copernicus global land operations cryosphere and …...mask (white=volcanic soil) obtained after...

Copernicus Global Land Operations – Lot 2 Date Issued: 18.10.2018 Issue: I1.10

Copernicus Global Land Operations

“Cryosphere and Water” ”CGLOPS-2”

Framework Service Contract N° 199496 (JRC)

ALGORITHM THEORETHICAL BASIS DOCUMENT

WATER BODIES

PROBA-V 300M

VERSION 1

Issue I1.10

Organization name of lead contractor for this deliverable: VITO

Book Captain: I. Reusen (VITO)

Contributing Authors: L. Bertels (VITO)

D. Wolfs (VITO)


Document-No. CGLOPS2_ATBD_WB300m_V1

© C-GLOPS2 consortium

Issue: I1.10 Date: 18.10.2018 Page: 2 of 53





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)





Document Release Sheet





Change Record

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

06.09.2017 All First issue I1.00

I1.00 01.08.2018 All Revision after review meeting I1.10





TABLE OF CONTENTS

1 Background of the document ............................................................................................. 12

1.1 Executive Summary ............................................................................................................... 12

1.2 Scope and Objectives............................................................................................................. 12

1.3 Content of the document....................................................................................................... 12

1.4 Related documents ............................................................................................................... 12

1.4.1 Applicable documents ................................................................................................................................ 12

1.4.2 Input ............................................................................................................................................................ 12

1.4.3 Output ......................................................................................................................................................... 13

1.4.4 External Documents .................................................................................................................................... 13

2 Review of Users Requirements ........................................................................................... 14

3 Methodology Description .................................................................................................. 16

3.1 Overview .............................................................................................................................. 16

3.2 The retrieval Algorithm ......................................................................................................... 17

3.2.1 Outline ........................................................................................................................................................ 17

3.2.2 Basic underlying assumptions ..................................................................................................................... 19

3.2.3 Related and previous applications .............................................................................................................. 19

3.2.4 Alternative methodologies currently in use ............................................................................................... 21

3.2.5 Input data.................................................................................................................................................... 21

3.2.6 Output product ........................................................................................................................................... 25

3.2.7 Methodology............................................................................................................................................... 26

3.2.8 Limitations .................................................................................................................................................. 51

3.3 Quality Assessment ............................................................................................................... 52

3.4 Risk of failure and Mitigation measures ................................................................................. 52

4 References ........................................................................................................................ 53





List of Figures

Figure 1: General overview of the Water Bodies Detection Algorithm. .......................................... 16

Figure 2: Outline of the Water Bodies Detection Algorithm. ........................................................... 18

Figure 3: Location in Northern South-Sudan (10°0’N, 31°80’E). a) False water bodies were

detected because of dark soils. b) By applying the extra check on the MWEM most of the

commission errors could be prevented. .................................................................................. 20

Figure 4: Location in Southern Spain (38°40N, 7°4’W). a) Due to the strict Water Bodies Potential

Mask parts of the lake were not detected as water. b) The MWEM overrules the WBPM and as

such reduces the omission errors. ......................................................................................... 21

Figure 5: a) This Google Earth image shows an area over the Alps (Upper left corner: 48°08’25” N,

5°40’0” E). b) The permanent glacier mask for the same area in (a). ..................................... 23

Figure 6: a) Google Earth image showing part of northern Ethiopia and southern Eritrea with the

dark volcanic soils manually delineated (red polygons). b) Dekad MC10_20140521 for the

same area with the derived dark volcanic soils shape file overlaid. c) The final volcanic soil

mask (white=volcanic soil) obtained after rasterizing the shape file. ....................................... 24

Figure 7: a) The high resolution Maximum Water Extent product of JRC’s Global Surface Water

dataset shows where in the last 32 years water was ever detected (blue colored). b) The

PROBA-V 300 m resolution Maximum Water Extent Mask is derived from the Maximum Water

Extent product of JRC’s Global Surface Water dataset. Both images have the PROBA-V 300

m raster overlaid (red). The shown water reservoir is located in India (25°25’N, 77°55’E). ..... 25

Figure 8: MC10 algorithm flow. ..................................................................................................... 28

Figure 9: Decision tree for MC10 observation type classification. .................................................. 29

Figure 10: a) Part of the 90 m spatial resolution GLSDEM over Rift Valley in Ethiopia. b) The

detected lowest points for this area are colored yellow. The larger sized colored areas are

areas of equally low elevation which correspond to some smaller lakes in the region. ........... 31

Figure 11: Expanding the initially detected lowest point by systematically rising an imaginary water

level in steps of 1 m. The corresponding 90 m spatial resolution pixels are indicated by the

dots at the bottom. ................................................................................................................. 32

Figure 12: a) Part of the GLSDEM over Rift Valley. b) The detected potential WBs for this area.

The detailed window shows a potential WB in the center (crossing of red lines). The horizontal

and vertical profiles are according to the red lines over the images. On the horizontal profile,

the wetland (1) near lake Hora (2) and the potential water body (indicated by the long red

arrow) are seen. On the vertical profile part of the wetland (1’) near lake Hora (2), the potential

water body (indicated by the long red arrow) and lake Bishoftu (3) are seen. ......................... 33

Figure 13: a) Part of image MC10_20131021 taken over the Rift Valley in Ethiopia (SWIR, NIR and

Red bands assigned to the RGB channels resp.). b) The WBPM for the same area in (a)





(white=potential water body, black=no water body possible). The plot shows the vertical

GLSDEM profile according to the red line. Marked with the blue boxes are the seven potential

WBs. The arrows indicate the location of lake Abijato (1) and lake Langano (2). ................... 35

Figure 14: The HSV color space. .................................................................................................. 36

Figure 15: The first part of the WBDA comprises invalid data filtering. .......................................... 37

Figure 16: The removal of cloud shadow by dilatation of the cloud indicated pixels. a) Dekad

MC10_20140511 over Argentina (center at 31°33’50”S, 65°10’37”W). b) The Water Bodies

detection result without delate filtering. Due to cloud shadows lots of false WB detections

appear. c) After de clouds are dilated by its circular neighborhood of twelve pixels most of the

false detections are masked by the dilated cloud. The red oval shows a false detection which

could not be reached by the dilated cloud. d) Dekad MC10_20140511 over Argentina (center

at 31°45’58”S, 65°4’44”W). e) An existing small lake is shown in the lower left part of the

image, besides some false detections can be seen near to the clouds. No cloud dilatation was

done yet. f) After cloud dilatation the false detection don’t appear, a small part of the lake is

now obstructed by the dilated cloud as indicated by the red oval. .......................................... 38

Figure 17: The second part of the WBDA comprises Incompatibility Masking. .............................. 39

Figure 18: The last part of the WBDA comprises actual Water Body Detection based on threshold

levels on the HUE and VALUE bands. The thresholds consist of two parabolic functions. ..... 40

Figure 19: 2D scatter plot of the Vietnam test area showing the final threshold levels. Land pixels

are indicated by the dark grey dots, reference WBs (obtained from Landsat 8) are indicated by

the colored plus symbols for different ranges of water surface ratios. A further refinement of

the thresholds is not possible because WB pixels and land pixels are intermixed. ................. 43

Figure 20: Location of the Landsat scenes used to define the detection method. .......................... 44

Figure 21: Workflow for the extraction of reference water bodies using Landsat-8 scenes. An as

much as possible cloud free scene is searched for the required geographical location and time

period. After pre-processing and HSV color transform of the Landsat-8 scene, WB detection

was done using a Decision Tree Classifier (DTC). The obtained Landsat-8 WBs are

subsequently used to calculate the Water Surface Ratio using the spatial information from the

PROBA-V image. ................................................................................................................... 45

Figure 22: Decision Tree Classifier for WB detection on Landsat-8 images. A check on different

bands and on cloud cover removes incompatible pixels from the scene. A check on HUE and

VALUE is used to detect water bodies. .................................................................................. 46

Figure 23: 2D scatter plot of the WB frequency (WBf) and the number of continuous temporal WB

observations (mctWBs) for the Rift Valley test area calculated for the 31 available dekads. The

Water Body Occurrence levels are indicated by the different colored areas. .......................... 51





List of Tables

Table 1: GCOS requirements for areas of water bodies as Lakes and Land cover Essential Climate

Variables (GCOS-200, 2016) ................................................................................................. 15

Table 2: Thresholds for water body detection are applied on the HUE and VALUE, which are

obtained after HSV color transformation of the SWIR, NIR and Red bands, on the NDVI and

on the VALUE as an additional check besides the NDVI threshold check (NV). ..................... 19

Table 3: Spectral characteristics of the PROBA-V bands .............................................................. 22

Table 4: Status Map bit mapping of the PROBA-V data ................................................................ 22

Table 5: Output product legend. .................................................................................................... 26

Table 6: Output QUAL product legend. The colored entries are the occurrence values. ............... 26

