gio global land component - lot i · 2015-07-02 · gio-gl lot 1, gmes initial operations date...

GIO-GL Lot 1, GMES Initial Operations

Date Issued: 30.06.2015

Issue: I2.10

Gio Global Land Component - Lot I

”Operation of the Global Land Component” Framework Service Contract N° 388533 (JRC)

PRODUCT USER MANUAL

WATER BODIES (WB)

VERSION 1 (SPOT-VGT, PROBA-V)

AFRICA

Issue I2.10

Organization name of lead contractor for this deliverable: VITO

Book Captain: Bruno Smets (VITO)

Contributing Authors: Davy Wolfs (VITO)

R. D’Andrimont (UCL)

Jacobs Tim (VITO)



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Document-No. GIOGL1_PUM_WB-V1 © GIO-GL Lot1 consortium

Issue: I2.10 Date: 30.06.2015 Page: 2 of 36

Dissemination Level PU Public X

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)



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Document Release Sheet

Book captain: Bruno Smets Sign Date 30.06.2015

Approval Roselyne Lacaze Sign Date 03.07.2015

Endorsement: Michael Cherlet Sign Date

Distribution: Public



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Change Record

Issue/Rev Date Page(s) Description of Change Release

28.03.2013 All Geoland2 document, Issue I1.13, 08.06.2011 I1.00

I1.00 10.03.2015 All Updated to Copernicus template, removed

GWW layer, added PROBA-V

I2.00

I2.00 30.06.2015 All Update after external review and according to

release I2.10 of the ATBD

I2.10



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TABLE OF CONTENTS

1 Background of the document ................................................................................................ 9

1.1 Executive Summary .................................................................................................................... 9

1.2 Scope and Objectives ................................................................................................................. 9

1.3 Content of the document ........................................................................................................... 9

1.4 Related documents .................................................................................................................. 10

1.4.1 Applicable documents ................................................................................................................................ 10

1.4.2 Input ............................................................................................................................................................ 10

1.4.3 External document ...................................................................................................................................... 10

2 Review of Users Requirements ............................................................................................ 11

3 Algorithm ............................................................................................................................ 14

3.1 Overview ................................................................................................................................. 14

3.2 Retrieval Methodology ............................................................................................................ 15

3.2.1 Background ................................................................................................................................................. 15

3.2.2 Input data ................................................................................................................................................... 15

3.2.3 Processing steps .......................................................................................................................................... 19

3.2.4 Limitations .................................................................................................................................................. 26

4 Product Description ............................................................................................................. 28

4.1 File Format ............................................................................................................................... 28

4.2 Product Content ....................................................................................................................... 30

4.2.1 Image Files .................................................................................................................................................. 30

4.2.2 ‘WB’ quicklook image ................................................................................................................................. 32

4.3 Product Characteristics ............................................................................................................ 32

4.3.1 Projection and Grid Information ................................................................................................................. 32

4.3.2 Spatial information ..................................................................................................................................... 33

4.3.3 Temporal Information ................................................................................................................................. 33

4.3.4 Data Policies ................................................................................................................................................ 34

4.3.5 Access and Contacts ................................................................................................................................... 34

5 Validation ........................................................................................................................... 35

6 References ........................................................................................................................... 36



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List of Figures

Figure 1: Algorithm overview .......................................................................................................... 15

Figure 2: Spectral Response Functions of VGT1, VGT2, and the three PROBA-V cameras,

superimposed with a spectrum of green grass. ....................................................................... 18

Figure 3 : Coastal erode (arid mask), blue = sea, white = detection area, grey = erode area ........ 19

Figure 4 : SWB algorithm representation. T means that a threshold is applied. ............................. 20

Figure 5 : Data flow of the seasonality detection ............................................................................ 23

Figure 6: Zone for which the VGT4AFRICA was designed (in black) ............................................. 26

Figure 7: Region of Interest. Blue (inner) rectangle indicates water bodies algorithm boundary. Red

(outer) rectangle indicates product delivery boundary. ............................................................ 29

Figure 8: The cut-out of tiles ........................................................................................................... 33



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List of Tables

Table 1: GCOS requirements for water bodies as Essential Climate Variables (GCOS-154, 2011)

................................................................................................................................................. 12

Table 2: geoland2 User Requirements for Water Bodies ( 1 the relative accuracy; 2 absolute

accuracy) ................................................................................................................................. 13

Table 3: Spectral characteristics of VEGETATION-2 sensor .......................................................... 16

Table 4: Spectral characteristics of the PROBA-V sensor .............................................................. 17

Table 5: SWB thresholds description .............................................................................................. 21

Table 6 : Consecutive detection rule ............................................................................................... 22

Table 7 : List of flags for the seasonality detection. ........................................................................ 26

Table 8: Definition of the continents. ............................................................................................... 29

Table 9: WB layer legend ................................................................................................................ 30

Table 10: WBFLAG legend ............................................................................................................. 31

