SUPPORT TO THE EVALUATION OF THE SHORT-TERM EXPLOITATION OPPORTUNITIES FOR ERS DATA WITHIN THE AGRIBUSINESS MARKET

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1 SUPPORT TO THE EVALUATION OF THE SHORT-TERM EXPLOITATION OPPORTUNITIES FOR ERS DATA WITHIN THE AGRIBUSINESS MARKET Judith Johnston, Jacquie Conway, Meryl Strang National Remote Sensing Centre Limited Delta House, Southwood Crescent, Southwood, Farnborough, Hampshire, GU14 ONL, UK. s: ABSTRACT The first objective of this study was to investigate the capabilities of ERS-2 SAR data (supplemented by one optical image) for producing fully validated regional area estimates of important crops. This study included the development of a sampling strategy in order to determine whether subsets of the region could be extrapolated to obtain similar area estimates to those obtained for the entire region. The second objective was to identify short-term opportunities and to make recommendations for increasing the exploitation of SAR data within the Agribusiness market sector. This entailed the identification of the information requirements of the agribusiness sector, and an assessment of the capability of current radar sensors. ERS-2 data were acquired over the majority of East Anglia (UK) during the 1997/1998 growing season. All radar data were radiometrically and geometrically corrected using NRSC s TSAR software. An IRS LISS-III optical image was also acquired (May 1998) and geocorrected. The ERS-2 data were classified for the winter and summer period using NRSC s SAR CLASSIFIER and the optical image was classified using ERDAS IMAGINE software to produce regional classifications. The purpose of the optical image was to improve the area estimates for the summer period. To monitor large areas on a monthly basis is currently not cost effective so a sampling strategy was devised and the regional classifications were used to extract data for 30 mini-sites. Area estimates were recalculated from the mini-sites using a defined extrapolation methodology. The crop area estimates extrapolated from the mini-sites were compared to estimates obtained for the whole region. It was assumed that if results compared well at a regional level, the sampling methodology could also be applied at a national level. The classifications have been validated and the region and mini-site area estimates have been compared with area estimates derived from [27]. The area estimates obtained for the mini-sites were considerably different to those obtained for the entire region. Further research into sampling strategies and extrapolation methodologies will therefore be required. The area estimates derived from the optical image were superior to those derived from the SAR imagery. This project indicates that current radar sensors cannot satisfy the user requirements of the agribusiness sector. Future SAR sensors, including ENVISAT (multi-polarisation data) and TERRASAR (higher spatial resolution, multi-band, multi-polarisation data), may be able to provide improved results. AGRIBUSINESS REQUIREMENTS The information requirements of the agribusiness market were identified using findings of previous user requirement studies and NRSC in-house knowledge of the market. The majority of agribusiness contributors in the study were from Spain, France and the UK. The sectors involved included fertiliser and agrochemical manufacturers/distributors, agronomy service provision companies, seed companies, machinery manufacturers, food processors, grain millers and merchants, importers/exporters and commodity brokers, insurance companies and co-operatives. Nutrient status emerged as the most common information requirement, whilst crop identification and growth stage are the least in demand for the respondents represented. Certain expected national differences emerge, such as the greater demand for water stress information in Spain, and the greater demand for disease information in the UK.

