a satellite based data collection and crop yield forecasting … · a satellite based data...

A SATELLITE BASED DATA COLLECTION AND

CROP YIELD FORECASTING SYSTEM

FOR THE SAHEL REGION

ORET NE/ID07027

Scientific Final Report

July 2016

A satellite based data collection and crop yield forecasting system for the Sahel region 3

A SATELLITE BASED DATA COLLECTION

AND CROP YIELD FORECASTING SYSTEM

FOR THE SAHEL REGION

Cooperation Project

ORET NE/ID07027

Scientific Final Report

July 2016

Authors

Andries Rosema, Steven Foppes,

Wim Joost van Hoek, Joost van der Woerd.

EARS Earth Environment Monitoring BV

Kanaalweg 1

2628 EB Delft

The Netherlands

Email: [email protected]

Mohamed Yahya Ould Mohamed Mahmoud,

Brahima Sidibé, Seydou Bréhima Traore,

Kalamou Maman, Hadiza Aboubakar,

Alhassane Agali and Zalagou Moussa.

Centre Régional Agrhymet

BP 11011

Niamey

Niger

Email: [email protected]

Scientific final report of the project Establishment of a Satellite Based Data Collection and Crop

Yield Forecasting System for the Sahel region, commissioned by Centre Régional Agrhymet. The

project was co-funded by the Centre Régional Agrhymet and the Government of the Kingdom of

The Netherlands through Grant Agreement NE/ID07027 related to ORET project NE/ID07027.

This report may be referred to as: Rosema, A; Foppes, S; Van Hoek, W.J; Van der Woerd, J,

(2016) “A Satellite Based Data Collection and Crop Yield Forecasting System for the Sahel

region”, Scientific final report of ORET project NE/ID07027, EARS, Delft, the Netherlands, July

2016.

Cover: Dogon village at the Bandiagara Escarpment, Mali.


ACKNOWLEDGEMENT

This project has been approved and supported by the Nigerian Ministry of Finance and the

Agrhymet Regional Centre. We especially thank Dr. Maty Ba Diao and Mohamed Yahya Ould

Mohamed Mahmoud, Issa Martin Bikienga, Brahima Sidibé, Seydou Bréhima Traore, Kalamou

Maman, Hadiza Aboubakar, Alhassane Agali and Zalagou Moussa of the Agrhymet Regional

Centre. We are grateful for their interest and support during project initiation, development and

implementation.

The authors also wish to thank the many people and national experts from the dedicated project

teams, Alfari Issoufou, Soumana Djibo, Traore Seydou, Djabi Bakari, Songoti Henri, Souley

Yero Kadiadiateu, Dan Karami Ado, Adamou Moussa, Boukari Oumar, Namodji Luci from

Direction de Development Pastoral (DDP), the University of Niamey and Centre National de

Télédétection (CENATEL) for their work and valuable interaction on the understanding, as

well as establishment and maintenance of the EWBMS system, as well as for keeping data

reception performance optimal, analyzing and spreading the measured data, results and

information.

The project would not have been possible without the grant received from the Government of

the Netherlands, through the ORET organization. We are grateful to the Ministry of Foreign

Affairs for the fair and proper settlement of all administrative, financial and contractual matters

related to this project.


CONTENTS

1. INTRODUCTION 9

1.1 Drought, desertification and food production 9

1.2 CILSS and Agrhymet's mandate 10

1.3 Implementation project 12

1.4 Project objectives 12

1.5 Project approach 12

1.6 Project deliverables 13

2. ENERGY AND WATER BALANCE MONITORING SYSTEM (EWBMS) 15

2.1 Data reception 16

2.2 Compositing 17

2.3 Calibration 18

2.4 Rainfall processing 18

2.5 Energy balance processing 20

2.5.1 Atmospheric correction 20

2.5.2 Cloud detection 23

2.5.3 Global radiation 23

2.5.4 Net radiation 24

2.5.5 Sensible heat flux 24

2.5.6 Photosynthesis light use 25

2.5.7 Evapotranspiration 26

3. DROUGHT MONITORING AND YIELD FORECASTING INFORMATION 27

3.1 Agricultural drought monitoring 27

3.2 Meteorological drought monitoring 27

3.3 Hydrological drought monitoring 28

3.4 Start of the growing season 28

3.5 Crop yield estimation 28

4. PRODUCT VALIDATION 29

4.1 Rainfall 30

4.1.1 Validation approach 33

4.1.2 Niger 33

4.1.3 Burkina Faso 34

4.1.4 Mali 35

4.1.5 Senegal 37


4.2 Global radiation 38

4.3 Net radiation 40

4.4 Evapotranspiration 41

4.5 Crop yield 43

4.5.1 Data sources 43

4.5.2 Senegal 43

4.5.3 Mali 50

4.5.4 Burkina Faso 56

4.5.5 Niger 63

5. SAHEL DESERTIFICATION MONITORING INFORMATION 71

5.1 Climatic zoning of desertification 71

5.2 Desertification monitoring products 71

5.3 Trends 72

6. USER INVOLVEMENT 75

6.1 Project website 75

6.2 Project FTP site 75

6.3 Training of users 75

6.4 Growing season bulletins 75

6.5 User application 76

6.6 Users feedback 76

7. CONCLUSIONS 77

8. REFERENCES 79

ANNEX 1 : PROVINCES AND ADMINISTRATIVE REGIONS 81

ANNEX 2 : START OF THE GROWING SEASON

INFORMATIVE BULLETIN 83


1. INTRODUCTION

The Sahelian zone of West Africa is 400 to 800 km wide and more than 4000 km long. It is one

of the very large semi-arid zones of the world, characterized by summer rainfall varying from

100 mm in the north to 600 mm/year in the south. About one quarter of the territory of Chad,

Mali, Mauritania, Niger, Senegal and Burkina Faso belongs to this zone and about one third of

the population lives there. In this region rainfall is not only scarce but also highly irregular. Dry

and wetter periods interchange in an unpredictable manner. In combination with a steadily

increasing population pressure and the introduction of more modern agricultural techniques, the

ecosystem is periodically overstrained and finally deteriorated. Periods of drought have a direct

effect on crop production and food security and on longer term may lead to permanent loss of

productive capacity, i.e. to desertification.

Drought cannot be prevented. To mitigate the effects of drought it is necessary to develop

flexible response policies and mechanisms in terms of agricultural production systems,

environmental protection measures and related decision making. Flexibility, however, implies

the need for timely, quantitative and reliable information for decision makers and farmers to act

upon. Such information, however, was not, not timely or insufficiently available. The proposed

project will to a considerable degree satisfy the need for such information.

1.1 Drought, desertification and food production

Desertification results mainly from an imbalance between generation and exploitation of plant

biomass. The generation of biomass depends on a range of factors, but those that are most

variable are soil and precipitation. In the desert regions of the world rainfall is insufficient to

sustain plant growth. These regions are bordered by semi-arid zones, which are historically the

domain of nomadic cattle breeders. They used to move with the seasons to those areas where

precipitation and consequently biomass production were most favorable. Due to an increasing

population, however, their number and cattle increased while at the same time sedentary

farming more and more expanded into these marginal semi-arid lands, thus restricting the

traditional domain of the pastoral population.

In these semi-arid regions rainfall is irregular in both space and time. Periods of favorable

precipitation are interchanged with period of drought, which may last several years. Particularly

during drought the imbalance between the generative capacity of the land and its exploitation

becomes overstrained, often leading to irreversible damage. The herbal vegetation is consumed

early and/or trampled and has little chance to produce seed. As a result the quality and quantity

of the vegetation is less during the next season. In this way a negative spiral sets in, which

within a few years may lead to denudation of the land.

During such dry spells also the crops of sedentary farmers fail and also their soils remain

exposed to the elements. These are then subject to further, usually irreversible degradation as a

result of high radiation, leaching and erosion. Besides degradation of the environment, there is

a most serious consequence for the population. Income and food resources are lost, which may

result on the short term in famine and even starvation. Farmers tend to abandon their land and

draw off. Their land is likely to deteriorate and its capacity to produce food is lost. Therefore it


is very important to spot drought and reduced food production in an early stage. Early warning

of national and international authorities will support efficient decision making and targeted

relief actions. By bringing food from surplus areas famine can be prevented. Bridging programs

for farmers and cattle breeders may help to maintain the productive capacity of these areas.

1.2 CILSS and Agrhymet's mandate

The Permanent Interstate Committee for Drought Control in the Sahel region (CILSS) was

established in 1973 in order to coordinate the fight against desertification among the Sahel

countries and to co-operate in solving the adverse effects of prolonging drought, thus finding a

new ecological balance in the region. The highest authority is the Council of Ministers of the

13 member states, which meets twice every year. Day to day management is in the hands of the

Executive Secretary. The member countries are shown in figure 1. The head office is in

Ouagadougou, Burkina Faso.

