Dr Rosie Bryson 1 & Mr Keith Norman 2. Mr Gary Holmes Infoterra Ltd, Delta House, Southwood Crescent, Farnborough GU14 0NL

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1 Integrating SAR and Optical products for Crop Management (ISOCROP*): Case studies to demonstrate the practical application of remote sensing technologies. Dr Rosie Bryson 1 & Mr Keith Norman 2 1 Velcourt Ltd, e-space South, 26, St Thomas Place. Ely Cambs. CB7 4EX. 2 Velcourt Ltd. Woodside Cottage, Church Lane. Hagworthingham. Spilsby. PE23 2LJ Mr Gary Holmes Infoterra Ltd, Delta House, Southwood Crescent, Farnborough GU14 0NL * ISOCrop is a shared-cost project (contract EVG1-CT ) co-funded by the Research DG of the European Commission within the RTD activities of a generic nature of the Environment and Sustainable Development sub-programme (5 th Framework Programme). Introduction The intensification of agriculture in Northern Europe over the last forty years has resulted in the greater use of variable inputs such as fertilisers and pesticides as well as the removal of field boundaries to reduce fixed costs thereby improving efficiency. A sustainable agriculture sector, by definition, must be economically viable. However, with increasing exposure to Global markets and agricultural reforms through EC policy it is necessary to not only have an efficient agriculture sector in terms of production but one that is also mindful of the environment and the consumer (Bryson et al., 2000). Farmers are now faced with increasing demands from both environmental legislation and buyers to follow good agricultural practice. These economic and environmental pressures are increasingly affecting crop management decisions and driving the optimisation of the rates at which fertilisers and agrochemicals are applied to fields. In order to help with the decision making process the arable farmer has attempted to measure aspects of the crop or soil considered to be important. Due to the large areas that need to be covered, and the limitations of time and human resource, in the past it has only been possible to obtain an approximate mean value of any parameter on a field basis in the majority of cases. This may be a mean value from five soil samples traditionally taken in a W shape (often irrespective of field size), from an estimate of disease severity from crop walking or by recording weed patches at harvest (Blackman et al, 2000). However, the use of remote sensing technologies now has the potential to revolutionise data acquisition relating to crop and soil parameters by farmers and their advisers. For example, measurements of parameters such as soil type and nutrient status may be possible by electro-magnetic induction (King & Dampney, 2000), field zone differences due to soil type from historical yield maps (Steven & Miller, 1997), crop canopy size from optical sensors (Hatfield & Pinter, 1993) and crop dry matter from radar sensors (Anderson et al, 2003). Until recently these technologies, although able to provide within-field, spatial, biophysical information to the farmer, were expensive and labour intensive. 1

2 Recent advances in sensor technology, data acquisition, modelling and processing coupled with planned future satellite missions now mean that there is a very real opportunity to obtain near-real time data for crop parameters to support on-farm decision making at relatively low cost, over significant land areas and without the need for a large amount of human resource. This paper describes early findings from the practical application of optical and radar sensing techniques from an EC funded project, ISOCrop* to support the decision making processes on farm to manage winter wheat crops. Project Background The primary aim of the ISOCrop project was to develop techniques to retrieve crop parameters from Synthetic Aperture Radar (SAR) imagery, and to develop the framework for integrating these capabilities with Superspectral Optical products through satellitebased crop monitoring services. Superspectral product development has reached an advanced stage, but it is acknowledged that sustainable remote monitoring services will need to integrate multiple information sources to overcome issues such as revisit times, satellite availability and cloud cover. SAR has shown strong potential for the retrieval of substitutional and supplementary information layers, but the maturity of the research is several years behind the optical equivalent. Within the EC project, SAR and superspectral optical imagery has been used to simulate datasets from planned European satellite missions, along with intensive ground measurements of crop parameters. The aim was to provide the basis for SAR retrieval development techniques and investigations of SAR- Optical synergies. Satellites as a sensor platform Satellite imagery provides the means to monitor whole regions and provide spatial information products at an affordable per hectare cost, and requires no capital investment from the farmer. Current satellite data sources are largely unable to meet the needs of farmers and agronomists (for reasons of pricing structure, information content, timeliness or resolution) but a number of European initiatives are under way to address the needs of these users with improved data sources (e.g. TerraSAR, XStar, Cosmo-Skymed). Superspectral visible/nir research has reached an advanced stage of readiness for providing operational services, thanks in part to Framework IV activities (Jaquemoud & Baret, 1990, Jaquemoud 1993). Products have been developed which have very high information content and are able to support a wide range of practical farm management decisions. However, it is recognised that cloud cover will act as a barrier to service provision in many parts of the world, due to its effect on timeliness of product delivery. The role of SAR A major objective of the ISOCrop project is to develop cloud-independent substitute products from SAR data, which contain sufficient information to fill the temporal gap in superspectral optical product deliveries. With cloud-independent substitute products established, a solid and complete basis will be available for operational services in all regions of Europe. The body of SAR research to date has indicated that dual-band; polarimetric data are capable of detecting variations in biomass, which could act as substitute information for crop monitoring services. Furthermore, the research has shown that SAR data have the potential to provide additional information such as surface soil moisture and variations in canopy moisture content, which cannot be retrieved from optical data sources. SAR-based products could therefore complement optical products and act as a key element in making reliable crop monitoring services available to growers in all 2

