SADC activities on the use of GIS and RS for Agricultural Meteorology

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SADC activities on the use of GIS and RS for
Agricultural Meteorology

• T. Tamuka Magadzire
• SADC Regional Remote Sensing Unit, USGS/FEWSNET

WMO/FAO Training Workshop on GIS and Remote
Sensing Application in Agricultural Meteorology
for SADC Countries. November 14-18,
2005 Gaborone, Botswana
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Outline

• Background to SADC Region
• SDC RRSU Background
• Available EO-based data
• Modelling Applications of EO data
• End-user information products
• RRSU Database
• Partnerships - GMFS

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The SADC Region - Background
Southern African Development Community

• 14 Member States.
• 200 million people.
• Varied climate regions.
• Mostly uni-modal rainfall systems (bi-modal in
  the north).
• Varied cropping systems.
• Maize (corn) dominant crop
• Cassava and tubers important in the north.
• Rain fed agriculture irrigation only
  significant in South Africa and Zimbabwe.

Prone to floods and droughts.
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Climatic Hazards in SADC
The SADC Region - Background

• Floods and droughts are the major climatic
  hazards in the SADC Region.
• Serious drought in 1991-92
• Flooding in Mozambique, Zimbabwe, Botswana and
  South Africa in 2000
• Cyclones Eline and Gloria responsible.
• 4 million people affected. Lessons learnt.
• SADC Disaster Management Strategy formulated.
• Further flooding in ensuing years (e.g. in 2003
  from Cyclone Delfina (January) and Cyclone Japhet
  (March))
• Serious droughts between 2001 and 2005 in several
  SADC countries

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1995-96
1996-97
1997-98
1998-99
2000-01
1999-2000
2001-02
2002-03
2003-04
2004-05
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SADC RRSU Organizational Context

• The SADC Secretariat is comprised of four
  directorates, including the Food, Agriculture and
  Natural Resources (FANR) Directorate
• The SADC Regional Remote Sensing Unit (RRSU) is a
  project within the FANR Directorate

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Institutional Setting

• SADC RRSU Cooperating Partners

Technical support and training. Emergency food assessments. Supply of satellite data.
Technical support and training. Vulnerability assessment activities. Support to the Regional Disaster Management Strategy. Supply of satellite data.
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Main Objective of RRSU

• Strengthen national and regional capabilities in
  the area of Remote Sensing, Agrometeorology and
  GIS.
• Support early warning for food security and
  natural resources and disaster management.
• Principal contact institutions
• National Meteorological Services (NMSs).
• National Early Warning Units (NEWUs).
• National Disaster Management Units.

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SADC RRSU Operational Activities

• Training of agro-meteorologists in the use of
  satellite imagery products and GIS for early
  warning for food security.
• Monitoring crops, vegetation and weather
  developments during the crop growing period using
  satellite images and GIS techniques.
• Developing and maintaining database of satellite
  images, maps and associated data.

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RRSU Agromet GIS Training

• Creating trained experts in RS and GIS
  applications.

• National staff seconded to RRSU
• Backstopping missions organized for on-the-job
  training in Member States.

• Subject- or application- specific workshops
  conducted at national and regional levels.

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SADC Region Early Warning Information flow

• Outgoing satellite-based information and analysis

NEWU
NEWU
NEWU
NEWU
NEWU
NEWU
NEWU
NEWU
NEWU
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SADC Region Early Warning Information flow

• Incoming ground-based information and analysis

NEWU
NEWU
NEWU
NEWU
NEWU
NEWU
NEWU
NEWU
NEWU
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Available EO-based Data

• Available satellite-based data used for Agromet
  activities are vegetation products and rainfall
  estimates.
• These products are analyzed and further processed
  into application specific products for flood and
  drought monitoring by USGS/FEWSNET and RRSU

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Monitoring Rainfall Activity

• Rainfall Estimate (RFE) images.
• Combine satellite images with rain gauge
  observations.
• RRSU receives RFE images from USGS EROS Data
  Center.