The mission of CILSS may be described as follows:

1. To study the problems of food security and life stock management in the Sahel, in order to

define adequate strategies and effective policies for sustainable development of the sub-

region.

2. To coordinate at the sub-regional level the whole of considerations and actions to control

food, ecological and demographic constraints which prevent a sustainable economic

growth.

3. To collect, process and diffuse quantitative and qualitative information in order to inform

and alert the member states and the international community on the ecological and human

problems in the sub-region.

Figure 1: The member countries of CILSS.


4. To contribute to the coordination and development of policies for research and combat of

the effects of drought and desertification.

5. To promote the realization of sub-regional actions, which strengthen the co-operation

between the Member States in their joint fight against the effects of drought and

desertification, and to ensure the follow-up of these actions, thus contributing to regional

integration.

6. To contribute to the regional coordination of the emergency aid obtained in the sub-

regional framework, and to promote is use as factor of development.

CILSS is considered one of the most successful organisations in Africa in setting up a truly

regional cooperation in food security and early warning.

Created in 1974, AGRHYMET is a specialized agency of CILSS. The objectives of

AGRHYMET Regional Centre are as follows:

1. To contribute to food security and increased agricultural production in member countries

of CILSS and ECOWAS.

2. To improve the management of natural resources of the Sahel and West Africa.

3. To provide information and training of development agents and their partners in the fields

of agro-ecology in the broadest sense (agroclimatology, hydrology, plant protection, etc.).

It is a tool oriented regional center, specialized in science and technology, applicable to the

sectors of agricultural development and the development of rural and natural resource

management.

Over the years, the AGRHYMET has established itself as a regional center of excellence in:

1. Training of cadres of the Sahel and elsewhere

2. Agro-meteorological and hydrological monitoring at the regional level

3. Agricultural statistics and crop monitoring

4. Regional data banks

5. Management and dissemination of information on the monitoring of natural resources in

the Sahel

6. Documentation: Agrometeorology, plant protection, environmental monitoring,

desertification, natural resource management, etc.

7. Maintenance of meteorological instruments and electronic equipment

8. Strengthening cooperation interstate through the exchange of technology and methodology.

It is for this reason that executives of the Centre are increasingly approached by bilateral and

multilateral agencies. (USAID, FAO, WHO, IRD, CIRAD) and is engaged, in connection with

CILSS, in international meetings on food security, sustainable development, management of

natural resources and the fight against desertification.


1.3 Implementation project

The Center is, in terms of its scientific and technical capabilities on the one, and its operational

mandate on the other side, the best possible institution in West Africa for successful

implementation of a drought monitoring and crop yield forecasting system. The main

component is the Energy and water balance monitoring system (EWBMS) developed by the

Dutch remote sensing company EARS.

The EWBMS will in a natural way complement the existing facilities and capabilities and

because of its level of integration and timeliness it will allow Agrhymet to produce and

distribute innovative drought and crop yield forecasting products to the member countries, in

near real time.

The monitoring system is to become a major tool at Agrhymet. It will help to carry out its

mandate, namely to monitor and document agrometeorology, crop growth and desertification.

Furthermore, the project is used to disseminate this environmental information to CILSS states.

1.4 Project objectives

The objectives of the proposed project are as follows:

1. To establish an MSG based Energy and Water Balance Monitoring System (EWBMS) for

the CILSS region at Agrhymet.

2. To establish a drought and desertification monitoring sub-system (DMS) that will generate

regional drought and desertification maps, relevant to the UNCCD.

3. To establish a crop and pasture yield forecasting sub-system (CYFS), that will provide 10

daily crop yield forecasts during the growing season.

4. To calibrate, test and validate these systems.

5. To implement end-user systems in the CILSS member countries, which will allow end-

users to view and analyze the EWBMS drought and crop yield forecasting data products

generated at Agrhymet and provided to them by electronic mail.

6. To train experts from the partner and end-user organizations in understanding and using

the technology.

1.5 Project approach

The project was carried out in 5 phases. These phases were defined in the work plan and consist

of a number of work packages for which one of the partners has the main responsibility.

Although these phases are described separately in the work plan, in practice, these phases

overlap in time.

In the first phase, the system definition phase, the project methodology was reviewed. EARS

visited Agrhymet to provide detailed theoretical background presentations and demonstrations

on the EWBMS, the Drought and desertification monitoring system (DMS) and the Crop yield

forecasting system (CYFS). A state of the art description of the system was provided in this

phase. Effectively, during the system definition phase, the EARS and Agrhymet agree on what

the delivery of the systems comprises and what work is to be done in the other phases.


The work done in the second phase, the system development phase, was meant to develop the

existing system towards the requirements of CILSS, which were defined in the first phase.

Validation is also part of the development phase. A more extensive elaboration on validation is

found in chapter 3. The system installation phase, the third phase, was meant to implement and

operationalize the components. The already existing MSG receiving system at Agrhymet's

premises was interfaced with a small PC network. The software installed on the PC network is

the interface to the EWBMS, DMS and the CYFS.

A major part of the project was the fourth phase, the capacity building phase, serving to enable

the Agrhymet staff to operate the system, to develop trust in the products and to generate

dedicated products for end-users. Furthermore, Agrhymet can assist end-users in understanding

and using the provided products.

The last phase was the end-user implementation phase. Dedicated training of and cooperation

with end-users in CILSS countries was done to enable end-users to interpret and apply

EWBMS products to their own needs. Feedback from the end-users helped us to improve

products and delivery.

1.6 Project deliverables

The Dutch supplier, in co-operation with the counterpart, will provide the following systems

and services: Energy and Water Balance Monitoring Software system (EWBMS).

1. Drought and Desertification Monitoring Software sub-system (DMS).

2. Crop Yield Forecasting Software sub-system (CYFS).

3. A Data Base of Historical Data Products (10 years)

4. Imageshow-2, dedicated EWBMS data analysis and visualization software tool,

performing a range of end-user oriented operations, such as:

5. Image inspection and presentation

6. Mathematic operations

7. Time series analysis

8. Polygon averaging

9. Zoning, level slicing

10. Contouring and masking

11. Map editing

12. EWBMS and Imageshow2 user manuals

13. Testing of EWBMS climatic products using existing ground data.

14. Testing the CYFS crop yield forecasting products using existing reported yields.

15. Training of 5 experts at Agrhymet in understanding and using the EWBMS, CYFS and DMS.

16. Training of 30 end-users from the member countries in using and analyzing the EWBMS,

CYFS and DMS products.

17. Studying and possibly developing phyto-sanitary applications of the EWBMS in

cooperation with Agrhymet and regional organizations in the field of pest early warning.

18. Project final report

19. Diffusion the project results to the member states by means of a project web site and a

project evaluation symposium


2. ENERGY AND WATER BALANCE MONITORING SYSTEM

(EWBMS)

Meteorological satellites have been used mainly for weather analysis and forecasting. Since the

1980’s new applications, related to the energy and water balance of the earth surface, have

emerged. Surface reflectance, measured in the visible wavelength band (VIS) enables the

estimation of the amount of solar energy that is absorbed by the ground. Surface temperatures,

measured in the thermal infrared band (TIR) enable the assessment of the partitioning of this

absorbed energy between sensible and latent heat, the latter representing the evapotranspiration of

water. Geostationary meteorological satellites provide thermal infrared and visible data at 3 or 5

km resolution. Polar orbiting meteorological satellites may also be used to measure planetary

temperatures. But, the lower repeat coverage makes them less suitable for cloud and rainfall

monitoring. The time of data capture, the large scan angles and the variable imaging geometry

makes them also less valuable for energy balance monitoring.

The EWBMS is a set of software modules that are executed step by step. The primary input of

the EWBMS are raw satellite data obtained through a receiving dish. The outputs are useful

products like precipitation, evaporation, drought indices and yield forecasts. The EWBMS

software modules are extensively described in a user manual. A summary of the most essential

steps in the EWBMS, including their theoretical backgrounds, is provided in this chapter.

In figure 2 an overview of EWBMS, as used in this project, is shown. Images from the

geostationary satellite MeteoSat are received quarter hourly. Cloud top level frequencies or

"cloud durations" are determined. From the hourly full image data, composites are prepared

which represent local noon and local midnight VIS and TIR values. The extracted data are then

processed to quantitative, spatially continuous image maps of rainfall, radiation, sensible heat

flux, temperatures and evapotranspiration. Besides the satellite images, hardly any additional

input is needed. Only ground point precipitation data, used for generating the rainfall maps, are

required.