Table 11: MVM legend .................................................................................................................... 31

Table 12: PW legend ...................................................................................................................... 31

Table 13: Quicklook color coding scheme ...................................................................................... 32



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List of Acronyms

ATBD Algorithm Theoretic Basis Document

CCD Charged Coupled Device

CLDVW Contrast Local of the Difference NDVI-NDWI

CLMIR Contrast Local of MIR

CLV Contrast Local of NDVI

CLW Contrast Local of NDWI

Dekad 1/3th of a month, starting at day 1, 11 or 21

ECV Essential Climate Variables

EumetCast EUMETSAT’s Broadcast System for Environmental Data

FP Framework Program of the European Community for research, technological

development and demonstration activities

GCOS Global Climate Observing Systems

Geoland2 FP7 funded project on land monitoring

GIO GMES Initial Operations

GL Global Land

GMES Global Monitoring for Environment and Security

GTN-L Global Terrestrial Network Lakes

IFOV Instantaneous Field of View

JRC Joint Research Centre of the European Union at Ispra, Italy

MIR Middle Infrared

MODIS Moderate Resolution Imaging Spectroradiometer

MVM Manually Validated Map

NDVI Normalized Difference Vegetation Index

NDWI Normalized Difference Water Index

NIR Near Infrared

NRT Near-real time

PROBA-V satellite for earth observation

PUM Product User Manual

RGB Red, Green and Blue

S1 1 day Synthesis product from PROBA-V

SMAC Simplified Method for the Atmospheric Correction

SRTM Shuttle Radar Topography Mission

SPOTx Satellite for earth Observation

SWIR Short Wavelength Infrared

TOC Top Of Canopy

VGT VEGETATION sensor on SPOT platform

WB Water Bodies

XML eXtensible Markup Language

ZIP compression format



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1 BACKGROUND OF THE DOCUMENT

1.1 EXECUTIVE SUMMARY

The Global Land (GL) Component in the framework of GMES Initial Operations (GIO) is earmarked

as a component of the Land service to operate “a multi-purpose service component” that will

provide a series of bio-geophysical products on the status and evolution of land surface at global

scale. Production and delivery of the parameters are to take place in a timely manner and are

complemented by the constitution of long term time series.

The Global Land service currently provides a Water Bodies version 1 product that monitors

the dynamics of water bodies over Africa at 1km resolution. This product results from the

algorithm calibrated over semi-arid regions and developed at African continental scale: the

Small Water Bodies (SWB) algorithm. In addition, a Permanent Water mask is provided as a

static layer. The algorithm was run on images from SPOT-VEGETATION (SPOT-VGT) but

the sensor is terminated as Earth Observation mission by May 2014. The algorithm was

tuned to the PROBA-V 1km datasets to ensure the continuity of the service.

The Product User Manual (PUM) is a self-contained document which gathers all necessary

information to use the product on an efficient and reliable way.

This document describes the Water Bodies V1 products from SPOT-VEGETATION or PROBA-V

data. The product is calculated on a 10-daily basis, and made available to the user in near-real

time every 10 days. The product is provided for the entire Africa continent, although it has been

primary designed for the semi-arid region (Sahel).

1.2 SCOPE AND OBJECTIVES

The Product User Manual (PUM) is the primary document that users have to read before handling

the products.

It gives an overview of the product characteristics, in terms of algorithm, technical characteristics,

and main validation results.

1.3 CONTENT OF THE DOCUMENT

This document is structured as follows:

Chapter 2 recalls the users requirements

Chapter 3 presents a description of the algorithm.



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Chapter 4 describes the technical characteristics of the product.

Chapter 5 summarizes the validation procedure and the results.

1.4 RELATED DOCUMENTS

1.4.1 Applicable documents

AD1: Annex II – Tender Specifications to Contract Notice 2012/S 129-213277 of 7th July 2012

AD2: Appendix 1 – Product and Service Detailed Technical requirements to Annex II to Contract

Notice 2012/S 129-213277 of 7th July 2012

1.4.2 Input

Document ID Descriptor

GIOGL1_SSD Service Specifications of the Copernicus Global Land

Service

GIOGL1_ATBD_WB-V1 Algorithm Theoretical Basis Document of the Water Bodies

Version 1

GIOGL1_ATBD_PROBA2VGT Algorithm Theoretical Basis Document of PROBA-V pre-

processing

GIOGL1_SVP Service Validation Plan Copernicus Global Land Service

GIOGL1_QAR_WBV1 Quality Assessment Report of the SPOT/VGT and

PROBA-V WB V1 products

1.4.3 External document

Document ID Descriptor

SPOT-VGT http://www.spot-vegetation.com/index.html

http://www.spot-vegetation.com/index.html



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2 REVIEW OF USERS REQUIREMENTS

According to the applicable document [AD2], the users’ requirements define the features required

for the water bodies.

Definition: permanent and seasonal water bodies, natural and man-made, independently

of their size. Include but are not restricted to the lakes of the Global Terrestrial Network for

lakes (GTN-L).

Geometric properties:

o The baseline pixel size shall be 1km.

o The target baseline location accuracy shall be 1/3 of the at-nadir instantaneous field

of view.

o Pixel co-ordinates shall be given for centre of pixel

Geographical coverage:

o Geographic projection: regular lat-long

o Geodetical datum: WGS84

o Pixel size: 1/112° - accuracy: min 10 digits

o Coordinate position: centre of pixel

o Global window coordinates:

Upper Left:180°W-74°N

Bottom Right: 180°E 56°S

o African window coordinates:

Upper Left: 26°W – 38°N

Bottom Right: 60°E - 35°S

Time definitions:

o As a baseline, the biophysical parameters are computed by and representative of

dekad, I.e. for ten-day periods (“dekad”) defined as follows: days 1 to 10, days 11 to

20 and days 21 end of the month for each month of the year

o The output data shall be delivered in a timely manner, i.e. within 3 days after the

end of each dekad.

Ancillary information:

o The number of measurements per pixels used to generate any synthesis period

o the per-pixel date of the individual measurements or the start-end dates of the

period actually covered



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o quality indicators, with explicit per-pixel identification of the cause of anomalous

parameter result

Accuracy requirements:

o Baseline: wherever applicable the bio-geophysical parameters should meet the

internationally agreed accuracy standards laid down in the document “Systematic

Observation Requirements for Satellite-Based Products for Climate”. Additional

details to the satellite based component of the Implementation Plan for the Global

Observing System for Climate in Support of the UNFCCC are available in the

document WMO GCOS-#154, 2011 (see Table 1)

o Target: considering data usage by that part of the user community focused on

operational monitoring at (sub-) national scale, accuracy standards may apply not

on averages at global scale, but at a finer geographic resolution and in any event at

least at biome level.

Regarding this latter accuracy requirement, the water surface biophysical variable corresponds to

two different Essential Climate Variables (ECV), i.e. the “land cover” and the “lakes”.

Variable/ Parameter

Horizontal Resolution

Vertical Resolution

Temporal Resolution

Accuracy Stability

Maps of land-cover type

250 m N/A 1 year

15% (maximum error of omission and commission

in mapping individual

classes), location accuracy better than 1/3 IFOV

with target IFOV 250m


in mapping individual

classes), location accuracy better than 1/3 IFOV


Areas of GTN-L

Equivalent to 250 m

N/A monthly


in lake area maps), location accuracy better than 1/3 IFOV



in lake area maps), location accuracy better than 1/3 IFOV


Table 1: GCOS requirements for water bodies as Essential Climate Variables (GCOS-154, 2011)

Additional user requirements



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The users requirements expressed during geoland2/BioPar are shown in Table 2. Three accuracy

levels were defined: Optimal, Target and Threshold.

Table 2: geoland2 User Requirements for Water Bodies ( 1 the relative accuracy;

2 absolute accuracy)

Accuracy

Threshold Target Optimal

Water Bodies 1 20% 15% 10%

Seasonality 2 1 dekad

The above requirements are indicative. They have to be adapted to the Global Land products and

clarified by the user board of the Global Land service.



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3 ALGORITHM

3.1 OVERVIEW

The Water Bodies product identifies pixels covered by water. The areas of water bodies are

understood here with respect to the instrument resolution, i.e. surfaces more or less covered by

water with a size of about 1 km2 (Bartholomé and Combal, 2006).

At continental scale, it is difficult to expect a water body to have sufficiently specific properties to be

recognized easily, everywhere and in any condition from space. Indeed, from an ecological point of

view, the only thing that is sure is that a water body is characterized by permanent or seasonal

water logging. Due to the large geographical context, a large range of vegetation conditions may

be expected over these surfaces. Moreover the spectral behavior of water may vary according to

sediment load and the development of micro-organisms. Therefore there is no particular spectral

signature to confidently rely on for water body identification.

The termination of SPOT-VGT on May 2014 and the use of datasets from a different sensor

(PROBA-V) imply the adaptation of the methods to detect water.

Erreur ! Source du renvoi introuvable. shows that the algorithm is performing two algorithm

steps:

1. Detection of the water body, based on the 10-daily Top Of Canopy reflectance input and

the previous dekad reflectances. It provides the Small Water Bodies (SWB) layer using the

ARID mask input to filter out coastal effects.

2. Calculation of the seasonality parameters from the water body, based on the temporal

profile of 45 dekads. It calculates the Water Bodies Start Date (WBSTARTDATE), Water

Bodies End Date (WBENDDATE) and Water Bodies Flag (WBFLAG) layers.



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Figure 1: Algorithm overview

Both processing steps are described in more details in the next paragraphs.

