Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 3 Spectral signatures of objects. Chairman: G. Guyot, Liaison: N. J. J. Bunnik

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: The derivation of a simplified reflectance model for the estimation of LAI. J. G. P. W. Clevers

Document type:: Multivolume work

Structure type:: Chapter

216 2 LITERATURE ON ESTIMATING LAI 2.1 Indices for estimating LAI The green, red and infrared reflectances may be used as variables for estimating LAI. Much recent research has been aimed at establishing combinations of the reflectance factors in different wavelength bands, to minimize the undesirable disturbances of differences in soil background or atmospheric conditions. However, when using some combination of reflectances, one should be careful not to loose sensitivity to variations in LAI after complete soil cover has been reached. This also means that the infrared reflec tance should play a dominant role in such a combination. The earliest investigations involved the infrared/ red ratio {MSS(7/5)} (e.g. Rouse et al., 1973, 1974). Rouse and his colleagues found that this ratio was especially useful for estimating crop characteristics by correcting the radiances measured by earth- observation satellites (e.g. Landsat), for eliminating seasonal sun angle differences for minimizing the effect of atmospheric attenuation. The same authors also used the "vegetation index" {VI = MSS(7-5)/ (7+5)} for this purpose. In order to avoid negative values a transformed vegetation index {tVI = /VI + 0.5'} was also used in practical applications. Model simulations done by Bunnik (1978, 1981) show that these indices may be useful for estimating soil cover, but are only slightly sensitive for variations in LAI after complete soil cover has been reached. This is also confirmed by the results of e.g. Asrar et al. (1984), Hatfield et al. (1984), Holben et al. (1980). Moreover, in the study of Clevers (1986b) and of this paper, calibrated reflectance factors were used, so there was no reason to correct them for atmospheric attenuation. Seasonal sun angle differences were assumed to be minimal. In order to find an index independent of soil influence Richardson & Wiegand (1977) introduced the perpendicular vegetation index (PVI). However, in order to apply the PVI the reflectance of the soil has to be known, and often it is not. A similar approach for suppressing variations in soil background was developed by Kauth & Thomas (1976). They applied a heuristic linear transformation in the four dimensional data space provided by Landsat MSS measurements of vegetation for different soils, resulting into a greenness index. This transforma tion was only directly applied to four spectral bands. Gray & McCrary (1981a, 1981b) applied the concept of a difference between an infrared and a visible spectral band using data from NOAA satellites. This index is comparable to the greenness index of Kauth & Thomas, as far as greenness is a weighted difference between infrared and visible bands. None of the authors who used the infrared-red difference deduced this index from any physical model. Therefore, the mathematical description of the relationship of such an index to crop characteristics such as LAI differs from author to author, being derived in an empirical way. 2.2 Reflectance models Canopy-modelling studies also enable relationships between reflectance values and crop characteristics to be studied. The main aim of physical reflectance models suitable for agricultural crops is to obtain a better understanding of the complex interaction between solar radiation and plant canopies. Essentially, there are two classes of physical reflectance models: numerical and analytical models. Bunnik (1984) has reviewed several models. An example of a numerical model has been described by Idso & De Wit (1970). In this model, radiative transfer is determined by scattering and absorption for discrete leaf layers. Goudriaan (1977) improved and extended this model by calculating a numerical solution for upward and downward diffuse fluxes within nine sectors of each hemisphere for each discrete layer. One of the earliest analytical models was described by Allen & Richardson (1968). It was based on a theory of Kubelka & Munk (1931) which describes the transfer of isotropic diffuse flux in perfectly diffusing media. In the analytical model, upward and downward fluxes are expressed by differential equations. Allen et al. (1969) extended this model in order to include scattering of direct solar flux by using the Duntley equations (Duntley, 1942) . The first analytical model incorporating both illumination and observation geometry was developed by Suits (1972) and is an extension of the model developed by Allen and his colleagues. Suits's model also incorporates plant canopy structural (with a drastic simplification) and optical properties. When model simulations are carried out with varying view angle, Suits‘s simplifications appear to be too drastic (Verhoef & Bunnik, 1981) . Therefore, Verhoef (1984) extended the Suits model further by including scattering and extinction functions for canopy layers containing fractions of oblique leaves (inclined leaves). He did not introduce the drastic simplification of canopy geometry to exclusively horizontal and vertical components as used by Suits, but he used a discretized set of frequencies at distinct leaf angles. This model is called the SAIL model (Scattering by Arbitrarily Inclined Leaves). Kimes & Kirchner (1982) have developed a three- dimensional model, which describes the radiative transfer for heterogeneous scenes by subdividing the scene into modules. Complicated physical reflectance models usually simulate reflectances for varying crop characteristics and incorporate simplified structural and optical properties of the canopy. Although for practical applications a simple function of reflectances for estimating LAI is preferred, a physical basis is unavoidable in order to deduce the sort of relation ship between such a function and LAI (semi-empirical model). Therefore, a new model will be introduced resulting in a simple correction for soil background. A mathematical relationship between this correction and LAI will be described. This will be verified by means of calculations with the SAIL model. 3 SIMPLIFIED REFLECTANCE MODEL FOR VEGETATION The main requirements for a simplified reflectance model for vegetation are: 1. it should be possible to estimate LAI; 2. it should describe the relationship between reflectance and LAI by more or less physically defined parameters; 3. it should correct for soil background in order to enable a multitemporal analysis to be done; 4. it should be as simple as possible (preferably resulting in some sort of index). If a remote sensing technique is used in a visible band while looking downwards from some distance, the sensor will be unable to detect whether soil in the shadows is obscured by leaves. For this reason soil cover is redefined by taking the sun-sensor geometry into account (conventionally, for a green canopy, soil cover is defined as the relative vertical projection of the canopy on the soil surface). A prerequisite for ascertaining bare soil according to the new definition is that the soil must clearly contrast with the vegetation. When the sun is shining the soil should be directly illuminated by the sun (figure la). Further, the soil must be visible for the detector to be able to classify it as soil (figure lb). The fraction of soil that satisfies both conditions (i.e. soil that is

Access restriction

Copyright

fullscreen: Remote sensing for resources development and environmental management (Volume 1)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation