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 application of a vegetation index in correcting the infrared reflectance for soil background. J. G. P. W. Clevers

Document type:: Multivolume work

Structure type:: Chapter

221 sled cap - a poral develop- andsat. ata, Purdue Lve transfer L. Opt. 21: A factor Df spectral In remote sources. Rem. zur Optik der -601. stinguishing tion. 2. D.W. Deering, the Great ellite-1 aington D.C., J.A. Schell nal advance- ect) of Final Report, 1 & ectance Univ., 5 pp. directional . Sens. Envir. af layers modelling: 25-141. nee of crop etermined by roc. Int. Objects, udies. Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 The application of a vegetation index in correcting the infrared reflectance for soil background J.G.P.W.Clevers Dept, of Landsurveying and Remote Sensing, Wageningen Agricultural University, Netherlands ABSTRACT: The simplified reflectance model described earlier (Clevers, 1986a) for estimating leaf area index (LAI) is further simplified for one specific situation. In this model the infrared reflectance is corrected for soil background and subsequently used for estimating LAI by applying the inverse of a special case of the Mitscherlich function. In the specific situation that the reflectances of bare soil in the green, red and near- infrared are equal, the corrected infrared reflectance is ascertained as the difference of total measured in frared and red reflectances. This approach was confirmed by simulations with the SAIL model. The above concept was tested at the experimental farm of the Wageningen Agricultural University, by using reflectance factors ascertained in field trials with multispectral aerial photography. The soil type at the experimental farm yielded nearly equal reflectances in the green, red and infrared at some moisture content. The difference between measured infrared and red reflectances provided a satisfactory approximation of the corrected infrared reflectance. The estimation of LAI by this corrected infrared reflectance for real data yielded good results in this study, resulting in the ascertainment of treatment effects with larger precision than by means of the LAI measured in the field by conventional field sampling methods. 1 INTRODUCTION In the visible region of the electromagnetic spec trum, vegetation absorbs much radiation and shows a relatively low reflectance (e.g. Bunnik, 1978; Clevers, 1986c). This is especially true in the red region, because of the large absorption of this ra diation by the chlorophyll in the leaves. In the visible and near-infrared region the reflectance and transmittance of a green leaf are approximately equal (e.g. Goudriaan, 1977; Youkhana, 1983). For a crop canopy this implies that in the visible region only the reflectance of the upper layer of leaves determines the measured reflectance of that canopy. In the near-infrared region the spectral reflectance of leaves is high and there is hardly any infrared absorptance by a green leaf. In this situation leaves or canopy layers underneath the upper layer contribute significantly to the total measured re flectance. This multiple reflectance indicates that the infrared reflectance may be a suitable estimator of LAI. Soil reflectance has an important influence on the relationship between infrared reflectance and LAI. At low soil cover, soil reflectance contributes strongly to the measured reflectance in the differ ent spectral bands. Soil moisture content is not constant during the growing season and differences in soil moisture content greatly influence soil re flectance. If a multitemporal analysis of remote sensing data is required, a correction has to be made for soil background when ascertaining the re lationship between infrared reflectance and LAI. Clevers (1986a) has described a simplified, semi- empirical, reflectance model for estimating LAI. First of all, soil cover was redefined as: the ver tical projection of green vegetation, the relative area of the shadows included, seen by a sensor poin ting vertically downwards, relative to the total soil area (in this definition soil cover depends on the position of the sun). Next, the simplified re flectance model derived by Clevers was based on the expression of the measured reflectance as a compo site reflectance of plants and soil: the measured reflectance in the green, red and near-infrared spectral bands is a linear combination of the appar ent soil cover (new definition) and its complement, with the reflectances of the plants and of the soil as coefficients, respectively. For estimating LAI a corrected infrared reflectance was calculated by subtracting the contribution of the soil from the measured reflectance. Combining the reflectance measurements obtained in the green, red and infra red spectral bands, enables one to calculate the corrected infrared reflectance, without knowing soil reflectances. The main assumption was that there is a constant ratio between the reflectances of bare soil in different bands, independent of soil moisture content: this assumption is valid for many soil types. Subsequently this corrected infrared re flectance was used for estimating LAI according to the inverse of a special case of the Mitscherlich function. This function contains two parameters that have to be estimated empirically. Simulations with the SAIL model (introduced by Verhoef, 1984) con firmed the potential of this simplified (semi-empir- ical) reflectance model for estimating LAI. The starting point of the study of this paper was the simplified reflectance model for the estimation of LAI, introduced by Clevers (1986a), using cali brated reflectance factors. For a specific soil type a simple vegetation index was derived for correct ing the infrared reflectance of green vegetation for soil background. Then the mathematical relationship between this index and LAI, derived by Clevers, was applied for estimating LAI. This approach was veri fied by means of calculations with the SAIL model and with real field data. 2 DERIVATION OF A VEGETATION INDEX 2.1 Summary of the simplified reflectance model The simplified reflectance model derived by Clevers (1986a) was based on the equation: r = r v . B + r s . (1-B) (1) r = total measured reflectance r = reflectance of the vegetation r V = reflectance of the soil B S = soil cover.

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