Zhongxin Chen , Qingbo Zhou, Jia Liu, Limin Wang, Jianqiang Ren, Qing Huang, Hui Deng, Li Zhang, Dandan Li1Key Laboratory of Resources Remote Sensing and Digital Agriculture, MOA

2Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 1000813Research Department, Remote Sensing Application Center, MOA, Beijing 100081zxchen@mail.caas.net.cnIGARSS 2011, Vancouver, 24-29 July, 2011

CHARMS - CHINA AGRICULTURAL REMOTE CHARMS - CHINA AGRICULTURAL REMOTE SENSING MONITORING SYSTEMSENSING MONITORING SYSTEM

Outline

• Bcakgrounds• System Structure of CHARMS• System Implementation• System Components

– Crop acreage monitoring– Soil moisture monitoring– Crop growth and yield– Information dissemination

• Conclusions and Perspectives

BackgroundsBackgrounds

3

Global Food SecurityGlobal Food SecurityFood

Security

Climate Change

Global mean temperature increased by 0.7℃ in 20 th century, and increased another 0.1℃ recently

Population

Booming ~ 7billion9 billion in 2050

Land

Decreasing and degradation

Overview

• Since 1983, monitoring yield of winter wheat in North China Plain (pilot study)

• Key research projects during each 5-year plan

• Several (quasi-)operational Crop Remote Sensing Monitoring systems– CMA

– CAS

– MOA

– Emerging SSB

– ….

China Agriculture Monitoring with Remote

Sensing (CHARMS system in MOA)

• Since 1998, run every year• Operational running in the whole nation

• Monitoring key crops and Grassland– Acreage change

– Growth

– Yield & productivity

– Environment & disasters, etc.

– Grassland degradation

– Grass-livestock balance, etc.

System structure of CHARMSSystem structure of CHARMS

Remotely Sensed Data

Info Inversion Models or Algorithms

Ag-Info Monitored

by RS

Validation Data

Ground In situ Data

Met. Obs.

Ground Truthing

Agro-Info Monitoring Network

Theory of Physics & Agronomy

Expert Knowledge

Info Distribution & Service

Ag. Management & Policy-making

Image Processing

Geom. Corr

Atmos. Corr

Mosaic

Data fusion

……

Auxiliary Information

Other RS Info

Basic Database工作站

多个服务器

服务器

Logic of Crop Monitoring System with Remote Sensing

System Components

Professional data processing(index, information extration)

Basic data handeling（ inqury, subset, merge)

Agriculture Remote Sensing Monitoring

Data management (input, edit, organize)

management tools Database engine

kernal data modules

Local files

Local database

Remote database

Remote files

Data m

anagem

ent

layerF

unction

layerA

pplication layer

Highlights of the system

• A set of standards or protocols• Workflow-driven machanism• Modular structure• Distributed C/S and B/S hybrid system

System ImplementationSystem Implementation

Organization of CHARMS Activities

In-situ Crop Monitoring Sites

System componentsSystem components

Crop monitoring• Data

– TM, CBERS, SPOT, IRS, HJ-1, Aster, Envisat– IKONS, QUICKBIRD– EOS-MODIS, NOAA-AVHRR, AWiFS

• Methodology– Change detection for acreage– Stratified sampling and scaling up method– Ground truthing

• Monitored crops: wheat, maize, rice, soy bean, cotton, canola, sugar-cane

Remote sensing data for 2 consecutive years

Common areas

Subsetting for basic monitoring units

Omit non-cropland

Non-supervised classification

Supervisved classification

mannual modification

in-situ samples

crop acreage change

accuracycontrol

Cropland map

Monitoring results

for previous year

Crop spatial sampling frame

Landsat TM & Validation in Huabei Plain

2003 ， RGB:432 2004 ， RGB:432

Zouping, Shandong

WheatBuilt-up

Zhangqiu, Shandon

20062006 20072007

In-situ investigation

Deferential GPS ， quadrat size 500*500m2

• Winter wheat ： 2299• Maize ： 1024• Spring Wheat ： 273• Soy Bean ： 431• Cotton ： 694• Early Paddy Rice ： 476• Late Paddy Rice ： 960

Total sampled quadrats (in 2009): 6157

Wanning, HainanPaddy Rice

2008 2009

Time Span: Nov 24, 09 – Dec 7, 09Data Source: EOS/MODIS

Growth Monitoring for Winter Wheat

Legend

BetterNormalWorse

Could/ND

Soil Moisture Status of Cropland Time ： May 5-23, 2005

Date Source : EOS-MODS

LegendHeavy

ModerateLight

NormalMoistDesertCloudWaterSnow

Frosted

China Agriculture Remote-sensing Monitoring System (CHARMS) -Yield

26

Grassland Productivity in 2009 vs 2008

Grassland Productivity in 2009 vs 5-yr Mean

(a) (b) (c)

Phenology of Winter Wheat

Turning-green(a) 、 Heading(b) 、 Maturity(c)

Phenology of Wheat and Maize vs. Observation

Crop Mapping

Flood in South China, July 2003 (Dongting Hu)

监测时间： 2004 年 3 月 28 日数 据 源： EOS/MODISSand Storm, 2004-3-28

气团中心

越冬作物

无沙尘区域

沙尘区域

云

水体

Snow Harm in Feb 2008

Earthquake Impact on Cropland in Wenchuan 2008

Agro-Information Distribution Calendar

Soil MoistureCrop GrowthCrop AcreageCrop Yield

Conclusion and PerspectivesConclusion and Perspectives

Conclusion and Perspectives

• CHARMS is an extendable remote sensing agriculture monitoring system

• Further new functions or components can be added to the system upon new demands

• Future system will not only focus on agriculture monitoring from remote sensing, but also contribute more in decision making in agricultural management and food security

Short-term warning Agricultural Production MonitoringMarket information system

Monitoring Vulnerable GroupsNutrition Surveillance System

Cropping patterns monitoring Crop growth monitoring and yield estimation

Assessment of yield increase potentials Cropping patterns dynamics modeling

Warning System of Food SecurityMedium and long-term warning

Early Warning System on Food Security

Acknowledgements

The research was supported by the NSFC project (no. 40930101), and MOST the international corporation project (2010DFB10030), MOA 948 program project(no. 2010-S2, and 2009-Z31), and EU FP-7 E-Agri project with contract no. 270351.

Thanks Pei Zhiyuan, Xu Bin, Yang Peng, Wu Wenbin for providing related information

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