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Deliverable 2.1
Essential Variables (EV) List
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 821918
enviroLENS – D2.1: Essential Variable (EV) List
Start date of project: 01-12-2018
Duration: 24 months
Deliverable number 2.1
Deliverable title Essential Variables (EV) List
Deliverable due date 31-05-2019
Lead beneficiary AUTH
Work package WP2
Deliverable type Report
Submission date: 31.05.2019
Revision: Version 1.0
Dissemination Level
PU Public X
PP Restricted to other programme participants (including the Commission Services)
RE Restricted to a group specified by the consortium
CO Confidential, only for members of the consortium (including the Commission Services)
Project acronym: enviroLENS
Project title: Copernicus for environmental law enforcement support
Project number: 821918
Instrument: Horizon 2020
Call identifier: H2020-SPACE-2018
Topic DT-SPACE-01-EO-2018-2020
Type of action Innovation action
enviroLENS – D2.1: Essential Variable (EV) List
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Title:
Essential Variable (EV) List
Author(s)/Organisation(s):
Petros Patias/ AUTH
Natalia Verde/ AUTH
Giorgos Mallinis/AUTH
Maria Tasopoulou/ AUTH
Contributor(s):
Franziska Albrecht/ GeoVille
Mariza Pertovt/ Sinergise
Short Description:
Deliverable 2.1 includes a final list of selected Essential Variables (EVs) that will be compiled on the basis of current
capabilities of the EO-Toolset (see D5.1 EO-Toolset) and the perspective of available sensors to be added during the
project. Further the document details already available pre-processing capabilities and implemented biophysical
variables chosen by AUTH who has long standing expertise on EVs (as demonstrated through previous projects and as
described in scientific publications). Also, user requirements developed from the perspective of EO processing are
considered Finally, the EV list will be finalised based on the estimation of required work and availability of external open
source components.
Keywords:
Essential Variables, Earth Observation, modelling, EO toolset
History:
Version Author(s) Status Comment Date
0.1 Petros Patias Initial Draft 01.04.2019 0.2 Natalia Verde
Giorgos Mallinis Further Input 15.05.2019
0.3 Maria Tasopoulou Giorgos Mallinis
Finalization 30.05.2019
Review:
Version Reviewer Comment Date
1.0 Franziska Albrecht Final version, ready for submission 31.05.2019
enviroLENS – D2.1: Essential Variable (EV) List
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Table of Contents
1 Introduction .................................................................................................................................................... 9
2 Current and future capabilities of the EO-toolset ......................................................................................... 11
2.1 Available sensors .................................................................................................................................... 11
2.1.1 Sentinel-2 ......................................................................................................................................... 11
2.1.2 Landsat-8 .......................................................................................................................................... 11
2.1.3 Landsat-5 .......................................................................................................................................... 11
2.1.4 Landsat-7 .......................................................................................................................................... 12
2.1.5 Aqua/Terra MODIS ........................................................................................................................... 12
2.2 Sensors to be implemented in the future .............................................................................................. 13
2.3 Pre-processing capabilities ..................................................................................................................... 13
2.3.1 Atmospheric correction for Sentinel-2 data ..................................................................................... 13
2.3.2 Automated cloud and cloud shadow identification in Sentinel imagery .......................................... 13
2.4 Band colour composites ......................................................................................................................... 14
2.5 Spectral indices ...................................................................................................................................... 15
3 Essential variables ......................................................................................................................................... 16
3.1.1 Essential Climate Variables (ECV) ..................................................................................................... 16
3.1.2 Essential Biodiversity Variables (EBVs) ............................................................................................. 16
3.1.3 Essential Renewable Energies Variables (EREV) ............................................................................... 17
3.1.4 Essential Geodetic Variables (EGVs) and Essential Earth Rotation Variables (EERVs) ...................... 17
3.1.5 Essential Ocean Variables (OCVs) ..................................................................................................... 17
3.1.6 Essential Water Variables (EWVs) .................................................................................................... 18
3.1.7 Essential Sustainable Development Goals Variables (ESDGV) .......................................................... 18
3.1.8 Urban Essential Variables (UEVs) ..................................................................................................... 18
3.1.9 Essential Societal Variables (ESVs) ................................................................................................... 18
3.1.10 Essential Agriculture Variables (EAVs) ............................................................................................ 19
3.1.11 Essential Protected Area Variables (EPAVs) ................................................................................... 19
4 USE-CASE requirements and EVs linkages..................................................................................................... 20
4.1 Case 1: Environmental Impact Assessment Process in the Energy Projects ........................................... 20
4.2 Case 2: Force Majeure Events in the Energy Contracts .......................................................................... 20
4.3 Case 3: Oil and Gas Pipeline Monitoring For Safety and Environment ................................................... 21
4.4 Case 4: Forest Law Enforcement and Governance ................................................................................. 21
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4.5 Case 5: Infrastructure development in Protected Areas ........................................................................ 22
4.6 Case 6: Land-use change in the context of disaster risk reduction ......................................................... 22