Table 7: Status Map (SM) bit mapping. ......................................................................................... 30

Table 8: Landsat-8 scenes used to define the PROBA-V water bodies detection thresholds. ....... 44

Table 9: The results for eight different settings of the threshold levels. The overall accuracy (OA),

omission errors (OE) and the commission errors (CE) are listed in percentage. The OEs are

given for six ranges of minimum WSR . The figures are calculated over the 22 test regions for

the period Oct2013 – May2014. The green indicated entries show the best results and are the

final threshold levels. ............................................................................................................. 48

Table 10: Omission and commission errors are caused by several reasons. ................................ 52





List of Acronyms

ACCAm Automatic Cloud Cover Assessment - modified

ATBD Algorithm Theoretical Basis Document

DEM Digital Elevation Model

DTC Decision Tree Classifier

ECV Essential Climate Variables

ESA European Space Agency

GCOS Global Climate Observing System

GIO GMES Initial Operations

GL Global Land

GLIMS Global Land Ice Measurements from Space

GLSDEM Global Land Survey Digital Elevation Model

GMES Global Monitoring for Environment and Security

GSW Global Surface Water

GWW Global Water Watch

HSV Hue, Saturation and Value colour system

IFOV Instantaneous Field Of View

LC Land Cover

MC Mean Compositing

MC10 10-day mean compositing synthesis

NDVI Normalized Difference Vegetation Index

MWE-GSW Maximum Water Extent product of JRC’s Global Surface Water

MWEM Maximum Water Extent Mask

NIR Near Infrared

NMOD Number of valid Observations used in 10-daily compositing period

NOBS Number of satellite overpasses within the 10-daily compositing period

NSIDC National Snow and Ice Data Centre

PGM Permanent Glaciers Mask

PROBA-V Vegetation instrument on board of PROBA satellite

QUAL Quality Layer

RGB Red, Green and Blue colour system

S1-TOC The PROBA-V daily Top-Of-Canopy synthesis products

SM Status Map

SMAC Simplified Model for Atmospheric Correction

SPOT Satellite Pour l’Observation de la Terre

SRTM Shuttle Radar Topography Mission

SVP Service Validation Plan

SWIR Short Wavelength Infrared

SZA Solar Zenith Angle

TOA Top of Atmosphere

TOC Top of Canopy





UNFCCC United Nations Framework Convention on Climate Change

USGS United States Geological Survey

VITO Vlaamse Instelling voor Technologisch Onderzoek (Flemish Institute for Technological Research), Belgium

VSM Volcanic Soils Mask

WB Water Body

WBDA Water Bodies Detection Algorithm

WBO Water Bodies Occurrence

WBPM Water Bodies Potential Mask

WBPV PROBA-V Water Bodies product

WSR Water Surface Ratio

2D Two dimensional





1 BACKGROUND OF THE DOCUMENT

1.1 EXECUTIVE SUMMARY

The Copernicus Land Service continuously monitors the status of land territories to generate geo-

information at both local and global scale. Its Global Land component provides several bio-

geophysical products describing the status of the land surface on a global scale and its evolution.

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

complemented by the constitution of long time series.

This Algorithm Theoretical Basis Document (ATBD) describes the method for detecting Water

Bodies (WB) using PROBA-V at 333m resolution on a global scale. The method for deriving the

thresholds and the evaluation of the performance of the algorithm are based on the approach

developed by Pekel et al., 2014 and on the methods developed for the PROBA-V 1 km product as

described in GIOGL1_ATBD_WB1km-PROBAV-V2.

1.2 SCOPE AND OBJECTIVES

The scope of this document is to describe the theoretical basis and justification that underpins the

implementation of the Copernicus Global Land Water Bodies product . It details the methodology

applied on the PROBA-V 333m data. Moreover, it provides an evaluation of the algorithm

performance and a description of the limitations.

1.3 CONTENT OF THE DOCUMENT

This ATBD is structured as follows:

• Chapter 1 (this chapter) introduces the product

• Chapter 2 reviews the user requirements

• Chapter 3 describes the methodology used to generate the Water Bodies product

• Chapter 4 lists the references used in this document

1.4 RELATED DOCUMENTS

1.4.1 Applicable documents

AD1: Annex I – Technical Specifications JRC/IPR/2015/H.5/0026/OC to Contract Notice 2015/S

151-277962 of 7th August 2015

AD2: Appendix 1 – Copernicus Global land Component Product and Service Detailed Technical

requirements to Technical Annex to Contract Notice 2015/S 151-277962 of 7th August 2015

1.4.2 Input

Document ID Descriptor





CGLOPS2_SSD Service Specifications of the Global Component of

the Copernicus Land Service.

1.4.3 Output


CGLOPS2_PUM_WB300m_V1 Product User Manual for version 1 of the 300 m

Water Bodies product from PROBA-V.

CGLOPS2_QAR_WB300m_V1 Validation Report describing the results of the

scientific quality assessment for version 1 of the 300

m Water Bodies product from PROBA-V.

1.4.4 External Documents


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

PROBA-V PUM

Products User Manual of PROBA-V data, available on

http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/probav-

products_user_manual_v2.2_0.pdf

Commenté [BL1]: RID 1408: document updated

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

http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/probav-products_user_manual_v2.2_0.pdf

http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/probav-products_user_manual_v2.2_0.pdf





2 REVIEW OF USERS REQUIREMENTS

According to the applicable document [AD2], the user’s requirements relevant for Water Bodies

PROBA-V 300 m are:

• 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.

• Geometric properties:

o Location accuracy shall be 1/3rd of the at-nadir instantaneous field of view.

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

• Geographical coverage:

o Geographic projection: regular latitude/longitude

o Geodetical datum: WGS84

o Coordinate position: center pixel

o Pixel size: 1/336° - accuracy: min 10 digits

o Global window coordinates:

▪ upper left: 180°W - 75°N

▪ bottom right: 180°E - 56°S

• Ancillary information:

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

period actually covered

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-#200, 2016) (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.

Commenté [BL2]: RID 1383





Regarding this latter accuracy requirement, the water surface biophysical variable

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

“lakes”.

Table 1: GCOS requirements for areas of water bodies as Lakes and Land cover Essential Climate

Variables (GCOS-200, 2016)

Variable Horizontal

resolution

Temporal

resolution

Accuracy Stability

Areas of

lakes

Equivalent

to 250m

Monthly 5% (maximum error of

omission and commission

in lake area maps);

location accuracy better

than 1/3 of instantaneous

field-of-view (IFOV) with

250m target IFOV

5% (maximum error of

omission and

commission in lake area

maps); location accuracy

better than 1/3 of

instantaneous IFOV with

250m target IFOV

Maps of

land-cover

type

250m 1 year 15% (maximum error of

omission and commission

in mapping individual

classes); location accuracy

better than 1/3 of

instantaneous field-of-view

(IFOV) with 250m target

IFOV

15% (maximum error of

omission and

commission in mapping

individual classes);

location accuracy better

than 1/3 of instantaneous

field-of-view (IFOV) with

250m target IFOV





3 METHODOLOGY DESCRIPTION

3.1 OVERVIEW

The Global Land Water Bodies 300 m Version 1 product is a 10-daily synthesis product derived

from Top of Canopy (TOC) PROBA-V 333 m data. The product consists of 2 layers, the basic

Water Bodies (WB) layer which tells which pixels contain water and which not, and the Quality

Layer (QUAL) which tells something about the water body’s occurrences and as such can be used

to retrieve additional information to the basic detection layer. As shown in Figure 1, the Water

Bodies Detection Algorithm (WBDA) consists of four major steps.

• The PROBA-V daily Top-Of-Canopy synthesis products (S1-TOC) are first composited in a

10-daily synthesis (MEAN COMPOSITE, MC10) according the method of Vancutsem et al.

(2007). During the compositing step the MC10 Status Map is calculated.

• The SWIR, NIR and RED bands are then transformed to HUE, SATURATION and VALUE

using a RGB to HSV transformation (COLOR TRANSFORM).

• Subsequently, the application of specific threshold values on HUE and VALUE applied per

pixel while taking into account the MC10 Status Map, WB Potential Mask (WBPM),

Permanent Glacier Mask (PGM), the Volcanic Soils Mask (VSM) and the Maximum Water

Extent Mask (MWEM), allows water body detection (WB DETECTION).

• Finally an occurrence is calculated and, as such, provides additional qualitative information

(WB OCCURRENCE).

Figure 1: General overview of the Water Bodies Detection Algorithm.





3.2 THE RETRIEVAL ALGORITHM

3.2.1 Outline

A schematic overview of the Water Body Detection Algorithm (WBDA) is shown by the decision

tree in Figure 2.

Developing the WBDA involved three main steps which are described in the following paragraphs:

• Invalid data filtering

• Incompatibility masking

• Water body detection

Although WB detection is based on thresholds on the HUE and VALUE, the description of the

different steps of the Decision Tree is given in their actual sequence.