2 This work is funded by the European Space Agency, ESRIN, Via Galileo,00044 FRASCATI, Italy. The most desirable products in order of priority include: Nitrogen status, Crop Condition/vigour map, Soil Maps, Lodging risk map, Final yield prediction, Crop establishment map, Confirm final yield map, Crop identification map. The fact that many individual information requirements were of a similar level of importance suggests a demand for an ERS service to meet multiple information needs with its products. The preference for a suite of products has been expressed by farmer/agronomist focus groups in the UK and by individuals in agricultural institutions and agribusiness. Furthermore, given the high temporal frequency and urgency of information requirements for crop management, product dissemination is a major issue and expense. The more information requirements are met by a package of products, the more acceptably the costs of service provision and data dissemination will be absorbed. Few applications (except for example disease detection) require turn-around of less than seven days to be of value. The remaining agribusiness requirements are satisfied by data delivered days or even weeks after acquisition, which places less pressure on the structure for product dissemination, even if the medium of delivery is hard copy or CD-Rom. The overall frequency requirements are predominantly for weekly or fortnightly information during the main growing period. Many of the delivery demands made by survey respondents are ideals. In fact, a monthly (ERS-based) service may well be acceptable where the current sources are weak or non-existent. In addition, there are also significant demands for data on a monthly or annual basis, which should not present a problem to image acquisition. Recent projects to determine the validity of an operational service to farmers and growers have been based on multispectral optical instruments. The products were deemed to be of great potential value to the agricultural community, but an operational service could not be provided due to cloud cover issues. It is likely that in order to offer a robust service, optical and radar data will need to be combined. It has been shown that current radar systems can provide a method of determining crop group as designated by the European Union s subsidy scheme, but are not able to discriminate at the species or sub-species level. The ability of ERS SAR to identify crop groups by means of a classification technique, based on known ground truth, is tested within this study. SATELLITE DATA AND PROCESSING Data Acquired In order to achieve the objectives relating to crop area estimates, it was necessary to identify a sequence of SAR images over the winter and summer growing seasons (September June 1998). Seven images were chosen to cover the full cereal season from planting and early growth in October-December through to full development and ripening in Spring and Summer to enable iterative updates of area estimates through the growing season. One optical image was ordered to improve the results of the summer crop classification, due to the significant proportion of bare soil at this time. A further reason for ordering an optical image in summer is that higher radiance improves spectral response enabling fields to be identified more clearly. Many of the field boundaries were also digitised from the optical image. It was initially proposed that a Landsat TM image would be ordered but only one cloud-free IRS-1C (LISS III) scene was available within the given time period. The scene covered a significant proportion of East Anglia, including the majority of the arable land and ground truth. Processing SAR Data Once a SAR CD was received at NRSC, a processing scheme was used to prepare the data for later analysis. This scheme involved the following stages, performed sequentially: 1. Reading the SAR CD onto the system; 2. Selecting 9 Ground Control Points (GCPs) between the SAR image and a reference map; 3. Radiometrically correcting (calibrating) the data; and 4. Geometrically correcting the data.

3 A unique software tool, the NRSC in-house Topographic SAR (TSAR) tool, was utilised to perform all radiometric and geometric corrections. The ERS-2 calibration algorithm applied follows ESA specifications [22] which recommend additional corrections be applied for the effects of Power Loss due to the Analogue-to-Digital Conversion and Topographic Incidence Angle. The mathematical specification of the calibration applied is: 2 Ac = A 2 sin α i PowerLoss sin α ref (1) where Ac is the corrected pixel value, A is the uncorrected pixel value, α i is the incidence angle of the radar beam on the target and α ref is the reference angle. Each of these corrections were routinely applied to the SAR data using the TSAR software system. The incidence angle used in the equation, α i, is the angle between the incident angle beam and the normal to the topography at that point (see Fig. 1). In areas of steep terrain relief, this correction can be significant. The significant amount of terrain distortion found in SAR data suggests that even in areas of low variation in relief, terrain correction must be applied to place targets in their true relationship to each other. For this project, all terrain correction was applied using TSAR, a DEM (50ft contour spacing, +/- 25ft accuracy) (see Fig. 2.), and a GCP file derived earlier in the processing chain. Optical Data The geocorrection of the optical image (IRS-1C LISS-III) employed a more simple procedure than the methodology described for the SAR imagery. This is because terrain distortion in optical images is much less important than that for SAR images. Furthermore, the region of East Anglia is particularly flat meaning that orthometric correction need not be applied. The Wash α The Thames Fig. 1. Definition of incidence angle αi Fig. 2. The DEM of the site

4 SAMPLING STRATEGY Introduction It is currently not cost-effective to cover large areas monthly using EO data to generate estimates of crop areas. Firstly the data and processing effort would be enormous, making it difficult to process the data within a short time-scale. Secondly, the cost would not be economical. It is anticipated that a reduced area could be observed using EO data, and the results of the classification extrapolated to cover a larger area. In order to develop a regional sampling strategy, a literature review was carried out. Description of Sampling Strategy The three major components to be considered when designing a sampling strategy are: Sampling unit (pixels or polygons); Sampling design (e.g. random sampling); Sample size (number of samples). Sampling Unit In this instance the population was defined as being all of the field boundaries within the region. The area was classified on a per field rather than per pixel basis as recommended in previous studies, for example [4]. This is because SAR imagery is inherently speckly in nature. The sampling unit was defined by a square known as a segment. Previous work has shown that optimum segment sizes range from 140 ha to 200 ha [6]. A segment size measuring 1.5 km 2, which is equivalent to 225 ha, was chosen for this project. Although slightly larger in size than Carfagna and Gallego have recommended, this segment size was chosen because it enabled the area of interest to be divided into equally sized segments. Sampling Design It was decided to sample systematically, allowing samples to be distributed evenly over the entire region of interest. The sampling design chosen for this project is known as UNALIGNED SAMPLING or stratified systematic unaligned sampling. This type of sampling combines the advantages of a regular grid (providing even coverage) and randomisation. It has been used on many occasions for land cover classifications, for example [36], [18]. Sample Size The final component of the sampling strategy was the size of the sample. For areas with less than a dozen categories, the overall classification accuracy may be estimated with a minimum of 50 samples. If the accuracy of each category has to be defined then a minimum of 50 samples per category is required [29]. Other work also suggests that a minimum of 50 samples should be taken for each category being classified, for example [19], [25]. It was decided that a minimum of 50 samples of winter cereals, summer crops and oilseed rape would be necessary to achieve a reasonable accuracy for each category (80% correct, Standard error ~ 5%). The following equation was used to make this assumption were p = percentage correct, q = percentage incorrect, n = number of samples and SE = standard error. SE% = p% q% n 5.66% = 80%20% 50 (2) [19]