5 Essential Variables modelling ........................................................................................................................ 23
5.1 Burned Area (ECV) and Burn Severity (ECV) modelling .......................................................................... 23
5.2 Built-up area ........................................................................................................................................... 25
5.3 Soil Sealing .............................................................................................................................................. 27
5.4 Land Cover .............................................................................................................................................. 29
6 Final Essential Variables list........................................................................................................................... 30
7 References .................................................................................................................................................... 31
Appendix .......................................................................................................................................................... 34
Spectral indices for the sensors available within the EO-Toolset ................................................................. 34
Sentinel-2 indices ...................................................................................................................................... 34
Landsat-8 spectral indices ......................................................................................................................... 39
MODIS spectral indices ............................................................................................................................. 40
Essential Variables list ............................................................................................................................... 42
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List of Figures
Figure 1: General Workflow for computing EVs for monitoring ...................................................................... 10
Figure 2: Cloud detection algorithm within the EO toolset based on Braaten et al. (2015) ............................ 13
Figure 3: Cloud detection algorithm within the EO toolset based on Hollstein et al. (2016) ........................... 14
Figure 4 :Spectral Response Curves of burned areas and of healthy vegetation, US Forest Service, 2018 ...... 20
Figure 5 Burned Area Index (BAI) and Normalized Burn Ratio (NBR) formulas indices, ((35) .......................... 23
Figure 6: Burned Area and Burn Severity workflow modelling ........................................................................ 24
Figure 7: Computation of NBR (middle image) and ΔNBR (right image) for the area of Alexandroupolis –
Greece (left image – orthorectified). Dark areas indicate burned areas. ......................................................... 25
Figure 8 Built Up area workflow modelling using EO ready data (spatial data from data repositories) ........... 26
Figure 9 Soil Sealing workflow modelling using satellite imagery .................................................................... 27
Figure 10: Computation of Soil Sealing for the designated area of Velipoje – Albania for the time period 2009-
2018 ................................................................................................................................................................. 28
Figure 11: Computation of Soil Sealing for the designated area of Velipoje – Albania for the time period 2015-
2018 ................................................................................................................................................................. 28
Figure 12: Computation of Land Cover using available spatial data and satellite imagery .............................. 29
List of Tables
Table 1: Readily available composites in EO-toolset for the Sentinel-2, Landsat and MODIS sensors ............ 14
Table 2 ΔNBR Burn Severity Categories, USGS FireMon program ................................................................... 21
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Abbreviations
ABT Aichi Biodiversity Targets
ARD Analysis-Ready-Data
BAI Burnt Area Index
EAV Essential Agriculture Variable
EBV Essential Biodiversity Variable
ECV Essential Climate Variable
EERV Essential Earth Rotation Variable
EERVs Essential Earth Rotation Variables
EGrVs Essential Gravimetric Variables
EGV Essential Geodetic Variable
EGVs Essential Geodetic Variables
EO Earth Observation
EOPs Earth orientation parameters
EOVs World Ocean Circulation Experiment
EPAV Essential Protected Area Variable
EREV Essential Renewable Energies Variable
ESDGV Essential Sustainable Development Goals Variable
ESV Essential Societal Variable
ETM+ Enhanced Thematic Mapper Plus
EVs Essential Variables
EWV Essential Water Variable
GBO4 Global Biodiversity Outlook
GEOGLAM GEO’s Global Agricultural Monitoring initiative
GEOGLOWS GEO-Global Water Sustainability
GGOS Global Geodetic Observing System
GOOS Global Ocean Observing System
IAG International Association of Geodesy
IGWCO Integrated Global Water Cycle Observations
IMBeR Integrated Marine Biosphere Research
IOC Intergovernmental Oceanographic Commission
LAI Leaf Area Index
LiDAR LIght Detection And Ranging
MBON Marine Biodiversity Observation Network
MODIS Moderate Resolution Imaging Spectroradiometer
MSS Multispectral Scanner
MSS Multispectral Scanner
NBR Normalized Burn Ratio
OBIS Ocean Biogeographic Information System
OCV Essential Ocean Variable
OLI Operational Land Imager
SDG Sustainable Development Goals
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TIRS Thermal Infrared Sensor
TM Thematic Mapper
UEV Urban Essential Variable
UNFCCC United Nation Convention on Climate Change
USGS United States Geological Survey
VHR Very High Resolution
WOCE World Ocean Circulation Experiment
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1 Introduction
The concept of Essential Variables (EVs) has emerged within the remote sensing community in recent years.
The EVs, having previously defined as a minimal set of variables that determine the system’s state and
development, have attracted considerable interest not only for remote sensing scientists, but from several,
diverse thematic groups and communities. The driving forces behind this evolution relates primary to the
need to support national to global monitoring, reporting, research, and forecasting of complex earth
systems. In addition, the necessity for an essential set of parameters that could be used for monitoring
progress towards the goals of different thematic communities as well as to the requirement to support
consistent, objective and temporal information provision for policy development and implementation is also
a critical issue for the development of EVs. Moreover, considering the availability of sensor data with similar
characteristics from different satellites orbiting around earth, there is a need to standardize the extracted
information independently of the observational platform and the processing algorithms and provide this
information in a more streamlined, comprehensible form to end-users not well acquainted with the remote
sensing technology and terminology.