Figure 2: Outline of the Water Bodies Detection Algorithm.


3.2.2 Basic underlying assumptions

Water bodies have specific spectral features that mostly distinguish them from the other Earth

surface objects. The red, NIR and SWIR bands are HSV-color transformed. After that a threshold

on the HUE and VALUE bands are applied for the detection of water bodies. To avoid confusion

with other objects on the Earth’s surface which have identical spectral properties as water bodies,

additional data layers are used: the permanent glacier mask, volcanic soil mask, water body

potential mask and maximum water extent mask are used to reduce commission errors.

3.2.3 Related and previous applications

The water bodies detection algorithm for the PROBA-V 300 m collection is related to the water

bodies detection algorithm developed for the PROBA-V 1 km and SPOT/VGT collection. Two

additional changes have been implemented.

• The thresholds for water body detection have been changed. Table 2 gives an overview of

the thresholds for the different collections, i.e. PROBA-V 1 km, SPOT/VGT and PROBA-V

300 m. There is a small difference for the thresholds between the PROBA-V 300 m

collection and the PROBA-V 1 km collection. In this case the HUE threshold for the 300 m

collection had to be increased from HUE = 100 (1 km) to HUE = 120. This is explained by

the higher spatial resolution. Small objects or areas which have a significant influence on

the overall spectral signature of the PROBA-V pixel become more important at higher

resolution. In case they have a spectral signature identical to water bodies they might

cause the pixel to be detected as a water body. At lower resolution their spectral

contribution is less and the pixel is not detected as a WB.

Table 2: Thresholds for water body detection are applied on the HUE and VALUE, which are obtained

after HSV color transformation of the SWIR, NIR and Red bands, on the NDVI and on the VALUE as

an additional check besides the NDVI threshold check (NV).

Thresholds PROBA-V

1 km SPOT-VGT

PROBA-V 300 m

VALUE 0.14 0.15 0.14

HUE 100 100 120

NDVI 0.32 0.42 0.32

NV 0.11 0.13 0.11

• The Maximum Water Extent product of JRC’s Global Surface Water (MWE-GSW) Dataset

provides information on all the locations ever detected as water over the last 32-year period

(1984 - 2015), (Pekel et al., 2016). This dataset is used to derive the Maximum Water

Extent Mask (MWEM) at PROBA-V 300 m resolution. This mask is used as an extra check

on detected water bodies to reduce commission errors, i.e. water bodies can only be

present on those locations ever detected as water over the 32-year period. Figure 3 shows

the WB occurrence results for an area in Northern South-Sudan. Without the MWEM (a)

Commenté [BL3]: RID: 1384





false detections occur due to dark soils. With the MWEM (b) the false detections don’t

occur and commission errors will be drastically reduced. To reduce omission errors, the

mask overrules the Water Bodies Potential Mask (WBPM). The WBPM, which was derived

from the Global Land Survey Digital Elevation Model (GLSDEM) (USGS, 2008), indicates

on which locations water bodies might be present. Because the GLSDEM was a snapshot

in time it might be too strict in some cases. Figure 4 shows the WB occurrence results for

an area in Spain. Without the MWEM (a) the water reservoir is poorly detected. With the

MWEM (b) the water reservoir is detected in its full size and omission errors will be

drastically reduced.

Figure 3: Location in Northern South-Sudan (10°0’N, 31°80’E). a) False water bodies were detected

because of dark soils. b) By applying the extra check on the MWEM most of the commission errors

could be prevented.





Figure 4: Location in Southern Spain (38°40N, 7°4’W). a) Due to the strict Water Bodies Potential

Mask parts of the lake were not detected as water. b) The MWEM overrules the WBPM and as such

reduces the omission errors.

3.2.4 Alternative methodologies currently in use

The Global Surface Water Dataset of JRC (Pekel et al., 2016) is a collection of water products

derived from the Landsat archive (1984 – 2015). The dataset contains different files, i.e. Water

Occurrence, Occurrence Change Intensity, Seasonality, Recurrence, Transitions, Maximum Water

Extent and Water History. All files are made freely available. The Global Surface Water Dataset of

JRC is a reference to water occurrence in the past while the PROBA-V WB 300 m is a near real

time product, reflecting the recent status of global water occurrence.

3.2.5 Input data

Six different sets of input data are used in the Water Bodies Detection Algorithm, i.e. S1-TOC,

MC10 Status Map, WB Potential Mask (WBPM) derived from the Global Land Survey Digital

Elevation Model (GLSDEM), Permanent Glaciers Mask (PGM), Volcanic Soils Mask (VSM) and

Maximum Water Extent Mask (MWEM) derived from the Global Surface Water – Maximum Water

Extent data layer

3.2.5.1 The top of canopy daily synthesis reflectances (S1 TOC) and status map (MC10)

Global PROBA-V level 3 Top of Canopy (S1-TOC) daily synthesis products are used as input for

the mean compositing. More details can be found on the website:

Commenté [BL4]: RID 1386. Need to revise?





https://earth.esa.int/web/guest/data-access/browse-data-products/-/article/proba-v-1km-synthesis-

products-s1-toa-s1-toc-and-s10-toc.

The PROBA-V S1-TOC synthesis product ensures a daily coverage between Lat. 35° N and 75° N,

and between 35° S and 56° S, and a full coverage every two days around the equator (between

35°S and 35°N). The S1 Level 3 Top of Canopy (atmospheric corrections applied) daily synthesis

product is provided at 333 m spatial resolution. Surface reflectance is available for four spectral

bands corresponding to the selected measurement (Table 3). The atmospheric correction is

performed using SMAC 4.0 (Rahman and Dedieu, 1994). Standard input data layers includes

Normalized Difference Vegetation Index (NDVI), geometric viewing and illumination conditions,

reference to date and time of observations four reflectance bands (Table 3) and a status map

containing identification of snow, ice, shadow, clouds, land/sea for every pixel (Table 4). The data

is stored in a szip compressed hdf5 file.

Table 3: Spectral characteristics of the PROBA-V bands

Spectral band Wavelength

BLUE 0.447 – 0.493 µm

RED 0.610 – 0.690 µm

NIR 0.770 – 0.893 µm

SWIR 1.570 – 1.650 µm

Table 4: Status Map bit mapping of the PROBA-V data

Bit Name Description

1 -3 Observation

000: clear

010: undefined

011: cloud

100: snow/ice

4 Land/sea mask 0: sea

1: land

5 SWIR quality flag 0: invalid data

1: valid data

6 NIR quality flag 0: invalid data

1: valid data

7 RED quality flag 0: invalid data

1: valid data

8 (Most significant) BLUE quality flag 0: invalid data

1: valid data

Besides the mean compositing of the reflectance bands, a status map is constructed. It is used as

an extra check in the WB detection algorithm to filter out invalid pixels.





3.2.5.2 The Water Bodies Potential Mask (WBPM)

Pixels in hilly terrain having low reflectance values due to shadow or dark vegetation are often

confused with WBs. To minimize these commission errors, a Water Bodies Potential Mask

(WBPM) is derived from the Global Land Survey Digital Elevation Model (GLSDEM) (USGS, 2008).

3.2.5.3 The Permanent Glaciers Mask (PGM)

To avoid commission errors due to confusion with permanent glaciers, which often have similar

spectral properties than WBs, a Permanent Glacier Mask (PGM) was constructed. The most recent

permanent glacier data was downloaded from the National Snow and Ice Data Centre (NSIDC,

December 2016) as a shape file which was subsequently rasterized to the PROBA-V image world

size in order to obtain the Permanent Glacier Mask (PGM). When a pixel is indicated as permanent

glacier, it cannot be a water body. Figure 5(a) shows a Google Earth image over the Alps, the

corresponding PGM is shown in (b).

Because the extent of glaciers worldwide are strongly influenced by climate (global warming), the

PGM needs to be frequently updated. An update of the permanent glacier data becomes frequently

available at the NSIDC.

Figure 5: a) This Google Earth image shows an area over the Alps (Upper left corner: 48°08’25” N,

5°40’0” E). b) The permanent glacier mask for the same area in (a).

3.2.5.4 The Volcanic Soils Mask (VSM)

Locations comprised of dark volcanic soils which are spread over wider flat areas are easily

confused with water bodies. To account for these potential commission errors, a Volcanic Soil

Mask (VSM) was made. The geographical locations obtained from the Holocene Volcano List, set-

up by the Global Volcanism Program of the Smithsonian Institution, National Museum of Natural

History (http://www.volcano.si.edu/), were used to locate volcanoes and their dark volcanic soils.