Figure 2: EWBMS flowchart

2.1 Data reception

The primary input of the EWBMS at Agrhymet are satellite images, as obtained from the

EUMETSAT receiving system at Agrhymet, and near real time rain gauge data, as obtained

through the WMO-GTS system. Both input file types are received via the receiver dish at

Agrhymet. The GTS data reception and preprocessing module processes rainfall data from

ground stations. Agrhymet receives these data through the WMO Global Telecommunications

System (GTS) that is broadcasted on EUMETSAT’s EUMETCAST (Figure 3).

Figure 3: The EWBMS flowchart with an emphasis on the GTS processing module


The GTS data are extracted from the GTS bulletins received by the reception system, then

decoded and written to a Microsoft Access data base. The raw satellite images are sent to a

local computer and stored for further calibration and processing into composite images and

cloud duration files.

A summarizing flowchart of the input preparation module is shown in figure 4. The computer

doing the processing of the particular program is shown the gray bar on top. The programs

being run are located in the orange bar. The involved file types are located in the blue bar. The

locations of these files are written in the red bar.

2.2 Compositing

The main input data for the energy balance are the local noon and local midnight composites,

representing the situation at local noon or midnight in one image. In the pre-processing these

images are composed from the hourly planetary albedo and planetary temperature images. For

each pixel in the image, the time of local noon or midnight is calculated based on its longitude

position, and expressed in GMT (Greenwich Mean Time). Then, the two hourly images closest

in GMT-time to the local noon or midnight are selected. Pixel values for the composite images

are calculated by interpolating the pixel values of the two selected hourly images. Both

composites of the planetary albedo and planetary temperature are produced at (local) noon (see

figure 5). Local midnight composites are only calculated for the planetary temperatures, since

albedo during midnight is of negligible importance to the energy balance.

Figure 4: A scheme of the computers, programs, filetypes and locations involved in the GTS

processing module


Figure 5: Planetary albedo noon composite (in %, left) and planetary temperature noon composite (in Kelvin, right) of

June 1st, 2005

When one of the two hourly images, closest in time to the local noon or midnight of a pixel, is

not present, no interpolation is carried out. Therefore, if hourly images are missing, the

composite images will show distinct lines. When 3 or more subsequent hourly images,

necessary for the creation of the composite images, are missing, no composite image for that

noon or midnight is created.

2.3 Calibration

Right after the satellite data reception, both cloud durations and composed local noon and local

midnight images are calibrated. The visual bands are combined and calibrated by a vicarious

calibration, using mainly sand desert areas with a fairly stable planetary albedo. The thermal

infrared radiance of an observed object in the infrared spectrum, measured by satellite, is

directly related to the temperature of that object. The thermal infrared images are calibrated by

the on-board black body calibration coefficients of the satellite data. Applying the calibration

methods to the received satellite images, results in a planetary albedo and a planetary

temperature image for each hour.

2.4 Rainfall processing

The estimation of the spatially distributed precipitation is based on two sources of information:

(1) point precipitation data from meteorological stations and (2) cloud frequency data derived

from the thermal infrared channels of the MSG satellites. Point data are obtained from the

WMO Global Telecommunication System (GTS). The GTS data consist of meteorological

measurements from approximately eleven thousand meteorological stations spread over the


globe. 95% of these measurements are available within six hours through the GTS. In the Sahel

region about 50 stations report frequently to the WMO-GTS network, on average 70% of the

time. In other parts of Western Africa, availability of rainfall measurements on the GTS is very

limited.

To correct the incoming solar radiation for the energy balance mapping, cloud cover for each

pixel is monitored around (local) noon from 9:00 – 15:00. Incoming radiation earlier or later

than that time interval is considered negligible, thus cloud cover is not monitored then. For the

precipitation processing, cloud durations are monitored during the whole day. The cloud top

temperature is proportional to the height above the ground: a typical lapse rate is –6.5 °C per

1000 m. Based on analysis of image histograms, four cloud level classes are discriminated. The

corresponding temperatures and heights are shown in table 1. For every hour a planetary

temperature image is available, for each pixel is determined if there is a cloud present and to

which cloud class the cloud belongs. The results are stored in 4 images (one for every cloud

class), each dekad. The dekadal multilevel cloud class durations are used as input for the

precipitation mapping.

In the past several methods have been developed to create rainfall fields from meteorological

satellite data. Well known is the so-called Cold Cloud Duration (CCD) technique, which relates

the presence of very high and “cold” cloud tops to rain gauge measurements. Calibration is

done on historical data sets. EARS method uses four cloud levels (see Table 1) hence also

lower cloud levels, associated with frontal precipitation, are accounted for. The method

combines rain gauge data and cloud durations in near real time to calibrate.

Table 1: Definition of cloud top levels and corresponding temperatures and heights.

CLOUD LEVEL TEMPERATURE RANGE HEIGHT RANGE

Cold < 226 K > 10.8 km

High 226 – 240 K 8.5 – 10.8 km

Medium high 240 – 260 K 5.2 – 8.5 km

Medium low 260 – 279 K 2.2 – 5.2 km

EWBMS rainfall processing starts with the derivation of a multiple ‘local’ regression between

the satellite derived cloud data and the precipitation data for each pixel that contains a rain

gauge. This ‘local’ regression is based on the station under consideration and its 11 nearest

neighbours. The resulting equation for station j is:

Pj,est = Σ(aj,n · CDj,n) (1)

Where CDn is the cloud duration (frequency) at cloud level n. The regression equation,

however, is an imperfect estimator of precipitation P. Therefore at each station the residual Dj

between the estimated and the observed precipitation is determined:

Dj = Pj,obs – Pj,est (2)


Subsequently, the regression coefficients aj,n, bj from (3.1) and the residual Dj from (3.2) are

interpolated between 6 precipitation stations, using a weighed inverse distance method, so as to

obtain the corresponding values for pixel i. The spatially distributed precipitation is finally

calculated pixel by pixel with:

Pi,est = Σ(ai,n · CDi,n) + Di (3)

Note that the estimated precipitation at the location of a station is always equal to the reported

precipitation.

2.5 Energy balance processing

Energy balance theory describes how available solar energy is distributed to air temperature,

surface temperature and evaporation. In the EWBMS, these components are calculated from

surface temperatures and planetary visual reflectance measured by MeteoSat. The most basic

formulation of the energy balance is the following:

LE = In – H – E – G (4)

where:

LE = latent heat flux (W/m2)

In = net radiation (W/m2)

H = sensible heat flux (W/m2)

E = photosynthetic electron transport (W/m2)

G = soil heat flux (W/m2)

The energy used for evaporation (LE) is equal to the net radiation at surface level minus the

energy used to warm the air (S), the energy consumed for photosynthetic activities (E) and the

soil heat flux (G). On a daily average, G equals zero. This makes the equation as follows:

LE = In – H – E (5)

The following paragraphs explain how the individual components of the energy balance are

calculated.

2.5.1 Atmospheric correction

Radiation observed by MeteoSat is reflected by or emitted from the earth surface through the

atmosphere towards the sensors. Measurements may therefore be interfered by the atmosphere.

The measured planetary albedo differs from the surface albedo because of scattering and

absorption by air molecules. Emitted thermal radiation is altered by its transmission through the

atmosphere, mainly because of water vapor. To correct for this, an atmospheric correction is

carried out.


Atmospheric correction in the visual band is done with the transmission model by Kondratyev

(1969). Originally, the model of Kondratyev only accounts for backscatter and ignores

atmospheric absorption. In the applied model for the EWBMS, absorption is introduced, with a

fixed value of 10%. The upward and downward flux of radiation in the visual band is described

by two simultaneous differential equations:

I/ = + a.I - b.J (6)

J/ = - c.J + d.I (7)

where:

a = (+k) / cos(is)

b = 2

c = 2(+k)

d = /cos(is)

= Backscatter coefficient of light ( 0.1)

k = absorption coefficient of light ( 0.03)

is = solar zenith angle

These differential equations are solved analytically. The two functions derived are the

following:

A = f (A’, τm

) (8)

t = f (A, τd) (9)

where:

A’ = 30 day minimum of observed planetary albedo (-)

A = surface albedo (-)

m

= assumed minimum optical depth of the atmosphere (-)

d = daily optical depth of the atmosphere (-)

t = transmission through the atmosphere

Equation 8 relates surface albedo to the planetary albedo and the optical depth. Equation 9

relates atmospheric transmission with surface albedo and optical depth. First, a minimum

optical depth of 2.2 is assumed. Then, from this optical depth, we calculate the surface albedo

(A) from the 30 day minimum planetary albedo (A’). Next, daily optical depth is then obtained

from the daily planetary albedo Ad’ and A. Lastly, this calculated daily optical depth (

d) is

used for the calculation of atmospheric transmission: essential for the global radiation.


