Application of remote sensing techniques for crop identification and crop area estimates in Europe during the winter season is a 'virtual reality' problem, in the sense that no or very low agricultural vegetation cover is actually present on the ground. As a consequence, during most of the season, agricultural targets are principally the soils on which crops will grow later on, and it is not possible to identify directly crop species. For this reason, the acquisition of optical spaceborne imagery in the framework of the MARS project begins only from March on, when crops start developing their plant structure. The first crop area estimates are not available before mid-April, if the weather conditions are favourable.
Nevertheless, earlier estimates can be obtained, taking advantage of the specific sensitivity of the ERS SAR to important soil properties such as surface roughness. These soil properties and their evolution over time are not casual, as far as agricultural surfaces are concerned. Thus, a reasoning and processing methodology has been designed to exploit in a comprehensive way the causal relationships existing between soil properties as observed with ERS during the winter and crop cultivations.
A methodology to identify non-cultivated surfaces and major crop types should be robust regarding influences of meteorological factors on the ERS radar cross-section over time , and should be applicable whatever the study site. To this end, it must combine appropriately a multidisciplinary knowledge:
These synthetic channels provide us with an understandable picture of the causes and of the history of the ERS radar cross-section of agricultural targets during the observation period. The first two channels provide a representation in which urban, forest, grasslands/bare soils, and agricultural themes can already be separated using an ERS time series . The third channel carries information regarding the history of field preparation and crop phenology, thus allowing crop identification in relation to the crop calendars.
A real-time pre-operational ERS experiment has been carried out to provide very early surface estimates of non-cultivated land and major crops over three significantly different study sites (40 x 40 km) of the MARS project during the winter 1994-95.
During ERS-1 Phase F, 19 PRI frames (ascending and descending) were acquired over the three test sites, from 15 Nov. 1994 to 18 Feb. 1995:
- Albacete (Spain): 4 dates, 5 frames
- Bologna (Italy): 7 dates, 7 frames
- Chartres (France): 4 dates, 7 frames.
The ERS SAR data were delivered within an average period of 10 days after acquisition, meeting the time constraints requirements dor data delivery for the Rapid Estimates Activity of the MARS project.
This site, located within one of the richest agricultural areas in Europe, is of particular importance for the estimation of cereal production in France. As it is shown in the multitemporal colour composite image (Fig.1), the site is hilly, with altitude variations up to about 400 m, but generally gentle slopes. The synthetic channels carrying the selected information are produced on a per-pixel basis (Fig. 2).
Figure 1. Multidate ERS SAR imagery over the Chartres test site. Processed ERS-1 images: 28 Nov. in red, 4 Jan. in green and 10 Feb. in blue.
Figure 2. Chartres test-site. Synthetic channels, with the 'backscatter' channel in red, the 'variation' channel in green, and the 'history' channel in blues.
The final 8-class classification is presented in Figure 3, where:
Figure 3. Final classification of the Chartres test-site (8-classes).
Examination of the classification shows that it presents a good robustness to the relief characteristics of this site. Table 1 summarises the comparison between surfaces estimates retrieved from this ERS classification and the Spot-based estimates of the MARS project available in May 1995. Since the classes identified by the two classifications are slightly different, classes have been grouped into land-use families. Good agreement is found between the two classifications.
Table 1. Chartres test site. Comparison of early surface estimates using ERS (March 1995) and Spot (May 1995) multitemporal data.
Crops ERS-1, 4-dates Spot, 2-dates (areas in ha) 28 Nov.-10Feb. 4 Mar.-5May -------------- ---------------- ------------- Non cultivated 14225 11499 Winter wheat 68178 70622 Other cereals 5357 10015 Rape seed 12834 11360 Summer crops 24368 25784 Non-agriculture 35038 30720
This test site includes most of the richest Italian agricultural region: Emilia-Romagna. It consists in a flat plain, densely populated, located between the Po river in the north and the Apennines mountains in the south.
Figure 4 is a colour composite image which illustrates the complex land-use fragmentation of this site. In this case, information selection using the synthetic channels simplifies image interpretation for crop identification, and improves the statistical result of the clustering.
Figure 4. Multidate ERS imagery over the Bologna test site. Processed ERS-1 images: 15 Nov. in red, 5 Dec. in green and 11 Jan. in blue.
The final 9-class classification is presented in Figure 5, where:
Figure 5. Final classification of the Bologna test-site (9-classes).
Table 2 gives the comparison between surfaces estimates retrieved from this ERS classification and the Spot-based results.
Table 2. Bologna test site. Comparison of early surface estimates using ERS (March 1995) and Spot (May 1995) multitemporal data.
Crops ERS-1, 7 dates Spot, 2-dates (areas in ha) 15 Nov.-17 Feb. 21 Mar.-3 May ---------------- --------------- ------------- Non-cultivated 13029 13844 Rice 636 438 Orchards, etc. 23888 29280 Winter cereals 56139 43840 Sugar beets 23005 22295 Potato + maize 10463 10330 Spring crops 8729 7332 Non-agriculture 24109 32640
Although important differences can be noted for non-agricultural surfaces and winter cereals, these results show a good overall agreement for other crops.
This test site was chosen to evaluate the method in presence of moderate and strong relief. The dominant grey tones in Figure 6 indicate that meteorological conditions were almost constant (presistent drought) during this winter. The coloured areas are mainly due to the effects of tillage and irrigation. Mountainous areas present important confusion due to the changes in look angle (phase F) and in incidence angle.
Figure 6. Multidate ERS imagery over the Albacete test site. Processed ERS-1 images: 6 Dec. in red, 12 Jan. in green and 18 Feb. in blue.
This problem will be of less importance for the future due to the operational scenario of ERS-2.
The final 9-class classification obtained over this site is presented in Figure 7, where:
Figure 7. Final classification of the Albacete test-site (9 classes)
Within mountainous areas, the output of the clustering procedure reflects the misclassification due to strong relief.
Nevertheless, applying the mask used for the Spot data on this site, it is clear that:
The total user-cost of the project has been traced during the course of the experiment. The overall user-cost for ERS data, personnel and
running costs was under 5500 ECU per site.
Nevertheless, in the actual operational ERS phase (repeat-pass orbit), data and processing costs could be considerably reduced if the test sites are chosen to coincide with ERS PRI frames.
Results show that early estimates of non-cultivated areas and crop areas using ERS data can be carried out in operational conditions, using a methodology which is:
Finally, the total user-cost of such a project is particularly competitive, using the actual operational ERS satellites system.
 Kattenborn G, E Nezry & G De Grandi, 1993: High-resolution detection and monitoring of changes using ERS-1 time series, Proc. 2nd ERS-1 Symp., Hamburg 11-14 Oct. 1993, ESA SP-361, Vol.1, pp.635-642.
 Kohl HG, E Nezry, M Mróz &H De Groof, 1994: Towards the integration of ERS SAR data in an operational system for rapid estimates of acreage at the level of the European Union, Proc. 1st Workshop on ERS-1 Pilot Projects, Toledo, 22-24 June1994, ESA SP-365, pp.433-441.
 Ulaby FT & MC Dobson, 1989: Handbook of radar scattering statistics for terrain, Artech House Inc., Norwood (MA), USA.
 Laur H, 1989: Analyse d'images radar en télédétection:Discriminateurs radiométriques et texturaux, Ph.D. dissertation, Univ. Toulouse, 250 p.
 Lopes A, E Nezry, R Touzi & H Laur, 1993: Structure detection and statistical adaptive speckle filtering in SAR images, Int. J. Remote Sens., Vol.14, no. 9, pp.1735-1758.