Volcanic soils can be easily located on Google Earth using the information from the Holocene

Volcano List. As shown in Figure 6(a), these areas were manually delineated on the Google Earth


http://www.volcano.si.edu/





image (February 2015). The polygons were exported to shape files as shown in (b) and

subsequently rasterized to a VSM (c). Small errors made during delineation disappear when

rasterizing to the lower resolution PROBA-V pixel size. The geological situation in the volcanic

areas of southern America, i.e. Chili and Peru, is very complex and delineating the volcanic soils in

these areas was not always easy. It is therefore very well possible that some smaller regions in this

area are still missing in the VSM. On the other hand future eruptions might produce new dark

volcanic soils leading to false detected WBs. In both cases an update of the VSM is needed.

Figure 6: a) Google Earth image showing part of northern Ethiopia and southern Eritrea with the dark

volcanic soils manually delineated (red polygons). b) Dekad MC10_20140521 for the same area with

the derived dark volcanic soils shape file overlaid. c) The final volcanic soil mask (white=volcanic

soil) obtained after rasterizing the shape file.

3.2.5.5 The Maximum Water Extent Mask (MWEM)

The Maximum Water Extent product of JRC’s Global Surface Water (MWE-GSW) dataset is used

to create the Maximum Water Extent Mask (MWEM). This dataset provides information on all the

Commenté [BL6]: RID: 1387





locations ever detected as water over the last 32-year period. The GSW dataset has a spatial

resolution of 1/4000° and therefore needs to be resampled to the PROBA-V 300 m, 1/336° spatial

resolution. For each PROBA-V 300 m pixel, the corresponding pixels in the MWE_GSM are

searched for. A pixel is set in the MWEM if at least one of the corresponding pixels was found to

have ever contained water in the MWE-GSM product (Figure 7a, b).

Figure 7: a) The high resolution Maximum Water Extent product of JRC’s Global Surface Water

dataset shows where in the last 32 years water was ever detected (blue colored). b) The PROBA-V

300 m resolution Maximum Water Extent Mask is derived from the Maximum Water Extent product of

JRC’s Global Surface Water dataset. Both images have the PROBA-V 300 m raster overlaid (red). The

shown water reservoir is located in India (25°25’N, 77°55’E).

3.2.6 Output product

The water bodies product consists of two layers:

• The Water Bodies (WB)

The values of the digital numbers in the first band (WB) are summarized in Table 5. The

values are kept consistent with the version 1, 1 km product over Africa.





Table 5: Output product legend.

Value Label

0 Sea

70 Water

251 No data

255 No water

• The Quality Layer (QUAL)

Next to the Water Bodies layer, a second Band that acts as a Quality Layer (QUAL) is

added in the product and can be used to retrieve additional information to the basic

detection layer (WB). The Quality Layer represents Water Bodies Occurrence (WBO) if the

base layer indicates “Water” or the source of masking if the base layer indicates “No water”

or “No data”. The values of the digital numbers in the QUAL are summarized in Table 6.

Table 6: Output QUAL product legend. The colored entries are the occurrence values.

Value Occurrence

0 Sea

71 Very Low

72 Low

73 Medium

74 High

75 Very High

76 Permanent

151 Lowland vegetation

152 Mountain vegetation

241 Glacier

242 Volcanic

243 Mountain no vegetation

244 Lowland no vegetation

251 No data

252 Cloud

253 Snow

254 SZA > 65°

3.2.7 Methodology

In order to come to the Water Bodies product the collected PROBA-V imagery needs to be

processed in a number of steps:





3.2.7.1 Mean compositing

The MC10 composites (SWIR, NIR, RED, BLUE) are obtained using the mean compositing (MC)

method (Vancutsem et al., 2007) applied on the PROBA-V S1 daily reflectance values over a 10-

days window. The mean compositing algorithm improves the radiometric quality of the temporal

synthesis by averaging the reflectances and therefore reduces the random component of the

noise. Figure 8 depicts the processing flow of the MC10 algorithm.

The mean composite is calculated, pixel by pixel, across the four spectral bands simultaneously.

For each pixel location, the input data over a time period of 10 days is handled sequentially.

The Mean Compositing step keeps track of the number of observations used to perform the

compositing operation. The first number of observations (NOBS) represents the original number of

overpasses available during the compositing period, irrespectively of the actual radiation values or

observation status.

The compositing algorithm always tries to return the most optimal result. To achieve this, only the

most optimal observations are used. The observation type is determined as shown in Figure 9. If

one or more radiometric bands quality flags in the S1-TOC SM show a bad quality, the new

observation type will be undefined. Next, the observation bits of the S1-TOC are taken into

account. If the observation of the S1-TOC is not ‘clear’, the observation is copied. The original

cloud detection of the PROBA-V collection 0 data, proved to do an under detection of clouds.

Hence, for each pixel with a ‘clear’ observation in the S1-TOC SM, an additional classification is

done base on the method of Vancutsemet al., 2007. The thresholds values have been adapted to

reflect the spectral difference between the SPOT-VGT 1km collection used in the reference

method and the PROBA-V 333m collection. To determine if the classified observation is a better

observation type or not, the following convention is used: clear observation pixels are considered

to be more optimal then snow. Snow pixels are considered to be more optimal then cloud pixels.

Cloud pixels are more optimal then undefined observation pixels. If for example the algorithm

would detect within a given dekad a total of 7 observations with 3 cloudy and 4 clear pixels, only

the latter 4 are used in the compositing. As such, only identical observation type pixels of the

most optimal type are accumulated in the sum and contribute to a second set of ‘number of

observations’, indicating the total observations of the most optimal observation type. This number

of observations (NMOD) is used to calculate the composite mean value.

Both NOBS and NMOD are stored in their own dataset in the MC10 file.






Figure 8: MC10 algorithm flow.





Figure 9: Decision tree for MC10 observation type classification.





Once all daily input data of a pixel is calculated, the status map is generated and the mean

composite value for all four spectral bands is calculated.

In addition to the mean reflectances for each band, the NMOD and the NOBS, the Mean

Compositing step also provides a status map (SM) (Table 7) for each pixel. The status map mimics

the bit positions as used in the PROBA-V input data Status Map.

Table 7: Status Map (SM) bit mapping.

Bit Name Description

1 -3 Observation

000: clear

010: undefined

011: cloud

100: snow/ice

4 Land/sea mask 0: sea

1: land

5 Mean composite SWIR quality flag 0: no mean composite

1: valid mean composite

6 Mean composite NIR quality flag 0: no mean composite


7 Mean composite RED quality flag 0: no mean composite


8 (Most

significant) Mean composite BLUE quality flag

0: no mean composite


The observation status (bits 1 to 3) is based upon the most optimal observation used for the mean

composite.

The land/sea mask bit (bit 4) is an exact copy of the PROBA-V land/sea mask.

Bits 5, 6, 7 and 8 are quality flags for the mean composite spectral bands. The quality flag is set to

1 when a mean composite could be calculated and is set to 0 in case no compositing is done. The

latter only occurs when there was no valid observation to be used in the compositing (NMOD

equals 0).





3.2.7.2 Construction of the Water Bodies Potential Mask

This mask was derived from the GLSDEM, which has a 90 m horizontal and 1 m vertical resolution,

in three steps:

1. Search for the lowest points in the terrain.

A pixel is a candidate lowest point and a potential water body when the pixel elevation is lower

than or equal to its eight neighbours. Therefore, the 8 pixels neighbourhood of each GLSDEM

pixel is evaluated. Figure 10a shows part of the GLSDEM over the Rift Valley in Ethiopia, in

Figure 10b the detected lowest points are shown (in yellow).

Figure 10: a) Part of the 90 m spatial resolution GLSDEM over Rift Valley in Ethiopia. b) The detected

lowest points for this area are colored yellow. The larger sized colored areas are areas of equally low

elevation which correspond to some smaller lakes in the region.

2. Filtering and expanding the detected lowest points.

The next step in generating the WBPM is expanding the detected lowest points depending on the

topography. For each detected lowest point, an imaginary water level is raised in steps of 1m till

the maximum rise of 5m or the flooding1 condition is reached. As long as the edge of the potential

WB is not flooded, its area is extended according to the raised level, i.e. all neighbouring pixels

having the additional elevation are added to the potential WB area. This is schematically shown in

1 Flooding means detection of a pixel in the expanded area with an elevation lower than the actual rise level,

e.g. if the imaginary water level is raised to 405 m and there is a pixel in the expanded area which has an

elevation of 404 m, than the flooding condition is reached.





a two dimensional representation in Figure 11. The initial detected lowest point contains two pixels.

Rising the water level 1 m will expand the potential WB with an additional 5 pixels. Because no

flooding occurs the water level is increased again with 1 m. Now, 3 additional pixels are added to

the potential WB. Further, rising the water level will start flooding the WB because the first pixel

elevation right to the expanded WB is now lower. The pixels initially detected as lowest point and

surrounded by eight neighbours of equal height are marked as ”Level-1” in the potential WBs map.

