Using a MODIS Index to Quantify MODIS-AVHRRs Spectral Differences in the Visible Band

Abstract

Spectral band differences are a critical issue for progressing into an integrated earth observation framework and in particular, in sensor intercalibration. The differences are currently normalized using spectral band adjustment factor (SBAF) that is generated from hyperspectral data. In this context, the current study proposes a method for calculating moderate-resolution imaging spectroradiometer (MODIS)-advanced very high resolution radiometers (AVHRRs) SBAF in the visible band, using the MODIS surface reflectance data. The method involves a uniform ratio index calculated using the MODIS 552-nm and 645-nm bands, and a sensor-specific quadratic equation, producing SBAF data at 500-m spatial resolution. The calculated SBAFs are in good agreement at site scale with literature reported data (relative error < 1.0%), and at local scale with Hyperion-derived data (total uncertainty ≈ 0.001), and significantly improve MODIS-AVHRR surface reflectance data consistency in the visible band (better than 1.0% reflectance units). The calculation is more sensitive to atmospheric effects over the vegetated areas. At global scale, MODIS-AVHRRs SBAFs are generally large (>1.0) over densely vegetated areas and extremely low over deserts and barren lands (0.96-0.98), indicative of large MODIS-AVHRRs differences. Deserts show temporally stable SBAF values, while still suffer from intra-annual BRDF effects and short-term cloud contamination. By means of daily MODIS data, the proposed method can produce ongoing SBAF data at a spatial scale that is comparable to AVHRRs. It increases the sampling of MODIS-AVHRRs image pairs for intercalibration, and offers insight into spectral band conversion, finally contributing to an integrated earth observation at moderate spatial resolutions.

Analytical Relationship between Two-Band Spectral Vegetation Indices Measured at Multiple Sensors on a Parametric Representation of Soil Isoline Equations

“…One type includes pure soil-dependent variables, while another corresponds to reflectance-dependent variables. In other words, the latter is the effect of higher-order terms in soil isoline equations in the intersensor VI relation model, which are mathematically defined as elements of G ε in Equation (30). The effect in the VI relation model is unavoidable factors propagated from soil isoline non-linearity under a separating operation for soil isoline equations proposed in this study.…”

“…These studies addressed clearly the importance of resolving land cover dependencies upon intersensor calibration from a theoretical point of view. Most remarkable was Fan et al (2014) introducing and employing a theory of radiative transfer and showing its potential as a feasible approach for intercalibration among multiple sensors [25,28,30,31]. Their work inspired us to further investigate the issue of land cover dependency over the intersensor VI-to-VI relationships.…”

Compositing the Minimum NDVI for Daily Water Surface Mapping

“…Due to different spectral response functions (relative spectral responses), however, the inter-sensor NDVI differences should be adjusted in advance before any compositing. In most cases, a linear equation is qualified for a reliable spectral adjustment [70][71][72]. Within the framework of NDVI intercalibration, moderate-resolution and high-resolution images can be combined for water surface mapping.…”