For the thermal infrared band a different method of atmospheric correction is used. The relation

between the planetary temperature (T0') and the surface temperature (T0) is described as:

a'0m

a0 TT)icos(

kTT (10)

where:

k = atmospheric correction coefficient

im = satellite zenith angle

Ta = air temperature at the top of the atmospheric boundary layer (K)

The air temperature at the top of the boundary layer (Ta), is obtained on the basis of a linear

regression between the noon and midnight pixel temperatures, as illustrated in figure 6. An

estimate of the air temperature is found for the case of perfect heat transfer so that T0,noon =

T0,midnight = Ta. The top of the atmospheric boundary layer varies at daytime usually between

one and two kilometres. A map of the air temperature at the top of the boundary layer covering

the whole region is obtained by applying this method to a shifting window of 200*200 km.

In order to calculate the correction coefficient, the driest pixels in the image are selected and

are assumed to correspond with the condition of no evapotranspiration. For each pixel a

dryness index (DI) is calculated, which is defined as follows:

n

a0

I

T'TDI

(11)

260

270

280

290

300

310

320

330

340

350

360

260 270 280 290 300 310 320

midnight planetary temperature (Kelvin)

noon

pla

net

ary

tem

pera

ture

(K

elv

in)

Ta

To' max

LE = 0

To max

Figure 6: Derivation of reference temperatures from the scatter

gram of planetary noon and midnight temperatures


Where In is the net radiation. For the driest pixels in the image it is assumed that the latent heat

flux (LE) is zero and therefore the net radiation equals the sensible heat flux (H). Once the

sensible heat flux for the driest pixels is known, the corresponding surface temperature can be

calculated from the net radiation and air temperature with T0 = Ta + In/. Because in equation

10 the correction coefficient (k) is then the only unknown variable, its value can be determined.

The correction coefficient is applied to the whole image. After the correction coefficient and

the air temperature are known, it is possible to calculate the surface temperature for each pixel.

These surface temperatures may then be used for calculating the sensible heat flux.

2.5.2 Cloud detection

The EWBMS calculates evapotranspiration for both cloudy and cloud free conditions.

Estimation of evapotranspiration under cloudy conditions differs from the estimation from a

clear sky atmosphere. A cloud detection algorithm is used to separate cloudy pixels from cloud

free pixels.

Four criteria are defined to distinguish cloudy pixels from non-cloudy pixels. If one of the

following criteria is true, the pixel is considered cloudy.

1) threshold1 > A’ / A’min

2) threshold2 > T0’noon, max / T0’noon

3) T0’noon T0’midnight – threshold3

4) T0’noon Ta

2.5.3 Global radiation

Global radiation [W/m2], Ig, is the product of the transmission (t) of solar radiation through the

earth’s atmosphere, solar constant [W/m2], S, and the solar inclination angle [°], i. For every

quarter hour, the instantaneous global radiation is calculated. The daily average of global

radiation is calculated from these quarter hourly global radiation values.

Instantaneous global radiation follows from:

Ig = t * cos(i) * S (12)

In the EWBMS, the solar constant S is considered to be constant and fixed at 1367 W/m2. The

solar inclination angle varies per location, day of the year and time of the day and is calculated

every quarter hour. To calculate the instantaneous transmission, a pixel first needs to be

indicated as either a bright sky atmosphere pixel or a clouded atmosphere pixel. The

transmission through a bright sky atmosphere is estimated with the earlier described

transmission model by Kondratyev (1969). The transmission through clouded sky is estimated

by a model after Kubelka-Munk, which relates cloud transmission with planetary albedo and

ground albedo.

t = f (A, A’) (13)


Furthermore, the global radiation at noon is calculated based on noon composites in the visual

and thermal bands. Noon global radiation is used later in the processing to calculate noon

surface temperature under cloudy conditions.

2.5.4 Net radiation

Once the temperature data fields have been derived, the algorithm continues with the

calculation of the radiation components. There are both shortwave (solar) and longwave

(thermal, terrestrial) radiation components involved. The net radiation (In) represents the

radiation absorbed at the surface and converted into heat. It may be calculated as follows:

In = (1-A) Ig – Ln (14)

The longwave radiation term, denoted Ln, consists of two components: (1) the upward

longwave radiation from the surface (Lu) and the downward longwave radiation from the

atmosphere (Ld). These long wave radiation fluxes, according to the law of Stephan-Bolzman

(Edmonds, 1968), depend on respectively the surface and the air temperature. However, also

the emission coefficients have to be taken into account. The net long wave radiation is

formulated as follows:

Lnet = Lupward – Ldownward = t*(ε0*σ*T04) – εa*σ*Ta

4 (15)

It is assumed that upward and downward longwave radiation under clouds cancels out.

Therefore, net longwave radiation is neglected under clouds.

2.5.5 Sensible heat flux

Sensible heat flux (H) in Watts/m2 is a measure for heat exchange between the surface and

atmosphere. Heat exchange (H) with the atmosphere happens through turbulence and radiation

and is therefore generally expressed as follows:

H = Hc + Hr (16)

Since H is a function of an exchange coefficient and temperature difference it is rewritten as

shown below:

H= c (To - Ta ) + r (To - Ta ) (17)

H = (To - Ta ) (18)


where:

c = C * va

r = 3

0 T4

= c + r

T0 = daily average surface temperature

Ta = boundary layer temperature

C = drag coefficient (W.m-3

.s.K-1

)

va = average wind speed (m.s-1

)

0 = earth surface emissivity (-)

= Stefan-Bolzman constant (W.m-2

.K-4

)

T = Mean temperature (K) = (To + Ta ) / 2

The atmospheric sensible heat transfer coefficient () is the sum of the turbulent sensible heat

transfer coefficient (c) and the radiative sensible heat transfer coefficient (r). Fixed values of

the average wind speed and the earth surface emissivity are used. Therefore, the difference

between the surface temperature and the air temperature determines the magnitude of the

sensible heat flux (H). Rewriting equation 16 results in:

H = (αc + αr ) (T0 – Ta )=Cva(T0 – Ta ) + 4ε0σT 3(T0 – Ta ) (19)

2.5.6 Photosynthesis light use

When vegetation is present, part of the global radiation is used for photosynthetic electron

transport (E) and the fixation of CO2. The amount of energy used may be estimated with:

E = * (1-A) * Ig * Cv (20)

where:

= photosynthetic light use efficiency on a daily basis (-)

Cv = fraction of the surface covered by vegetation (-)

The photosynthetic light use efficiency is estimated on the basis of the Photosystem

Deactivation Model (Rosema et al. 1998). The vegetation cover Cv is not known independently.

It is clear however that presence of vegetation is usually characterised by high

evapotranspiration values. We therefore use the relative evapotranspiration (LE/LEp) as a proxy

of crop coverage.

Cv = LE/LEp LE / (0.8*In) (21)


2.5.7 Evapotranspiration

Evapotranspiration (E) [W/m2] is obtained from the energy balance formulation as follows:

Enc = In – H – P (22)

Net radiation (In), sensible heat flux (H) and photosynthetic light use (P) are known in non-

cloudy conditions; E can be calculated. In clouded conditions, it is assumed that the Bowen

ratio (β), the relation between the evaporative heat flux and the sensible heat flux, is the same

as the last non cloudy day. This is formulated as follows:

Ec = In / (1+ β) (23)


3. DROUGHT MONITORING AND YIELD FORECASTING

INFORMATION

3.1 Agricultural drought monitoring

Agricultural drought is indicated by the evapotranspiration drought index (EDI). The EDI is

defined as the actual evapotranspiration (LE) over the potential evapotranspiration (LEp) on a

timescale of one or several months. The index is similar to the relative evapotranspiration (RE).

The EDI however is more indicative on the average condition of vegetation over a longer time

period.

EDI = Eact / Epot (24)

The agricultural drought of an individual year can be compared to other years via the difference

agricultural drought index that is formulated as follows:

EDIdiff = (EDIact – EDI10yravg) / EDI10yravg (25)

The difference index provides insight in whether an individual year is drier or wetter than the

longer term average. When applied to a period of the growing season, the EDIdiff can be used as

indicator for crop growth conditions relative to other years.

3.2 Meteorological drought monitoring

The meteorological drought index (MDI) is defined as the precipitation [mm] over the potential

evapotranspiration [mm] expressed as a percentage on a timescale of one or several months,


MDI = Pcum / Ecum (26)

A meteorological drought index < 100% indicates that precipitation was lower than potential

evapotranspiration. Consequently, precipitation could not supply enough water for optimum

plant growth. A PDI higher than 100% however, does not necessarily mean that optimum plant

growing conditions are met, whereas an unknown part of the rainfall is lost to runoff and deep

percolation. A meteorological drought > 100% does imply that there was some runoff or deep

percolation.