The other initially detected lowest points and the expanded pixels are marked as “Level-2”. The

two levels are used in the last step when resizing the potential WBs map to the PROBA-V

resolution to obtain the final WBPM.

Figure 11: Expanding the initially detected lowest point by systematically rising an imaginary water

level in steps of 1 m. The corresponding 90 m spatial resolution pixels are indicated by the dots at

the bottom.

Figure 12 shows a detail from an area over Rift Valley. The horizontal and vertical profile plots are

taken according to the red lines in the images. The focus location of the plots is placed in the

center of a detected potential WB as shown by the crossing red lines in the detail images. In the

plots, this location is marked by the vertical red line. As seen in the detail window on the horizontal

profile, one additional potential WB was detected marked (1). On the vertical profile two additional

potential WBs were detected marked (1’) and (3).





Figure 12: a) Part of the GLSDEM over Rift Valley. b) The detected potential WBs for this area. The

detailed window shows a potential WB in the center (crossing of red lines). The horizontal and

vertical profiles are according to the red lines over the images. On the horizontal profile, the wetland

(1) near lake Hora (2) and the potential water body (indicated by the long red arrow) are seen. On the

vertical profile part of the wetland (1’) near lake Hora (2), the potential water body (indicated by the

long red arrow) and lake Bishoftu (3) are seen.

3. Deriving the WBPM

In the final step, the 90 m spatial resolution potential WBs map is re-sampled to the PROBA-V 300

m spatial resolution. For each pixel in the PROBA-V image, the corresponding pixels in the 90m

potential WBs map are located (which is 121 pixels). The resized (300 m) WBPM pixel is indicated

as a potential WB in two cases:

- At least one of the corresponding pixels was labelled as “Level-1”;

- At least nine of the corresponding pixels (7%, a figure empirically defined) were labelled as

“Level-2”.





Figure 13(a) shows a visual representation of the PROBA-V image over the Rift Valley test area

and the derived WBPM for the same area in (b). Indicated are the lakes Abijato and Langano. A

profile according to the red line is taken over the area for which the GLSDEM profile is shown in

the plot. The seven detected potential WBs over this profile are indicated by the transparent

colored boxes.

The WBPM was made based on the GLSDEM for which data was collected in the early 2000s.

Because of the high resolution of the GLSDEM dataset (90 m spatial and 1 m vertical), the derived

WBPM (300 m spatial resolution) reveals the finest detail as shown in Figure 13(b). Although the

Earth’s topography changes only notably over large geological time scales, natural events

(earthquakes) or anthropogenic activities (dam building) can influence the topography on shorter

time scales. Therefore, when such events take place, the WBPM needs to be updated.

The resulting 300 m WBPM (1=”potential water body”, 0=”no water body possible”) is used as an

extra input in the WB DETECTION processing step. The PROBA-V 300 m world image contains

120.960 samples x 47.040 lines or 5.7*109 pixels. Of those amount 29% (1.7*109 pixels) consists

of land mass (i.e. not ocean), 50.5% (833.3*106 pixels) of those land mass is indicated as potential

water body.





Figure 13: a) Part of image MC10_20131021 taken over the Rift Valley in Ethiopia (SWIR, NIR and Red

bands assigned to the RGB channels resp.). b) The WBPM for the same area in (a) (white=potential

water body, black=no water body possible). The plot shows the vertical GLSDEM profile according to

the red line. Marked with the blue boxes are the seven potential WBs. The arrows indicate the

location of lake Abijato (1) and lake Langano (2).

3.2.7.3 Colorimetric transformation

The Global Water Watch (GWW) algorithm (Pekel et al., 2005) was originally developed and

optimized for a window extending from Senegal (17.7° West) to India (85° East) and from the

equator to the Mediterranean sea (37.4°North) in the framework of the Global Watch project (Pekel

et al., 2005). Later on, the methodology was improved and adapted to MODIS and validated over

Africa (Pekel et al., 2014).

The approach is based on a transformation of the RGB (Red, Green and Blue) color space into

HSV (Hue, Saturation and Value) that decouples chromaticity and luminance (Figure 14). The HSV

color space is also commonly used in image processing. It is a nonlinear transformation of the

RGB color space using equations 1, 2 and 3 presented in Figure 14, with R mapped to SWIR

band, G to NIR band and B to the RED band. The Hue (H) is defined as the dominant wavelength





of the perceived color. It is the visual perceptual property corresponding to the categories called

yellow, blue, green, etc. Hue is considered as an angle between a reference line and the color

point, going from 0° to 360°. The Saturation (S) is defined as the degree of purity of the color and

may be intuitively considered as the amount of white mixed in a color. This component represents

the radial distance from the cone center going from 0 to 1. The nearer the point is to the center, the

lighter is the color. The Value (V), which is a brightness approximation, represents the height of the

axis of the HSV cone, going from 0 to 1. This axis describes the gray levels.

𝑉 = 𝑚𝑎𝑥(𝑅, 𝐺, 𝐵) (1)

𝑆 =𝑉−𝑚𝑖𝑛(𝑅,𝐺,𝐵)

𝑉 (2)

𝐻 =

{

(60° ∗

𝐺−𝐵

𝑉−𝑚𝑖𝑛(𝑅,𝐺,𝐵)+ 360°)𝑚𝑜𝑑 360° 𝑖𝑓 𝑉 = 𝑅

60° ∗𝐵−𝑅

𝑉−𝑚𝑖𝑛(𝑅,𝐺,𝐵)+ 120° 𝑖𝑓 𝑉 = 𝐺

60° ∗𝑅−𝐺

𝑉−𝑚𝑖𝑛(𝑅,𝐺,𝐵)+ 240° 𝑖𝑓 𝑉 = 𝐵

(3)

Figure 14: The HSV color space.

This color space presents three interesting properties: (i) it is intuitive as it represents the colors as

interpreted by human brain; (ii) the chromaticity (H and S) and Value (V) are decoupled, reducing

the problem of brightness level inconsistencies of the pixel perceived color (i.e. spatially and

temporally) resulting from various observation conditions. This property is fundamental, as the

automatic detection technique has to be independent of all effects affecting the received spectral

signal. Practically, a way to solve this issue is to collapse the 3-dimensional space into a 2-

dimensional sub-space keeping only the Hue and Value components of the perceived color.

Among the 2 components, (iii) H represents a qualitative spectral index.

By analyzing the distribution of the pixels in the Hue - Value space, the pixels can be classified as

“water” and as “no water” based on thresholds (see Pekel et al., 2014).





3.2.7.4 Invalid data filtering

Figure 15: The first part of the WBDA comprises invalid data filtering.

The first part of the Water Body Detection Algorithm (WBDA) comprises invalid data filtering

(Figure 15). The status of each image pixel is defined by the Status Map of MC10. Pixels indicated

as “undefined” in the MC10 Status Map are classified here as “No Data”.

A low sun elevation causes elongated shadows which are observed in the remote sensing imagery

as darkened Earth surfaces. These might be detected as water bodies when their VALUE drops

below the threshold level. A check on the Solar Zenith Angle (SZA) prohibits the detection of

invalid WBs, i.e. when a pixel has a SZA larger than 65° no WB detection is done and the pixel is

classified as “No Data”.

The next step in the WBDA is filtering the clouds, the cloud shadows and snow, using the

information from the MC10 Status Map. Pixels indicated as “Cloud” or “Snow” are not considered in

the WB detection algorithm.

Cloud shadows on the Earth’s surface often cause the VALUE to drop below the threshold and are

as such regularly classified as WBs. Therefore, to avoid confusion due to cloud shadow in the

immediate neighbourhood of the initial defined cloud pixel, an additional dilate filtering is

performed. In case a pixel is indicated as “Cloud” in the MC10 status map, its surrounding twelve

neighbouring pixels (the circular neighbourhood with radius 2 pixels, i.e. they total twelve pixels)

are considered as “Cloud” as well and are therefore not taken into account for WB detection.

Figure 16 shows the result of cloud shadow masking over two locations in Argentina having a dark

soil background (a, b). Before cloud dilation was performed numerous false detected water bodies

can be seen (b,d). These false detections are caused by the cloud’s shadow. The cloud dilatation,

by a circular neighbourhood of twelve pixels around the cloud pixels, most false detections are

prohibited. The circular neighbourhood of twelve pixels was empirical defined and is a trade-off

between commission and omission errors (c,f).






Figure 16: The removal of cloud shadow by dilatation of the cloud indicated pixels. a) Dekad

MC10_20140511 over Argentina (center at 31°33’50”S, 65°10’37”W). b) The Water Bodies detection

result without delate filtering. Due to cloud shadows lots of false WB detections appear. c) After de

clouds are dilated by its circular neighborhood of twelve pixels most of the false detections are

masked by the dilated cloud. The red oval shows a false detection which could not be reached by the

dilated cloud. d) Dekad MC10_20140511 over Argentina (center at 31°45’58”S, 65°4’44”W). e) An

existing small lake is shown in the lower left part of the image, besides some false detections can be

seen near to the clouds. No cloud dilatation was done yet. f) After cloud dilatation the false detection

don’t appear, a small part of the lake is now obstructed by the dilated cloud as indicated by the red

oval.





