Abstract
The use of data from multiple sensors is often required to ensure data coverage and continuity, but differences in the spectral characteristics of sensors result in spectral index values being different. This study investigates spectral response function effects on 48 spectral indices for cultivated grasslands using simulated data of 10 very high spatial resolution sensors, convolved from field reflectance spectra of a grass covered dike (with varying vegetation condition). Index values for 48 indices were calculated for original narrow-band spectra and convolved data sets, and then compared. The indices Difference Vegetation Index (DVI), Global Environmental Monitoring Index (GEMI), Enhanced Vegetation Index (EVI), Modified Soil-Adjusted Vegetation Index (MSAVI2) and Soil-Adjusted Vegetation Index (SAVI), which include the difference between the near-infrared and red bands, have values most similar to those of the original spectra across all 10 sensors (1:1 line mean 1:1R2 > 0.960 and linear trend mean ccR2 > 0.997). Additionally, relationships between the indices’ values and two quality indicators for grass covered dikes were compared to those of the original spectra. For the soil moisture indicator, indices that ratio bands performed better across sensors than those that difference bands, while for the dike cover quality indicator, both the choice of bands and their formulation are important.
Highlights
Remote sensing data are widely used for vegetation, environmental, hazard and land process monitoring and assessment from local through to global scales
Since index values from different sensors are not directly comparable, this paper aims to investigate the effects of differing spectral response functions (SRFs) of various very high spatial resolution sensors on the cross-calibration of numerous spectral indices in the context of cultivated grasslands, that are typically found on dikes and levees that do not have a hardened cover
This paper examined the effects of differing spectral response functions (SRFs) on the cross-calibration of a large number of indices across various very high spatial resolution sensors for cultivated grasslands
Summary
Remote sensing data are widely used for vegetation, environmental, hazard and land process monitoring and assessment from local through to global scales. Data from multiple sensors are often used in order to ensure coverage and continuity, due to limitations of satellite revisit time [1], cloud cover [1,2] as well as satellite design life [3]. Data obtained from different sensors are not directly comparable [1,4]. One of the main causes of differences in remote sensing data is the difference in spectral response functions (SRFs) between sensors [5,6,8,9,10]. The effect may be such as to mask subtle natural variability that is of interest [8,10]
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