Thus, integrated Earth Sensing, envisioned by early 1990’s (1) can be nowadays a reality, facilitating efficient,
reliable and affordable monitoring of our planet from global to local scales through the assimilation of
remote sensing observations and in situ measurements. In recent years alongside the growing societal and
environmental challenges and requirements for Earth’s monitoring at multiple spatial and temporal scales,
there has been a great improvement in EO development and research (sensor technology and image
processing). Spaceborne and airborne, EO multispectral, hyperspectral, thermal, LiDAR datasets
characterized by their synoptic view and extended spatial coverage collection capabilities, can offer the
potential to monitor Earth system and its components at local-to-global scales through appropriate
indicators and variables (2). Finally, in-situ instruments, which provide over limited geographic extent
accurate, high-frequency and high-quality measurements, are also crucial for providing ground-based
calibration and validation for the EO data.
Yet, even nowadays, challenges still exist since the growing and sometimes heterogeneous volume of raw EO
data available for local, regional, and national decision-making requires time-consuming processing
workflows for information extraction. These workflows incorporate significant costs and complexity, despite
hardware/software improvement and the availability of Analysis-Ready-Data (ARD) that facilitates faster and
more consistent analysis with lower user efforts. Furthermore, it shouldn’t be neglected that only in few
cases does the EO measure the actual variables of interest, but rather is used to infer or estimate variables
through expert-based selection of retrieval algorithms or models. Quite often it is also needed to integrate
diverse EO (and non-EO) data for Earth’s system monitoring, in order to meet multiple user requirements
across different scales, environmental settings and applications, generating non-standardized, fragmented
and heterogeneous information. In addition, EVs definition is an open process, which ideally should be based
in simultaneous top-down and bottom-up approach, trying to address both scientific questions and needs
and tackle societal benefit areas.
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Regardless of the EV Type and the specific theme, global requirements for characterizing a variable as
“Essential”, are that this variable is critical for characterizing a specific environmental system or process, it is
sensitive to detect change at different scales, it is technically feasible using scientifically understood methods
to observe or derive the variable on a global scale and it can be generated and archived on an affordable
way, relying on coordinated observing systems and proven current technology (3). Overall, an essential
requirement is that within each theme, a limited number of variables should be identified. This will not only
minimize technical and scientific requirements related to data formats, data processing, big data handling,
bias correction, data harmonization etc., but it will contribute to the cost-effectiveness of the framework,
allowing early detection of changes and improved communication between science, policy and the society
(4).
Concluding, the proposed methodology of identifying and computing an EV and the area of interest and the
reporting level of the monitored phenomenon are closely interdependent parameters. Specifically, by setting
the spatial level (i.e. global, regional, country level etc.) and the required time intervals of the monitoring, we
can define which EV can be used for the monitoring. In addition, the definition of the EV defines the
temporal and spectral analysis of the satellite imagery and in general the EO data products that will be
utilized for the computation of each EV.
The methodology proposed by the Laboratory of Photogrammetry and Remote Sensing of the Department
of Surveying Engineering of the Aristotle University of Thessaloniki (PE&RSLab), (5) suggests computing each
EV as a set of critical or complementary variables which are individual sub-parameters that when combined
can quantify the EV. Within that framework, one EV can potentially contribute to multiple case studies, and a
given observation can be linked to more than on EV (6). This can enable a potential reduction of the number
of observations required to deliver the EVs and thus monitoring a phenomenon (Figure 1).
Figure 1: General Workflow for computing EVs for monitoring
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2 Current and future capabilities of the EO-toolset
2.1 Available sensors
2.1.1 Sentinel-2
Dedicated to supplying data for Copernicus services, Sentinel-2 carries a multispectral imager with a swath of
290 km. The imager provides a versatile set of 13 spectral bands spanning from the visible and near infrared
to the shortwave infrared, featuring four spectral bands at 10 m, six bands at 20 m and three bands at 60 m
spatial resolution.
2.1.2 Landsat-8
The Landsat-8 satellite launched on February 11, 2013, orbits the Earth in a sun-synchronous, near-polar
orbit, at an altitude of 705 km (438 mi), inclined at 98.2 degrees, and circles the Earth every 99 minutes. The
satellite has a 16-day repeat cycle with an equatorial crossing time: 10:00 a.m. +/- 15 minutes. The Landsat-8
satellite carries 2 instruments namely, Operational Land Imager (OLI)- and Thermal Infrared Sensor (TIRS).
