Biases in estimated vegetation indices from observations under cloudy conditions

Abstract. Field observations of vegetation indices (VIs) from drones and aircraft provide higher spatial resolution than satellites. Vegetation indices are derived from ratios of spectral reflectivity measurements. The reflectivity is measured in a relative way by periodic reference measurements over reflectance panels. This requires cloud-free or at least stable cloud conditions between reflectance panel measurements. This assumption is often violated, with the effect that wavelength-dependent scattering and absorption of radiation by clouds lead to a distortion of the below-cloud spectral downward irradiance F^↓(λ) and thus affects estimates of VIs.

This paper presents combined atmosphere-vegetation radiative transfer (RT) simulations to systematically investigate cloud-induced biases in remotely sensed VIs derived from below-cloud measurements. The biases in VIs have been investigated for the general case of two-band VIs, 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 found to be almost insensitive to changes in cloud conditions, while greater biases were identified for the NDWI. The EVI was also found to be susceptible to cloud changes, leading to biases of 0.36 in the selected example with biases in the estimated leaf area index of 1.3.