Spectral compatibility of vegetation indices across sensors: band decomposition analysis with Hyperion data

Abstract

Abstract. Vegetation indices (VIs) are widely used in long-term measurement studies of vegetation changes, including seasonal vegetation activity and interannual vegetation-climate interactions. There is much interest in developing cross-sensor/multi-mission vegetation products that can be extended to future sensors while maintaining continuity with present and past sensors. In this study we investigated multi-sensor spectral bandpass dependencies ofthe enhanced vegetation index (EVI), a 2-band EVI (EVI2), and the normalized difference vegetation index (NDVI) using spectrally convolved Earth Observing-l (EO-I) Hyperion satellite images acquired over a range of vegetation conditions. Two types of analysis were carried out, including (1) empirical relationships among sensor reflectances and VIs and (2) decomposition of bandpass contributions to observed cross-sensor VI differences. VI differences were a function of cross-sensor bandpass disparities and the integrative manner in which bandpass differences in red, near-infrared (NIR), and blue reflectances combined to influence a VI. Disparities in blue bandpasses were the primary cause of EVI differences between the Moderate Resolution Imaging Spectroradiometer (MODIS) and other course resolution sensors, including the upcoming Visible Infrared Imager / Radiometer Suite (VIIRS). The highest compatibility was between VIIRS and MODIS EVI2 while A VHRR NDVI and EVI2 were the least compatible to MODIS.

Estimation of crop gross primary production (GPP): I. impact of MODIS observation footprint and impact of vegetation BRDF characteristics

“…Relative change in "a" (y = ax + b) The values of any particular VI for same field may vary when determined from different sensors (Kim et al, 2010;Miura et al, 2006). For instance, the US-NE1 CI green during 7/11-7/20 of 2001-2004 computed from the field measured surface reflectance provided by UNL (field CI green : 9.6-11.4) (Gitelson et al, 2006) was greater than the CI green of the same field at the same time period derived from MODIS surface reflectance (MODIS CI green : 7.0-8.8).…”

Towards decadal soil salinity mapping using Landsat time series data

“…Generally, the regression method is relatively straightforward. The regression model can be obtained via comparison of dual images (Miura et al, 2008), hyperspectral data (Kim et al, 2010) and simulated data (Gonsamo and Chen, 2013). However, while the model is generally thought to vary with land cover (Miura et al, 2006), the regression method may be suitable for a unique land cover, e.g.…”

Spatial autocorrelation of ndvi and gvi indices derived from landsat/tm images for soybean crops in the western of the state of Paraná in 2004/2005 crop season

“…According to KIM et al (2010), vegetation indices such as NDVI, are widely used in long-term measurement studies of vegetation changes, including seasonal vegetation activity and interannual vegetation-climate interactions. The utility of the NDVI index has been demonstrated in various areas, such as precision agriculture (PARISE & VETTORAZZI, 2005), study of the vegetation phenology and monitoring rice cultivation areas (CHEN et al, 2011).…”