Search Results (5)

Vegetation isoline equations describe analytical relationships between two reflectances of different wavelengths. Their applications range from retrievals of biophysical parameters to the derivation of the inter-sensor relationships of spectral vegetation indexes. Among the three variants of vegetation isoline equations introduced thus far, the optimized asymmetric-order vegetation isoline equation is the newest and is known to be the most accurate. This accuracy assessment, however, has been performed only for the wavelength pair of red and near-infrared (NIR) bands fixed at ∼655 nm and ∼865 nm, respectively. The objective of this study is to extend this wavelength limitation. An accuracy assessment was therefore performed over a wider range of wavelengths, from 400 to 1200 nm. The optimized asymmetric-order vegetation isoline equation was confirmed to demonstrate the highest accuracy among the three isolines for all the investigated wavelength pairs. The second-best equation, the asymmetric-order isoline equation, which does not include an optimization factor, was not superior to the least-accurate equation (i.e., the first-order isoline equation) in some cases. This tendency was prominent when the reflectances of the two wavelengths were similar. By contrast, the optimized asymmetric-order vegetation isoline showed stable performance throughout this study. A single factor introduced into the optimized asymmetric-order isoline equation was concluded to effectively reduce errors in the isoline for all the wavelength combinations examined in this study. Full article

Differences between the wavelength band specifications of distinct sensors introduce systematic differences into the values of a spectral vegetation index (VI). Such relative errors must be minimized algorithmically after data acquisition, based on a relationship between the measurements. This study introduces a technique for deriving the analytical relationship between the VIs from two sensors. The derivation proceeds using a parametric form of the soil isoline equations, which relate the reflectances of two different wavelengths. First, the derivation steps are explained conceptually. Next, the conceptual steps are cast in a practical derivation by assuming a general form of the two-band VI. Finally, the derived expressions are demonstrated numerically using a coupled leaf and canopy radiative transfer model. The results confirm that the derived expression reduced the original differences between the VI values obtained from the two sensors, indicating the validity of the derived expressions. The derived expressions and numerical results suggested that the relationship between the VIs measured at different wavelengths varied with the soil reflectance spectrum beneath the vegetation canopy. These results indicate that caution is required when retrieving intersensor VI relationships over regions consisting of soil surfaces having distinctive spectra. Full article

The relationship between two reflectances of different bands is often encountered in cross calibration and parameter retrievals from remotely-sensed data. The asymmetric-order vegetation isoline is one such relationship, derived previously, where truncation error was reduced from the first-order approximated isoline by including a second-order term. This study introduces a technique for optimizing the magnitude of the second-order term and further improving the isoline equation’s accuracy while maintaining the simplicity of the derived formulation. A single constant factor was introduced into the formulation to adjust the second-order term. This factor was optimized by simulating canopy radiative transfer. Numerical experiments revealed that the errors in the optimized asymmetric isoline were reduced in magnitude to nearly 1/25 of the errors obtained from the first-order vegetation isoline equation, and to nearly one-fifth of the error obtained from the non-optimized asymmetric isoline equation. The errors in the optimized asymmetric isoline were compared with the magnitudes of the signal-to-noise ratio (SNR) estimates reported for four specific sensors aboard four Earth observation satellites. These results indicated that the error in the asymmetric isoline could be reduced to the level of the SNR by adjusting a single factor. Full article

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. Full article

This paper introduces derivations of soil line influences on two-band vegetation indices (VIs) and vegetation isolines in the red and near infra-red reflectance space. Soil line variations are described as changes in the soil line parameters (slope and offset) and the red reflectance of the soil surface. A general form of a VI model equation written as a ratio of two linear functions (e.g., NDVI and SAVI) was assumed. It was found that relative VI variations can be approximated by a linear combination of the three soil parameters. The derived expressions imply the possibility of estimating and correcting for soil-induced bias errors in VIs and their derived biophysical parameters, caused by the assumption of a general soil line, through the use of external data sources such as regional soil maps. Full article