To compare drought in an individual year with previous years, a difference meteorological

drought index is use. The difference index is formulated as follows:

MDIdif = (MDIact – MDI10yravg) / MDI10yravg (27)


3.3 Hydrological drought monitoring

The hydrological drought index (HDI) indicates how much precipitation relates to actual

evapotranspiration. It expresses the fraction of precipitation that is evapo(trans)pired and it is


HDI = Pcum / Eact (28)

Therefore, it is a measure for the water amount of that remains available for deeper percolation

or surface runoff. Also for this drought index, there is a variant to compare and individual year

drought index with the years before, the difference hydrological drought index, formulated as

follows:

HDIdiff = (HDIact – HDI10yravg) / HDI10yravg (29)

3.4 Start of the growing season

The agricultural growing season starts when the relative evapotranspiration is above 65%. The

65% RE threshold indicates that vegetation is transpiring. Evapotranspiration only takes place

when the plants leaves are above the soil surface. As such it is used as a uniform proxy to

indicate a starting level of the growing season across the entire region. The season is considered

to start on all locations where the yearly maximum NDVI is at least 0.5. This is considered the

region where agricultural activities take place.

3.5 Crop yield estimation

The most limiting factor for crop growth in the Sahel is water availability. It is for that reason

that the EARS crop yield estimation is based on growing season average relative

evapotranspiration estimates. The EWBMS relative evapotranspiration provides a substantial

part of information on plant growth conditions in terms of water availability during the growing

season. According to Kassam (1979) there is a linear relation between relative

evapotranspiration (RE) and relative yield (RY).

(1-RY) = k*(1-RE) (30)

In this equation k is a crop specific drought sensitivity factor. This factor varies between 0.9

(sorghum, millet) and 1.25 (maize). The method accounts for the variability in yield as a result

of the drought conditions. Therefore, it enables to compare drought conditions in different

years. As there are many factors that influence the absolute crop yield (in kg/ha), the effects of

seasonal drought are expressed in terms of the difference from the previous 5 year average or

“difference yield” (DY):

DY = RY/RYavg -1 (31)


4. PRODUCT VALIDATION

Validation results EWBMS radiation data was evaluated with ground measurements of

radiation from the AMMA (African Monsoon Multidisciplinary Analyses) database

(Redelsperger et al., 2006). Strong correspondence is found between daily averages of ground

measurements and EWBMS estimations. The validation compared satellite derived data with

ground reported data quantitatively in scatter plots, and qualitatively in maps. EWBMS data

shows to be of additional and highly needed value in monitoring spatial and temporal

distribution of several meteorological and hydrological parameters, drought and crop yield.

Furthermore, combined ground and satellite derived precipitation data is also compared to

ground measurements through using the Jack knifing method (Miller, 1974). Many ground

stations do not report consecutively and there is reason to doubt the quality of the ground

measurements. Correlation between ground data and satellite estimations are very variable,

highly depending on consecutive and qualitative coverage of reporting stations.

At last, crop yield reports have been compared with EWBMS crop yield products. It is shown

that although crop yield depends on a large amount of variables, drought conditions during the

growing season, as monitored by the EWBMS, show clear correlation with reported yielded

amounts. Meteorological ground data is scarcely available and accessible and crop yield reports

are not always reliable or comparable to satellite data, quantitative validation does not give

complete insight in performance of EWBMS data. Therefore, a qualitative validation was done

as well. It showed correspondence between reported drought and crop failures and EARS data.

The most critical factor in determining crop yield, water availability, plays a dominant role in

large scale crop failures in the Sahel region. During critical years, drought related crop yield

anomalies emerge above other random variability in reported crop yield. It has been shown that

EARS data detects these critical situations in an early stage.

Validation is needed to develop trust in the provided information. Products are tested on their

validity and accuracy with accessible and independent historical data on radiation, rainfall, the

overall water balance and crop yield. Validation provides additional insight in the value and

quality of EWBMS products compared to ground data. It shows the systems’ benefits and the

shortcomings. Furthermore, validation is used to improve the EWBMS.

Ground data is often referred to as ‘ground truth’. However, ‘ground truth’ is an invalid term,

since meteorological data is susceptible to sampling errors. With high spatial and temporal

variability in space and time and a limited sampling rate, also ‘ground truth’ data does not

represent the ‘true’ value it pretends to be. Ground data should better be referred to as a general

objective reference. Although the aim still is to show correlation between different estimators

of meteorological data, perfect correlation can never be expected here since we are comparing

point ground data with 3x3 km satellite data. With crop yield ground data, we encountered

another issue. Although we have used it as an objective reference, since it is the only ground

data we have found, we expect that it is subject to manipulation. Unrealistic year to year


changes (more than 300 or 400% change in one single year) occur in the dataset. Correlation of

ground yield data with EWBMS yield data is therefore disputable and as will be shown,

correlations are not high. The validation is not meant to ‘proof’ that EWBMS data is fully

consistent with ground data, but it is aimed at showing that EWBMS data indeed adds valuable

information on climatological conditions.

4.1 Rainfall

As we use ground observations (Figure 7 and Table 2) in our method to estimate rainfall and

these same observations are used for validation, a so-called Jack Knife validation method is

used. With Jack knifing, an observation station is left out of the data set used in the rainfall

field calculations while the precipitation for that location is calculated again. The EWBMS

rainfall value and the rainfall measured at that location on the ground, provides one validation

data pair. The procedure is repeated as many times, as there are rainfall stations. In this way as

many independent rainfall validation data pairs are obtained as there are rainfall stations. This

validation data set is then analysed by means of regression. The results of the Jack-knife

validation give a good indication of the quality of the rainfall mapping method. However, the

actual mapping results will be better, as the nearest and thus most influential precipitation data

point is not used as input in the Jack-knife run.

Figure 7: Locations of all rainfall stations in the Sahel region considered in the validation


Table 2: Rainfall WMO stations in the Sahel region considered in the validation

Country WMO nr Station Latitude Longitude Missing

values

Niger 61024 AGADEZ 16.967 7.983 37%

61036 TILLABERY 14.2 1.45 13%

61043 TAHOUA 14.9 5.25 10%

61045 GOURE 13.983 10.3 21%

61049 N'GUIGMI 14.25 13.117 14%

61052 NIAMEY-AERO 13.483 2.167 6%

61075 BIRNI-N'KONNI 13.8 5.25 21%

61080 MARADI 13.467 7.083 18%

61090 ZINDER 13.783 8.983 8%

61091 MAGARIA 12.983 8.933 15%

61096 MAINE-SOROA 13.233 11.983 13%

61099 GAYA 11.883 3.45 9%

Country WMO nr Station Latitude Longitude

Missing

values

Mali 61223 TOMBOUCTOU 16.717 -3 39%

61226 GAO 16.267 -0.05 46%

61230 NIORO DU SAHEL 15.233 -9.35 48%

61233 NARA 15.167 -7.283 51%

61235 YELIMANE 15.117 -10.567 50%

61240 HOMBORI 15.333 -1.683 51%

61250 MENAKA 15.867 2.217 67%

61257 KAYES 14.433 -11.433 46%

61265 MOPTI 14.517 -4.1 27%

61270 KITA 13.067 -9.467 44%

61272 SEGOU 13.4 -6.15 37%

61277 SAN 13.333 -4.833 47%

61285 KENIEBA 12.85 -11.233 36%

61291 BAMAKO/SENOU 12.533 -7.95 21%

61293 KOUTIALA 12.383 -5.467 26%

61296 BOUGOUNI 11.417 -7.5 44%

61297 SIKASSO 11.35 -5.683 40%


Table 2 (continued): Rainfall WMO stations in the Sahel region considered in the validation

Country WMO nr Station Latitude Longitude Missing values

Senegal 61600 SAINT-LOUIS 16.05 -16.45 7% 61612 PODOR 16.65 -14.967 42% 61627 LINGUERE 15.383 -15.117 8% 61630 MATAM 15.65 -13.25 7% 61641 DAKAR/YOFF 14.733 -17.5 7% 61666 DIOURBEL 14.65 -16.233 46% 61679 KAOLACK 14.133 -16.067 7% 61687 TAMBACOUNDA 13.767 -13.683 8% 61695 ZIGUINCHOR 12.55 -16.267 8% 61697 CAP-SKIRRING 12.4 -16.75 8% 61698 KOLDA 12.883 -14.967 56% 61699 KEDOUGOU 12.567 -12.217 77%

Burkina Faso 65501 DORI 14.033 -0.033 4% 65502 OUAHIGOUYA 13.583 -2.433 4% 65503 OUAGADOUGOU 12.35 -1.517 8% 65505 DEDOUGOU 12.467 -3.483 11%

65507 FADA N'GOURMA

12.067 0.35 10%

65510 BOBO-DIOULASSO

11.167 -4.3 11%

65516 BOROMO 11.733 -2.917 9% 65518 PO 11.167 -1.15 10% 65522 GAOUA 10.333 -3.183 15%


In this section, Jack-knifing results for stations in Mali, Burkina Faso, Senegal and Niger are

reported for the period 2005-2014. GTS precipitation data from other countries in West Africa

are too scarce to be useful for validation. A total number of 50 stations (see Figure 7 and Table

2) reported on average 70% of all the dekads in 2005-2014. A station dekad value is considered

to be missing if that station has reported less than 75% of the time in a dekad. Due to

malfunctioning of the meteorological station or the data communication lines, erroneous reports

can always be expected. Some reports are excluded automatically, but since variance of rainfall

both in time and space is very high, thresholds to exclude reports are very soft.

