Remote Sensing of Crop Acreage and Crop Mapping in the E-Agri Project Chen Zhongxin

1. Remote Sensing of Crop Acreage and Crop Mapping in the E-Agri Project Chen Zhongxin Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences

2. Outline • I. The Objectives for WP5 • II. Main Tasks in WP5 • III. Research Plan and Activities

3. I. The Objectives for WP5 • Adapt and design in-situ segment sampling method set up crop area extrapolation models for the study areas (sampling and scaling-up) • Select the optimal remote sensing classification options for crop area in spectral and temporal terms • Generate crop area estimates with in-situ sampling and remote sensing • Analyze errors (sampling and non-sampling) and costs for crop area monitoring with remote sensing • Demonstrate the selected technology in the study areas

4. II. Main Tasks in WP5 WP51 WP53 • To adapt and design segment sampling method • To establish the crop area spatial extrapolation model for the study area • To execute the segment sampling and track sampling in the study areas • To collect the remote sensing data . • To pre-process and classify the satellite images • To select the best classification option in both spectral and temporal terms • To generate the area estimates using the ground sampling dataset WP52 WP54

5. II. Main Tasks in WP5 WP52 WP54 • To generate the area estimate using best classification option • To generate the area estimate combining regression and remote sensing • Analysis of sampling and non-sampling errors • Analysis of mapping costs • to evaluate what is the impact on the mapping accuracy when no or very limited ground survey (for example based on the track sampling) is conducted. WP55 WP56

6. Participating Institutions • VITO (WP51,52, 53, 54, 55,56) • CAAS (WP51,52) • AIFER (WP51, 52) • INRA (WP53, 54) • DRSRS (WP56)

7. III. Preliminary Research Plan • Data Preparation and Collection • In-situ sampling and extrapolation • Remote Sensing Classification of Crop • Error analysis • Generate crop acreage estimates from in-situ and remote sensing data

8. Data collection and preparation • Background data • GIS maps (land use, administrative, road, soil, vegetation, contour, crop, geology, geomorphology, hydrology) • Socio- economic statistical data for 10 yr • Crop calendar and phenology • Climate data • In-situ data: field segments and tracks • Remote sensing imagery • Time series of LR images • HR images

9. Data collection and preparation • Remote sensing imagery • Time series of LR images: MODIS, AVHRR, AWiFS, VEGETATION, • HR images: TM, ALOS, SPOT, IRS, HJ-1 • VHR images: QB, IKONOS, Aerial

10. 平原 丘陵 山区平原

11. Crop Mapping for Winter Wheat in Anhui, 2009

12. In-situ data from field segments • 50 samples @ 1km x 1km • With 25 km intervals • Winter wheat and maize • Existing samples 500m x 500m • Study region size 40000km2?

13. Technical flow of spatial sampling scheme

14. Samples Spatial distribution in Faku county Samples Spatial distribution in Fengtai county Samples Spatial distribution in Dehui County

15. Fig 4.2 Distribution of sample village Fig 4.3 Distribution of sample plots in sample village

16. 样方地点：河南省新乡市原阳县葛埠口镇 采集时间：2008.11.12样方面积：228586.97平方米冬小麦面积：196359.90平方米

17. In-situ Segments 2009 2008

18. In-situ sampling and extrapolation • Selection of sampling frame • spatial vs. non-spatial • Sampling methods: • Random • Systematic • Stratification • Remote sensing sampling • Extrapolation (scaling-up) • Relevant to sampling method • Regression with remote sensed info

19. Remote Sensing Classification of Crop • Hard classification vs. soft classification • Hard for HR images • Soft for LR time-series data with sub-pixel classification • Automation vs. visual interpretation • Supervised vs. unsupervised classification

21. The sub-pixel classification result

22. ALOS：10m,2009-3-20 QuickBird：0.61m,2009-3-25

23. Error analysis • Sampling error • Non-sampling error • Cost analysis

24. Generate crop acreage estimates • From in-situ segment and track sampling • Get crop acreage estimate based on statistics • HR remote sensing info • Direct pixel count for full coverage • Regression if sampled • LR remote sensing • Regression with HR or in-situ samples • Sub-pixel classification

25. Activities • Define the research regions (C, M, K) • Background data collection • Remote Sensing data collection/ processing • Field survey (2-3 times) • Sampling and extrapolation model • Remote Sensing classification • Error analysis • Generate crop estimate • WP5.6?

26. Define the research regions (C, M, K) • China – Huaibei, Anhui • Moroco - ? • ? Kenya? • Time: asap (1 month? Before April 30)?

27. Background data collection (research regions) • Socio- economic statistical data for 2001-10 • Climate data for 2001-10 • GIS maps (land use, administrative, road, soil, vegetation, contour, crop, geology, geomorphology, hydrology) • Crop calendar and phenology • Time: 6 months (before September 30)

28. Remote Sensing data collection • Time series of LR images: MODIS, AVHRR, AWiFS, VEGETATION, • HR images: TM, ALOS, SPOT, IRS, HJ-1 • VHR images: QB, IKONOS, Aerial • Time: • 3 months for first datasets • progressively

29. Remote Sensing Image Processing • Geometric correction • Radiometric correction • Time series preparation • Derived parameters (VIs, Ts, etc.) • Phenology • Time:

30. Field surveys • 2-3 times for winter wheat and maize • 50 samples 1kmx1km (500m x 500m?) • Track servey • Time: April, August of 2011, 12 and 13 for China • For Moroco?

31. Sampling and extrapolation model • Remote Sensing classification • Error analysis • Generate crop estimate • WP5.6?

32. Thanks for Your Attentions!