The product is associated with a separate product package, representing two static layers:

1. Manual Validation Mask (MVM) (see )

2. Permanent Water Mask (PW) derived from the 90m SRTM SWBD Water Body Data PSG

v2.0 [Caroll et al., 2009] and the MOD44W product.

3.2 RETRIEVAL METHODOLOGY

3.2.1 Background

The original algorithm (Gond et al., 2004) was developed for the VGT4AFRICA WB (SWB)

products by the JRC as part of the FP4 “improvements for the VEGETATION mission” shared cost

action project.

3.2.2 Input data

Two different sets of input data are used in the Water Bodies Detection Algorithm, i.e. 10-dailyTop

Of Canopy reflectances (S10-TOC) and a coastal erode mask (ARID mask).



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3.2.2.1 S10 Top Of Canopy reflectances

3.2.2.1.1 SPOT-VGT

The VGT-S10 (Top of Canopy 10-daily synthesis reflectance) products are generated and provided

by the SPOT VEGETATION program through VITO (http://free.vgt.vito.be/).

Since April 1998 and till May 2014, the VEGETATION sensor has been operational on board the

SPOT 4 and 5 earth observation satellite system. It provides a global observation of the world on a

daily basis. The instrumental concept relies on a linear array of 1728 CCD detectors with a large

field of view (101°) in four optical spectral bands described in Table 3. Although very similar, some

differences between VEGETATION 1 and VEGETATION 2 instruments have to be noticed [SPOT-

VGT], particularly regarding the spectral sensitivity (Table 3).

Acronym Centre (nm) Width (nm) Potential Applications

B0 450 40 Continental ecosystems - Atmosphere

B2 645 70 Continental ecosystems

B3 835 110 Continental ecosystems

SWIR 1665 170 Continental ecosystems

Table 3: Spectral characteristics of VEGETATION-2 sensor

The spatial resolution is 1.15 km at nadir and presents minimum variations for off-nadir

observations. The 2200 km swath width implies a maximum off nadir observation angle of 50.5°.

About 90% of the equatorial areas are imaged each day, the remaining 10% being imaged the next

day. . For latitudes higher than 35° (North and South), all regions are acquired at least once a day.

The multi-temporal registration is about 300 meters.

The 10-day synthesis (S10) provides surface reflectance values based on a selection of the “best”

measurements on the entire period. The actual selection is based on the maximum NDVI value

from the clear observations within the compositing period (Holben, 1986).

3.2.2.1.2 PROBA-V

PROBA-V Top Of Canopy (S10-TOC) 10-daily synthesis products are generated and provided by

the PROBA-V project through VITO (http://proba-v.vgt.vito.be/ ).

http://free.vgt.vito.be/

http://proba-v.vgt.vito.be/



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The PROBA-V satellite was launched at 6 May 2013 and was designed to bridge the gap in space-

borne vegetation measurements between SPOT-VGT (March 1998 – May 2014) and the upcoming

Sentinel-3 satellites, scheduled for launch in 2015/2016. The mission objective is to ensure

continuity with the heritage of the SPOT-VGT mission.

PROBA-V flies at an altitude of 820 km in a sun-synchronous orbit with a local overpass time at

launch of 10:45 h. Because the satellite has no onboard propellant, the overpass times are

expected to gradually differ from the at-launch value. The instrument has a Field Of View of 102o,

resulting in a swath width of 2295 km. This swath width ensures a daily near-global coverage

(90%) and full global coverage is achieved every 2 days.

The optical design of PROBA-V consists of three cameras. Each camera has two focal planes, one

for the short wave infrared (SWIR) and one for the visible and near-infrared (VNIR) bands. The

VNIR detector consists of four lines of 5200 pixels. Three spectral bands were implemented,

comparable with SPOT-VGT: BLUE, RED, and NIR (see Table 4). The SWIR detector contains the

SWIR spectral band and is a linear array composed of three staggered detectors of 1024 pixels.

The spectral response functions of the four spectral bands from the three PROBA-V cameras are

compared to those of SPOT/VEGETATION1 and 2 in Figure 2.

The PROBA-V S10-TOC synthesis product provides the ground surface reflectance in the four

spectral bands corresponding to the selected measurement, the atmospheric correction being

performed using SMAC 4.0 [Rahman et Dedieu, 1994]. The selection of the pixel in the 10-days

composite is done using the maximum NDVI value from the clear observations within the

composite period (Holben, 1986).

To provide a consistent time-series with the SPOT-VGT dataset, the PROBA-V reflectance bands

are spectrally corrected before applying the algorithm [GIOGL1_ATBD_PROBA2VGT].

Acronym Centre (nm) Width (nm) Potential Applications

B0 463 46 Continental ecosystem - Atmosphere

B2 655 79 Continental ecosystem

B3 845 144 Continental ecosystem

SWIR 1600 73 Continental ecosystem

Table 4: Spectral characteristics of the PROBA-V sensor



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Figure 2: Spectral Response Functions of VGT1, VGT2, and the three PROBA-V cameras,

superimposed with a spectrum of green grass.