3.2.7.5 Incompatibility masking

Figure 17: The second part of the WBDA comprises Incompatibility Masking.

The second part of the WBDA comprises incompatibility masking (Figure 17) to mask out natural

and man-made objects on the earth’s surface, having spectral properties similar to WBs, which

cause false detection and commission errors.

Glaciers are relatively bright surfaces with different shades of blue and grey which regularly result

in spectral properties similar to WBs. The permanent glacier mask (PGM) excludes these areas

from WB detection.

An important source of false WB detection is caused by dark soils, mainly dark volcanic soils. The

volcanic soil mask (VSM) excludes these areas from WB detection.

Because areas with distinct topographic features such as slopes or extreme height differences can

impossibly hold WBs, these areas are also excluded from WB detection. This is done using the

water body potential mask (WBPM) in combination with the Maximum Water Extent Mask

(MWEM). The potential WB pixels have value 1 in these masks; those which cannot be potential

WBs have value 0 in both masks and are therefore not considered. This means that the MWEM

overrules the WBPM, i.e. if the WBPM indicates a potential WB and the MWEM indicates that

these was never water, than it will not be a potential WB. An additional check on the NDVI (4)

value (the threshold of 0.32 was empirically defined) is used to distinguish between ‘Mountain no

vegetation’ and ‘Mountain vegetation’.





𝑁𝐷𝑉𝐼 = 𝜌𝑁𝐼𝑅−𝜌𝑅𝐸𝐷

𝜌𝑁𝐼𝑅+𝜌𝑅𝐸𝐷 (4)

Finally, vegetated surfaces, e.g. large overhanging trees or floating plants, prohibit the detection of

WBs underneath. Besides, vegetated surfaces often cause confusion with WBs. Therefore, the

NDVI is used to identify the vegetated areas. Pixels having an NDVI value higher or equal to 0.32

(the threshold was empirically defined) and having a WBPM pixel set (locating lowland areas

where holding a WB is possible) are considered to be “Lowland Vegetation”.

Some WBs have a high NDVI value (due to algae load or adjacency effect of the surrounding

vegetation) and are as such classified as “Lowland Vegetation”. To avoid these omission errors an

additional check on VALUE is added, this threshold level is denoted as NV. If the VALUE of a pixel

which is designated as “Lowland Vegetation”, is less than 0.11 (a value empirically defined) it is

considered as “Water” depending on the thresholds of HUE and VALUE.

3.2.7.6 Water body detection

In the previous steps, i.e. ‘Invalid Data Filtering’ and ‘Incompatibility Masking’ the PROBA-V MC10

images are filtered before the actual WB detection takes place. Only pixels having following

conditions are considered for WB detection:

- SZA less than 65°;

- SM does not indicate cloud or snow;

- Not indicated as “Permanent Glacier” or “Dark Volcanic soil”

- Indicated as a maximum water extent pixel;

- NDVI value less than 0.32 or, if not, VALUE less than 0.11.

The pixels surviving the previous conditions are in the finals step compared against different

threshold levels on the HUE and VALUE bands to finally decide if they belong to water bodies or

lowland (Figure 18)

Figure 18: The last part of the WBDA comprises actual Water Body Detection based on threshold

levels on the HUE and VALUE bands. The thresholds consist of two parabolic functions.







Threshold values on the HUE and VALUE bands are used for detecting WBs worldwide. The

threshold consists of two quadratic functions which were empirically defined. The left part (HUE = 0

till 34) can be written as:

𝑉𝑎𝑙𝑢𝑒𝐿 ≤(34−𝐻𝑢𝑒𝐿)

2∗𝑆𝑐𝑎𝑙𝑒𝐿

2+ 𝑂𝑓𝑓𝑠𝑒𝑡𝐿 † (5)

With:

𝐻𝑢𝑒𝐿 = [0…34]

𝑆𝑐𝑎𝑙𝑒𝐿 =0.41

342

14.0=LOffset

The right part (HUE = 34 till 120) is a bit more complex because it is based on a parabola rotate

over 0.2° for which the rotated coordinates can be written as:

𝑥 = 𝐻𝑢𝑒 ∗ cos(𝛼) − 𝐻𝑢𝑒2 ∗ 𝑠𝑖𝑛(𝛼) (6)

𝑦 = 𝐻𝑢𝑒 ∗ 𝑠𝑖𝑛(𝛼) + 𝐻𝑢𝑒2 ∗ cos (𝛼) (7)

This equation can be solved using the standard form:

𝑥 =−𝑏±√𝑏2−4𝑎𝑐

2𝑎 (8)

And from which only one solution is used to calculate the corresponding VALUE threshold:

𝑥 =−(−cos(𝛼)) − √(−cos (𝛼))2−4∗𝑠𝑖𝑛(𝛼)∗𝐻𝑢𝑒𝑅

2∗𝑠𝑖𝑛(𝛼) (9)

𝑉𝑎𝑙𝑢𝑒𝑅 ≤(𝑥∗𝑠𝑖𝑛(𝛼)+ 𝑥2∗cos (𝛼))∗𝑆𝑐𝑎𝑙𝑒𝑅

2+ 𝑂𝑓𝑓𝑠𝑒𝑡𝑅 † (10)

With:

𝛼 = 0.2°

𝐻𝑢𝑒𝑅 = [34…360] − 48.5 ‡

𝑆𝑐𝑎𝑙𝑒𝑅 =1

95000 ‡‡

14.0=ROffset

† The division by two was done to sharpen the parabolic curve in order to reduce the commission

errors.





‡ The value 48.5 was empirically defined; it is used to broaden the parabolic curve (higher values

will broaden the curve).

‡‡ The value 95000 in the scale factor was empirically defined; it defines the height of the

parabolic curve (higher values will lower the curve).

The parabolic thresholds consists of a left and a right part. The OffsetL and OffsetR directly

influence the “minimum level” of the parabolic function. They must at all times have identical values

otherwise there will be a ‘jump’ in the function. The maximum HUE is always in the right part and is

contained in the HueR parameter. This one immediately defines the max HUE value. In other

words these parameters directly define the shape and level of the parabolic function as inherent

defined by the math functions.

Figure 19 shows the 2D scatter plots of the Vietnam test areas. The 2D scatter plot has the HUE

on the x-axis and VALUE on the y-axis. The location of the pixels from the reference WBs are

indicated by the colored plus symbols and this for three ranges of Water Surface Ratio (WSR), i.e.

blue for WSR = 0.95 to 1, cyan for WSR = 0.8 to 0.95 and magenta for WSR = 0.7 to 0.8. The

other WSR ranges are not indicated as they would overrule the figure. The non-WB pixels, which

are denoted as land, are indicated by the dark grey dot symbols. The final thresholds levels are

indicated by the red lines. Although the final thresholds are tuned to detect as much WBs as

possible, not all WBs can be detected because they are intermixed with land pixels.






Figure 19: 2D scatter plot of the Vietnam test area showing the final threshold levels. Land pixels are

indicated by the dark grey dots, reference WBs (obtained from Landsat 8) are indicated by the

colored plus symbols for different ranges of water surface ratios. A further refinement of the

thresholds is not possible because WB pixels and land pixels are intermixed.

3.2.7.7 Extracting reference water bodies

Defining the thresholds and demonstrating the maturity of the PROBA-V WBDA was done by

comparing the obtained WB detection results against reference water bodies derived from high

spatial resolution Landsat 8 images. In total 22 areas were selected worldwide within the time

period end of October 2013 till end of May 2014 (Table 8, Figure 20).

These 22 images were pre-processed and WB detection was done based on threshold values on

HUE and VALUE which were empirically defined. This could easily be done as the images were

cloud free, i.e. no cloud shadows and contained no structures that caused confusion with water

bodies. So a simple approach could be used as described below. The obtained L8 reference WBs

were subsequently manually checked against Google Earth images. Using this approach it was

fast and easy to obtain reference WBs based on Landsat 8.






Table 8: Landsat-8 scenes used to define the PROBA-V water bodies detection thresholds.

Figure 20: Location of the Landsat scenes used to define the detection method.

