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Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 
The use of multitemporal Landsat data for improving 
crop mapping accuracy 
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Alan S.Belward & John C.Taylor 
Cmnfield Institute of Technology, Bedfordshire, UK 
Six Landsat MSS scenes from between October 1979 and August 1980 were located for a test site in southern 
England. Each scene was independantly registered to the National Grid. Resampling to 50m pixel size was 
carried out with Bilinear interpolation. Ground error for each scene was less than 100m. These data were 
then matched to 2000ha of ground information giving the crop type and field boundaries for the 1979 - 1980 
growing season. Ten cover classes were identified; Winter Wheat, Winter Barley, Spring Barley, Oilseed 
Rape, Grassland, Sugar Beet, Peas, Beans, and Deciduous and Coniferous Woodland. Spectral coincident plots 
were drawn for all cover types from each image, and a decision tree applied to identify key bands / dates 
for maximum spectral separation of cover types. Supervised maximum likelihood classification was then used 
to produce crop classifications from both single date and multitemporal data. 
Classification accuracy was variable for the different cover classes. The multitemporal data gave better 
overall classification accuracies than the single date images. The best result was from a spring / early 
summer combination giving a mean classification purity of 70%. This is a 6% increase over the best single 
date classification from May, and 46% better than the worst from February. 
1. BACKGROUND 
The large area coverage and sequential nature of 
Landsat Multispectral Scanner (MSS) data and the 
opportunity for computer data processing offers the 
potential for relativly cheap, timely and accurate 
crop inventory (Baur, 1975). The Landsat MSS has been 
employed sucessfully for example, in crop inventory 
for land use stratification (Hay, 1974), the 
identification and area estimation of winter wheat 
(Morain and Williams, 1975), automatic corn-soya bean 
classification (Badwhar, 1984) and as an inventory 
system for agriculture in California (Wall et al, 
1984). Such work has involved both single date and 
multitemporal image sets. 
The accuracy with which individual crop types can be 
classified from Landsat data varies widely. Figures 
range from 80% accuracy of test field recognition 
reported by Baur et al, 1979, in their work on the 
identification and area estimation of corn and soya 
bean, to an overall crop classification accuracy of 
less than 50% found by Taylor et al, 1983, working on 
crop classification in the United Kingdom. 
The work by Baur et al shows that the 80% accuracy of 
test field recognition was achieved by using Landsat 
data for a 3 county area of Illinois, United States 
of America. In this region 81% of the total land area 
was cropped, and 71% of this crop land was planted 
with either corn or soybean. The aquisition date of 
the imagery used was from August, (identified 
elswhere in the same work as being the best date for 
spectral separation of the corn and soybeans).In 
contrast the 50% crop classification accuracy 
obtained by Taylor et al was from imagery aquired for 
Feltwell, Suffolk, United Kingdom. This is an area of 
mixed cropping in which a range of cereal, oilseed 
and root crops are produced in small, irregularly 
shaped fields. Image aquisition was also not idealy 
matched to the crop calendar. Imagery was used from 
April, where considerable overlap in crop spectral 
response exists for this region. 
Experimental proceedure may go part way to explaining 
the large difference in reported classification 
performance, but the contrasting agricultural 
situation and match between image aquisition and 
agricultural clalendar are probably of greater 
importance. Carlson and Aspiazu, (1975), endorse this 
view stating that, "Satellite coverage critically 
timed with a crop development calendar is noted to 
improve classifier effectiveness." The potential for 
further improvements through the use of the temporal 
dimension in crop surveys from space platforms has 
long been recognised, (Steiner, 1970), though there 
are few published cases where the technique has been 
applied for crop inventory. Those investigators who 
have used the technique have generally found that 
improvements in crop classification accuracy are 
found over the use of single date images. Von Steen 
and Wigton, (1976), found that overall classification 
accuracy of grass, cotton, corn and soybeans using 
three observation dates increased by 88% over the 
50.8% classification accuracy from the best single 
date image. Other work such as that by Carlson and 
Aspiazu, (1975), shows similar benefits from the use 
of multitemporal Landsat data for cropland acreage 
estimates. 
More recent work (Odenweller and Johnson, 1984) has 
used Landsat derived temporal-spectral profiles for 
crop identification, where a temporal-spectral 
profile is defined as "The multitemporal 
representation of Landsat data, optimally in the form 
of a green vegetation indicator, from a single 
labeling target". In their work Kauth and Thomas 
greeness component values were calculated for a 
series of targets and plotted against time as a 
temporal spectral profile. Individual cover types 
were then identified within the context of baseline 
proceedure analysis logic. Using this approach corn 
and soybean, could be separated from perennial crops 
and bare soil, then identified individually because 
of differences in the amplitude and shape of the 
Temporal - spectral profiles. 
Badwhar, again working on corn / soybean 
classification has developed an automatic 
corn-soybean classification from Landsat MSS using 
multitemporal data (Badwhar, 1984). This requires at 
least two image aquisitions which again are 
transformed to the Kauth and Thomas greeness