Abstract
Geostationary (GEO) satellite sensors provide earth observation data with a high temporal frequency and can complement low earth orbit (LEO) sensors in monitoring terrestrial vegetation. Consistency between GEO and LEO observation data is thus critical to the synergistic use of the sensors; however, mismatch between the sun–target–sensor viewing geometries in the middle-to-high latitude region and the sensor-specific spectral response functions (SRFs) introduce systematic errors into GEO–LEO products such as the Normalized Difference Vegetation Index (NDVI). If one can find a parameter in which the value is less influenced by geometric conditions and SRFs, it would be invaluable for the synergistic use of the multiple sensors. This study attempts to develop an algorithm to obtain such parameters (NDVI-based indices), which are equivalent to fraction of vegetation cover (FVC) computed from NDVI and endmember spectra. The algorithm was based on a linear mixture model (LMM) with automated computation of the parameters, i.e., endmember spectra. The algorithm was evaluated through inter-comparison between NDVI-based indices using off-nadir GEO observation data from the Himawari 8 Advanced Himawari Imager (AHI) and near-nadir LEO observation data from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference over land surfaces in Japan at middle latitudes. Results showed that scene-dependent biases between the NDVI-based indices of sensors were −0.0004±0.018 (mean ± standard deviation). Small biases were observed in areas in which the fractional abundances of vegetation were likely less sensitive to the view zenith angle. Agreement between the NDVI-based indices of the sensors was, in general, better than the agreement between the NDVI values. Importantly, the developed algorithm does not require regression analysis for reducing biases between the indices. The algorithm should assist in the development of algorithms for performing inter-sensor translations of vegetation indices using the NDVI-based index as a parameter.
Highlights
Satellite remote sensing has facilitated biosphere monitoring by, for example, mapping terrestrial vegetation over the past two decades using polar-orbiting, low earth orbit (LEO) satellites [1,2,3]
The Advanced Himawari Imager (AHI) view angle was closer to the principal plane (Figure 6b) and the reflectances were sensitive to the backscattering effects that increase the reflectances
The results indicate that the variability of Normalized Difference Vegetation Index (NDVI)-based index differences was comparable to that of the NDVI differences while biases between NDVI-based indices of sensors were mitigated by using the developed algorithm, as shown in previous subsection
Summary
Satellite remote sensing has facilitated biosphere monitoring by, for example, mapping terrestrial vegetation over the past two decades using polar-orbiting, low earth orbit (LEO) satellites [1,2,3]. The Meteosat Second Generation (MSG) program includes four satellites launched over the period 2002–2015 and carrying the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The sensor provides earth observation data across 12 spectral channels, including the visible and near-infrared (NIR) regions, with 3 km spatial sampling at sub-satellite points and a 15 min temporal resolution [4]. The first satellite of the Meteosat Third Generation program, a follow-on program of MSG, will be launched in 2021 [11] Sensors onboard these satellites provide enhanced temporal, spatial, and spectral capabilities relative to previous generations of GEO sensors. The temporal resolution of a full disk in the Himawari 8 Advanced Himawari Imager (AHI) is 10 minutes, and its spatial resolution is 0.5–1 km in the visible to NIR bands at sub-satellite points. Top-of-atmosphere (TOA)/surface reflectance products for the sensors in the new generation GEO satellites, as well as higher level products, have been generated (e.g., [14,15,16,17,18])
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