Joint characterization of vegetation by satellite observations from visible to microwave wavelengths: A sensitivity analysis

Abstract

This study presents an evaluation and comparison of visible, near‐infrared, passive and active microwave observations for vegetation characterization, on a global basis, for a year, with spatial resolution compatible with climatological studies. Visible and near‐infrared observations along with the Normalized Difference Vegetation Index come from the Advanced Very High Resolution Radiometer. An atlas of monthly mean microwave land surface emissivities from 19 to 85 GHz has been calculated from the Special Sensor Microwave/Imager for a year, suppressing the atmospheric problems encountered with the use of simple channel combinations. The active microwave measurements are provided by the ERS‐1 scatterometer at 5.25 GHz. The capacity to discriminate between vegetation types and to detect the vegetation phenology is assessed in the context of a vegetation classification obtained from in situ observations. A clustering technique derived from the Kohonen topological maps is used to merge the three data sets and interpret their relative variations. NDVI varies with vegetation density but is not very sensitive in semiarid environments and in forested areas. Spurious seasonal cycles and large spatial variability in several areas suggest that atmospheric contamination and/or solar zenith angle drift still affect the NDVI. Passive and active microwave observations are sensitive to overall vegetation structure: they respond to absorption, emission, and scattering by vegetation elements, including woody parts. Backscattering coefficients from ERS‐1 are not sensitive to atmospheric variations and exhibit good potential for vegetation discrimination with ∼10 dB dynamic range between rain forest to arid grassland. Passive microwave measurements also show some ability to characterize vegetation but are less sensitive than active measurements. However, passive observations show sensitivity to the underlying surface wetness that enables detection of wetlands even in densely vegetated areas. Merging the data sets using clustering techniques capitalizes on the complementary strengths of the instruments for vegetation discrimination and shows promising potential for land cover characterization on a global basis.