3.2.2.2 ARID Mask

To avoid confusion of detected water bodies at the coastal area, an erode zone of 25 km (being

approximately half of a detection cell, see paragraph Erreur ! Source du renvoi introuvable.) has

been installed, as shown in Figure 3. This erode zone is named ‘arid’ mask. Through this arid

mask, the detection of contrast between sea and land pixels, resulting in false detections, is

removed.

The water body detection is performed across the full continent, however any detected bodies at

the eroded area (25 pixels around the coast and permanent big lakes) are ignored and represented

by a sea value. The big lakes have a size larger than 45km2 and similarly as the coastal border,

false detections are removed through applying the arid mask.

As shown in Figure 3, the East African Great Lakes are coded as sea in SWB due to the arid

mask.



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Figure 3 : Coastal erode (arid mask), blue = sea, white = detection area, grey = erode area

3.2.3 Processing steps

As shown previously in Erreur ! Source du renvoi introuvable. there are two main processing

steps to generate the WB v1 product:

1. Water body detection

2. Seasonality calculation

The following sections briefly outline these steps. More information can be found in

[GIOGL1_ATBD_WB-V1].

3.2.3.1 Water Body detection

Water body identification is carried out each time a new 10-days synthesis (S10) product is made

available. Surface water is detected using the spectral “anomalies” of the water pixels according to

their surrounding environment, as shown in Figure 4. This is done by computing the difference

between “regional” average of the channels and each pixel value for specific spectral bands or

indexes such as NDVI and NDWI. Considering that we are looking for targets that are more or less



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the size of instrument ground resolution, i.e. 1 km2 and that water bodies are rather rare features in

the landscape, it was assumed that a “region” of about 2000 km2 would be sufficient to minimize

the weight of water body spectral properties in the average. Therefore, for each pixel, the average

value of the channels and derived indices are computed on a 45 × 45 km cells, then the difference

between the pixel’s original value and the average are computed. Water body identification is

finally obtained by simply applying thresholds on these final values.

Figure 4 : SWB algorithm representation. T means that a threshold is applied.

Normalized Difference Vegetation Index (NDVI) is a ratio used to characterize the vegetation

amount. It is defined as “NDVI = (NIR-Red) / (NIR+Red)”. The average (mean) of NDVI computed

on 45 pixels x 45 pixels cells is known as NDVIMEAN.

Normalized Difference Water Index (NDWI) is a ratio used to characterize the water and moisture

in the pixel. It is defined by Gao (1996) as “NDWI= (NIR-MIR) / (NIR+MIR)”. The average (mean)

of NDWI computed on 45 pixels x 45 pixels cells is known as NDWIMEAN.



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The Shortwave InfraRed (SWIR) variable corresponds to the reflectance in the middle infrared, i.e.

in our case to the band of VGT or PROBA-V (MIR). The average (mean) of SWIR computed on 45

pixels x 45 pixels cells is known as SWIRMEAN.

Four difference values are calculated per pixel, before classifying the pixel through applying

threshold values, as shown in Table 5:

1. Contrast Local Vegetation (CLV) is the difference between the NDVIMEAN and the NDVI

pixel value.

2. Contrast Local Water (CLW) is the difference between the NDWIMEAN and the NDWI pixel

value

3. Contrast Local Difference Vegetation Water (CLDVW) is the difference between NDVIMEAN

and NDWIMEAN

4. Contrast Local Mid InfraRed (CMIR) is the difference between SWIRMEAN and the SWIR

pixel value.

Table 5: SWB thresholds description

Indices Description Thresholds

VGT

Thresholds

PROBA-V

Classification

CLDVW The difference between the mean of NDVI-

NDWI on the 45x45 pixels window and its

value for each pixel [Gond et al., 2004]

CLDVW >

0.08

CLDVW > 0.09 Water

CLMIR Mean of MIR channel on 45x45 km centred

sliding window minus MIR pixel value [Gond

et al.,2004]

CLMIR > 0.05 CLMIR > 0.06

NDWI (NIR-MIR)/(NIR+MIR) [Gao, 1996] NDWI > - 0.05 NDWI > -0.041

CLV Mean of NDVI on 45x45 km centred sliding

window minus NDVI pixel value [Gond et

al.,2004]

CLV < -0.1 CLV < -0.11 Humid

Vegetation

CLW Mean of NDWI on 45x45 km centred sliding

window minus NDWI pixel value [Gond et al.,

2004].

CLW < -0.1 CLW < -0.11

To avoid commission errors, the algorithm evaluates the consecutive detection result of the current

and previous dekad (see



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Table 6).