3 regions of Europe, and beyond. There is a need to improve techniques for retrieving crop parameters from SAR data, bringing this technologies capabilities more in line with those already attained for superspectral optical data. There is also a need to establish how these SAR products can be integrated with the established superspectral optical products into operational satellite-based services for growers, agronomists and agribusiness. Wheat as a model crop Winter wheat was chosen as the subject for research within the ISOCrop project. This is the crop for which the vast majority of the necessary structural and radiative transfer modelling has been carried out. A substantial amount of research has already been undertaken in retrieving wheat crop parameters from SAR data, linking architectural and radiative transfer models. Within this project these models have been applied to crops grown under field conditions and will be used to develop products suitable for satellite data sources. Wheat is also the crop for which superspectral optical products are most advanced, with biophysical products at the operational stage and higher-level application products at the pre-operational stage. Wheat was also chosen on the basis of its economic importance in Europe and the high number of management decisions that may potentially be supported by agronomic map products. Approach The aim of the experimental work within the ISOCrop project was to examine key biophysical crop parameters in commercially grown winter wheat crops and use these to develop and refine models of superspectral optical and SAR imagery data. With the application of these models to the image data, derived biophysical parameter product maps were supplied to the farm managers at the two main experiment sites and evaluated to see whether they could be used to support decision making on farm. In some cases image product maps were obtained for fields where uniform treatments had be applied, in other cases, large experimental plots were variably treated with nitrogen, growth regulators, fungicides and irrigation in order to obtain contrasting crop structure and condition. This paper will briefly describe some of the image products and a few cases studies where they were used to improve and support decision making in a commercial situation. SAR and superspectral optical imagery model development and refining are not described here as it is out of the scope of this paper. SAR image acquisition The SAR data was acquired by the E SAR system (Figure 1), operated by DLR (Deutsches Zentrum fuer Luft- und Raumfart Ev) of Germany. E-SAR was configured to acquire X and L-band multi-polarisation data in order to simulate the performance of the planned TerraSAR satellites. The imaged area dimensions were 3 x 5 km at each site. 3

4 Figure 1 Dornier D0 228 with E-SAR installed Optical image acquisition The optical data was acquired by the hyperspectral Specim AISA system (Figure 2), operated by Infoterra Ltd. AISA was configured to acquire 11 channels of data ( superspectral ) in the visible/nir region in order to simulate the performance of the planned XStar satellite. The spatial; resolution of the sensor was <10m with a flight line swath width of km. Figure 2 - AISA system installed in Piper Seneca aircraft 4

5 Field sites Field experimentation was carried out at two sites, one in the UK at Vine Farm, Shingay cum-wendy, near Cambridge and the other in Spain El Soto farm, Ruyales del Agua Nr Lerma. At each site primary and secondary fields were established over two seasons. At the primary sites large scale field plots were established (72m x 60m UK and 75m x 60m Spain) and variable inputs applied to the plots. These included sowing date (early v s late e.g Sept. v s Dec.), and rate differences (e.g. 150 v s 250 seeds/m 2 ), high, low and standard nitrogen (e.g kgn/ha), high, low and standard fungicides (3 v s 1 applications), high and low plant growth regulators (2 v s 1 application) and irrigation (Spain only 100%, 50% and 25% of standard applied). Within the large scale plots smaller areas were defined (24m x 24m UK and 30m x 30m Spain) in order to carry out ground measurements of crop parameters. On the secondary fields, no variable applications were made but four sampling areas were defined each being 24m x 24m in the UK and 30m x 30m in Spain. These samples areas were selected to provide as much contrast of the field as possible. Within all sample areas ground truth measurements were carried out to coincide with the SAR and optical image acquisition. In Spain, SAR and Optical image acquisition flights were carried out on two occasions and in the UK three. The aim was to acquire image and ground truth data as simultaneously as possible and at key growth stages where image products could be used to support input decisions. A wide range of ground truth measurements were carried in order to support the SAR and optical data models and image product definition. These included leaf area index (LAI) (measured using a Delta-T Sunscan Delta-T Cambs.), crop biomass, leaf transmittance (correlated to chlorophyll content using a SPAD Minolta meter), crop height, leaf angle distribution, plant part dimensions (stem length, leaf thickness, leaf length, ear length etc.), yield and soil parameters (moisture, conductivity, temperature and roughness). Case studies 1) Whole farm management Nitrogen. 5

6 Priority fields for early or more frequent nitrogen applications Fields where total N or the number of applications could be reduced Figure 3. LAI field maps superimposed on field map image of Vine farm Cambridge, UK. Comparison of image products of LAI at early growth stages helped to support prioritisation of nitrogen. Areas of low LAI (LAI <0.75 red-orange on map) at GS31 required early or more frequent applications of nitrogen whereas more advanced, larger crop canopies (LAI >1.0 light to dark green) could have reduced applications of nitrogen. The potential benefits from improved nitrogen timing were to help optimise the crop canopy thereby optimising yield. Reducing nitrogen inputs to more advanced crops reduced lodging risk and pollution due to nitrogen into water courses through run-off. 6