A lot more reports are doubtful, but anyhow used for the results presented hereafter.

4.1.1 Validation approach

Comparison between EWBMS and ground measured rainfall in this section is done by means

of volume difference, root mean square difference (RMSD), Pearson’s R2, and a visual

impression of a scatter plot. The volume difference over 6 years is given as a percentage of the

reported volume over 6 years and shows how reported and EWBMS estimated series are

biased. RMSD measures the average of the squares of the differences, and gives an indication

of difference per time step. It is reported for 1 dekad, 1 month, and 3 months, representing

different time scales in the growing season: rainfall in a certain dekad could be the trigger for

farmers to start planting or sowing; 1 month represents a phase in crop growth, while 3 months

may cover a whole growing season. Pearson’s R2 indicates the variability of the difference

between EWBMS and ground measured rainfall. To assess the different coefficients relative to

the average, the reported average per dekad is also given, as is the percentage of missing

values. In the scatter plot pairs of reported (on x-axis) and Jack-knife estimated rainfall (on y-

axis) of all stations are plotted. Without any differences between reported and EWBMS

estimated rainfall, volume difference and RMSD would be 0 mm, R

2 would be 1, and in the

scatter plot all the dots are on the 1:1 line. However different measurement scales and errors in

both field measurements and satellite derived data explain the deviation from the ideal

situation.

4.1.2 Niger

Except for 1 station in central Niger, all the stations are situated in the southern area of Niger.

(see figure 7). Pairs of reported (on x-axis) and Jack-knife estimated rainfall (on y-axis) of all

12 stations for the period 2005-2014 are plotted in figure 8. For all 12 stations together,

Pearson’s R2 is 0.42. The Jack-knife estimated total volume is country averaged 15% larger

than the reported volume. The amount of erroneous rainfall reports in Niger is relatively large.

In table 3 results for each station separately are given. Pearson’s R2 range from 0.17 to 0.78 and

volume differences range from –5% to 30%, while at 10 out of 12 stations Jack-knife total

volumes are higher than reported volumes. Agadez in central Niger, N’Guigmi in the southeast

and Tahoua and Maradi, which are all remote stations (in terms of neighbouring stations), score

low on R2 and high on volume difference.


Table 3: Jack knife rainfall validation results for Niger

Station

Rep.

avg.

dekad

Vol.

Diff.

(est. -

rep.)

RMSD

Dekad

RMSD

1 month

RMSD

3 month R

2

Missing

Values

AGADEZ 3.19 39.49 8.46 13.87 24.88 0.22 37%

TILLABERY 10.21 19.73 18.68 33.42 59.30 0.37 36%

TAHOUA 8.48 29.68 17.07 29.87 53.24 0.47 10%

GOURE 9.66 17.18 19.23 34.51 66.24 0.43 21%

N'GUIGMI 6.64 37.28 18.72 33.71 65.17 0.40 14%

NIAMEY-AERO 12.41 10.70 20.50 36.45 67.01 0.42 6%

BIRNI-N'KONNI 12.51 -4.87 16.07 28.61 45.24 0.53 21%

MARADI 10.68 29.01 16.49 28.05 44.34 0.51 18%

ZINDER 11.84 0.95 16.54 29.69 52.06 0.45 8%

MAGARIA 12.08 13.84 73.52 117.41 52.22 0.37 16%

MAINE-SOROA 10.35 7.62 16.29 27.85 35.42 0.48 14%

GAYA 18.02 19.23 28.76 50.27 91.35 0.35 9%

4.1.3 Burkina Faso

The 9 stations in Burkina Faso available for Jack-knife validation are well distributed

throughout the country (see figure 7). Pairs of reported (on x-axis) and Jack-knife estimated

rainfall (on y-axis) of all 9 stations for the period 2005-2014 are plotted in figure 9. For all 9

stations together, Pearson’s R2 is 0.38. Country averaged, there is no difference between Jack-

knife estimated precipitation and reported precipitation. In table 4 specifications and results for

each station in Burkina Faso separately are given. Pearson’s R2 range from 0.31 to 0.55 and

volume differences range from –21% to 11%. Volume differences in 6 out of 9 stations are

lower than 10%. The monthly RMSD’s are lower than the reported average of 1 month. Since

the R2 is high, volumes of Jack-knife estimated and reported rainfall coincide, stations are

Figure 8: Dekad rainfall scatter plots of observed versus Jack knife estimate

rainfall, with Pearson’s R2 and volume difference in Niger


nicely distributed throughout the country and the number of missing values is low, EWBMS

rainfall maps, especially on time scales of month or largerR4TYUIin Burkina Faso is expected

to provide country wide representative quantities of precipitation.

Figure 9: Dekad rainfall scatter plots of observed versus Jack knife estimated rainfall, with Pearson’s R2 and volume

difference in Burkina Faso

Table 4: Jack knife rainfall validation results for Burkina Faso

4.1.4 Mali

The 17 stations in Mali available for Jack-knife validation are situated in the southern and

central areas of Mali. Pairs of reported (on x-axis) and Jack-knife estimated rainfall (on y-axis)

of all 17 stations for the period 2005-2014 are plotted in figure 10. The majority of data points

are close to the 1:1 line, which indicates that a substantial part of the estimations are close to

the reported rainfall. For all 17 stations together, Pearson’s R2 is 0.44 and the Jack-knife

estimated total volume is 4% larger than the reported volume. In table 5, the results for each

Station Rep. avg.

dekad

Vol. diff.

(est. -

rep.)

RMSD

dekad

RMSD

1 month

RMSD

3 months R2

Missing

values

DORI 12.75 3.50 21.08 36.61 63.82 0.31 4%

OUAHIGOUYA 13.85 11.10 20.48 35.55 13.85 0.52 4%

OUAGADOUGOU 20.48 6.21 24.42 42.38 20.48 0.55 8%

DEDOUGOU 22.08 6.19 37.90 65.79 22.08 0.33 11%

FADA N'GOURMA 20.79 9.66 24.52 42.53 20.79 0.52 10%

BOBO-DIOULASSO 27.68 -3.35 41.12 71.42 27.68 0.37 11%

BOROMO 22.88 4.65 26.38 45.73 22.88 0.51 9%

PO 25.81 -21.37 30.02 52.10 25.81 0.55 10%

GAOUA 25.56 2.07 28.91 50.10 87.13 0.31 15%


separate station are given. Pearson’s R2 ranges from 0.15 to 0.80 and volume differences range

from –30% to 54%. Especially estimated and reported rainfall of the 4 most northern situated

stations (Menaka, Gao, Tombouctou and Hombori), vary considerably. The stations in the

southern and western parts of the country score well on R2 and volumes, and monthly RMSD’s

are mostly lower than the reported average of 1 month. It shows that the method performs

better when more ground stations are available to complement the satellite estimations.

Figure 10: Dekad rainfall scatter plots of observed versus Jack knife estimated rainfall, with Pearson’s R2 and volume

difference in Mali

Table 5: Jack knife rainfall validation results for Mali

Station Rep. avg.

dekad

Vol. diff

(est. -

rep.)

RMSD

dekad

RMSD

1 month

RMSD 3

months R2

Missing

values

TOMBOUCTOU 6.50 11.92 15.50 26.920 47.02 0.18 39%

GAO 3.87 54.47 10.64 18.481 3.87 0.24 46%

NIORO DU SAHEL 15.41 8.39 17.49 30.301 15.41 0.43 48%

NARA 19.42 -9.36 18.95 32.710 19.42 0.54 51%

YELIMANE 18.03 -3.73 16.56 28.757 18.03 0.68 50%

HOMBORI 11.90 17.19 15.58 27.061 11.90 0.32 51%

MENAKA 6.92 18.56 9.71 16.873 6.92 0.19 67%

KAYES 21.93 -6.85 26.62 46.208 21.93 0.51 46%

MOPTI 15.68 13.59 25.04 43.476 75.82 0.59 27%


Table 5: (continued) Jack knife rainfall validation results for Mali

Station

Rep. avg.

dekad

Vol. diff

(est. - rep.)