Table 6 : Consecutive detection rule

Dekad ‘N-1’ Dekad ‘N’ Output

Water Water Water

Water or humid vegetation Water Water

Water or humid vegetatoin Humid Vegetation Humid Vegetation

Humid Vegetation Humid Vegetation Humid Vegetation

Land Water or Humid Vegetation Land

3.2.3.2 Seasonality detection

The detection of seasonality is organized in three main steps, as depicted on Figure 5,

1. The first step consists in organizing the detections of small water bodies in time series. For

each pixel, a history of 45 dekads (1 Year + 3 months) is available.

2. In the second step, the date detection algorithm searches for the most recent start and end

of season. Depending on the type of season, a flag value is also defined.

3. In a third step, the dates are assigned to each pixel and saved to files. The output of the

seasonality are three dynamic images representing start date, end date and status flag.

The detection of the seasonality (Step 2 in Figure 5) is made by analyzing time series for each

pixel. A season must have at least two detections out of three in a row. The seasonality algorithm

detects time frames during which these conditions are met. The start of season corresponds to the

first date of the most recent identified period of detections. The end of season is not necessarily

visible at the time of the observation.



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Figure 5 : Data flow of the seasonality detection

Across the season period, the flag status is updated whenever applicable as shown in Table 7:

If the end of the current season was detected, flag is set to 1. In this situation the season is

said to be complete.

After a complete season, a positive detection may occur at the date of the observation. In

this case, another positive detection, or a negative immediately followed by a positive



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detection will be need to classify the detection as a start of season (or to discard it). Hence,

the detection receives the flag 2, but the start and end of season maps still store the dates

of the preceding start and end of season.

In the case where a detection, candidate for validation as a start of season, and that no

valid detection occurred before (during the 45 dekads of observations), the detection

receives flag 3 (two examples are given in Table 7). In this situation the season end date is

identical to start season and should be ignored.

If the start of the current season was detected, however the end has not been detected,

flag is set to 4. In this situation the season end date is set to current decade (running

towards end of season) and should be ignored.

If only single date events were seen in the time series, the pixel receives flag 5, and the

date of this single date event is stored in the products.

In the case where the end of season is still visible but the start too ancient (older than 45

dekads), and no new valid season occurred, the season receives flag 6. The start season

date should be ignored.

Flag 0 corresponds to the ocean and flag 255 corresponds to dry land. They are used to

display the images with different colors for these two no-data types.



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0 Ocean

255 Land

1 Season start and end available, season length ≥2dekads

2 New possible start in the most recent dekad, products start and end give date of the former validated season

3 Start of season to be confirmed by next observation, no valid season was observed before

4 Season still going on, ends in future

5 Only single date events were visible

Flag 2

1 2 3 4 5 6 7 8 9 10 11

Dekad

De

tec

tio

n

Flag 3

1 2 3 4 5 6 7 8 9 10 11

Dekad

De

tec

tio

n

Flag 3

1 2 3 4 5 6 7 8 9 10 11

Dekad

De

tec

tio

n

Flag 4

1 2 3 4 5 6 7 8 9 10 11

Dekad

De

tec

tio

n

Flag 5

1 2 3 4 5 6 7 8 9 10 11

Dekad

Det

ectio

n

Flag 1

1 2 3 4 5 6 7 8 9 10 11 Dekad

Detection



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6 Start of season falls out of the time series scope

Table 7 : List of flags for the seasonality detection.

3.2.4 Limitations

The algorithm was validated over a large area in Sahel (Gond et al., 2004). Key conclusions of the

authors are that commission errors are in the order of 10%, whereas omission errors can be

higher, due to the low resolution of the instrument used.

Other limitation of the algorithm is the extent of the zone for which it was designed. To overcome

this limitation, a mask corresponding to the validated zone (named MVM) is available for the user

to mask the extent were the algorithm is not relevant (see Figure 6).

Figure 6: Zone for which the VGT4AFRICA was designed (in black)

The size of the sliding window in comparison with the size of the water body to detect doesn’t allow

the detection of large water bodies. Further, they indicate negative NDWI due to coppery soils.

However, besides the problems mentioneed by the authors, the major issue is that this method

was developed for local application over specific bioclimatic area and is not suited for continental

detection. Nevertheless, this method was taken into account for the development of a more generic

algorithm.

Flag 6

1 2 3 4 5 6 7 8 9 10 11

Dekad

Det

ectio

n



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Another limitation is on the edges of the coast (25km) where the pixels are masked out using a

mask to avoid detection of contrast between sea and land pixels at the coastal zone. Also any

detection on the shore of the big lakes are similarly masked out.

Despite the adjustment of the threshold values, the agreement between SPOT-VGT and PROBA-V

SWB products reaches a probability of only 60%.



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4 PRODUCT DESCRIPTION

The Water Bodies V1 (WB1) product follows the following naming standard:

g2_BIOPAR_WB_<YYYYMMDDHHmm>_<AREA>_<SENSOR>_V<Major.Minor.Run>

where

<YYYYMMDDHHmm> gives the temporal location of the file. YYYY, MM, DD, HH, and mm

denote the year, the month, the day, the hour, and the minutes, respectively.

