A novel spectral index for estimating leaf water content using infrared atmospheric window edge bands

Atmospheric windows allow satellite sensors to efficiently collect surface spectral data with minimal atmospheric interference. In particular, water vapor exhibits weak absorption in the solar spectrum at the edge of the infrared atmospheric window. It is highly sensitive to variations in the moisture content of surface vegetation, soil, and other land cover features. Equivalent water thickness (EWT) is an essential biophysical parameter that reflects vegetation water content and indicates vegetation health. This study aims to develop a generalized spectral index within the infrared atmospheric window edge to accurately estimate vegetation water content. The PROSPECT is utilized to simulate the impact of various biophysical parameters on leaf reflectance. This study primarily focused on examining the relationship between spectral reflectance and the EWT of fresh leaves across different vegetation species at the leaf scale. In addition, atmospheric transmittance and radiance at 400-2500 nm were simulated using MODTRAN to determine the atmospheric window region. The results show that the 1276-1342 nm spectral range (at the edge of the infrared atmospheric window) is more sensitive to EWT estimation. Based on this sensitive band, the infrared atmospheric window index (IAWI) is proposed. In comparison to commonly utilized spectral indices, the IAWI introduced in this study exhibited a robust correlation. At the canopy scale, the validity of IAWI-estimated vegetation EWT under varying LAI and VZA conditions was analyzed using the PROSAIL model, with R-2 > 0.79. This study proposes a novel spectral index for estimating vegetation water content using the reflectance of the infrared atmospheric window region, providing a theoretical basis for vegetation water content estimation by satellite remote sensing. These results will aid in tracking critical properties and processes within vegetation and broader ecosystems.