7 2) Whole farm management (Vine farm, Cambridge UK) lodging risk. In some cases, biophysical parameters such as chlorophyll content and LAI from optical data were used to derive more advanced image products such as a lodging risk maps. In this case below high lodging risk is shown as red and low risk yellow. Again, it may be seen that certain fields have a high priority (red fields) for the use of growth regulators, whereas the low risk fields (green) may require fewer treatments. High lodging risk fields indicating a need for a higher plant growth regulator input,e.g.split CCC/ Moddus plus Terpal Figure 4. Lodging risk maps superimposed on field map image of Vine farm Cambridge, UK. 7

8 3) Manipulation of canopy characteristics. Using variable seed rates and inputs such as nitrogen, fungicides and PGRs (Figure 5) large scale plots were created that had contrasting canopy characteristics as previously described. These were used to compare image products and to support model development whilst mimicking likely crop stands found in commercial farm situations. Figure 5. Large scale plot layout at Vine farm, Cambridge, UK. At GS33 clear differences in canopy size could be seen from the LAI maps of the experimental area (Figure 6). The larger crop canopies (LAI >3.0)(pale and dark green) of the High N:High seed rate and High N:Low seed rate could be clearly seen. This was in contrast to the much smaller crop canopy (LAI<1.5) (red) seen in both the Low N:High seed rate and Low N:Low seed rate plots (Figure 6). Interestingly, at a later growth stage (GS39) differences in canopy height were also apparent from the SAR derived field maps. As seen in Figure 7 - crop height differences of 3-5cm could be seen. 8

9 Low N: High Seed rate Low N: Low Seed rate High N: High Seed rate High N: Low Seed rate Figure 6. LAI map of primary experimental area with differential plots at GS33 at Vine farm, Cambridge. UK. Shorter crop in low seed rate blocks Taller crop in high seed rate blocks 9

10 Figure 7. SAR (L & X band) derived crop height at GS39, Vine Farm. Cambridge. UK 4) Irrigation management Lerma Spain Figure 8 below demonstrates that LAI image maps can be used to identify problems with irrigation systems and the potential yield loss associated with this. The map below is of the primary field in Lerma, Spain (2002) and is irrigated by a line irrigator which travels from the top right hand side to the top left hand side and then back from the bottom left hand side to the bottom right hand side. The very small LAI measurements (dark red) seen as a line in the centre of the image are the wheelings from the irrigator. It can be clearly seen in this image that the top half of the field is well irrigated with a large crop canopy (green area). However, where there have been problems with the water supply from the irrigator in the bottom right hand corner of the image (red area) the crop is under severe stress. If the lack of water continued then almost total crop loss could occur. Poor or inadequate irrigation results in a significant loss of leaf area index (April 2002 image) leading to yield loss and/or total crop failure. Figure 8. LAI map of the primary field at Lerma, Spain showing irrigated and nonirrigated areas. The above case studies are just a few examples of the practical application of both optical and SAR image products that have been investigated within the ISOCrop project. Although the SAR image products are at the very early stage of development compared with the optical images early indications are that they will provide very good logistical support to the optical products. 10

11 Conclusions and discussion. As financial and legislative pressures increase on the agricultural sector across Europe effective crop management, the decision making process, traceability and cross compliance will become increasingly important. The preliminary findings from the case studies described here, and others, demonstrate that remote sensing technologies using optical and radar derived data can:- a) enable differences in the growing crop to be identified, in real time, b) be used as a way of visualising a large number of hectares, all on one day, on a relative basis to help manage resources and reduce fixed costs, c) support input management decisions to optimise inputs, reduce environmental contamination and maximise yields. d) improve forward planning and logistics at the farm enterprise level, e) assist in cropping and set aside management decisions to support future cross compliance legislation, f) provide a full range of derived products throughout the season Although it is apparent from the work described here that remote sensing technologies offer a management solution to support the changes that will inevitable occur within the agricultural industry over the next few years, it is important to remember that for the farm manager, sustainability is only achievable through profitability. With this in mind there are several important issues which need to be addressed if remote sensing technologies are truly going to be of value to, and adopted by, the agricultural industry:- 1) the value of the technology to the farmer or advisor must outweigh the cost. 2) If the cost of acquiring the technology increase fixed costs the there needs to be a significant reduction in variable costs or an increase in gross margin, 3) Delivery of remotely sensed data to the decision-maker needs to be timely, consistent and reliable, 4) The value of the opinion, experience and inherited knowledge of the farming community should not be underestimated. Acknowledgements The authors of the paper wish to acknowledge the funding of the ISOCrop project from the EC* and also the contribution of the consortium partners who are Infoterra Gmbh, BAE Systems, Astrium SAS, Asistencia Técnica Industrial, S.A.E. and BASF. References on request. 11