Estimating Vegetation Water content with Hyperspectral data for different Canopy scenarios: Relationships between AVIRIS and MODIS Indexes

Three linked leaf and canopy radiative transfer models were used to assess uncertainties in three vegetation architectures for the relationships between canopy water content and Equivalent Water Thickness (EWT). The leaf radiative transfer model PROSPECT was linked to SAILH, rowMCRM, and FLIM canopy reflectance models to generate synthetic spectra for a range of leaf and canopy parameters under closed, rowstructured, and forest canopy architectures, respectively. Leaf water content (Cw) and leaf area index (LAI) were used to calculate canopy water content. Leaf and canopy parameters that affect the retrieval of EWT, estimated by the MODTRAN-based fitting technique, were used to investigate their influence on the water content estimates. Results showed a consistent relationship between retrieved EWT and canopy water content for the different simulated architectural scenarios. It was shown that EWT was negatively affected by leaf dry matter and soil background. Retrievals of EWT from hyperspectral Advanced Visible Infrared Imaging Spectrometer (AVIRIS) imagery at three study sites were then used for cross-validation of the Moderate Resolution Imaging Spectrometer (MODIS) data, assessing the behavior of NDVI, EVI, NDWI, and SIWSI as potential indicators of vegetation water content. All four MODIS indexes showed consistent agreement with retrievals of EWT from AVIRIS imagery at the agricultural site and the savanna–shrub site, with EVI having the highest correlation. However, at the conifer forest study site the two water indexes, NDWI and SIWSI, yielded better agreement with retrievals of EWT than NDVI and EVI. The performance of NDVI was inconsistent across sites. This manuscript demonstrates the importance of canopy architecture when estimating EWT by showing that large errors are obtained when EWT estimates derived from absorption feature curve-fitting are applied to different canopy types. These errors are propagated in simple indexes that produce inconsistent results when applied to divergent vegetation conditions.Portions of this study were supported by National Aeronautics and Space Administration (NASA) under Grant No. NNG0 4GQ42G “Global Estimation of Canopy Water Content” and Contract No. NAG5-9360 “New EOS Data Products to Improve Biogeochemical Estimates” and support from the Cotton Foundation, #5002-008-00, “Evaluation of Advanced Hyperspectral Remote Sensing for Detection of Cotton Water Stress and Irrigation Refinement”. Financial support was provided to P.J Zarco- Tejada under the “Ramón y Cajal” and “Averroes” Programs from Spain are acknowledged. Financial support to D. Riaño under the “Ramón y Cajal” Program of Ministry of Science and Technology from Spain is acknowledged.Peer reviewe