RMSD

dekad

RMSD 1

month

RMSD 3

months R2

Missing

values

KITA 23.65 12.75 18.70 32.44 56.08 0.77 44%

SEGOU 17.00 25.09 22.99 39.76 69.13 0.75 37%

SAN 17.51 18.91 18.89 32.79 56.42 0.72 48%

KENIEBA 22.68 15.67 22.55 39.17 68.02 0.80 36%

BAMAKO/SENOU 32.03 -25.86 35.70 61.96 108.05 0.72 21%

KOUTIALA 22.80 12.60 27.38 47.48 81.76 0.47 26%

BOUGOUNI 23.61 15.22 20.16 34.90 60.49 0.64 44%

SIKASSO 38.02 -30.19 29.26 50.79 88.13 0.75 40%

4.1.5 Senegal

The 12 stations available for Jack-knife validation in Senegal are well distributed throughout

the country (see figure 7). Pairs of reported (on x-axis) and Jack knife estimated rainfall (on y-

axis) of all 12 stations for the period 2005-2014 are plotted in figure 11. For all 12 stations

together, Pearson’s R2 is 0.56. The Jack-knife based estimated total volume is 17% larger than

the reported volume, i.e. the EWBMS precipitation method tends to overestimate rainfall rates

in Senegal. In table 6 results for each station separately are given. Pearson’s R2 values range

from 0.15 to 0.56 and volume differences range from –30% to 47%. The monthly RMSD’s are

usually lower than the reported average of 1 month.

Figure 11: Dekad rainfall scatter plots of observed versus Jack knife estimated rainfall,

with Pearson’s R2 and volume difference in Senegal


Table 6: Jack knife rainfall validation results for Senegal

Station

Rep. Avg.

dekad

Vol. diff.

(est. - rep.)

RMSD

dekad

RMSD 1

month

RMSD 3

months R2

Missing

values

SAINT-LOUIS 8.47 18.40 24.64 42.72 74.3 0.29 8%

PODOR 4.05 38.68 13.84 23.99 4.0 0.15 42%

LINGUERE 13.08 -5.37 23.99 41.51 13.1 0.36 8%

MATAM 10.53 29.92 25.74 44.67 10.5 0.36 7%

DAKAR/YOFF 15.64 -1.40 36.10 62.67 15.6 0.40 7%

DIOURBEL 7.58 43.24 15.96 27.53 7.6 0.51 46%

KAOLACK 17.37 14.84 27.37 47.52 17.4 0.53 7%

TAMBACOUNDA 18.97 10.84 56.07 97.35 19.0 0.37 8%

ZIGUINCHOR 40.23 -30.19 47.18 81.59 140.7 0.56 8%

CAP-SKIRRING 33.54 7.36 46.24 80.01 134.12 0.55 8%

KOLDA 9.90 47.28 19.95 34.66 60.31 0.41 56%

KEDOUGOU 12.79 29.10 12.67 22.02 37.97 0.40 77%

4.2 Global radiation

Daily average global radiation ground data from two West-African meteorological stations was

obtained from the AMMA database. Ground data are compared to EWBMS data with a grid

size of 0.03° by 0.03° (about 3 km by 3 km in West Africa). Comparison of the two datasets

shows that the EWBMS global radiation data show a high correlation on daily time scale for

Djougou (9.70° N, 1.67° E, northern Benin), in figure 12, and Agoufou (15.35° N, 1.42° W,

eastern Mali) in figure 13. Their respective correlation coefficients are 0.81 for Djougou and

0.72 for Agoufou. Differences between EARS data and ground measurements result most likely

from temporal variability in clear sky optical depth. Although optical depth is taken into

account, its spatial and temporal variability can’t be calculated with the available spectral

bands. However, since differences between ground and satellite measurements are very small,

we consider them satisfactory for application.

Statistics on how the two locations Djougou and Agoufou compare in the available years are

shown in table 7. It is shown that global radiation estimations for Djougou are generally closer

to ground measurements than estimations for Agoufou. This variability in performance might

result from atmospheric conditions (e.g. dust) that are not sufficiently considered in the global

radiation estimation method.


0

50

100

150

200

250

300

350

400

[W/m

2]

Date

Agoufou, Mali

Ground EWBMS

Figure 13: Time series of ground data and EWBMS data on global radiation in Agoufou in 2006

0

50

100

150

200

250

300

350

400

[W/m

2]

Date

Ground EWBMS

Figure 12: Time series of ground data and EWBMS data on global radiation in Djougou in 2006

Djougou, Benin


Table 7: Summary of validation statistics of global radiation

Location Year Daily R2

RMSD daily 10 daily R2

RMSD 10 daily

Djougou 2005 0.80 24.2 0.82 12.5

2006 0.81 18.8 0.87 10.0

2007 0.82 20.1 0.88 9.7

Agoufou 2005 0.68 44.5 0.66 26.9

2006 0.72 33.1 0.68 24.5

2007 0.75 33.3 0.84 21.7

4.3 Net radiation

Daily average net radiation ground data from the meteorological station at Djougou, retrieved

from the AMMA-database is compared to EWBMS data in figure 14. Only the year 2007 was

available. During December until April, we observe a significant overestimation of the net

radiation of about 25 W/m2 both during cloudy days and non-cloudy days, which is about 30%

of the total net radiation. This either might result from an underestimated surface albedo or

from underestimated outgoing longwave radiation or from a combination of these

underestimations. From May to November, the overestimation mainly occurs during cloudy

days. Nevertheless, there is a high correlation on daily time scale. The correlation coefficient is

0.76. Unfortunately, we do not have other net radiation data available in the region.

Figure 14: Time series of ground data and EWBMS data on net radiation in Djougou in 2007

0

50

100

150

200

250

[W/m

2]

Date

Djougou, Benin

Ground EWBMS


4.4 Evapotranspiration

Since very little to no reliable turbulent flux measurements are available for the region, it is

impossible to validate the latent heat or actual evapotranspiration data directly. Such stations

have been used in earlier projects in China, but are very expensive. There is however another

approach of validating the actual evapotranspiration: by considering the water balance.

Precipitation – Evapotranspiration – change in storage = Run off

We assume that the change in storage over a longer period of time equals zero.

According to Mahe et al. (2008), the average yearly run-off in West Burkina catchments is

between 2 and 8%. In West Burkina we have randomly chosen 44 locations in the agricultural

areas near the towns of Dande, Solenzo, Tougan and Dedougou. For these arbitrary locations

we have extracted the EWBMS precipitation and actual evapotranspiration time series for the

period 2005-2010. These time series have been averaged, so as to get an approximation of a

larger area average.

The resulting precipitation and evapotranspiration time series are plotted in figure 15. The

temporal distribution of the data is typical, with precipitation only during the summer season,

but a more spread-out distribution of the evapotranspiration all over the year and depending on

the radiation level. As it is difficult to judge the longer term water balance from this graph, we

have also plotted the cumulative precipitation and evapotranspiration in figure 16. The

difference is very small, confirming the information that there is little run-off in this area. The

longer term water balance appears to be in the order of 5-10%. This is very well in line with the

earlier cited 2-8% run-off.

This exercise shows that the EWBMS satellite derived precipitation and evapotranspiration

data are very consistent in terms of water volume in multi-year time scale. In the previous

section we have already shown that the EWBMS precipitation is in line with the rain gauge

data. The patterns and volumes suggest that the EWBMS satellite derived actual

evapotranspiration indeed provide information on and may be used as an input for crop yield

calculations. The validation of crop yields estimated and predicted in this way is the subject of

the yield validation exercises discussed in the next chapter.


Water balance

44 locations in W-Burkina

0

50

100

150

200

250

mm

wate

r/d

ek

ad

Precipitation Actual evapotranspiration

Figure 15: Time series of average precipitation and actual evapotranspiration for 4*11

arbitrarily chosen locations in W-Burkina around Dande, Solenzo, Tougan and Dedougou

Cumulative water balance

44 locations in W-Burkina Faso

0

1000

2000

3000

4000

5000

6000

mm

wate

r

Precipitation Actual evapotranspiration

Figure 16: Time series of cumulative average precipitation and actual evapotranspiration for 44

arbitrarily chosen locations in W-Burkina


4.5 Crop yield

Data that are used to validate the EWBMS difference yield products comes from CountryStat at

both GAUL0 and GAUL1 district level. Although ground data is considered as ground truth,

there are several possible sources of errors in this data:

- Using harvested instead of planted area

- Errors in weighing harvested crop

- Census sample survey not representative (too small, not random, restricted area)

- Typing or writing errors

- Extrapolation of data to fill in gaps

- Political and economic conditions influencing reported yields.

It is important to note that both ground and satellite derived yield data contains errors.