The Operational Land Imager includes eight “narrow” spectral bands with 30 m spatial resolution and a
“wider” panchromatic band with 15 m spatial resolution:
• Band 1 Coastal (0.43 - 0.45 µm) 30 m
• Band 2 Blue (0.450 - 0.51 µm) 30 m
• Band 3 Green (0.53 - 0.59 µm) 30 m
• Band 4 Red (0.64 - 0.67 µm) 30 m
• Band 5 Near-Infrared (0.85 - 0.88 µm) 30 m
• Band 6 SWIR 1(1.57 - 1.65 µm) 30 m
• Band 7 SWIR 2 (2.11 - 2.29 µm) 30 m
• Band 8 Panchromatic (PAN) (0.50 - 0.68 µm) 15 m
• Band 9 Cirrus (1.36 - 1.38 µm) 30 m
The Thermal Infrared Sensor (TIRS) includes two spectral bands with 100 m spatial resolution:
• Band 10 TIRS 1 (10.6 - 11.19 µm) 100 m
• Band 11 TIRS 2 (11.5 - 12.51 µm) 100 m
2.1.3 Landsat-5
Landsat-5 orbited the planet at an altitude of 705 km more than 150,000 times from 1984 to 2013 being the
longest-operating Earth-observing satellite sensor in history, transmitting over 2.5 million images of land
surface conditions around the world. The Landsat-5 satellite carried 2 instruments namely, Multispectral
Scanner (MSS) - and Thematic Mapper (TM). TM was an across-track mechanical scanner provided
multispectral images of Earth’s surface with 8-bit radiometric resolution and 30 m spatial resolution. The TM
included seven reflective spectral bands with 30 m spatial resolution and a thermal band with 120 meters
spatial resolution:
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• Band 1 Blue (0.45 - 0.52 µm) 30 m
• Band 2 Green (0.52 - 0.60 µm) 30 m
• Band 3 Red (0.63 - 0.69 µm) 30 m
• Band 4 Near-Infrared (0.76 - 0.90 µm) 30 m
• Band 5 Near-Infrared (1.55 - 1.75 µm) 30 m
• Band 6 Thermal (10.40 - 12.50 µm) 120 m
• Band 7 Mid-Infrared (2.08 - 2.35 µm) 30 m
2.1.4 Landsat-7
The Landsat-7 satellite orbits the Earth in a sun-synchronous, near-polar orbit, at an altitude of 705 km (438
mi), inclined at 98.2 degrees, and circles the Earth every 99 minutes. The satellite has a 16-day repeat cycle
with an equatorial crossing time: 10:00 a.m. +/- 15 minutes. Landsat-7 carries the Enhanced Thematic
Mapper Plus (ETM+) sensor, delivering 8-bit images with 256 grey levels.
The ETM+ includes seven reflective spectral bands (six “narrow” spectral bands with 30 m and a “wider”
panchromatic band with 15 meters spatial resolution) and a thermal band with 60 m spatial resolution:
• Band 1 Blue (0.45 - 0.52 µm) 30 m
• Band 2 Green (0.52 - 0.60 µm) 30 m
• Band 3 Red (0.63 - 0.69 µm) 30 m
• Band 4 Near-Infrared (0.77 - 0.90 µm) 30 m
• Band 5 Near-Infrared (1.55 - 1.75 µm) 30 m
• Band 6 Thermal (10.40 - 12.50 µm) 60 m Low Gain / High Gain
• Band 7 Mid-Infrared (2.08 - 2.35 µm) 30 m
• Band 8 Panchromatic (PAN) (0.52 - 0.90 µm) 15 m
2.1.5 Aqua/Terra MODIS
The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the Terra and
Aqua satellites at an orbit of 705 km. Both Terra- and Aqua-MODIS instruments view the entire surface of
the Earth every 1 to 2 days, acquiring data across 36 spectral MODIS bands from 0.4s to 14.4 μm. Available
on the EO-Toolset is the MCD43A4 product, providing the 500 meter reflectance data of the MODIS “land”
bands 1-7 adjusted using the bidirectional reflectance distribution function to model the values as if they
were collected from a nadir view.
The MCD43A4 product includes seven reflective spectral bands:
• Band 1 Red) (620–670 µm) 500 m
• Band 2 Near-Infrared (841–876 µm) 500 m
• Band 3 Blue (459–479 µm) 500 m
• Band 4 Green (545–565 µm) 500 m
• Band 5 SWIR-1 (1230–1250 µm) 500 m
• Band 6 SWIR-2 (1628–1652 µm) 500 m
• Band 7 MWIR (2105–2155 µm) 500 m
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2.2 Sensors to be implemented in the future
The EO-toolset includes currently a suite of sensors facilitating analysis and monitoring of Earth’s surface
from global to national scales. Yet, based on the preliminary identification of user needs of the law and
environmental communities, VHR data availability is a non-functional requirement. As such, very-high spatial
resolution sensors as described in detail D5.1, will be implemented within EO-toolset.
2.3 Pre-processing capabilities
2.3.1 Atmospheric correction for Sentinel-2 data
There are two atmospheric correction filters available, both of which use precomputed data:
• DOS1 being simpler and somewhat less accurate as well as available only for RGB bands.
• ATMCOR is the one based on Sen2cor and is available for all bands, except for band 10, but might
not be available for all tiles. In this case the system will use DOS1 calculation instead for those
tiles.
2.3.2 Automated cloud and cloud shadow identification in Sentinel imagery
Automated cloud and cloud shadow identification algorithms designed for Sentinel-2 satellite images
provides the means of including only clear-view pixels in image analysis and efficient cloud-free compositing.
Two algorithms are available for cloud detection within the EO-toolset
1. The modified Braaten, et al (7) cloud detection algorithm
2. The Hollstein et al. (8) algorithm for the detection of clouds, cirrus, snow, shadow, water and clear
sky pixels.
The first one (see Figure 2) is a modified version of the relatively simple cloud detection algorithm from
Braaten, et al (7) based on simple thresholding is available within EO-toolset using B11, B03 and B04:
Figure 2: Cloud detection algorithm within the EO toolset based on Braaten et al. (2015)
function index(x, y) {
return (x - y) / (x + y);
}
function clip(a) {
return Math.max(0, Math.min(1, a));
}
let bRatio = (B03 - 0.175) / (0.39 - 0.175);
let NGDR = index(B03, B04);
let gain = 2.5;
if (B11>0.1 && bRatio > 1) { //cloud
var v = 0.5*(bRatio - 1);
return [0.5*clip(B04), 0.5*clip(B03), 0.5*clip(B02) + v];
}
if (B11 > 0.1 && bRatio > 0 && NGDR>0) { //cloud
var v = 5 * Math.sqrt(bRatio * NGDR);
return [0.5 * clip(B04) + v, 0.5 * clip(B03), 0.5 * clip(B02)];
}
return [B04, B03, B02].map(a => gain * a);
enviroLENS – D2.1: Essential Variable (EV) List
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The second one is based on the individual bands, ratios and difference of bands considering several Sentinel-
2 bands (Figure 3).
