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:: 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Processing of raw digital NOAA-AVHRR data for sea- and land applications. G. J. Prangsma & J. N. Roozekrans

Document type:: Multivolume work

Structure type:: Chapter

64 3-3- Cloud detection techniques One of the major problems in satellite retrievals of Earth-surface parameters is the correct identification and removal of cloud contaminated pixels. Currently available techniques (Saunders, 1985) are only partially automatic in nature and include an essential interactive stage, leaving the decision on cloud clearance to an experienced operator. However, to cope with the ever increasing stream of remotely sensed environmental data, we decided to embark on a fully automated cloud identification scheme. Algorithms for temperature retrievals require pixels to be flagged cloud contaminated even with cloud covers as low as 1$, the error in the derived brightness temperature having increased to the 0.2 K level and higher already. During the course of our developments we found that the usual cloud detection techniques can be used in an automatic scheme if semi-transparent clouds can be identified by a new procedure. In this context we developped the "channel 4 - channel 5" - technique. The brightness temperature difference between channels 4 and 5 can be used to detect semi transparent cloud-layers, especially thin cirrus. This type of cloud, though frequently occuring, is hard to identify with other cloud detection techniques, because the radiance of thin cirrus in both visible and infra-red channels is very low. The small difference in effective emissivity between channels 4 and 5 can cause brightness temperature difference (T^ - T b c) between the two channels as high as 6 K for semi-transparent cloud layers with an effective emissivity of 60$ (Inoue, 1985). Thresholds, linearly related to T b ^ for T b c are "empirically" determined. These have been obtained by analyzing a large number of AVHRR-images of different areas, seasons and times of the day. By selecting obviously cloudfree and obviously cloudy pixels and plotting the reswults in a 2-D histogram (T b c versus T b ^ - T b 5) thresholds for land and sea’nave been determined (see fig. 1): -> T b , 5 CLOUDLAYER WITH VARIABLE OPTICAL DEPTH CLOUDFREE SEA- OR LAND- SURFACE Figure 1: 2-D histograph showing principle of "ch4 - ch5" - technique. sea:cloudfree if T b ^ - T b 5 < 0.065 * T b 17.255 land:cloudfree if T b ^ - T b 5 < 0.094 * T b 5 -25.11 (brightness temperatures in’degrees kelvin) Land/sea discrimination is based on a linear combination of the channel 1 and channel 2 albedos. 3.4. Navigation/geometric correction Navigation of satellite images involves assigning a latitude and longtitude to any point in the satellite image. For accurate navigation results, the exact position of the satellite must be known at any time, simple trigonometry then giving the latitude and longitude for every pixel (in principle). The NOAA-information Service publishes every other day orbital elements of the operational NOAA- satellites. These elements can be used together with the recording date of a scanline to determine the coordinates of the sub-satellite-point in this line and after that the positions of the pixels on the scanline. In this way a navigational grid can be generated with which an image can be resampled in any desired map-projection. 4. DETERMINATION OF EARHT-SURFACE TEMPERATURES The brighness temperature in the three (or two) thermal-IR channels of the AVHRR are being used to derive true Earth-surface temperatures. The different characteristics of sea and land surface require different approaches. 4.1. Sea surface temperature (SST) 4.1.1. Multi channel technique Many uncertainties hamper the measurements of the Earth-surface temperature from space. Nevertheless for a sea-surface some of these can be eliminated. In the thermal IR wavelengths the emissivity of sea water is high and relatively constant, radiometric efficiency is particularly high on account of the nature of the Planck function at temperatures near 300 K and verification with in-situ measurements is possible. In these circumstances by far the largest source of measurement error remains in the estimation of the atmospheric correction. The wavelengths of the AVHRR thermal infra-red channels have been chosen in such a way that atmospheric effects are different for each channel. The transmittance in these so called "window" is dependent not only on the water vapour concentration but also on its vertical distribution. The SST can be obtained from a linear combination of the brightness temperatures: SST = a ♦ .? a. T,., where N is the number of . .0 i=l 1 Ai channels used. The coefficients a Q , a i are determined, empirically or theoretically, to give the optimum performance in a given set of atmospheric conditions believed to joinly represent those in a particular area, or period, or for the whole globe. The correct determination of the coefficients is critical for the accurate measurements of SST from satellites. A good set of coefficients for the North East Atlantic Ocean and North Sea was determined by a group of the Rutherford Appleton Laboratory in the UK. (Llewellyn-Jones, 1984). The coefficents are scan-angle dependent. During daytime the "split"- window (channels 4 and 5) technique must be used, because the reflection of solar energy in channel 3 is too high. During nighttime a "triple" window technique gives more accurate results provided channel 3 is not too noisy. 4.1.2. Verification In October 1985 the Oceanographic Research ; — F: departme measurem temperat fig. 2). derived ; October The in-s: 15.00 GM' The SST': determini in-situ t satelliti the in-s: by the s< R ut her f 01 than the between j SST is m coefficic RMS 0.241 statistic is 0.022 These st£ publishec 4.2. Lane 4.2.1. Te For the c of AVHRR- for the £ 1 . the 2. Lack Price (19 window te can also He estima LST's , us caused by compared variation 4.2.2. Va In co-ope

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