Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications

Consistent NDVI time series are paramount in monitoring ecological resources that are being altered by climate and human impacts. An increasing number of natural resource managers use web-based geospatial decision support tools that integrate time series of both historical and current NDVI data derived from multiple sensors to make better informed planning and management decisions. Representative canopy reflectance and NDVI data were simulated for historical, current and future AVHRR, MODIS and VIIRS land surface monitoring satellites to quantify the differences due to sensor-specific characteristics. Cross-sensor NDVI translation equations were developed for surface conditions. The effect of a range of atmospheric conditions (Rayleigh scattering, ozone, aerosol optical thickness, and water vapor content) on the sensor-specific reflectance and NDVI values were evaluated to quantify the uncertainty in the apparent NDVI for each sensor. MODIS and VIIRS NDVI data are minimally affected by the atmospheric water vapor, while AVHRR NDVI data are substantially reduced by water vapor. Although multi-sensor NDVI continuity can be obtained by using the developed cross-sensor translation equations, the interactions between the spectral characteristics of surface vegetation and soil components, sensor-specific spectral band characteristics and atmospheric scattering and absorption windows will introduce uncertainty due to insufficient knowledge about the atmospheric conditions that affect the signal of the Earth's pixels at the time of data acquisitions. Processing strategies and algorithm preferences among data streams are also hindering cross-sensor NDVI continuity.