Remote Estimation of Canopy Water Content in Different Crop Types with New Hyperspectral Indices

Abstract

A diverse range of vegetation indices have earlier been developed for the remote estimation of canopy water content (CWC), but most of them are not universally applicable. The aim of this study is to define new indices valid for a wide variety of crop types, that allow to obtain CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain), which consists of field data including water content and other biophysical parameters collected for 6 different crops (lucerne, corn, potato, sugar beet, garlic and onion) and associated TOC reflectance spectra acquired by the HyMap airborne sensor. Specifically, Water Absorption Area Index (WAAI) has been defined as the area between the spectrum with null water content, i.e. a straight line whose slope depends only on the reflectance at 800 nm, and the spectrum between 911 and 1271 nm. On the other hand, it is proposed the Depth Water Index (DWI), which is a simple index, applicable to those sensors with lower spectral resolution, based on the spectral depths estimation produced by the water absorption at 970 and 1200 nm. These algorithms outperform commonly used indices in predicting CWC, being applicable to heterogeneous zones, with a R2 of 0.8 and 0.7, respectively, using an exponential fit.