Biases in estimated vegetation indices from observations under cloudy conditions

Abstract

Field observations of vegetation indices (VIs) are derived from ratios of spectral reflectance data that are collected by drones and aircraft, providing higher spatial resolution than satellites. These reflectance data require periodic reference measurements over calibrated reflectance panels under cloud-free conditions. However, the reference measurements are partly performed in cloudy situations with the effect that wavelength-dependent scattering and absorption of solar radiation by clouds affects the subsequently derived VIs. This paper quantifies these effects using combined atmosphere-vegetation radiative transfer (RT) simulations. We study the general case when VIs are obtained from reflectance ratios of two wavelengths, and for the special cases of the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the enhanced vegetation index (EVI). For the general case of two-band VIs the lowest sensitivity to cloud changes was found for wavelength combinations below 1400 nm and outside the water vapor absorption bands. The NDVI was almost insensitive to changes in cloud conditions, while greater biases were identified for the NDWI. The EVI was most susceptible to cloud changes, with biases of 0.2 in the selected example. This lead to biases in the estimated leaf area index of 0.9. Biophysical properties derived from EVI, such as gross primary product, are also affected with variations of up to ±2 gCm-2d-1 in the selected cases.