Figure 3: Cloud detection algorithm within the EO toolset based on Hollstein et al. (2016)
2.4 Band colour composites
Any individual band of a multispectral digital image can be displayed as grayscale (panchromatic) image,
where the lowest-value pixels are displayed as black, the highest-value pixels are displayed as white, and
pixels with intermediate values are displayed in corresponding shades of grey (9).
Alternatively, true-color (or natural colour) and false-color composites work under the premise that a
computer screen will display no more than three image bands at a time, each matched to one of three
primary colour ramps: blue, green, and red. True colour composites depicts its features in natural colour
(similar to the human perception) while the false colour composites allow to visualize the wavelengths the
human eye does not see. Within the EO-toolset, several colour composites are available for each sensor (see
Table 1)
Table 1: Readily available composites in EO-toolset for the Sentinel-2, Landsat and MODIS sensors
Sentinel-2 Landsat-8 Landsat 5-7 MODIS
Natural color (B04,B03,B02) Natural color (B04, B03, B02)
Natural color (B03,B02,B01)
True Color (B01,B04,B03)
Color Infrared (vegetation) (B08,B04,B03)
Pansharpened natural color (B04,B03,B02)
Color Infrared (vegetation) (B04,B03,B02)
False color (urban) (B12,B11,B04)
Color Infrared (vegetation) (B05,B04,B03)
Short-wave Infrared (B07/05,B04,B02)
Agriculture (B11,B08,B02)
Vegetation Index (B08,B04)
Moisture Index (B8A - B11)
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Sentinel-2 Landsat-8 Landsat 5-7 MODIS
Geology (B12, B04,B02)
Bathymetric (B04,B03,B01)
Atmospheric penetration (B12,B11,B8A)
SWIR (B12,B8A,B04)
NDWI (B03,B08)
SWIR (B02,B11,B12)
2.5 Spectral indices
Several spectral indices have been implemented within the EO-Toolset for each sensor. A detailed reference
is given in the appendix (Appendix 7.1)
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3 Essential variables
The EV approach foresees the definition of a minimum set of variables that can be enough to decipher and
fully characterize the state, trend and future evolution of the Earth’s system in a cost-, time- and work-
efficient approach (10). The following sections include a detailed review of the all the recent progress in the
field of the EVs definition in different societal sectors and scientific disciplines.
3.1.1 Essential Climate Variables (ECV)
The first community that “introduced” the process of EVs identification was the weather and climate one. In
the 1990s, gaps in knowledge of climate and declining core observational networks in some countries,
emphasized the need of a limited set of critical variables that should be observed, providing empirical
evidence on status and evolution of climate, and guidance on mitigation and adaptation measures (10).
Accordingly, the ECV were defined as a ‘physical, chemical, or biological variable or a group of linked
variables that critically contributes to the characterization of Earth’s climate’ (11).
This definition was endorsed by the United Nation Convention on Climate Change (UNFCCC) and other
international bodies and programmes. ECVs datasets provide nowadays the empirical evidence needed to
understand and predict the evolution of climate, to guide mitigation and adaptation measures, to assess
risks and enable attribution of climatic events to underlying causes, and to underpin climate services. The
current ECVs list includes 50 variables in the three domains of Atmosphere, Ocean and Land (12).
3.1.2 Essential Biodiversity Variables (EBVs)
Essential Biodiversity Variables (EBVs) were proposed in 2013 by the biodiversity community to improve
harmonization of biodiversity data into meaningful metrics (4). Previously, it had been identified from the
Global Biodiversity Outlook (GBO4) monitoring progress towards Aichi Biodiversity Targets (ABT) (13), that
many of the indicators used for supporting the measurement of ABT, were characterized by insufficient data
standardization, global coverage, spatial resolution and long-time records (14).
The proposed EBVs have been grouped into six classes: genetic composition, species populations, species
traits, community composition, ecosystem structure, and ecosystem function (15). This concept has taken
root within wide segments of the theoretical and applied ecology communities. Furthermore, the idea
behind the original EBV concept was that at least one EBV per class should be monitored, while keeping the
set of EBVs limited is necessary to assure the usefulness of the EBV concept (16). Satellite-based EO as a
measurement tool for biodiversity-related indicators includes many proven advantages include synoptic view
of the Earth’s surface under constant conditions of solar illumination, consistent and systematic surface
observation, existence of multi-annual time series of observations, cost-effective for monitoring remote and
inaccessible areas. Therefore 14 out of the 22 candidate EBVs have a fully or partly remotely sensed
component and can be considered as Remote Sensing Essential Biodiversity Variables (RS-EBVs) (14)
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3.1.3 Essential Renewable Energies Variables (EREV)
Renewable Energy is a domain where no major dedicated EO network exists so far (17) while no formal
attempt by international bodies to validate EVs was undertaken (10). Renewable Energy Essential Variables
(RE-EVs) (12) or Essential Renewable Energies Variables (18) according to an earlier definition (12) are
variables that meet important requirements from RE stakeholders and are technical and economically
feasible for systematic observation and global implementation. Recently, RE-EVs were defined as a minimal
set of variables that determine the state of the energy system, being crucial for predicting its development,
and support metrics that measure its trajectory (18). Through GEO’s Energy Community of Practices (Energy
CoP) a provisional list of 18 variables has been identified (17).
