Drought monitoring with coarse resolution remote sensing at the

African continent level

O. Rojas (FAO) and F. Rembold (JRC)

One of the main water sources outside Moyale in Kenya runs dry. Photograph: Sarah Elliott/EPA (2009)

Problem

• Currently weather stations are sparse and provide discontinuous data

• Rainfall estimates have a bias and show deviations in different regions of Africa (Dinku et al. 2007, Lim and Ho 2000).

Objective

• The objective of the present study is to use the vegetation index derived from NOAA-AVHRR to calculate the probability of agricultural drought occurrence

Data• Vegetation Health Index (VHI) produced by the

Center for Satellite Applications and Research (STAR) of the National Environmental Satellite, Data and Information Service (NESDIS) (1981-2009). Weekly product, 16 km resolution.

• Normalized Difference Vegetation Index (NDVI) dataset from the NASA Global Inventory Monitoring and Modeling Systems (GIMMS) group. 15-day MVC, 8 km resolution. (1981-2006)

• A crop mask. The crop mask was constructed using FAO crop zones for 10 crops, and the Global Land Cover (GLC2000)

• The first sub-national administrative units from the Global Administrative Unit Layers (GAUL) database.

Administrative regions(GAUL)

Phenological-model(Average SOS and GFS)

Input data

Process and tools Intermediate and final outputs

VHI (1981-2010)

Weekly data16 km resolution

30 Sub-national maps with the percentage of agriculture area affected by drought

VHI < 35

PERIOD OF ANALYSIS 28 YEARS

GIMMS NDVI (1981-2006) 15-day data

8 km resolution

Agricultural crop mask(FAO crop zones x GLC2000)

Empirical probabilities at Sub-national level of having 30% and 50% of agriculture area affected by drought

30 VHI crop season average images

VHIi = a VCIi + b TCIi

VCIi = 100 * (NDVIi – NDVImin)/(NDVImax – NDVImin)

TCIi = 100 * (Tmax – Ti)/(Tmax – Tmin)

http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_ftp.php

Source: Kogan, F. 1995. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bulletin of the American Meteorological Society vol.76, No. 5 655-668 pp.

Period of analysis

SOS EOSEGF

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ND

VI

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Crop cycle (180 days)

Planting

Source: White, M.; Thornton, P. and Running, S. 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability, Global Biogeochemical Cycles 11 (2) 217-234

Crop mask, SOS and EGF

Figure 2. (a) Agricultural crop mask considering the main FAO crop zones and masking out the forest using GLC2000 for the following annual crops: pulses, sorghum, wheat, millet, maize, niebe, teff, yams, rice and barley. (b) The start of crop development stage (c) end of the grain filling stage.

A B C

From VHI to % of agricultural area affected by drought at sub-national level

- The method does not depend on rainfall (-estimates).

- Considers time and space dimensions of drought

- Good agreement with station level indices such as SWALIM’s CDI

temporal aggregation + spatial aggregation

Drought probability

- First crop season

- Second crop season

In Somalia probability > 35% to have 30% of agricultural area affected by drought in the first crop season and > 30% to have 50% affected in the second season!

Area affected by drought (VHI<35) at different scales of analysis

20

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Are

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Somalia Ethiopia Kenya

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Northern Africa Western Africa

Central Africa Eastern Africa

Southern Africa Western Indian Ocean Islands

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Somalia Ethiopia Kenya

Africa

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Eastern Africa

A

CBD

(A) Start of the crop development stage by administrative unit (B) Agricultural areas (C) Percentage of the agriculture areas with Vegetation Health Index (VHI) below 35 for the cropping season 2011/2012 (D) Enlarge area of (C) in Eastern Africa, VHI is temporally average.

Week 33, 21 August 2011

Week 33, 21 August 2011

Week 35, 4 September 2011

Week 35, 4 September 2011

Drought and La Niña in Eastern Africa

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JJA JAS ASO SON OND NDJ DJF JFM FMA MAM AMJ MJJ 1984

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JJA JAS ASO SON OND NDJ DJF JFM FMA MAM AMJ MJJ 2009

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Percentage of agriculture areas affected by drought for each cropping season when La Niña occurred.

2011(up-dated until 1st

dekad of July)

2009

Eastern Africa

1992/931992/93 2011/2012

2011/2012

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Somalia Ethiopia Kenya

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Somalia Ethiopia Kenya

- Going from average Land Surface Phenological date to near real time analysis at pixel level

Automatic determination of seasonality, number of growing seasons per year, and growing season breakpoints using Autocorrelation

LSP (Land Surface Phenology) parameters retrieval using model fit.

Original FAPAR time series Autocorrelation

2 GS per year

Double Hyperbolic Tangent model fitted to original data

Drought monitoring with VHI in near real time

Work in progress with M. Meroni, M.M. Verstraete, O. Rojas

Determination of phenological parameters Start of GS End of GS GS length Accumulated fAPAR Anomalies ..

Drought monitoring with VHI in near real time 1

(Sep-Dec 2010)

(Mar-Jun 2011)

Start of GSStart of GS

Drought monitoring with VHI in near real time 2

Some advantages & limitations• The validity of the VHI as drought detection tools relies on

the assumption that NDVI and LST (land surface temperature) at a given pixel will vary inversely over time, with variations in VCI and TCI driven by local moisture conditions.

• This assumption will be valid when water-not energy-is the primary factor limiting vegetation growth (highlands Ethiopia, South Africa-wheat areas)

• NDVI values may slightly vary due to soil humidity and depending on the particular anisotropy of the target (angular geometry and time of measurement)

• AVHRR is composed by data from several different NOAA satellites

Some advantages & limitations• AVHRR has the longest time series, offers a thermal

channel and data is available for downloading on the Web in near real time

• The method presented has the advantage to detected agricultural droughts (inter-annual analysis based on the crop cycle duration, restricted to the agricultural areas of the administrative division)

• The approach is better understood by the general users due to the crop cycle integration of the results compared with other RS indicators like for example ten-daily NDVI differences

Questions & discussion

ROJAS, O., VRIELING, A. REMBOLD, F. (2011) Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sensing of Environment 115 (2011) 343-352

ROJAS, O., VRIELING, A. REMBOLD, F. (2011) Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sensing of Environment 115 (2011) 343-352

Empirical probability

NnP /ˆ