|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||In-Season Crop Mapping Using Multi-Frequency SAR|
|Investigator||SHANG, JIALI - Agriculture and Agri-Food Canada, Research Branch|
Annual crop inventories from satellite data especially when available in-season provides valuable spatial information on changing land use and agricultural production. Agriculture land use monitoring is critical for forecasting, early warning, program response, policy development, and performance measurement. Timely geospatial information on crop inventory can be used to track a number of sustainable production issues and crop resources. This proposed research aims at developing a methodology to provide crop acreage estimate early in the season. Agriculture and Agri-Food Canada (AAFC) and the ChineseAcademy of Agricultural Engineering (CAAE) at the Ministry of Agriculture of China (MAC) have been working collaboratively on several multi-year projects to develop methods to derive annual crop inventories using EO data. Recent results have shown that satisfactory classification accuracies can be achieved using SAR data alone at the end of the growing season. This research will examine the potential of multi-frequency SAR data to derive crop acreage information early in the growing season. On-going research activities will continue collecting L-Band (ALOS PALSAR) and C-Band (RADARSAT-2 polarimetric) SAR data to identify crops in diverse agro-ecosystems. With the addition of the X-band SAR data, this project will permit the unique opportunity to demonstrate the delivery of in-season a crop inventory using multi-frequency (X- C- and L-Band) SAR. 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%) at the early stages of the growing season..
3. Determine whether incorporation of coherence change detection can assist in delivering crop type information.
The teams in China and Canada will use a decision tree method developed at AAFC for crop classification. In addition advanced non-parametric classification methods and classifiers designed specifically for polarimetric data will be explored using existing data over China. 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 60 images over three years and four sites. Data required are mainly dual polarization stripmap. 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. ALOS PALSAR will be acquired through a JAXA project lead by Mr.Pei. This research will produce a preliminary and final report on the contribution of TerraSAR-X for in-season 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-X for operational mapping, promoting the use of these data in both public and private agencies.
Back to list of proposals
|© DLR 2004-2016||