Moreover, the data contains different information. Where ground yield data includes the effects

of temporal changes in fertilizer and seed quality, EARS data only account for the changes in

yield due to drought. Furthermore, while obvious errors, such as a sudden 300% increase in

yield in the ground data, are easy to spot, it is not possible to eliminate all types of errors and

discern erroneous changes from those caused by change in climatological and technological

conditions. Consequently, an exact match cannot be expected and thus a completely

quantitative validation analysis is not applicable here.

4.5.1 Data sources

Ground data of 4 countries that seem to provide a relatively reliable dataset comes from Mali,

Burkina Faso, Senegal and Niger. Data for these countries were obtained from the CountryStat

website (http://www.countrystat.org).

Country Period

Senegal 2000-2009

Mali 2000-2007

Burkina Faso 1984-2011

Niger 2000-2010

Furthermore, see the reference list for several consulted reports and articles on droughts in

CILSS countries. However, regular reports are not abundant and they are quite general for all

countries.

4.5.2 Senegal

Senegal has 14 provinces (see Annex 1) and 11 of those frequently report on yearly yield

(ton/ha). As the country spans in the north-south direction more than 400 kilometres, it has a

range of climates. Northern Senegal has an arid climate with an average precipitation of

around 300 mm, whereas in southern Senegal the annual rainfall sums up to more than 1000

mm. Therefore, there are substantial yield differences between southern and northern

provinces. An exception is a large irrigation area in the dry province of St. Louis in the north

http://www.countrystat.org/


of Senegal. These areas are characterised with high evapotranspiration whereas the eastern

part of St. Louis barely shows evapotranspiration. Moreover, the north-south gradient is also

clearly visible. Figure 17 illustrates the presence of these areas. The north-south gradient is

clearly visible. The north has low relative evapotranspiration values (>60%) and the south

has high values (≈100%).

There is a high temporal variability in crop yield, both on local scale and on national

scale (figure 18, 19 and 20). 2003, 2004 and 2005 show significantly larger maize yields

for almost all provinces in Senegal. These exceptional years are according to IFC (2009)

resulting from “special projects (…), where maize is being grown under irrigation using

improved hybrid seeds” and “over reporting of maize production and yields during this 3-

year period”. In contrast, the millet yields in 2004 show a dip in many districts, whereas

2004 for maize has been a successful year for almost all crops. It shows that variation in

yield data does not necessarily link to climatological conditions, but for a substantial part

also to the quality of input. A direct comparison with EARS data is not possible since

elements other than drought appear to dominate the temporal variation in yield in

Senegal. Therefore, a conclusive answer on the quality of the reported yield data can’t be

made. However, qualitative analysis shows that EARS data still provides additional and

valuable information on growth conditions in terms of drought and its impact on yielded

amount.

Figure 17: Growing season average relative evapotranspiration 2008 in Senegal


0

1

2

3

4

5

6

ton

/ha

year

Reported Maize yield Senegal GAUL1 districts

St. Louis

Tambacounda

Dakar

Kaolack

Diourbel

Fatick

Kolda

Louga

Thies

Zinguichor

Figure 19: Time series of CountryStat reported Maize yield for GAUL1 regions in Senegal

0

1

2

3

4

ton

/ha

year

Reported Sorghum yield Senegal GAUL1 districts

St. LouisTambacoundaDakarKaolackDiourbelFatickKoldaLougaThiesZinguichor

Figure 20: Time series of CountryStat reported Sorghum yield for GAUL1 regions in Senegal

0

0,5

1

1,5

2

ton

/ha

year

Reported millet yield Senegal GAUL1 districts

St. Louis

Tambacounda

Dakar

Kaolack

Diourbel

Fatick

Louga

Thies

Zinguichor

Figure 18: Time series of CountryStat reported Millet yield for GAUL1 regions in Senegal


EARS data provides information on both the temporal and spatial variability in drought

conditions. Crop yields of maize and groundnuts during the 2002 growing season in

Senegal are reported to be severely reduced as a direct result of drought (IFC, 2009).

More drought resistant crops, such as millet, do not show notable reductions in 2002.

Also 2007 was a year with strong yield reductions, partly resulting from drought, but

according to IFC (2009) it is understood that shortages of fertilizers and non-availability

of proper seeds also have a part in decreased yields. It is difficult to distinguish between

drought and other factors impacting the crop yield. Nevertheless, EARS data, depicted in

figure 21 and figure 22, shows a distinct decrease of relative evapotranspiration in 2002.

It is the driest year in the time series. The spatial distribution of this decrease is presented

in figure 23. Also the year 2007 shows a significant below average evapotranspiration

value. On the other hand, the growing season of 2005 shows an above average relative

evapotranspiration for almost all provinces. These conditions must have added to the

above average yields in 2005 for maize, millet and sorghum in most districts.


55

60

65

70

75

80

85

90

95

100

[%]

year

Growing season average relative evapotranspiration Senegal GAUL1 districts

St.Louis

Tambacounda

Kaolack

Diourbel

Fatick

Kolda

Louga

Thies

Ziguinchor

Figure 21: Time series of EARS data on growing season average relative evapotranspiration for GAUL1

regions in Senegal

-15,00

-10,00

-5,00

0,00

5,00

10,00

15,00

[%]

year

Difference average relative evapotranspiration Senegal GAUL1 districts

St.Louis

Tambacounda

Kaolack

Diourbel

Fatick

Kolda

Louga

Thies

Ziguinchor

Figure 22: Time series of EARS data on difference average relative evapotranspiration for GAUL1

regions in Senegal


Results of a direct comparison of reported yield data to EARS data for maize, sorghum and

millet are shown in figure 24, 25 and 26. Ground reports correspond rather well with EARS

data, as they follow the 1:1 line closely. The results for reported maize compare the least

with EARS data (R2

maize = 0.60). It was shown and explained earlier that factors other than

climatological played a significant role in maize yield variability. For sorghum and millet,

there is a much stronger fit (R2

sorghum = 0.69 and R2

millet = 0.81), suggesting that inputs for

these crops are less variable. Yield is dominantly variable with climatological conditions.

-15,00

-10,00

-5,00

0,00

5,00

10,00

15,00

[%]

year

Difference average relative evapotranspiration Senegal GAUL1 districts

St.Louis

Tambacounda

Kaolack

Diourbel

Fatick

Kolda

Louga

Thies

Ziguinchor

Figure 23: Growing season average relative evapotranspiration anomaly in 2002 in Senegal

Figure 24: Scatterplot of EARS yield forecast data vs. CountryStat ground reported Maize yield data

0

0,5

1

1,5

2

2,5

3

0 1 2 3

Co

un

tryS

tat

fiel

d d

ata

[to

n/h

a]

EARS Yield forecast [ton/ha]

Senegal, Maize, 2002-2009

R2 = 0.60


R² = 0,8063

0

0,5

1

1,5

2

0 0,5 1 1,5 2

Co

un

tryS

tat

fiel

d d

ata

[to

n/h

a]


Senegal, Millet, 2002-2009

Figure 25: Scatterplot of EARS yield forecast data vs. CountryStat ground reported Millet yield data

Figure 14: Scatterplot of EARS yield forecast data vs. CountryStat ground

reported sorghum yield data

0

0,5

1

1,5

2

0 0,5 1 1,5 2

Co

un

tryS

tat

fiel

d d

ata

[to

n/h

a]


Senegal, Sorghum, 2002-2009

Figure 26: Scatterplot of EARS yield forecast data vs. CountryStat ground reported

Sorghum yield data


4.5.3 Mali

Mali has 8 provinces (see Annex 1) of which 6 provinces report regularly on the yearly yield

(ton/ha) since 1995. There is no yield data for 2004, a critically dry year and no data from the

province of Kidal. In Conijn et al. (2012) the accuracy of the yield is questioned, since different

datasets show different numbers. Consequently, quantitative validation of yields is difficult for

Mali.

The land locked country spans several climates, since its north-south distance is more than

1600 kilometers. In the southern province Sikasso, arid with a dry winter climate, we find

woody savannah whereas in the north, in the province Mopti, we find the Sahara desert.

Accordingly, we find the highest reported yields in Sikasso and the lowest in Mopti. In the

middle of Mali, we find the inland delta of the Niger river and as well as the Office du Niger, a

large area with irrigated agriculture fed by the Niger river. These areas are clearly visible in

figure 21.

The reported yield data, depicted in figure 28, 29 and 30 shows both a substantial temporal and

spatial variability. Note that the yield for maize is substantially higher than of sorghum and

millet. Maize yield is more than 1 ton/ha for most regions and some years even 1.5 tons/ha.

Sorghum and millet have yields below 1 ton/ha. 2006 is a good year for all crops in Sikasso,

where 20

a satellite based data collection and crop yield forecasting … · a satellite based data...

Documents