Landsat 8 scene Dekad Lat Lon ns nl

1 Argentina1 LC82270862014111LGN00 20140421 -36.40625000 -63.94196429 299 239

2 Argentina2 LC82310932014011LGN00 20140111 -46.29910714 -73.83482143 376 257

3 Australia LC81110782014131LGN00 20140511 -24.94196429 118.49553571 267 236

4 Bangladesh LC81380442014112LGN00 20140421 24.17410714 87.74553571 244 240

5 Bolivia LC82330692013310LGN00 20131101 -11.95089286 -66.85267857 232 239

6 Brazil LC82220742014028LGN00 20140121 -19.17410714 -51.55803571 248 237

7 Canada LC80180272014151LGN00 20140521 48.53125000 -80.06696429 332 246

8 China LC81210382014121LGN00 20140501 32.79910714 116.05803571 271 240

9 Congo LC81740662014140LGN00 20140521 -7.62053571 25.22767857 235 238

10 India LC81460422014072LGN00 20140311 27.04910714 76.05803571 249 240

11 Mali LC81950502013332LGN00 20131121 15.51339286 -2.25446429 234 239

12 Mexico LC80270422014118LGN00 20140421 27.04017857 -100.12053571 260 238

13 Poland LC81870222014087LGN00 20140401 55.60267857 21.60267857 393 250

14 RiftValley1 LC81680542013335LGN00 20131201 9.72767857 38.20089286 232 238



17 Rusia LC81770212014129LGN00 20140501 56.99553571 37.60267857 426 248

18 SouthAfrica LC81690802014121LGN00 20140501 -27.77232143 28.12946429 277 246

19 Spain LC82010332014121LGN00 20140501 39.95089286 -5.66517857 306 239

20 Turkey LC81730332013306LGN00 20131101 39.95982143 37.65625000 299 240

21 USA LC80430342014134LGN00 20140511 38.54017857 -121.87946429 281 242

22 Vietnam LC81240512014062LGN00 20140301 14.08482143 107.11160714 241 242

SPOT-VGTUpper Left CoordinateN° Area

2013/ 14





Figure 21 gives a schematic overview of the workflow used to extract the reference water bodies

from Landsat-8 scenes.

Figure 21: Workflow for the extraction of reference water bodies using Landsat-8 scenes. An as

much as possible cloud free scene is searched for the required geographical location and time

period. After pre-processing and HSV color transform of the Landsat-8 scene, WB detection was

done using a Decision Tree Classifier (DTC). The obtained Landsat-8 WBs are subsequently used to

calculate the Water Surface Ratio using the spatial information from the PROBA-V image.

The USGS ‘EarthExplorer’ was used to search for Landsat-8 OLI/TIRS cloud free scenes over

randomly selected geographical coordinates worldwide. Subsequently one cloud free scene or one

which is as much as possible cloud free, was downloaded and unpacked. The data was

subsequently pre-processed to convert the eleven bands to TOA radiance, the 9 OLI bands to TOA

reflectance and the 2 TIRS bands to at-satellite brightness temperature.

The ‘Automatic Cloud Cover Assessment – modified’ (ACCAm) was used for cloud detection.

ACCAm is a modified version of the ACCA (Irish et al., 2006) developed for Landsat-7. In this

modified cloud detection algorithm only the spectral cloud identification is retained and an

additional check on the cirrus band is added.

In the next step the ‘SWIR 1’, ‘NIR’ and ‘Red’ bands are color transformed to the HSV-color

system. This allows for an easy water bodies detection using thresholds on the HUE and VALUE

band. As shown by the Decision Tree Classifier (DTC) in Figure 22, the class ‘NoData’ is based on

a zero value in the TIR 1, SWIR 1, NIR or Red band. The ‘Cloud’ class is set according the cloud

mask generated by ACCAm (1 means cloud detected). In the final step water is separated from





land by a threshold value on HUE (≥ 160) and VALUE (< 0.4). These thresholds were empirically

defined. Because as much as possible cloud free images were used and the validation areas didn’t

contain surfaces that confuse with water bodies, water bodies detection could easily be done by

only applying these thresholds.

Figure 22: Decision Tree Classifier for WB detection on Landsat-8 images. A check on different

bands and on cloud cover removes incompatible pixels from the scene. A check on HUE and VALUE

is used to detect water bodies.

In the next step, the Landsat-8 corner coordinates were used to extract the corresponding area

from the PROBA-V Water Bodies (WBPV) product and this for the corresponding dekad by taking

into account the Landsat-8 acquisition date. Subsequently, for each pixel of the extracted WBPV

product, the Water Surface Ratio (WSRxy) was calculated. Therefore, for each WBPV pixel the

corresponding pixels from the Landsat-8 DTC result (Kxy) were extracted and the WSR was

calculated according (11):

𝑊𝑆𝑅𝑥𝑦 = (𝑁° 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑝𝑖𝑥𝑒𝑙𝑠)𝐾𝑥𝑦

(𝑇𝑜𝑡𝑎𝑙 𝑁° 𝑜𝑓 𝑣𝑎𝑙𝑖𝑑 𝑝𝑖𝑥𝑒𝑙𝑠)𝐾𝑥𝑦 (11)

The WSR has a value greater than zero and less or equal to one when ‘Water’ classified pixels are

present in the corresponding DTC result pixels. The WSR is not calculated when the cloud cover of

the corresponding Landsat-8 DTC pixels is more than 10% or if more than 10% of the pixels

contain no data. Pixels in the WSR image are indicated as ‘Cloud’ (WSR = 3) or ‘No Data’ (WSR =

0) respectively. When no ‘Water’ classified pixels are present in the corresponding DTC pixels the

WSR pixel is set to ‘Land’ (WSR = 2).






In the final step a confusion matrix (CMX) is calculated as described by Roy and Boschetti (2009)

and Padila et al. 2014. From these CMX the overall accuracy (OA), commission error (CE) and

omission error (OE) is calculated. The OE is calculated per minimum WSR range (WSRm), i.e.

0.95, 0.8, 0.7, 0.6 and 0.5 and per test area.

The confusion matrix can be written as:

Reference

WB NoWB

WBPV WB p11 p12

NoWB p21 p22

With:

− p11 = the number of pixels indicated as WB in both reference and WBPV and having a WSR larger or equal to the WSRm;

− p12 = the number of pixels not indicated as WB in the reference but indicated as WB in WBPV and having a WSR larger or equal to the WSRm;

− p21 = the number of pixels indicated as WB in the reference but not in WBPV and having a WSR larger or equal to the WSRm;

− p22 = the number of pixels not indicated as WB in both reference and WBPV and having a WSR larger or equal to the WSRm.

From the CMX the Overall Accuracy (OA), Commission Error (CE) and Omission Error (OE) can

be calculated as:

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑖𝑥𝑒𝑙𝑠 = 𝑝11 + 𝑝12 + 𝑝21 + 𝑝22 (12)

𝑂𝐴 = 𝑝11+𝑝22

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑖𝑥𝑒𝑙𝑠∗ 100 (13)

𝐶𝐸 = 𝑝12

𝑝11 + 𝑝12∗ 100 (14)

𝑂𝐸 = 𝑝21

𝑝11 + 𝑝21∗ 100 (15)

The average values for OA, CE and OE are calculated as the mean of the OA, CE and OE for all

test areas.

3.2.7.8 Defining the water body detection thresholds

The threshold levels were iteratively and empirically defined by different decision tree

classifications with different settings for the threshold levels and by comparison of the results by

using the reference WBs obtained from Landsat 8 images. Table 9 shows the comparison results

for different settings of the threshold levels which are calculated over the 22 test regions. The

figures give the mean calculated over all test regions. The Commission Error (CE) and Omission

Error (OE) are listed in the table. The OEs are calculated for six minimum Water Surface Ratios

(WSR) with the minimum WSR indicated in the table (e.g. a minimum WSR = 0.95 means only WB





pixels with a WSR >= 0.95 are considered as reference WB). Because the overall accuracy is

always extremely high (more than 97 %), due to the fact that the non-WB pixels outnumbers the

WB pixels, it is not an appropriate metric and not reported.

Table 9: The results for eight different settings of the threshold levels. The overall accuracy (OA),

omission errors (OE) and the commission errors (CE) are listed in percentage. The OEs are given for

six ranges of minimum WSR . The figures are calculated over the 22 test regions for the period

Oct2013 – May2014. The green indicated entries show the best results and are the final threshold

levels.