<AREA> gives the spatial coverage of the file. In our case, <AREA> is HxxVyy, the name of

the 10°x10° tile or the continent short name

<SENSOR> gives the name of the sensor used to retrieve the product, so PROBAV or VGT

<Major.Minor.Run> gives the version number of the product. “Major” increases when the

algorithm is updated. “Minor” increases when bugs are fixed or when processing lines are

updated (metadata, colour quicklook, etc...). “Run” increases each time, for a given

Mayor.Minor version, a new processing run has been performed. This can happen when for

some reason the PROBA-V input data is updated. The current Major version is 1.

4.1 FILE FORMAT

The Water Bodies V1 products are delivered every 10 days as a ZIP archive containing following

files:

Four single band GEOTIFF format containing the following layers:

o SWB: Water Bodies Detection

o WBSTARTDATE: Water Bodies Season Start

o WBENDDATE: Water Bodies Season End

o WBFLAG: Water Bodies Season Flag

o A quicklook image (SWB_QL) in a coloured GEOTIFF format. The quicklook is sub-

sampled to 25% in both X and Y direction and based on the SWB layer.

An xml file containing the metadata conforms to INSPIRE2.1.

An xsl file for displaying a friendly view of the above metadata file.

A text file containing the copyright of the product.

Each ZIP archive is a self-contained package and hence is not dependent on any other files. A

dedicated typology has been chosen to locate easily each tile when reading its name. The globe is



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divided in 36 tiles in longitude and 18 tiles in latitude according to Figure 7. Only the green tiles

over Africa (red rectangle), covering land, are generated and hence available. The products are

also provided by continents (grouping of a set of tiles) as defined in Figure 7 (red rectangle), and

named as in Table 8.

Figure 7: Region of Interest. Blue (inner) rectangle indicates water bodies algorithm boundary. Red

(outer) rectangle indicates product delivery boundary.

Note the actual calculation of water bodies is done according the VGT4Africa region definition,

hence longitude -26° to 60° and latitude 38° to -35°. Pixels falling outside the VGT4Africa region

(longitude -30°E to -26°E, latitude 40°N to 38°N and latitude -35°N to -40°N) are indicated with a

251-value (missing).

Short name Continent

AFRI Africa: 30°W – 60°E, 40°N – 40°S

Table 8: Definition of the continents.

The product is accompanied with two separate ZIPs that are delivered once, containing the following files:

One single band GEOTIFF format containing one of the following layers:



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o MVM: Manual Validation Mask

o PW: Permanent Water mask

An xml file containing the metadata conform to INSPIRE2.1.

An xsl file for displaying a friendly view of the above metadata file.

A quicklook image in a coloured GEOTIFF format. The quicklook is sub-sampled to 25% in

both X and Y direction and based on the SWB layer.

A text file containing the copyright of the product.

4.2 PRODUCT CONTENT

4.2.1 Image Files

4.2.1.1 Water Bodies layer

The values of the digital numbers in the first layer (SWB) are listed in Table 9.

Value Label

0 Sea

70 Free Water

150 Humid vegetation

220 Mixed

251 No data

255 Dry land

Table 9: WB layer legend

4.2.1.2 Season layers

The three season layers are coded as follows:

WBSTARTDATE contains the start date of the season, represented as the number of

dekads since 1st January 1980.

WBENDDATE contains the end date of the season, represented as the number of dekads

since 1st January 1980.

WBFLAG represents a set of values describing the quality of the seasonality detection (

Table 10).



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Table 10: WBFLAG legend

4.2.1.3 MVM layer

The values of the digital numbers in the Manual Validation Mask layer (MVM) are listed in Table

11.

Table 11: MVM legend

Value Label

0 Ocean

64 Pixel within erode zone

128 Pixel within validated zone

255 Dry land

4.2.1.4 PW layer

The values of the digital numbers in the Permanent Water layer (PW) are listed in

Table 12.

Table 12: PW legend

Value Label

0 Ocean

70 Permanent Water

255 Dry land

Value Label

0 Ocean

1 Season start and end dates available

2 New possible start in the most recent dekad

3 Start of season to be confirmed by next observation

4 Season still ongoing, ends in future

5 One single date event was visible

6 Start of season falls out of the time series scope

255 Dry land



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4.2.2 ‘WB’ quicklook image

The quicklook GEOTIFF file is derived from the SWB image. The spatial resolution is sub-sampled,

using nearest neighbour resampling, to 25% in both directions, hence a quicklook is 1/16th of the

size of the data layer.

The quicklook is coded in 1 byte (no scaling or offset used) and provided with an embedded color

table as presented in Table 13.

