices studied had an important structural component (as indicated by the strong correlation with NDVI), yet only the indices WI and NDWI additionally responded to water content. These results indicate that the WI and NDWI are sensitive to variations in canopy relative water content at the landscape scale.