5 CLASSIFICATION SAR Classification In circumstances when it is impossible to employ optical imagery, for example, excessive cloud cover, insufficient sun illumination, long revisit constraints or spectrally similar crops, it is possible to utilise SAR imagery. It has been recognised that there is no easy way to classify SAR data on a per pixel basis due to the speckly nature of the data. The development of classification schemes which classify crops in terms of mean values on a per field basis have enabled this problem to be overcome. Crop classification using SAR demands more than one image of an area. A single black and white image gives very little information about different types of vegetation cover on the ground. The acquisition of multi-temporal imagery enables one to utilise information throughout the season. The timing of the radar image acquisition needs to take into account differences in crop growing cycles. The classification methodology used image field statistics from fields of known crop cover (Ground data collected from the Area Frame Sampling Strategy in the European Union - United Kingdom, 1998 ) to classify other areas. This was performed using a supervised, field-based (rather than pixel-based) technique, applying the Maximum Likelihood (ML) algorithm. The SAR classification was run automatically using NRSC s in-house classifier. Data were classified for the winter (September to February) and summer (April to June) period into the following categories: winter cereals (WI1), summer crops (SM1), oilseed rape (RA1), permanent grassland (PG1), forest (FO1) and other (UK1). Confusion matrices were generated for the classifications. The region classifications were then used to extract data for the mini-sites. Optical Classification The problem of cloud cover prevents optical imagery being used on a routine basis. A further problem results from the fact that optical imagery requires spectral information in the visible and infrared regions of the crop canopy. The earliest estimates are therefore only possible after the canopy has developed significantly for the discrimination of spectral signature against bare soil background. For appropriate signal reception sun lighting also needs to be sufficiently high. In Europe this usually means that the earliest suitable acquisition date is early April [40]. The process for classifying the optical image was similar to that for the ERS scenes. A significant amount of cloud/ cloud shadow were present on the optical image and were removed from the scene. An unsupervised classification of the region was also carried out in order for a comparison to be made with the supervised classification. The optical image was classified into 20 classes using the ERDAS IMAGINE ISODATA algorithm. The results of the classifications were assessed using confusion matrices. The supervised classification was found to be more accurate so this classification was used to extract information for the mini-sites. EXTRAPOLATION The ground truth available for the project was located in two distinct blocks, one to the north and one to the south of the region. In order to have performed any valid regression technique (the typical extrapolation methodology employed within the remote sensing industry) it would have been necessary to have ground truth randomly spread throughout the region, in the same location as the mini-sites. It was therefore not possible to perform regression on the optical or SAR mini-sites. The alternative extrapolation method chosen was direct expansion. It was hypothesised that the high accuracy of the optical classification would enable a third option to be employed. The optical mini-sites were used as pseudo ground truth and regression was applied to the SAR mini-sites. However, this methodology proved to be statistically very poor due to the weak relationship between the SAR and optical classifications.