Value Description Color RGB value

0 ocean 12, 88, 235

70 free water 173, 216, 230

150 humid vegetation 0, 120, 0

220 “free water” and “humid vegetation” 144, 238, 144

251 missing 255,255,255

255 dry land 165, 42, 0

Table 13: Quicklook color coding scheme

4.3 PRODUCT CHARACTERISTICS

4.3.1 Projection and Grid Information

The product is displayed in a regular latitude/longitude grid (plate carrée) with the ellipsoid WGS

1984 (Terrestrial radius=6378km). The resolution of the grid is 1/112° (approx. 1km at the equator).

The reference is the centre of the pixel. It means that the longitude of the upper left corner of the

pixel is (pixel_longitude – angular_resolution/2.)

Each tile covers an area of 10°x10°, while the continental tiles cover a multiple of these 10°x10°

tiles as defined in Figure 7.



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Figure 8: The cut-out of tiles

Starting from the upper left centre pixel and taking 10° or 1120 pixels into account, the final tile is

an array of 1121 lines and 1121 columns taking the bottom/right upper/left half pixel into account

(Figure 8). As such there is an overlap between the tiles. For example, the last column of tile

H18V4 is the first column of the tile H19V4, and the last line of H18V4 is the first line of the tile

H18V5.

4.3.2 Spatial information

As shown in Figure 7, the product is provided from longitude -30°W to +60°E and latitude +40°N to

-40°S.

4.3.3 Temporal Information

The WB V1 demonstration product is a 10 day composite. Each composite starts on day 01, 11 or

21, hence the last composite of each month is variable dependent on the number of days available

in the month.

10° = 1120 pixels

1121 pixels = 10.0089°

10° = 1120 pixels

1121 pixels = 10.0089°



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The products are provided from 1998-04-01 till Near Real Time. However the seasonality

information is only available from 1999-07-01 because it requires 45 months of pre-processed

inputs.

4.3.4 Data Policies

Any use of the Copernicus WB V1 products implies the obligation to include in any publication or

communication using these products the following citation for the SPOT-VGT derived products

"The product was generated by the land service of Copernicus, the Earth Observation programme

of the European Commission. The research leading to the current version of the product has

received funding from various European Commission Research and Technical Development

programmes. The product is based on VEGETATION data ((c) CNES).”

, and for the PROBA-V derived products:

"The product was generated by the land service of Copernicus, the Earth Observation programme

of the European Commission. The research leading to the current version of the product has

received funding from various European Commission Research and Technical Development

programmes. The product is based on PROBA-V 1km data ((c) ESA and distributed by VITO).”

The user accepts to inform the production and distribution centre of their publications through the

following address: [email protected].

4.3.5 Access and Contacts

The product is available through the Copernicus Global Land service website at the address:

http://land.copernicus.eu/global/products/WB

Accountable contact: European Commission Directorate – General Joint Research Centre

Email address: [email protected]

Scientific, Production and Distribution contact:

Email address: [email protected].

mailto:[email protected]

http://land.copernicus.eu/global/products/WB





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5 VALIDATION

This original (SPOT-VEGETATION) Water Bodies product version 1 has been validated through

mapping the temporary surface water bodies in sub-Saharan western Africa [Haas et al., 2009].

The Quality Assessment of the SPOT/VGT and PROBA-V WB V1 is being performed following the

procedure described in the Service Validation Plan [Erreur ! Source du renvoi introuvable.SVP].

Omission and commission errors are both assessed [GIOGL1_QAR_WBV1].



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6 REFERENCES

Bartholomé, E., & Combal, B. (2006). Small water bodies - VGT4Africa User Manual (first Ed.). VGT4Africa User Manual (first ed.), Office for Official …. Retrieved from http://www.vgt4africa.org/PublicDocuments/VGT4AFRICA_user_manual.pdf

Carroll M.L., J. R. Townshend, C. M. DiMiceli, P. Noojipady, and R. A. Sohlberg, 2009, A new global raster water mask at 250 m resolution, International Journal of Digital Earth, vol. 2, 291-308.

Gao, B. G., 1996, NDWI — a normalized difference water index for remote sensing of vegetation

liquid water from space. Remote Sensing of Environment, 58, 257–266.

Gond, V., Bartholomé, E., Ouattara, F., Nonguierma, A., & Bado, L. (2004). Surveillance et cartographie des plans d’eau et des zones humides et inondables en régions arides avec l'instrument VEGETATION embarqué sur SPOT-4. International Journal of Remote Sensing. doi:10.1080/0143116031000139908

Haas, E., Bartholomé, E., Combal, B. , 2009, Time series analysis of optical remote sensing data

for the mapping of temporary surface water bodies in sub-Saharan western Africa. Journal of

Hydrology, 370, 52-63.

Holben, B.N., 1986. Characteristics of maximum-value composite images from temporal AVHRR

data. Int. J. Remote Sens. 7, 1417–1434.

Rahman, H., Dedieu, G., 1994. SMAC: a simplified method for the atmospheric correction of

satellite measurements in the solar spectrum. International Journal of Remote Sensing 15,

123-143.