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Cropland Mapping and area estimation method in CropWatch
Nana Yan, Miao Zhang, Bingfang Wu, Bo Chen RADI, CAS June, 2015
Outline
Background
Methodology in CropWatch
Recent updates of crop classification
Outlook
(FAO. How to Feed the World in 2050)
Food: a big issue for current and the future • By 2050 the world’s population will reach 9.7 billion, 34 percent higher than today. • Nearly all of this population increase will occur in developing countries.
More mouth to feed
We have to producing 70 percent more food for an additional 2.3 billion people by 2050
CropWatch Mission CropWatch aims at improving food information availability, quality and transparency To improve access to global information about the
worldwide production of major cereals and soybean Serve as a science-based Chinese voice on global food
security perception To provide additional, reliable information for developing
countries to fight against hunger Offer net-based services free
Sub-national for large countries
Crop type proportion (some countries)
National: 31 countries In addition to previous indicators, crop cultivated
area, time profile clustering
Regional: Major production zones In addition to CWAIS, Vegetation health index, uncropped arable land, cropping intensity, and maximum vegetation
condition index
Global: homogeneous crop mapping and reporting units Using CropWatch Agroclimatic Indicators (CWAIs) for rainfal, air
temperature, photosynthetically active radiation, and potential biomass
Increasing level of detail, from environmental-climatic to agronomic; from 25 km resolution to 16m
CropWatch Hierarchical approach
Arable land fraction Crop structure (China, USA, Canada)
NDVICrop areaNDVI cluster and profile
Global
7 Main Production
Zones
30+1 Key Countries
Sub countries
Input
Cropping IntensityCropped Arable Land Fraction (CALF)Vegetation Health Index (VHI)Maximum VCI
RainfallAir temperaturePARPotential biomass
Monitoring and Reporting Unit (MRU)Climatic Indicators (CI)
Agricultural patternFarming intensity Crop stress
Crop conditionYieldProduction
Crop conditionYieldProduction
Global food supply analysis
Output
CropWatch Hierarchical approach
Outline
Background
Methodology in CropWatch
Recent updates of crop classification
Outlook
Methodology in CropWatch
Crop area in China, Canada, Australia, Egypt, and US CALF=Cropped Arable Land Fraction
Crop area = Arable land area × CALF × Crop type proportion Remote Sensing based GVG survey
Crop area in other countries relies on the regression of crop area against cropped arable land fraction
Areai = a + b ∗ CALFi
Crop type proportion using GVG system GPS receiver Video camera GIS analysis system
GPS
GISVIDIO
Methodology in CropWatch
Crop type proportion sampling routes
Methodology in CropWatch
Sampling routes
> 20,000 km survey
Transfer GVG system to cellphone Carry out field survey in US.,
Canada, Australia, and Egypt Ongoing in Thailand and India
Methodology in CropWatch
August 2014 in Canada
September 2014 in Australia
ChinaCover2000 ChinaCover2005 ChinaCover2010
Arable land data are regularly updated
Globally rely on existing land cover map and use the combined arable land map
Methodology in CropWatch
Separation of cropped and uncropped arable land using 30m or higher res. data Cloud extraction using reflectance and temperature Vegetation index threshold method to separate cropped
and uncropped
Methodology in CropWatch
Methodology in CropWatch
Winter crops during the period of Oct 2014-Apr 2015
Cropped/uncropped
Cropped arable land fraction at global scale
Methodology in CropWatch
Max Min
Argentina
Global cropped and uncropped arable land map
Methodology in CropWatch
July to October 2014
Wheat Rapeseed Maize Soybean Other crops
Alberta 47 27 1 0 25
Saskatoon 52 26 1 0 21
Manitoba 41 25 9 13 12
Crop type proportion in three major producing provinces in Canada(%)
2013 （kha）
2014 （kha）
Variation （%）
National data（kha）
Difference （%）
Alberta 2796 2873 0.11 2747 5
Manitoba 1371 1408 0.07 1127 25
Saskatoon 5980 5538 -9.8 5313 4
Canada 10442 10105 -3.2 9658 5
Wheat area for Canada, 2014
Methodology in CropWatch Crop area estimates
maize rice wheat soybean Anhui 857 2604 2625 826 Chongqing 433 775 360 Fujian 474 Gansu 959 705 Guangdong 2116 Guangxi 2091 Guizhou 1068 799 Hebei 2974 2016 114 Heilongjiang 4865 2992 146 2643 Henan 3119 606 4945 428 Hubei 2257 1052 Hunan 4275 Inner Mongolia 2630 576 647 Jiangsu 445 2329 1990 284 Jiangxi 3207 Jilin 3509 718 316 Liaoning 2228 653 222 Ningxia 262 69 232 Shaanxi 815 164 1017 Shandong 3114 4103 281 Shanxi 1725 511 150 Sichuan 1421 2206 1266 Yunnan 1483 931 Zhejiang 485 China 35254 32174 23503 7559
Provinces of China 2014 (kha)
Outline
Background
Methodology in CropWatch
Recent updates of crop classification
Outlook
Recent updates of crop classification Using cropped and uncropped farmland dynamics
Cropped Uncropped
Recent updates of crop classification using radar
Weather independent
Suitable for operational monitoring
HJ data, SVM Merged data, MLC
Merged data, NETMerged data, SVM
wheat
cotton
House & road
tree
Shandong province, 2010
Multi-frequency Radar data effectively improve the accuracy, better than that using multi-temporal data
Field border can be easily identified by integration of Radar data and optical data
More sensitive for rice identification
Cro
p di
stri
butio
n m
ap in
201
2
Data source: 2012
Eight HJ-1 CCD with approx. 20 days interval
Overall accuracy: 0.91
Kappa coefficient: 0.88
Some fields with mixed crops
Object based crop classification
Hongxing Farm in Heilongjiang Province
Develop a method to refine the fields borders
Apply classifier to each pure fields/sub-fields to generate crop map
Object based crop classification
Original fields
Identify pure fields Subdivide the impure fields
Outlook
Cropped arable land fraction at early growing stage
as a early warning indicator
Introduce Radar data into the operational system
Objected-based method would be intensive study
