Crop Monitoring – SWALIM Workshop - Nairobi 12-13 June 2007 1 / 19 Session 3: Processes modeling Crop Monitoring for Food security: Contribution of Remote

Crop Monitoring – SWALIM Workshop - Nairobi 12-13 June 2007 1 / 19

Session 3: Processes modeling

Crop Monitoring for Food security:

Contribution of Remote sensing & future challenges

Olivier LEO, Felix REMBOLD, Michel MASSART, Oscar ROJAS

Agriculture and Fisheries Unit , IPSC

[email protected]

Potentialities and limitations in the use of remote-sensing for detecting & monitoring environmental change

in the Horn of Africa Expert workshop, Nairobi, 12- 13 June 2007


Outline

1. General background

2. Crop monitoring systems

Place of remote sensing

On going and future developments

3 Conclusions

Links with sustainable development


• MARS = Monitoring Agriculture with Remote sensing

• MARS STAT started 20 years ago – Support to EU Common Agricultural Policy (DG AGRICULTURE)– Focus on EU Member States and 7-10 crops of interest– Various actions covering areas estimates, agromet models, area frame

surveys, crop monitoring, rapid estimates, etc…

• MARS FOOD started in 2000– To address Food Security in support to DG DEV, AIDCO, RELEX, ECHO… – The EC is a main international donor

• FS budget-line 500 Mio €/ year since 1996 (cf. Reg. EC N° 1292/96).• Reinforced by Food Security Thematic Program 2007-2010(COM 2006/21).

– Adapt MARS STAT crop monitoring system, in collaboration with FAO– DG AIDCO involve MARS-Food as technical advisor

• Participation in CFSAM and ENA missions by UN institutions FAO / WFP…

General Background


A rather mature application, facing new Global challenges • Medium term (2010- 15) Increase of prices and volatility of Food

products– Bio fuel policies – Climate change or frequency of extreme events – Development of emerging countries (China, India)

• Longer term (2050) (cf FAO World Agriculture towards 2030-2050)– Population increase 8,9 Bio in 2050 (+ 45%/ 2000) – increased Food global demand to achieve 1st MDG – New pressure on land use – Effects of climate change ...

Strategic importance– of monitoring both crop areas and yields– In vulnerable zones and for the main producers

General Background


2000-2006

Mars STAT

Mars FOOD

During the 7th FWP (2007-2013), MARS intent to enlage its AOI

• To EU neighbouring countries and Black sea Region + emerging countries ( MARS STAT)

• To whole Sub saharan AFRICA + Central America (MARS FOOD)

• Shared global data sets and capacities

MARS regions of interest

7 th FWP

Mars STAT

Mars FOOD

?

MARS FOOD 2006

1 - Horn of Africa - 6 countries8 regional monthly bulletins

4 x (36) national 10 day bulletins 2- South & East Mediterranean countries 11 countries – 6 Bi monthly bulletins 3 - Russia & central Asia

15 countries, 6 bi-monthly bulletins 4 - South America MERCOSUR + Bolivia5 countries, 11 2 wks /Monthly Bulletins


• Agricultural statistics Production (Crop, Region) = Total area (C, R) x mean Yield (C, R)

• Crop monitoring – Near real-time activity along the campaign– to regularly provide information

• Qualitative (status of the crop, planting dates, etc)• Then quantitative (crop yield forecasts)

– for appropriate, timely, decision taking • Actions on the market and stocks • Request of Food Aid (Early Warning Systems) ...

• Crop monitoring answer only to 1 / 3 component of Food Security

• FS focus on total production or Yield– Areas are considered as rather constant – Crop Monitoring use total area planted– Mean yields forecasted by agro-meteo.models integrate areas with null-yield (due to drought or adverse climatic conditions)

Crop monitoring

Crop Production & Food Supply

Market & macro economic

contextVulnerability &

Food Needs


Types of RS information used in Crop monitoring

Crop monitoring

Purpose Resolution Frequency Products Comments Crop

development and yield forecast

Low resolution (1-5 km) SPOT VGT, NOAA AVHRR, METOP

Daily processed by 5-10 dd

Biophysical parameters, NDVI, DMP,

fAPAR, LAI…

Near real-time time-profiles

Long archive needed

Crop Mask (crop extension)

Medium Resolution (200-300m)

MODIS, MERIS

4- 12 dates / year

Land cover maps

Updated every 3 - 7 years

Crop Maps and area estimates

High resolution (10- 60m), Spot,

Landsat, Awifs, etc...