6 The areas occupied by the ground truth were not included when calculating the mini-site area estimates. The total area of the mini-sites equated to approximately 1.0% of the entire region with the exclusion of the ground truth. Reference [16] also used a sampling rate of about 1%. RESULTS Validation The SAR and optical classifications were validated from ground truth and confusion matrices were generated. The most accurate results were obtained from the optical supervised classification (88% overall accuracy). The unsupervised classification achieved an overall accuracy of 84%. The SAR winter and summer classifications obtained relatively low overall accuracies, 53% and 69% respectively. The use of SAR for the determination of oilseed rape acreage, in particular, was identified as unreliable. Area Estimates Census statistics In order to assess the significance of the region area estimates it was necessary to compare the results with auxiliary data obtained from [27]. The area estimates were calculated for the east of England region, a region somewhat larger than the region examined in this study. However, it was assumed that the percentages of each class computed from the census statistics were comparable to the percentages calculated from the classifications. General All of the region area estimates were compared to the census statistics for East Anglia and the following list shows the results in decreasing order of accuracy: Optical supervised classification area estimates; Optical unsupervised classification area estimates; SAR summer classification area estimates; SAR winter classification area estimates. SAR Winter Area Estimates A chi-square test was performed to compare the values from the entire region with those resulting from the mini-sites. There is a 91.94% probability that the mini-site area estimates derive from the same population as the region area estimates. This is a high probability, and the mini-site and region area estimates are most similar for this classification. The mini-sites have improved the area estimates of PG1, RA1 and FO1, in relation to the census statistics but worsened the estimates of UK1, WI1 and SM1. SAR SummerArea Estimates The region area estimates for the SAR summer classification are much better than those produced for the SAR winter classification. However, the mini-site area estimates do not compare well to the region area estimates. In fact the probability that the numbers derive from the same population is only %. The mini-sites have improved the area estimates of FO1, RA1 and PG1, in relation to the census statistics, but worsened the area estimates of SM1, WI1 and UK1. Supervised Optical Classification The supervised optical region area estimates are the best area estimates achieved within this study. The main problem identified with the optical classifications relates to cloud cover on the IRS scene. It is for this reason that the area of unclassified data is quite high. The mini-site area estimates are less similar to the region area estimates than they were for the SAR winter classification but are considerably better than for the SAR summer classification. There is a 27.34% probability that the mini-site area estimates belong to the same population as the region area estimates. The mini-sites have improved the area estimates of RA1 and SM1, in relation to the census statistics, but worsened the area estimates of UK1, WI1, PG1 and FO1.

7 COMBINING DATA The supervised optical region classification yielded area estimates most similar to the census statistics. The accuracy of this classification was higher than that obtained for any of the other classifications. The biggest problem with this classification was the large proportion of unclassified land. In order to reduce this, it was decided to use the results from the SAR summer classification to fill in the gaps. Combining the two datasets improved the area estimate of PG1 but worsened the area estimates of all of the other classes. Other possibilities of combining data were considered but none of these possibilities appeared to offer any potential for improving results. CONCLUSIONS AND RECOMMENDATIONS Conclusions The results of this project confirm that higher classification accuracies can presently be obtained from optical data than from currently available SAR data. The comparison of region area estimates with their corresponding mini-site area estimates has shown mixed results. Some classes compare well (for example, SM1 within the SAR winter classification) and others poorly (for example, SM1 in the SAR summer classification). It is difficult to offer any valid explanation for this, except for chance (relating to the location of the mini-sites). A further observation is that the mini-site area estimates have improved the area estimates of some classes, in relation to the census statistics, but worsened the area estimates of other classes. Recommendations and Business Plan Present Capabilities This project demonstrates that current radar sensors cannot satisfy the user requirements of the agribusiness industry. This is mainly because of the requirement for spatial accuracies greater than can be delivered using ERS-2 SAR. Agrochemical manufacturers and retailers require, at the very least, species information on the crop type being grown, and often will require variety information. This is because modern pesticides (and their recommended application rates) are variety specific. Current satellite-based radar has the capability to provide information on crop group but is unable to classify crops to species or sub species level. Seed producers are interested in any methodology that offers accurate planting information, but again, this is required at the species level. Sales of differing varieties of seed are currently used to indicate planting levels, and this information is passed on to grain traders and commodity brokers. This is a very cost-effective method of monitoring the planting of new varieties. Grain traders and commodity brokers are only profitable if they can buy commodities at a lower price than they can sell them. Generic crop type information is not of value to this sector because of the wide price differential between, for example, milling wheat and feed wheat. The premiums offered for milling wheat are based on the known planted area of those varieties that should produce wheat suitable for bread and biscuit making. A number of the grain traders interviewed in the course of this project indicated that they would be interested in a service that allowed them to monitor the growth of known varieties through the season. Present radar is unable to satisfy this need. A potential market growth area, for current sensors, is to provide crop inventory information to countries in Eastern Europe and the Baltic. In order to qualify for EU accession such countries will need to demonstrate their ability to provide agricultural statistics and to control subsidy claims under the Arable Area Payment Scheme (AAPS). The market sector will be government (Ministries of Agriculture) rather than agribusiness. This project has identified that current radar sensors do not satisfy the requirements of the agribusiness sector, but that improved radar sensors and systems will be essential if this market sector is to be served by EO. Optical sensors offer sophisticated products, but to-date have not met the critical timelines or reliability demanded by agriculture. The future is likely to be a combined radar/optical system, offering the benefits that accrue from both sensors.