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NOAA Rainfall Estimates

• Rainfall Estimates (RFE) are produced by NOAA for
  the FEWSNET activity, and distributed in southern
  Africa through RRSU
• Uses a number of datasets
• Meteosat data used to composite a CCD image at
  -38oC, a rainfall estimate is generated from the
  CCD using the GOES Precipitation Index (GPI). GPI
  CCD x 3
• WMO GTS rainfall data from approx. 1000 stations
  (not all stations used at any given time), and
  are taken as the true rainfall within 15-km
  radius of each station
• Two satellite microwave instruments, SSM/I
  (Special Sensor Microwave/Imager) and the AMSU
  (Advanced Microwave Sounding Unit), which acquire
  data every 6 hours and every 12 hours
  respectively.
• The four datasets are merged to produce an
  improved product

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RFE Related Activities

• SADC RRSU operates the WinTRES system and
  generates daily and dekadal Cold Cloud Duration
  (CCD) images from Meteosat-7 TIR images
• RRSU currently working on computer algorithms in
  collaboration with Botswana Met Services to
  enable the use of MSG Meteosat-8 images in CCD
  generation
• Interest has been expressed by SADC nationals in
  improving RFE using local rain gauge data
• Some workshops have been held by NOAA on
  implementation of their RFE production technique
  locally in Africa RRSU installed this technique
  locally for short time
• Limited by operational availability of rain-gauge
  information

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Monitoring Vegetation Condition

• Normalized Difference Vegetation Index (NDVI)
  images.
• Sources of NDVI are NOAA AVHRR (8km), SPOT VGT
  (1.1km) and MODIS (250m)

MODIS 250m
AVHRR 8 km
SPOT 1 km
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Seasonal Trends

• Time series curves for visualizing seasonal
  trends.
• Comparing against long-term (average) trends.
• Main crop-growing regions in SADC monitored.

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Monitoring Crop Condition WRSI

• The Water Requirements Satisfaction Index (WRSI)
  is a crop specific water balance approach that
  models the effect of seasonal rainfall
  availability on potential crop yields.
• Two approaches are used in the SADC region
  using satellite-based, distributed approach, and
  a ground-based point-specific approach
• The model is being used in several SADC countries
  to monitor crop water use with a view to yield
  forecasting and estimation. SADC RRSU is
  providing training
• Operational model run at USGS but modern
  modelling software now publicly available from
  FAO and USGS.

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Crop Water Balance Modeling
Water Requirements Satisfaction Index
Water Requirements Satisfaction Index (WRSI)
WRSI100AET/WR
Regression models
Yield Estimation
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WRSI Water Balance - Products
WRSI
WRSI Anomaly
Start of Season
Soil Water Index
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SWI, West Africa
WRSI Anom, East Africa
SOS, Southern Africa

• Can model for multiple regions or countries
• Can enter field information on planting, soils,
  maturity
• Can model using information for
• multiple planting dates
• multiple varieties (maturity periods)
• multiple crop types
• Range of outputs SOS, WRSI, WRSI Anom, SWI etc

WRSI, Zambia
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End-user Information Products

• A number of bulletins are produced to meet
  information requirements, including
• Regular agrometeorological updates at 10-daily
  and monthly intervals
• Ad-hoc Significant Weather Developments (SWD)
  bulletin which aims to provide timely
  highlights of developing weather patterns and
  their potential impacts to human lives and
  property
• Other special bulletins to address current or
  issues e.g. forecast interpretation drought alert

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Agro-Meteorological Update

• Rainfall
• Areas
• Crops
• Models

Agromet Up-dates
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Agro-Meteorological Update

• Rainfall
• Areas
• Crops
• Models

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Significant Weather Developments
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Examples from SWD bulletins
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RRSU Database

• Developed and maintained on central computer at
  the RRSU
• Abridged onto CD for external use.
• Simple and open data formats make data portable.

RRSU Standard Vector Data. Satellite
data. Raster images with climatic
parameters. Tabular data with agricultural
statistics and population data. Free WinDisp 3.5
4 software for data viewing. Current CD-ROM
version is 2.0. Details from rrsu_at_sadc.int
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RRSU Data holdings

• comprises both baseline datasets and earth
  observation datasets, compiled from a variety of
  sources
• uniform regional standard vector data set for
  SADC at a scale of 11 million was compiled as
  part of this dataset
• originated from the DCW
• updated using inputs from the SADC countries

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RRSU Data holdings

• Administrative (borders, subnational boundaries,
  cities)
• Elevation
• Land use and land cover
• Hydrology (water bodies, rivers, lakes)
• Infrastructure (roads, railroads, bridges,
  airports, utility lines)
• Soil
• Agriculture (crop zone maps)
• Climate (rainfall, temperature etc)
• Demography
• Satellite images