1- 3 dates / year

Land cover maps and statistics

Yearly Pos. Regression

Estim. with AFS Crop area estimates

Very High Resolution on AFS (< 1 m),

Ikonos, Quickbird, aerials

1 date / year

Crop statistics In situ data on sub sample

Natural hazards/ Floods

High – Medium Resolution on AOI, Radar

imagery

Ad Hoc Estimate of areas flooded

Within 5-10 days after the alert


ECMWF global daily + reference data (ERA 40)+ Ground stations

Crop monitoring

and Analysis

SPOT Vegetation daily data + Archive

Rainfall estimates (MSG)

Real time acquisition and

Pre-processing RemoteRemotesensingsensing

METEOMETEO

CropCropmodelsmodels

StatisticsStatistics

ExpertiseExpertise

Overall Processing chain

Regional / national CropBulletins & forecasts

Global 10 days products and indicators


MARS-FOOD Bulletins

Quantitative Yield estimate end of

campaignMaize Yield Estimate (t/ha)

Regular Quantitative crop

monitoring


Contribution of Remote sensing

REMOTE SENSING

CNDVI Type products

Dif. & probability indicators

Preprocessing 10 day products, CNDVI,

fAPAR, DMP, LAI etc…

Meteo Ground Station data

ECMWF Meteo model/ forecast

data

Real time acquisition and pre-processing

Grid AGRO-METEO Variables

CNDVI Time Profile

Agromet Indicators Crop Indicators

Specific Crop growth & development models

Crop Monitoring Indicators, Biomass, WSI, etc

Comparison with long time archives (10-30 years)

Preprocessing 10 day meteo variables,

T, P, ETP, etc

Long A

RC

HIV

E

Statistical Analysis and expertise

Trend analysis

Scernarii / Similar years

Simulation end of campaign CROP MONITORING BULLETINS

Qualitative : Crop condition / Geographical Quantitative: Yield forecasts

Databases Regional

Agricultural statisitics

“IN SITU” and other INFORMATION

Low Resolution Agromet Sat. (VGT,

AVHRR, METOP MSG)

REMOTE SENSING

CNDVI Type products






data



CNDVI Time Profile






T, P, ETP, etc

Long A

RC

HIV

E


Trend analysis




Databases Regional




AVHRR, METOP MSG)

3 main levels of Crop monitoring systems

– Level 1: data (pre) processing – Level 2: Crop development

modelling– Level 3: Statistical analysis

(Regression, etc) and/ or interpretation by experts

Present contribution of RS in the whole process

– 30 % of the whole tasks– < 20 % of the operational costs– Much higher but variable

weight on the final outputs (use by Analyst use of the various sources)


MAY 2007

#Y

#Y

#Y

#Y

Bari

Bay

Mudug

Gedo

Nugal

Sanag

Hiran

Lower Juba

Galgadud

Tog-Dheer

Bakool

AwdalGalbeed

LowerShabelle

MiddleJuba

MiddleShabelle

Somalia

Ethiopia

Djibouti

Mogadishu

Legend

Rivers

Provinces

Sea

< -300

-300 - -150

-150 - -50

-50 - -25

-25 - 25

25 - 50

50 - 150

150 - 300

> 300

APRIL 2007

#Y

#Y

#Y

#Y

Bari

Bay

Mudug

Gedo

Nugal

Sanag

Hiran

Lower Juba

Galgadud

Tog-Dheer

Bakool

AwdalGalbeed

LowerShabelle

MiddleJuba

MiddleShabelle

Somalia

Ethiopia

Djibouti

Mogadishu

Legend

Rivers

Provinces

Sea

< -300

-300 - -150

-150 - -50

-50 - -25

-25 - 25

25 - 50

50 - 150

150 - 300

> 300

Rainfall anomalies in Somalia Monthly rainfall anomaly (difference with 1974 – 2003 normal ), based on ECMWF data


Current situation in Somalia

Crop cycle progress index ( percentage) for the main Sorghum regions at the end of May 2007

