Ross NELSON Earth Resources Branch NASAIGoddard Space Flight Center Greenbelt, MD 20771 Reducing Landsat MSS Scene Variability Reflectance calculations were most effective overall for reducing interscene variability; however, band ratioing proved most useful on the bright targets. INTRODUCTION LANDSAT DIGITAL TRANSFORMATIONS HE SPECTRAL VARIABILIn of a particular land-cover The Landsat MSS sensor response to radiance T feature over time is a function of changes in from an unchanging target is affected by factors the spectral response characteristics of the target which may be grouped into three generic catego- and viewing conditions. In terms of land-cover as- ries: sessment, the first factor may be considered useful information, the second, noise. Accounting for the Viewing Factors: Sensor response varies due to latter without affecting the former is important in changes in the sun-target-sensor geometry (Potter, situations where multiple scenes are used to assess/ 1974; Malila et al., 1975; Kowalik et al., 1982). monitor land cover. Studies which utilize multiple Atmospheric Factors: Sensor response varies due to acquisition Landsat MSS data must be concerned changes in the capability of the atmosphere to with apparent scene changes which are due to the transmit and scatter radiation (Rogers and Peacock, effects of changing sun angle, atmospheric condi- 1973; Potter, 1974; Turner et al., 1974; Hulstrom, tions, or sensor differences. Correcting or removing 1974; Fraser, 1974; Fraser et al., 1977; Dozier and scene noise should interest those involved with dig- Frew, 1981). ABSTRACT:Landsat 1, 2, and 3 MSS data acquired for six different nonvegetated targets over a three-year period were used to determine which of five transfor- mations was most useful for reducing between-scene variability. The following values were calculated from the MSS digital numbers (dn): (1) radiance; (2) reflec- tance; (3) reflectance corrected for changes in the Earthfsun distance; (4) normal- ized dn (normalizing equations proposed by ERIM researchers); and (5)band ratios. Results indicated that reflectance calculations were most effective overall for re- ducing interscene variability; ratios proved most useful on the bright targets. ital change detection and digital data base manipu- Systems Factors: Spectral variability is a function of lations which include multite~nporalor mosaicked the instrument used to detect target radiance (Slater, MSS scenes. 1979). Also, data preprocessing may be a source of The purpose of this study was to determine the variation (Grebowsky, personal communication). utility of various published transformations for re- Transformations which account for and remove ducing scene variability. These included calcula- variability associated with the viewing and systems tions of radiance and reflectance using linear models factors were tested in order to determine which available in the Landsat Data Users Handbook transforms were most effective. The MSS digital (USGS, 1979), linear normalizing equations empir- number (dn) responses acquired over unchanging ically derived by Environmental Research Institute targets over a period of three years were trans- of Michigan (ERIM) personnel, and band ratioing. In formed using the following calculations in order to addition, climatological data were assessed to de- determine if scene variability could be significantly termine the utility of archived weather information reduced. for further reducing scene variability. Radiance: The radiance calculation attempts to PHOTOGRAMMETRICENGINEERING AND REMOTESENSING, 0099-1112/85/5105-0583$02.25/0 Vol. 51, No. 5, May 1985, pp. 583-593 O 1985 American Society for Photogrammetry and Remote Sensing account for response differences between satellites. The satellite responses are corrected to Landsat 2 Digital numbers were converted to radiance mea- data processed prior to 16 July 1975, which corre- sures using transformations available in the Landsat spond to preprocessing steps implemented for all Data Users Handbook (USGS, 1979). LACIE (Large Area Crop Inventory Experiment) seg- dn ments. Radiance = -(L,, - L,in) + Lmin Dm, ERIM = (m:::') - (m- dn + b) where dn = digital number (unitless); Dm, = the maximum digital number where m, b = linear coefficients (Table 2). that can be recorded by the sat- dn = Landsat MSS digital number. ellite: 127 for bands 4, 5, and 8 = solar zenith angle. 6; 63 for band 7 (unitless); and L,,, L,,, = the saturation and threshold ra- The ERIM calculation results in a unitless digital diance levels, respectively, for number value corrected to a nominal sun angle. a given satellite and band; in Ratios: Ratioing has been used to reduce the ef- milliwatts per square centi- fects of solar zenith angle (Crane, 1971; Vincent, metre per steradian. These 1972, 1973) and topography (Holben and Justice, values are given in Table 1. 