8 Future Capabilities NRSC has been involved in a number of pan-european projects which investigated the needs of the agricultural community across Europe, and the benefits that could accrue from the use of remote sensing. The need for timely information was cited by all respondents, and this was not achieved using airborne optical instruments in the UK and France. It is hoped that radar will provide the mechanism for delivering a robust service to farmers and agronomists, especially early in the growing season, when cloud cover is high. Further research is required to determine whether radar can provide estimates of biomass at a field scale, in such a form that a rate of change map can be produced. The ongoing RADWHEAT project aims to develop radar sensor technologies to meet farmers' needs for cost-effective methods for measuring key crop parameters. This information will aid their decisions on crop inputs, particularly nitrogen fertiliser use. The project began in February 1999 and has a two-year duration. The consortium consists of ADAS Consulting Ltd (lead partner), BAE SYSTEMS Research Centre, Sheffield Centre for Earth Observation Science, with financial support from the BNSC LINK Programme, the Home Grown Cereals Authority and Matra Marconi Space (UK) Ltd (BNSC, 2000). One requirement that could be satisfied using radar data is crop vigour. Agronomists have shown a high degree of interest in the idea that rate of change maps, at field scale, could be produced throughout the growing season. A single crop vigour map, though interesting to many agronomists, in fact contains little useful information unless a pest or disease attack or fertilizer deficiency is apparent. The value of the rate of change map is that it monitors the growth of the crop over time, and allows semi-permanent, as well as transient, differences across the field to be identified. The benefit of this is that the transient differences, for example, disease infestation, can be identified and treated promptly. If the differences in vigour continue, it is likely that a more permanent feature, such as soil type or water holding capacity is the cause of the difference. The increased interest in precision or variable rate farming seen recently in Europe seems to have reached a plateau: this is possibly due to the fact that yield mapping provides only an historic picture of the crop. High resolution near real time remote sensing would offer farmers and agronomists a means to maximise yield whilst minimising inputs through the targeted and timely use of pesticides and fertilizers, and through improved soil management techniques. Another area in which remote sensing could offer a substantial benefit is in the detection of failed varieties of crops. New varieties that are offered to farmers are tested thoroughly before being accredited (e.g. by the Home Grown Cereals Authority in the UK), but have been known to perform poorly when widely available. An example of this is the oilseed rape variety, Sponsor, which failed to germinate in It was remote sensing (Area Subsidy Payment monitoring) that first highlighted the scale of this problem. We did not have the opportunity to monitor a failed crop, but research work indicates that timely radar would be an extremely effective method for determining germination failure early in the growing season. Future SAR Instruments Future SAR instruments will include ASAR and TerraSAR. A report produced by NRSC to DERA, ASAR and Radarsat Benefits for Crop and Area Determination in Europe (ARCADE), has demonstrated the potential advantages, which ASAR will have over the existing ERS2 SAR. A combination of Radarsat and ERS data were used to simulate ASAR. The new features on this instrument will include a steerable beam and dual polarisation. Although ASAR will have the ability to classify crops more accurately, it will probably be unable to classify a greater number of crops or provide information on crop variety. Airborne campaigns using, for example the Danish EMISAR or the German E- SAR (SHAC 2000), could be utilised to further simulate ASAR potential. The work carried out by the Technical University of Demark also indicates the advantages of using multi-polarisation data [38], [39]. The InfoTerra Project, in which NRSC is playing a lead role, will offer an X and L band, multi-polarised SAR known as TerraSAR. The proposed SAR will have a spatial resolution of better than 10m and a re-visit time of better than 10 days. It is likely that this instrument will offer farmers and agribusiness a timely, robust information service that is weather independent. Crop information at variety level may even be obtainable and potential services include crop condition maps, improved yield estimates and disease mapping. Several airborne campaigns, including SHAC 2000 and Pro-Smart 2 are presently being used to simulate TerraSAR capabilities. Ongoing market research indicates that the potential market for agricultural products will be 699Meuro in 2010.

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