3.1.4 Essential Geodetic Variables (EGVs) and Essential Earth Rotation Variables
(EERVs)
Efforts to identify essential variables have been initiated within the Global Geodetic Observing System
(GGOS) of the International Association of Geodesy (IAG) that provides the means for integrating ground-
and space-based geodetic and gravimetric observations. Modernizing the existing geodetic and gravimetric
infrastructure and homogenizing data processing are essential for consistent observations of Earth's time-
variable shape, rotation and gravity (19). Within this framework, IAG one of the eight semi-autonomous
Associations of the International Union of Geodesy and Geophysics, has prioritized the definition of the
respective essential variables and through its Bureau of Products and Standards established the Committee
on Essential Geodetic Variables (EGVs). The EGVs are observed variables that are crucial (essential) to
characterizing the geodetic properties of the Earth and that are key to sustainable geodetic observations
(19). Examples of EGVs might be the positions of reference objects, earth orientation parameters, ground-
and space-based gravity measurements (19). In parallel, the Committee on Essential Geodetic Variables also
aims to identify the Essential Gravimetric Variables (EGrVs) that can be used to enable a systematic
monitoring of the geodetic properties of the Earth (20). For the Earth’s rotation, the EERVs are proposed to
be the five Earth orientation parameters (EOPs), namely, the x- and y-components of polar motion (xp, yp),
the x- and y-components of nutation/precession (X,Y) and the spin parameter UT1 (21).
3.1.5 Essential Ocean Variables (OCVs)
Measurements of the status and trends of key indicators for the ocean and marine life are required for
ensuring their sustainable use and conservation (22). Observing status and trends of the global ocean dates
back to the 1980s and 1990s, when the World Ocean Circulation Experiment (WOCE) established an
essential set of observations, to meet regional sampling requirements (23). Essential Ocean Variables (EOVs)
are defined through the Global Ocean Observing System (GOOS), established under the Intergovernmental
Oceanographic Commission (IOC) and Marine Biodiversity Observation Network (MBON), in collaboration
with the Ocean Biogeographic Information System (OBIS), and the Integrated Marine Biosphere Research
(IMBeR) (24).
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Since EOVs are the Framework for Ocean Observing concept of the fundamental physical, biogeochemical,
and biological measurements needed for the scientific understanding of ocean phenomena and the
provision of applications in support of Societal Benefits (25), efforts are focused nowadays to the essential
biological and ecosystem variables to measure (26).
3.1.6 Essential Water Variables (EWVs)
The GEOSS Water Strategy Report submitted by the Integrated Global Water Cycle Observations (IGWCO)
Community of Practice (CoP) defined the EWVs as water variables/parameters that address specific user-
requirements for water management decisions and monitoring (27), including:
• Observational “monitoring” of key elements of the global and regional/local water cycle.
• Observations required by diagnostic and/or land surface/hydrological prediction models that are
used to generate derived products for the end-user communities
• Observational and model-derived variables and parameters required by users of water
data/information products as applied to various inter-disciplinary decision support systems and
tools
Based on these criteria, 10 primary and 9 supplementary variables were identified (27). The process of
further promoting and evolving the EWVs is currently supported by the GEO-Global Water Sustainability
(GEOGLOWS).
3.1.7 Essential Sustainable Development Goals Variables (ESDGV)
Reyes et al (28) after identifying the need of the global community to measure and monitor progress
towards Sustainable Development Goals (SDG), proposed for criteria for identifying Essential Variables for
the SDG domain. They suggested that the ESDGV should identify key features, processes and interactions are
critical for describing and projecting of social–ecological systems behaviour over time and space (capture
system essence, should support the transformative agenda of SDGs, based on knowledge about system
transformations and leverage points, should capture trade-offs or synergies between the SDGs or between
policy arenas requiring coordination and should be foundational and multipurpose.
3.1.8 Urban Essential Variables (UEVs)
UEVs are currently under development stage, mostly in the ERAPLANET project SMURBS (29). Global Urban
Observation and Information Initiative, included in the GEO 2012–2015 Implementation Plan, focused
among others in the identification of EO based essential urban variables and indicators for sustainable cities
(30).
3.1.9 Essential Societal Variables (ESVs)
Ehrilich et al. (31) introduced the term human societal system to indicate the integrated system that
combines human activities and earth system processes and the associated need to establish essential
variables to monitor this system and model human activities and the impact of climate induced hazards on
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society. Societal variables are essential in the quantification of processes such as urbanization, global
displacement of land use, the looming land scarcity, dynamic of the food system and water cycle. In the
study of Ehrilich et al. (31) EO datasets are demonstrated for calculating global built-up area and global
population densities. Linkages of these two ESVs with SGDs and policies to mitigate risks were also explored.
