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

Damen, M. C. J.

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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

219 ! through the (l-exp(-k.t)) die relation- ¡ctance and ìpirical >e used: (14) le for the of extinction ;ers are Finally the SOIL COVER (X) SAIL MO p EL SOIL COVER (X) SAIL MODEL SAIL MODEL SAIL MODEL W (15) >f the )EL isented earlier i with the ¡4) . This >n of plant section 2.2), ¡1 have been red re fie c- 24.2%) ; red reflec- = 12.1%) ; .ectance = .e: 45°) . > be vertically [le leaf were = 8%, red ice = 45%. Г the follow- ) .5) 5.0 (1.0) i factors were >r each of the i able to led soil .1 cover with >er) . Figure 3: Soil cover (new definition) as a function of green and red reflectance, respectively, for a spheric cal leaf angle distribution, xx : calculated points SAIL model — : simplified reflectance model. asymptotic values for the infrared reflectance, calculated from the SAIL model. A changing leaf angle distribution during the growing season of a crop may disturb the relationship between corrected infrared reflectance and LAI. However, Clevers also showed with real field data that leaf angle distribution of cereals may be considered constant during the vegetative and generative stage, respectively. A correction can be made for differences in soil moisture content by subtracting the contribution of the soil detectable by the sensor from the measured infrared reflectance (equation 6). If soil reflectance is known, equation (6) may be combined with e.g. equation (3) in carder to ascertain this corrected infrared reflectance. This method will be called method 0 (indicating that it cannot be applied without knowing soil reflectances explicitly). In practice, however, soil reflectances often are not known. Then equation (9) can be applied, taking into account the constant ratios of soil reflectance between spectral bands. This method will be called method 1. Results for both methods are given in figure 5. Both methods gave essentially the same results, which supports the validity of equation (9) for correcting the infrared reflectance for soil background. Because the only correction made is for soil visible to the eye and not for the soil underneath vegetation, some influence of soil background will still remain. This is illustrated in figure 6. Even with such a large range in soil reflectances, differences between curves were not very large. In reality, fluctuations in soil moisture content underneath vegetation will be less than those on bare soil. SAIL MODEL LAI SAIL MODEL Figure 5: Two methods for correcting for differences in soil moisture content in estimating LAI. Spherical leaf angle distribution (for explanation of symbols see figure 4). SAIL MODEL CORR. INFRARED REFL. <X) Figure 6: Influence of soil background on the regression of LAI on corrected infrared reflectance. il clearly :over, accor- red cor a dry soil 1 for a wet xort the new definition >n together itions (2) ìal definition ince is cor- cly this nr estimating 3 by using a black i does not juation (15) jure 4, : describing cared Le distribu- )) show that juite distinct SAIL MODEL Figure 4: LAI as a function of the infrared reflec tance for a black soil with a spherical leaf angle distribution. xx : calculated points SAIL model — : simplified reflectance model. (Rw is used for r . and a is used for a in this o°, lr graph; CV = coefficient of variation). A more extensive verification of the new model by means of calculations with the SAIL model for several leaf angle distributions and also for skylight only are presented by Clevers (1986b). 5 CONCLUSIONS 1. If soil cover is redefined as in chapter 3, then the reflectance in a spectral band in the visible region of the electromagnetic spectrum decreases linearly with increasing soil cover (equation 2 and 3) . 2. It was shown to be possible to get around the problem of an unknown soil moisture content (and so an unknown soil reflectance) in estimating LAI. Under the assumption that there was a constant ratio between the reflectance factors of bare soil in different spectral bands, independent of soil moisture content, a combination of green, red and infrared reflectances

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