Investigation of inter-sensor NDVI relationships based on analytical representation of soil isolines

Taniguchi, Kenta; Obata, Kenta; Yoshioka, Hiroki

doi:10.1109/igarss.2012.6352516

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Our intention was to eliminate the influences of the spatial resolution, the viewing and illumination geometries, and so on, from the influences of the sampling wavelength. Similar approaches have been explored in our pilot studies [52][53][54][55].…”

Section: Derivation Steps For Obtaining the Intersensor VI Relationshipsmentioning

confidence: 99%

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

2019

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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.

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Section: Derivation Steps For Obtaining the Intersensor VI Relationshipsmentioning

confidence: 99%

“…The use of the soil isoline for the intercalibration of VI data has been examined briefly in pilot studies [52][53][54][55], which indicated that the soil isoline equation may provide good insights into the intersensor spectral differences. This study attempts to further advance the derivation and numerical validation studies.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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“…2,3 The work needs to be extended to cover different types of VIs such as Ratio VI (RVI), Different VI (DVI), Perpendicular VI (PVI), Soil Adjusted VI (SAVI), and two-band Enhanced VI (EVI2). Applicability of our previous work needs to be investigated, where this study try to address.…”

Section: Introductionmentioning

confidence: 99%

Inter-sensor relationship of two-band spectral vegetation index based on soil isoline equation: derivation and numerical validation

et al. 2013

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Differences in spectral response function among sensors have known to be a source of bias error in derived data products such as spectral vegetation indices (VIs). Numerous studies have been conducted to identify such bias errors by comparing VI data acquired simultaneously by two different sensors. Those attempts clearly indicted two facts: 1) When one tries to model a relationship of two VIs from different sensors by a polynomial function, the coefficients of polynomial depends heavily on region to be studied: 2) Although increase of the degree of polynomial improves the translation accuracies, this improvement is very limited. Those facts imply that a better functional form than a simple polynomial may exist to model the VI relationships, and also that the coefficients of such a relationship can be written as a function of variables other than vegetation biophysical parameters. This study tries to address those issues by deriving an inter-sensor VI relationship analytically. The derivation has been performed based on a relationship of two reflectances at different wavelengths (bands), called soil isoline equation. The derived VI relationship becomes a form of rational function with the coefficients that depend purely on the soil reflectance spectra. The derived relationship has been demonstrated numerically by a radiative transfer model of canopy, PROSAIL. It is concluded that a rational function is a good candidate to model inter-sensor VI relationship. This study also shows the mechanism of how the coefficients of such a relationship could vary with the soil reflectance underneath the canopy.

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A comparison of NDVI intercalibration methods

Fan

Liu

2017

International Journal of Remote Sensing

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Investigation of inter-sensor NDVI relationships based on analytical representation of soil isolines

Cited by 8 publications

References 7 publications

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

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

Inter-sensor relationship of two-band spectral vegetation index based on soil isoline equation: derivation and numerical validation

A comparison of NDVI intercalibration methods

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