E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 Remote Sensing of Crop Acreage and Crop Mapping in the E-Agri Project Chen Zhongxin Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 Outline I. The Objectives for WP5 II. Main Tasks in WP5 III. Research Plan and Activities

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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 I. The Objectives for WP5

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 II. Main Tasks in WP5 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 WP51 WP53 WP52 WP54

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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. II. Main Tasks in WP5 WP52 WP54 WP55 WP56

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 Participating Institutions VITO (WP51,52, 53, 54, 55,56) CAAS (WP51,52) AIFER (WP51, 52) INRA (WP53, 54) DRSRS (WP56)

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 Crop Mapping for Winter Wheat in Anhui, 2009

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 In-situ data from field segments 50 1km x 1km With 25 km intervals Winter wheat and maize Existing samples 500m x 500m Study region size 40000km2?

E-Agri Project Kick-off Meeting, Mol, 24-25, Technical flow of spatial sampling scheme

E-Agri Project Kick-off Meeting, Mol, 24-25, Samples Spatial distribution in Faku county Samples Spatial distribution in Fengtai county Samples Spatial distribution in Dehui County

E-Agri Project Kick-off Meeting, Mol, 24-25, Fig 4.2 Distribution of sample villageFig 4.3 Distribution of sample plots in sample village

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011

In-situ Segments

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011

The sub-pixel classification result

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 ALOS 10m, QuickBird 0.61m,

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 Error analysis Sampling error Non-sampling error Cost analysis

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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?

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 Define the research regions (C, M, K) China – Huaibei, Anhui Moroco - ? ? Kenya? Time: asap (1 month? Before April 30)?

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 Background data collection (research regions) –Socio- economic statistical data for –Climate data for –GIS maps (land use, administrative, road, soil, vegetation, contour, crop, geology, geomorphology, hydrology) –Crop calendar and phenology Time: 6 months (before September 30)

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 Remote Sensing Image Processing –Geometric correction –Radiometric correction –Time series preparation –Derived parameters (VIs, Ts, etc.) –Phenology Time:

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 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?

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 Sampling and extrapolation model Remote Sensing classification Error analysis Generate crop estimate WP5.6?

E-Agri Project Kick-off Meeting, Mol, 24-25, 2011 Thanks for Your Attentions!