3.1.10 Essential Agriculture Variables (EAVs)
The process for defining Essential Agriculture Variables (EAVs) is currently driven by GEO’s Global Agricultural
Monitoring initiative (GEOGLAM). EAVs are defined by GEOGLAM as the minimum set of variables needed
from their community to understand state and change in agricultural systems. The preliminary proposal is
formulated upon a tiered (hierarchical) approach that includes Core Agriculture Indicators, EAVs and
Supporting EVs from other themes (communities). The current tentative list of EAVs includes crop area, crop
type, crop condition, crop phenology, crop yield and crop management and agricultural practices.
3.1.11 Essential Protected Area Variables (EPAVs)
Within the ECOPOTENTIAL project1 an initiative was launched to identify essential variables through a
bottom-up approach. A three-tiered organization for the organization of the variables considering the
absolute or high-level of commonality regarding their use across diverse Protected Areas as well as their
technical dimensions was followed for the different storylines.
The EPAVs were identified using the following criteria (32):
• EVs must be observable and sensitive to change
• EVs can include core system variables
• EVs must be scalable
• EVs can include variables that are unique to a given system or ecological process
• EVs should be ecosystem agnostic
Within this community, a tentative list of 7 variables were identified including ecosystem extent and
fragmentation, precipitation, population abundance, taxonomic diversity, land use, land cover and net
primary productivity.
1 http://www.ecopotential-project.eu/
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4 USE-CASE requirements and EVs linkages
4.1 Case 1: Environmental Impact Assessment Process in the Energy Projects
As the detailed definition of the context of the specific Use-Case is in progress, linkages with EVs will be
developed accordingly at a later stage.
4.2 Case 2: Force Majeure Events in the Energy Contracts
The aforementioned use cases focus on transnational agreements for the implementation of international
cross-border energy infrastructure projects. Based on the information requirements, two important EVs
were identified, providing information on potential damages in the natural environment. For instance, the
impacts of a devastating fire in a big-scale construction project on the human and natural ecosystem in the
area of interest can be quantified using the following Essential Variables:
1) ECV: Burned Area
The essential variable of the Burned Area can be quantified using EO data and more specific, by computing
the Normalized Burn Ratio (NBR) to highlight areas that have been burned.
2) ECV: Burn Severity
The NBR is also used to estimate burn severity. Namely, in order to quantify the burn severity, EO data are
collected for an area of interest, before and shortly after the fire. Thus, imagery collected before a fire will
have very high near-infrared band values and very low mid-infrared band values. On the contrary, the
corresponding imagery after the fire will have very low near-infrared band values and very high-mid infrared
band values. Higher ΔNBR indicate more severe damage. Areas with negative ΔNBR values may indicate
increased vegetation productivity following a fire (33), (Figure 4).
Figure 4 :Spectral Response Curves of burned areas and of healthy vegetation, US Forest Service, 2018
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The general formula for the estimation of the Burn Severity is ΔNBR = estimation of the NBR before fire –
estimation of the NBR after fire. In addition, the United States Geological Survey (USGS) proposed a
classification table to interpret the burn severity using the following thresholds:
Table 2 ΔNBR Burn Severity Categories, USGS FireMon program
ΔNBR Burn Severity
<-0.25 High post-fire regrowth
-0.25 to -0.1 Low post-fire regrowth
-0.1 to +0.1 Unburned
0.1 to 0.27 Low-severity burn
0.27 to 0.44 Moderate-low severity burn
0.44 to 0.66 Moderate-high severity burn
> 0.66 to 1.30 High-severity burn
4.3 Case 3: Oil and Gas Pipeline Monitoring For Safety and Environment
As the detailed definition of the context of the specific Use-Case is in progress, linkages with EVs will be
developed accordingly at a later stage.
4.4 Case 4: Forest Law Enforcement and Governance
This use case will focus on illegal logging and deforestation monitoring in an Eastern European country.
Based on the information requirements, 5 EVs were identified as being more relevant to the specific case,
providing information on the forested extent within each area.
1) EBV: Ecosystem extent and fragmentation
Ecosystem extent refers to the location and geographic distribution of ecosystems across landscapes or in
the oceans, while ecosystem fragmentation refers to the spatial pattern and connectivity of ecosystem
occurrences on the landscape. Due to a lack of availability of time series data on ecosystem extent, and also
to the general lack of ecosystem maps in the first place, land cover is often used as a proxy for ecosystems.
Another approach to assessing change in ecosystem extent, which does not require use of a land cover
proxy, is to obtain a change map derived from image analysis of two images at different dates. The images
can be compared for changes in spectral properties, and without classifying the spectral signatures into
land cover classes, a change map can be produced which indicates where, on the ground, changes have
occurred (34).
2) ECV: Land cover
Land cover is defined as the observed (bio)-physical cover on the earth’s surface.
3) ECV: Leaf Area Index (LAI)
The LAI of a plant canopy or ecosystem, defined as one half the total green leaf area per unit horizontal
ground surface area, measures the area of leaf material present in the specified environment. LAI is defined
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as the one-sided green leaf area per unit ground area in broadleaf canopies, or as the projected needle leaf
area per ground unit in needle canopies.
4) EUV: Vegetation canopy cover
Vegetation canopy cover is defined as the share of ground covered by the vertical projection of the canopy
and is commonly expressed as a percentage. In addition, the international definition of a forest is based on
canopy cover.