1980; Justice et al., 1981) on sensor response. Ad- jacent band ratios and a common vegetation index Reflectance (at the top of the atmosphere): Re- were tested to determine how well such calculations flectance, a unitless number varying between 0 and removed scene variability. The ratios tested were: 1.0, is a measure of the percentage of light reflected from a given target. Radiance measures were con- band 41band 5 band 6hand 7 verted to reflectance measurements by accounting band 5lband 6 band 7hand 5 for the strength of the incoming solar radiation and The effects of changes in atmospheric conditions the angle of incidence of radiation on the target at on Landsat MSS response have been documented by the time of the overpass (USGS, 1979). This reflec- numerous authors. Potter (1974), Turner et al. tance measure assumes (1) that the target is lam- (1974), and Fraser et al. (1977) have documented bertian, and (2) that the affects of the atmosphere the effects of changing atmospheric conditions on on target response are lambertian. the ability to accurately classify MSS data. Atmo- IT spheric differences which affect MSS sensor response Reflectance = (Radiance) E . sin(a) (thereby affecting recognition capability) are a func- tion of changes in particulate concentrations (Fraser, where E = the solar constant for a given band at 1974), aerosol concentrations (Fraser et al., 1977), the top of the atmosphere (in mWIsq and aerosol and water vapor content (Dozier and cm) Frew, 1981). The effects of changing atmospheric band 4 = 17.70 band 6 = 12.37 haze levels on target response can be appreciable. band 5 = 15.15 band 7 = 24.91. Rogers and Peacock (1973) calculated that over 50 a = solar elevation, or 90-solar zenith angle percent of the MSS signal in bands 4, 6, and 7 was (degrees). attributable to atmospheric path radiance for a dark Reflectance, EarthlSun: The Earth is closest to water target. the sun in early January each year, and farthest away Numerous factors are available in weather records in early July. This variation can alter the strength of which may explain interscene variability. Three the incoming solar radiation by as much as 6.7 per- variables are found which may be related to haze cent.' The Earthlsun (EIS) distance variation was level. Two, relative humidity and precipitable taken into account to correct the solar constant water, describe water content characteristics of the values. One astronomical unit (AU) = 1.496 x surface and atmospheric column, re~~ectively.~Al- 10**11 metres, which is the nominal distance be- though no historical descriptors of particulate or tween the Earth and the sun. aerosol concentrations could be found, horizontal visibility measurements were used to characterize Reflectance, EIS = AU2 . (Reflectance) atmospheric tran~parency.~Cloud cover adjacent to where AU is the Earthlsun distance (in astronomical the target may also affect sensor response. Clouds units, Nautical Almanac Office, 1975-1978). in the vicinity of a target may decrease satellite re- sponse to that target due to the resampling filter ERIM Transformations: The ERIM transforms cor- rect for satellite differences and correct for sun angle by normalizing to a 39 degree solar zenith angle. Precipitable water is the amount of rain which would be generated &om the atmosphere if all the moisture con- densed and fell. ' Difference in light intensity between 3 January and 3 Turner et al. (1974) and Malila et al. (1975) express July: 3 January, AU = 0.9833, 3 July, AU = 1.0167. In- reservations concerning the use of horizontal visibility to tensity Difference = (1/.98332 - 111.01672)*100.0 describe atmospheric conditions. Visibility, however, was = 6.7% considered in lieu of an alternative. REDUCING LANDSAT MSS SCENE VARIABILITY TABLE1. LINEARCOEFFICIENTS USED TO CALCULATERADIANCE VALUESFROM LANDSATMSS DIGITALNUMBER RESPONSES(FROM LANDSAT DATA USERS HANDBOOK, USGS (1979) OR ROBINOVE(1982)). THEU~ITS ARE MILLI~VATTS PERSQUARE CENTIMETRE PER STERADIAN. Landsat 1 Landsat 2* Landsat 2* Landsat 3t Landsat 3t band Lm,, Lma Lnn,,, La Lnun Lax Lnun 'max Lmln Lax 4 0 2.48 0.10 2.10 0.08 2.63 0.04 2.20 0.04 2.59 5 0 2.00 0.07 1.56 0.06 1.76 0.03 1.75 0.03 1.79 6 0 1.76 0.07 1.40 0.06 1.52 0.03 1.45 0.03 1.49 7 0 4.00 0.14 4.15 0.11 3.91 0.03 4.41 0.03 3.83 * Landsat 2 coeffic~entslisted for data processed before (first two columns) and after 16 July 1975 t Landsat 3 coeffic~entsl~sted for data processed before (first two columns) and aRer I June 1978 used in preprocessing the MSS data (Grebowsky, mitted. The remaining three targets were located at personal communication). Conversely, clouds near the northern end of the Gulf of California (path 41, the target may, through reflection, increase the row 38, see Figure 2). Two of the three targets were amount of light falling on the target andlor may in- quartz sand north and west of the Gulf of California. crease atmospheric path radiance over the target. The third target included deep water in the Gulf Rainfall may also affect target response because soil, south of the mouth of the Colorado River.