First the thresholds are set according those used for WB detection on the PROBA-V 1 km

collection (VALUE = 0.14, HUE = 100, NDVI = 0.32 and the additional VALUE check beside the

NDVI check (NV) is set to 0.11). This first assessment gives a CE of 1.9 % and an OE of 12.4 %

for WSR 0.5 (entry 0). Notice that the user requirements (Table 1) define a maximum OE and CE

0.95 0.9 0.8 0.7 0.6 0.5

0

VALUE = 0.14, HUE=100,

NDVI=0.32, NV = 0.11,

Parabolic

1.9 0.6 0.9 2.3 4.6 7.9 12.4

1

VALUE = 0.15, HUE=100,

NDVI=0.32, NV = 0.11,

Parabolic

2 0.5 0.9 2 4.1 7.1 11.4

2

VALUE = 0.13, HUE=100,

NDVI=0.32, NV = 0.11,

Parabolic

1.8 0.6 1.1 2.6 5.2 8.9 13.6

3

VALUE = 0.14, HUE=110,

NDVI=0.32, NV = 0.11,

Parabolic

1.7 0.6 1 2.4 4.8 8.3 13

4

VALUE = 0.14, HUE=120,

NDVI=0.32, NV = 0.11,

Parabolic

1.3 0.6 1 2.5 5 8.6 13.4

5

VALUE = 0.14, HUE=125,

NDVI=0.32, NV = 0.11,

Parabolic

1.2 0.6 1.1 2.5 5.1 8.7 13.5

6

VALUE = 0.14, HUE=120,

NDVI=0.3, NV = 0.11,

Parabolic

1.3 0.6 1.1 2.6 5.2 8.9 13.8

7

VALUE = 0.14, HUE=120,

NDVI=0.32, NV = 0.13,

Parabolic

1.6 0.4 0.7 1.7 3.7 6.8 11

PROBA-V 333m (2013-14) CE (%)

OE (%)

Minimum WSR





of 15%. The VALUE threshold is subsequently increased to 0.15 which results in an increased CE

(entry 1), and decreased to 0.13 which results in increased OEs (entry 2). Because the CE or OE

errors increase, these settings were not retained. Increasing the HUE threshold to 110 and 120

(entry 3 and 4 vs entry 0) decreases the commission error with a slight increase of omission error.

Further increasing the HUE threshold to 125 (entry 5) slightly improves the commission error but

also the omission errors increases and therefore this option was not retained. Subsequently the

NDVI threshold was decreased to 0.3 (entry 6 vs entry 4) resulting in both increased CE and OEs.

Finally the NV value was increased showing better OEs, however the CE increased (entry 7 vs

entry 4). The last two settings were therefore not retained, so the final settings are according entry

4, which are identical to the settings for the PROBA-V 1 km collection except for the HUE threshold

which is increased to 120.

3.2.7.9 Water body occurrence estimation

To qualify the occurrence of the detected WBs, a Water Body Occurrence (WBO) is calculated for

each detected WB pixel in each dekad. This measure gives an idea about the permanency or

seasonality of the detected WBs. The WBO is added as a Quality Layer (QUAL) in the final output

product. To obtain a WBO map for each dekad, per pixel statistics are calculated by temporal-

sequential processing of the available dekads. If more than sixty-four2 cloud free MC10 composite

observations are available, only the last 64 cloud free observations including the actual observation

are used for calculating the statistics. This means that for the first dekads (observation started Oct.

2013) there are none or only a few dekads available for statistics.

The per-pixel temporal-sequential statistics are calculated over the available observation dekads

(max 64) and can be summarized as:

− Total number of temporal cloud free observations (ntObs)*

− Total number of temporal WB detections (ntWBs)

− Maximum number of continuous temporal WB detections (mctWBs)

* Note that after some time when many dekads are available ntObs will become fixed to 64.

From these statistics, the frequency of detected WBs can be calculated as:

( )( )

( )%100ntObsnsobservatiolcloudfreeroftemporaTotalnumbe

ntWBsonslWBdetectiroftemporaTotalnumbe=WBf (16)

As shown in Figure 23, the calculated WB frequency (WBf) and the maximum number of

continuous temporal WB detections (mctWBs) can be visualized in a 2D scatter plot which, in his

turn, is used for defining the occurrence threshold levels. The different occurrence levels are

2 To get an idea of the WB occurrence the observation period over which the statistics are calculated has to be long enough. An observation period over two years requires 72 dekads. However, for computational reason only 64 dekads are observed, information which can be stored in one “long word” (64 bits). This can be justified by the fact that per pixel not each dekad holds valid data due to cloud or cloud shadow, snow or an invalid SZA. This results in an observation period for most, if not all, of the pixels of more than 2 years.





indicated by the different colored regions in the figure. The slope and position of the four threshold

lines, marked L, M, H and VH, define the WBO threshold levels and are designed as such in the

first phase. These thresholds were defined independently of the calibration areas. They were

merely defined by common sense reasoning, i.e. WBs that appear with a high frequency (WBf) or

with long continuous observation periods (mctWBs) are most likely stable WBs that exist for a

longer period and therefore have a higher occurrence than those with lower frequency (WBf) of

shorter continuous observation periods (mctWBs). The advantage of defining the thresholds in this

way is that they can be easily adapted if needed. The defined occurrence threshold levels can be

expressed as:

𝑉𝑒𝑟𝑦 𝐿𝑜𝑤 = (𝑚𝑐𝑡𝑊𝐵𝑠 > 0) & (𝑚𝑐𝑡𝑊𝐵𝑠 < (2 + 𝛼𝐿 ∗𝑊𝐵𝑓)) (17)

𝐿𝑜𝑤 = (𝑚𝑐𝑡𝑊𝐵𝑠 ≥ (2 + 𝛼𝐿 ∗ 𝑊𝐵𝑓)) & (𝑚𝑐𝑡 𝑊𝐵𝑠 < (3 + 𝛼𝑀 ∗𝑊𝐵𝑓)) (18)

𝑀𝑒𝑑𝑖𝑢𝑚 = (𝑚𝑐𝑡𝑊𝐵𝑠 ≥ (3 + 𝛼𝑀 ∗𝑊𝐵𝑓)) & (𝑚𝑐𝑡 𝑊𝐵𝑠 < (4 + 𝛼𝐻 ∗ 𝑊𝐵𝑓)) (19)

𝐻𝑖𝑔ℎ = (𝑚𝑐𝑡𝑊𝐵𝑠 ≥ (4 + 𝛼𝐻 ∗ 𝑊𝐵𝑓)) & (𝑚𝑐𝑡 𝑊𝐵𝑠 < (5 + 𝛼𝑉𝐻 ∗𝑊𝐵𝑓)) (20)

𝑉𝑒𝑟𝑦 𝐻𝑖𝑔ℎ = 𝑚𝑐𝑡𝑊𝐵𝑠 ≥ (5 + 𝛼𝑉𝐻 ∗ 𝑊𝐵𝑓) (21)

𝑃𝑒𝑟𝑚𝑎𝑛𝑒𝑛𝑡 = 𝑊𝐵𝑓 ≥ 95 (22)

Where αx is the slope of the threshold line x, e.g.

𝛼𝑉𝐻 =5−0

0−60 (23)

The red line (o1) shows the minimum WBf in relation to the mctWBs. The slope depends on the

maximum number of observations (ntObs) made which in this case is thirty-one3 when all dekads

have valid data.

The plot shows that a certain pixel is classified as a ‘Very High’ WBO if it is identified as WB in at

least five consecutive dekads (point ‘m’ in the plot). Note that this number is independent of the

ntObs. As an example, if a pixel was detected as a WB in three consecutive dekads (with nObs =

31) its WBf will be 9.7 % and therefore will have a WBO “Medium” (point “a”). If this pixel would

have subsequently four additional WB detections in separate single dekads its WBf will rise to 22.6

% and its WBO will increase to “High” (point “b”).

Note also that if pixels have a WBf of at least 95% their WBO will be “Permanent”.

3 31 MC10 images were used, from 21 Oct. 2013 till 21 Aug. 2014.





Figure 23: 2D scatter plot of the WB frequency (WBf) and the number of continuous temporal WB

observations (mctWBs) for the Rift Valley test area calculated for the 31 available dekads. The Water

Body Occurrence levels are indicated by the different colored areas.

3.2.8 Limitations

Although minimization of omission and commission errors is achieved by the use of additional data

layers, WBPM, VSM, PGM, MWEM and NDVI they sometimes are inevitably. An overview of

reasons for omission and commission errors is listed in Table 10.





Table 10: Omission and commission errors are caused by several reasons.

Omission errors due to:

1 Small sized WBs in the order of the size of one PROBA-V pixel;

2 WBs with spectral properties that fall outside the defined thresholds;

3 New WBs not covered by the MWEM (32 years, 1984-2015).

Commission errors due to:

1 Cloud shadow caused by thin undetected clouds especially over dark dense vegetation and in mountainous areas;

2 Dark soils especially volcanic soils (which are not masked by the VSM);

3 Dark areas caused by anthropogenic activity, e.g. heavy industry;

4 Dark areas caused by shadow, e.g. high buildings in large cities;

5 Anthropogenic structures with spectral signatures equal to WBs, e.g. some agricultural fields, build-up areas;

6 Natural surfaces with spectral signatures very close to WBs, e.g. salt lakes;

To minimize confusion the user should use the product with attention to the QUAL which reflects

the history of detected WBs.

3.3 QUALITY ASSESSMENT

See CGLOPS2_QAR_WB300m_V1 upon public release.

3.4 RISK OF FAILURE AND MITIGATION MEASURES

In case the quality of the PROBA-V sensor degrades, the spectral response correction needs to be

adapted and the defined thresholds for Water Body detection need to be updated if necessary.





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