#Y

#Y

#Y

#Y

Bari

Bay

Mudug

Gedo

Nugal

Sanag

Hiran

Lower Juba

Galgadud

Tog-Dheer

Bakool

AwdalGalbeed

LowerShabelle

MiddleJuba

MiddleShabelle

Somalia

Ethiopia

Djibouti

Mogadishu

Legend

Rivers

Sea

0 Not planted

1 -25

25 - 45

45 - 65

65 - 85

85 - 99

100 End of cycle

Masked areas

Sorghum Water Satisfaction Index up to the end of May 2007

#Y

#Y

#Y

#Y

Bari

Bay

Mudug

Gedo

Nugal

Sanag

Hiran

Lower Juba

Galgadud

Tog-Dheer

Bakool

AwdalGalbeed

LowerShabelle

MiddleJuba

MiddleShabelle

Somalia

Ethiopia

Djibouti

Mogadishu

Legend

Rivers

Sea

Not planted

Crop failure

Poor yield

Mediocre yield

Average yield

Good yield

Masked areas


CNDVI profiles

Bay - High potential sorghum

0.0

0.1

0.2

0.3

0.4

0.5

0.6

M A M J J A S O N D J F

CNDVI

0

10

20

30

40

50

60

70

80

mm

rainfall 98-06 rainfall 07-08 CNDVI 98-06CNDVI 06-07 CNDVI 07-08

Bakool - Agro-pastoral sorghum

0.0

0.1

0.2

0.3

0.4

0.5

0.6


CNDVI

0

10

20

30

40

50

60

70

80

mm


L. Shabelle - Flood irrigated maize

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7


CNDVI

0

10

20

30

40

50

60

70

80

mm


M. Shabelle - Agro-pastoral cowpea

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7


CNDVI

0

10

20

30

40

50

60

70

80

mm


Crop development profile clearly above average in Bay and Bakool, close to average in the Shabelle regions


Future Developments

REMOTE SENSING

CNDVI Type products






data



CNDVI Profile





Rain fall estimates, ETa,

etc


T, P, ETP, etc

Long A

RC

HIV

E


Trend analysis




Crop cycle, & phenology

“Ensemble” forecasts

Direct input of Biopar

Databases Regional



Medium/ High Resolution

Satellite DATA

Crop Masks


AVHRR, METOP MSG)

Main identified or on going developments

• Use of MSG rainfall estimates

• Ensemble approaches• Updated and improved

crop masks• Ingestion of EO at level 2

– Readjust calendar / crop Phenology

– LAI, fAPAR, DMP…• Direct use of EO

indicators at level 3• Standardisation of

VPI,VCI, etc


Zooming for the 2 main Localities inside the state (Sennar and Singa)

Spatial Level of CNDVI analysis

2006

2007

EX in Sudan: SENNAR stateSorghum mean CNDVI : below average performance detected in 2006

In fact, 2006 season was• very good for the mechanized South of the state (Singa locality); • below average for the traditional agriculture in the North of the State (Sennar locality)


Case of rangeland monitoring• RS more used than Agromet models

• Specific requirements – Information on complex land covers

(perennial+grassland) & landforms– Calibration between RS indicators and usable

biomass, feeding value

• Models integrating Livestock information– Cf. PHYGROW (TAMU)

• Extra informations of interest – Water points, Small water bodies – Migration roads

• More rapid / regular information with direct involvement & Feed back to Pastoralists

– “ Feed” security – Management of the ressource– Conflict prevention ...

Courtesy of Robert KAITHO

Courtesy of Job ANDIGUE


• More a complete system than a model – Number of optimization / compromises between the different info. – Near real time processes, regular back up/ consolidations – Development & tests in parallel, before implementation with reprocessing

of the whole archive…

• Some SWOT considerations– Strengths: RS provides an unique, very valuable, wall-to-wall info.– Good consensus on state of the art approaches – Access, processing and storage capacities not limiting factors (LR,MR)– Weaknesses: Noise in complex landscapes/ saturation & clouds in

equatorial areas, still short time series – Opportunities: AMESD Program and VGT4 Africa Portfolio– Threats:

• “in situ” information (crop phenology, biomass measurements, etc) becomes rare and crucial

• Continuity of VGT program: Intercalibration or use of standard non sensor specific indicators

In summary …


Sustainable development • From yearly crop-monitoring to medium-longer term analysis and inputs in the

“LBRRD” ?

– Trend analysis, – identification of Hot Spots, – Prevention, mitigation and coping strategies…

• Agricultural / Rural Development Policies requires reliable estimates on the 2 components of production

– Cropped areas Margins of extension // Pressure on environment / land… – Mean yields margins of progress

• Agricultural statistics are crucial for Policy making – Land-cover maps do not provide accurate/ unbiased areas estimates – RS can support AFS with Ground survey and VHR (satellite or aerial) imagery

“LBRRD”

Crisis Post-Crisis Relief Rehabilitation Development

Emergency Long term

Prevention


Thanks you for your attention !

To know more• PPT of the recent CRAM workshop (Nairobi, March 07)

http://cram-forum.jrc.it/default.aspx

• Web sites of AGRIFISH, MARS Bulletins and products http://agrifish.jrc.it/

http://agrifish.jrc.it/marsfood/bulletins

http://agrifish.jrc.it/marsfood/ecmwf.htm

Documents

Crop Monitoring – SWALIM Workshop - Nairobi 12-13 June 2007 1 / 19 Session 3: Processes modeling Crop Monitoring for Food security: Contribution of Remote