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RRSU Data holdings
Administrative
National borders of SADC countries
Sub-national boundaries of SADC countries
National borders of SADC countries (FAO/GIEWS version)
Level 1 sub-national boundaries of SADC countries (FAO/GIEWS version)
Major cities and towns of SADC countries (FAO/GIEWS version)
Cities and towns of SADC countries
Urbanised areas of SADC countries
Cultural landmarks of SADC countries
Elevation
Digital elevation model
Elevation contours in SADC countries
Spot elevations in SADC countries
Land use and land cover
Land cover areas of SADC countries
Forest types in SADC countries
Managed areas (including national parks) in SADC countries
Centre points of managed areas in SADC countries
Hydrology
Small water bodies of SADC countries
Rivers of SADC countries
Surface water bodies of SADC countries
Perennial and non-perennial water layers in SADC countries
Wetland types in SADC countries
Lakes of SADC countries
Small islands and lakes of SADC countries
Small coastal islands of SADC countries
Infrastructure
Roads in SADC countries
Railroads in SADC countries
Road and Railroad Bridges in SADC countries
Airports in SADC countries
Utility lines in SADC countries
Soil
Soil types in SADC countries
Agriculture
Crop zone maps of SADC countries (FAO/GIEWS version)
Crop harvest dates of SADC countries (FAO/GIEWS version)
Crop planting dates of SADC countries (FAO/GIEWS version)
Historical crop statistics for the SADC countries
Crop Water Satisfaction index (1996 2005)
Start of rainfall season estimates (1996 - 2005)
Climate
Dekadal, long term average rainfall, temperature, evapotranspiration
Monthly, long term average radiation, humidity, wind
Satellite Rainfall estimates from 1995 to 2005
Demography
Population for SADC countries, by province
Satellite images
MODIS imagery (2000-2005)
AVHRR NDVI vegetation images (1981-2005)
Landsat imagery regional coverage for 1970s, 1990s, 2000s
ASTER (partial SADC coverage) imagery for late 2001, early 2002, and 2003
Meteosat thermal infrared and cold-cloud duration imagery
SPOT-4 VGT NDVI vegetation images (1998-2005)
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Partnerships - GMFS

• SADC RRSU has been collaborating with the GMFS
  consortium over the last couple of years
• GMFS is developing products for estimation of
  yield and area planted to crops, as well as other
  monitoring products
• Concentrating on using a combination of SAR and
  optical EO data to id crop extent and
  phenological stages
• GMFS has done some preliminary work for product
  development in Malawi, with potential for
  spreading to SADC region
• Products are currently being validated by GMFS

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Contacts

• RRSU Coordinator
• Dr. Kennedy Masamvu kmasamvu_at_sadc.int
• Regional Agrometeorologist
• Dr. Elijah Mukhala emukhala_at_sadc.int
• Database Specialist
• Mrs. Dorothy Nyamhanza dnyamhanza_at_sadc.int
• Research Assistant
• Mr. Blessing Siwela bsiwela_at_sadc.int
• GeoInformatics Scientist (USGS/FEWSNET Regional
  Rep. Southern Africa)
• Mr. T. Tamuka Magadzire tmagadzire_at_fews.net

Website http//www.sadc.int
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Re a leboha
Grazie
Zikomo
Thank You
Obrigado
Tinotenda
Gracias
Siyabonga
Asante sana
Merci Beaucoup
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Websites for cyclone monitoring

• Forecasted cyclone track
• http//www.npmoc.navy.mil/jtwc/newjtwc.html
• http//www.npmoc.navy.mil/jtwc/warnings/sh0203.gif
  
• Latest cyclone track
• http//www.meteo.fr/temps/domtom/La_Reunion/trajGP
  /data/home_trajGP.html
• Latest satellite imagery
• http//www.eumetsat.de/en/index.html?arealeft5.ht
  mlbody/en/m_area5.htmla500b0c0d0e0
• Other useful websites for extreme-weather
  monitoring
• http//www.sadc-hazards.net
• http//earlywarning.usgs.gov/adds
• http//www.cpc.ncep.noaa.gov/products/fews/briefin
  g.html
• http//www.dmc.co.zw
• http//grads.iges.org/pix/af.fcst.html
• http//www.fnmoc.navy.mil/PUBLIC/WXMAP/index.html
• http//metservice.intnet.mu/wsatpic.htm
• http//weather.yahoo.com/regional/AFRICAX.html

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NOAA RFE - Limitations

• Weaknesses in datasets
• Microwave inputs have 6hr and 12hr repeat rate
  estimates can either miss out some storms
  altogether, or overestimate rainfall when the
  satellite image is taken at the peak of a storm
• Rainfall is estimated most accurately in the
  vicinity of GTS gauges
• Meteosat-derived GPI estimates capture
  convectional rainfall very well. However, other
  rainfall types (e.g. orographic) are not
  estimated as accurately. Also cirrus clouds can
  cause over-estimation
• Note that the merging process makes these
  datasets complementary