Abstract
An analytical form of relationship between spectral vegetation indices (VI) is derived in the context of cross calibration and translation of vegetation index products from different sensors. The derivation has been carried out based on vegetation isoline equations that relate two reflectance values observed at different wavelength ranges often represented by spectral band passes. The derivation was first introduced and explained conceptually by assuming a general functional form for VI model equation. This process is universal by which two VIs of different sensors and/or different model equations can be related conceptually. The general process was then applied to the actual case of normalized difference vegetation index (NDVI) from two sensors in a framework of inter-sensor continuity. The derivation results indicate that the NDVI from one sensor can be approximated by a rational function of NDVI from the other sensor as a parameter. Similar result was obtained for the case of soil adjusted VI, enhanced VI, and two-band variance of enhanced VI.
Highlights
Satellite observations play an important role for global monitoring and characterization of the atmosphere and terrestrial environment in relation with Earth system science
Since we focus on the normalized difference vegetation index (NDVI) relationship, we assume that the reflectances of channel-1 and -2 are employed for both sensors, which is reflected in the subscripts of the reflectances for sensor-b
The derivation steps were first introduced and explained conceptually by assuming a general functional form of vegetation indices (VI) model equation. This universal technique of the derivation with the isoline equations was applied to the case of inter-NDVI relationship to identify functional form that is suitable to approximating the relationship
Summary
Satellite observations play an important role for global monitoring and characterization of the atmosphere and terrestrial environment in relation with Earth system science. In these studies, multi-year to multi-decadal observation records are often required to detect changes that have occurred or are occurring, and to identify trends and causes of those changes. In order to continue long-term satellite observations effectively, an interdisciplinary approach in the framework of interagency and international cooperation is indispensable. Numerous efforts have been made to continue and extend the long-term dataset acquired by the Advanced
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