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:: Mapping of available solar radiation at ground. Ehrhard Raschke & Martin Rieland

Document type:: Multivolume work

Structure type:: Chapter

307 L - Imi Tab. 2.1: list of variables to select mean standard atmosphere pro-files (input data -for radiative transfer model) (-max — L m i n temperature profile: subarctic summer midlatitude summer midlatitude winter subtropic tropic aerosol; surface: ocean wood vegetation (low) sand snow bare soil visibi1itv: Errors, which might occur because of this assumption are discussed in Chapter 3. L n involves the influence of the actual cloud cover N and the optical depth of the clouds within the image field of view of the radiometer. In our model we assume that any cloud increases the reflectance of the earth-atmosphere-system; that means: L c1 ou d >> L min mari time rural urban 10 km 23 km 50 km Tab. 2.2. : (a) System and data characteristics of the geostationary satellites Meteosat (ESA) and GMS (Japan) Meteosat GMS orbit (longitude) 0° E 140° E scan direction stepping S > N N > S scan E > W W > E no. of steps 2500 2500 spin rate (rpm) 100 100 wavelength (>im) IR 10 .5 - 12 .5 10.5 - 12.5 VIS 0 .4 - 1. 1 0.55 - 0.75 subsatel1ite resolution (km) IR 5 5 VIS 2.5 1.25 number of lines IR 2500 2500 VIS 5000 10000 samples per line IR 2500 6688 VIS 5000 13376 image-taking duration (min) 25 25 (b) ISCCP averaging and sampling scheme for data volume reduction Meteosat GMS Averaging of visible pixels to match IR resolution (2*1) -> (5km) 2 (6*4) -> (5kra) 2 Sampling of matched resolution pixels to obtain B1 1 out of (2*2) -> (10km) spacing Sampling of B1 to obtain B2 (B3) 1 out of (3*3) -> (30km) spacing The link between these theoretical calculations and the satellite measurements is the normalized reflected solar radiation M*„. Mr “ Mr m , n (2.5) Mr„ = ^Rm«K — Mrmin This might not be true in the case of the water surfaces (sunglint) or snow. Global maps of minimum radiances L ml „ are created by storing the lowest radiance value for each pixel location measured at a fixed local time over a period of one month. Some of them are shown in Fig. (2.3). is the upward radiance as it would be measured above a solid optically thick cloud deck. These values are obtained from statistical analysis of maps of maximum radiances for the period of one month, which we prepare in a similiar way as the minimum radiance maps. With the knowledge of L«m and L m «x and that of those functions M 00 and M 0n we are able to compute for the actul value of reflected radiation L of each pixel the global radiation using the equations (2.6), (2.3), (2.4) and (2.1) successively. The daily sums of global radiation are then estimated by use of Eq. (2.7). In Mo (2.7) Moc — Mood — In Moo Mod : daily sum of global radiation Mood : daily sum of global radiation. , cloudfree case N: number of measurements, which are available per day 3. Error considerations To estimate the accuracy of the model results (mainly: daily sum of global radiation), we have to distinguish between two species of error sources: (1) the model input parameters (e.g. L„ ln , L m .„, In, Moo) - After the procedure to find the minimum radiances Lmin during the period of one month there may be still some pixel left with cloud effects. Those pixels have to be detected. - The normalized reflected solar radiation or the 'effective cloud cover' L n may be over - or underestimated because of the isotropic assumption (Eq.2.6) of the radiation field. - The visibility, which influences the clear sky values Moo and Mood, cannot be adapted to special local conditions, if groundbased measurements are missing. The model is applied to data sets of two different geostationary satellite, Meteosat and GMS. (For some satellite data and system charcteristics see Tab. 2.2) Because these satellite instruments measure radiances L (Wm -2 sr _1 ) instead of exitances M ( Wm“ 3 ) it is assumed that the anisotropic characteristics of the normalized radiances L n is negligible. Supposed that most of these uncertainties occur statistically and not systematically, we can calculate their influence on the computed daily sums of global radiation. The result of such a sensitive study is shown in Fig. 3.1. The solid line in Fig. 3.1 represents the case, which only accounts uncertainties in L ml „, L m .* and L. They effect the accuracy of the final result mainly during cloudy conditions. The uncertainties in the

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