|TSM/TDM Science Team Meeting 2016|
|Science Team Meetings|
|How to Submit a Proposal|
|COFUR Price List (scientific use)|
|TanDEM-X Science Service System|
|Title||Integration of Multi-Frequency, Multi-Sensor, and Multi-Temporal Radar Data for Operational Annual Crop Inventory at the National Scale|
|Investigator||Shang, Jiali - Agriculture and Agri-Food Canada, Research Branch|
Annual crop inventories from satellite data provide valuable spatial information on changing land use and agricultural production. While this information can be supplied by multi-temporal optical data, obtaining sufficient data over a growing season to derive an operational annual inventory is problematic, particularly in areas with frequent cloud cover. Agriculture and Agri-Food Canada (AAFC) and the Chinese Academy of Agricultural Engineering (CAAE) at the Chinese Ministry of Agriculture have been working collaboratively on a multi-year project to develop methods to derive annual crop inventories using optical and SAR data. While results using optical data in combination with SAR have shown promise, methods using SAR data alone have shown lower accuracies, in comparison. This research will examine the potential of X-Band SAR data to derive annual crop inventories. On-going research activities are examining L-Band (ALOS PALSAR) and C-Band (RADARSAT-1/2, ASAR) SAR data to classify cropland in diverse agro-ecosystems. This project will permit the unique opportunity to demonstrate the delivery of a crop inventory using multi-frequency (X- C- and L-Band) SAR. With polarimetric RADARSAT-2 and PALSAR data being acquired over the Canadian site, acquisition of X-Band polarimetric data will evaluate the contribution of multi-frequency polarimetric variables in deriving crop information. The project objectives include:
1. Determine to what level of accuracy TerraSAR-X can classify crops in Canada and China.
2. Determine whether multi-frequency (X- C- and L-Band) SAR data alone can produce classification accuracies targeted by AAFC and CAAE (overall and individual accuracies of 85%).
3. Determine to what level of accuracy TerraSAR-X high resolution data can identify rice patties in southern China.
4. Determine whether polarimetric parameters can assist in delivering crop type information.
5. Determine whether a multi-frequency (multi-pol or polarimetric) data set can reach classification targets early in the growing season.
The teams in China and Canada will use a method developed at AAFC for crop identification. In addition advanced non-parametric classification methods and classifiers designed specifically for polarimetric data will be explored. Optimal pre-processing methodologies will be examined such as speckle filter selection. The inclusion of polarimetric-derived inputs into the classifier, such as decomposition parameters, will be explored. Classification of TerraSAR alone, and in combination with C- (RADARSAT-2) and L-Band (PALSAR) data, is planned. The project team is requesting a total of 42 images over two years and three sites. This includes standard and fine beam mode dual-pol TerraSAR data, and a limited number of polarimetric images. This project falls within the scope of existing internally funded research projects at AAFC and CAAE. RADARSAT-2 data are available through the Canadian government allocation and through an approved Canadian Space Agency SOAR project (#403). ALOS PALSAR is being acquired through a cal/val project led by the Canada Centre for Remote Sensing, as well as additional programming through the Alaska SAR Facility. This research will produce a preliminary and final report on the contribution of TerraSAR-X for crop mapping, as well as scientific publications. Workshops will be held to enhance the collaboration between the agencies in both countries. Results will demonstrate the applicability of TerraSAR for operational mapping, promoting the use of these data in both public and private agencies.
Back to list of proposals
|© DLR 2004-2016||