4.5 Case 5: Infrastructure development in Protected Areas
1) EUV: Built up area
Locations dominated by constructed surfaces or built environment, where dominancy implies >50%
coverage of pixels [16,20].
2) EUV: Soil sealing
Soil sealing can be defined as the destruction or covering of soils by buildings, constructions and layers of
completely or partly impermeable artificial material (asphalt, concrete, etc.).
3) ECV: Land cover
Land cover is defined as the observed (bio)-physical cover on the earth’s surface.
4.6 Case 6: Land-use change in the context of disaster risk reduction
As the detailed definition of the context of the specific Use-Case is in progress, linkages with EVs will be
developed accordingly.
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5 Essential Variables modelling
5.1 Burned Area (ECV) and Burn Severity (ECV) modelling
The essential variable of the Burned Area can be quantified using EO data and more specific, by computing
the Burn Area Index (BAI) or the Normalized Burn Ratio (NBR) for large fire zones greater than 500 acres, to
highlight burned areas.
The Burn Area Index (BAI) is computed using analysis ready satellite data and more specific, by utilizing the
red and near-infrared bands of the image. In addition, the Normalized Burn Ratio (NBR) image, was originally
developed for use with Landsat Imagery (Landsat TM and ETM+) utilizing the near-infrared (NIR) and
shortwave-infrared (SWIR) bands of the image.
Figure 5 Burned Area Index (BAI) and Normalized Burn Ratio (NBR) formulas indices, ((35)
The creation of a pre-fire and a post-fire NBR image and the generation of their difference can result to the
estimation of the Burn Severity (ΔNBR). Darker pixels of the final image indicate burned areas, while the
proposed classification of the results (severity thresholds) can lead to an initial estimation of the burn
severity. Figure 6 represents and indicative workflow using analysis ready satellite data.
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Figure 6: Burned Area and Burn Severity workflow modelling
Figure 7 represents the results of the aforementioned workflow in the area of Alexandroupolis – Greece.
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Figure 7: Computation of NBR (middle image) and ΔNBR (right image) for the area of Alexandroupolis – Greece (left image – orthorectified). Dark areas indicate burned areas.
5.2 Built-up area
Built-up area is a key parameter for the description of urban areas. An accurate estimation of the built-up
area can quantify the changes in an urban tissue providing vital information about the effects of urbanization
on biodiversity and the sustainability of urban ecosystems. Built-up areas refer to urban areas dominated by
artificial surfaces and more specifically to areas where the dominancy of built environment is more than 50%
in cover by non-vegetated, human-constructed elements (roads, buildings, infrastructure, industrial facilities
and more). In addition, artificial areas within the administrative boundary of an urban area covered by
vegetation (e.g. parks, gardens and more), are also considered as part of the built-up area (36). EO data
provide a reliable, low cost and efficient source of defining built-up areas. Thus, the remote sensing
community has developed various methods for the extraction of built-up areas combining spectral and
spatial indexes, utilizing multi-sensor data such as Landsat or Sentinel Imagery and Radar (SAR) data (37).
An indicative workflow for the computation of built-up areas, utilizing already available spatial data from
various data repositories are as follow:
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Figure 8 Built Up area workflow modelling using EO ready data (spatial data from data repositories)
Area Selection: The user will be able to select the exact location of the area of interest by providing the physical
address of a site (geocoding tool is needed) or by selecting a location on the map. In addition, in protected
landscapes the boundaries of a selected ecosystem can be provided by open access spatial data repositories
(e.g. Data repository of Natura20002 or Ramsar Site Information Service3, national data repositories and
more).
2 http://ftp.eea.europa.eu/www/natura2000/Natura2000_end2018_Shapefile.zip 3 https://rsis.ramsar.org/
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Set Time period: Select the time period and time intervals. The aforementioned selection will also define the
available satellite data that will be utilized for the process.
Image filtering and pre-processing: The process of image filtering and pre-processing is one of the most
crucial steps in each EV computation workflow. The available Satellite data as previously listed according to
their date of acquisition are further filtered to select the most suitable image for the process (i.e. selection of
the least cloudy image, radiometric enhancement of the selected image and more). Overall, the proposed
methodology suggests utilizing analysis ready data (e.g. Sentinel-2 BoA data (Bottomo of Atmosphere) or
Landsat Level-2 data and more). However, in cases where satellite imagery requires further pre-processing,
the proposed methodology also includes additional pre-processing algorithms (e.g. Landsat-7 correction due
to SLC (Scan Line Corrector) failure and more, atmospheric corrections).
5.3 Soil Sealing
Soil Sealing is defined as the destruction or covering of soils by buildings, constructions and layers of
completely or partly impermeable artificial material (asphalt, concrete, etc.). It can be computed using high
resolution satellite imagery (Figure 9).
Figure 9 Soil Sealing workflow modelling using satellite imagery
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represents the results of the aforementioned workflow in the designated area of Velipoje –Albania
represents the results of the aforementioned workflow in the designated area of Velipoje –Albania.
Figure 10: Computation of Soil Sealing for the designated area of Velipoje – Albania for the time period 2009-2018
Figure 11: Computation of Soil Sealing for the designated area of Velipoje – Albania for the time period 2015-2018
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5.4 Land Cover
Land Cover can be estimated using EO data from data repositories. In cases were ready available data are not
suitable for the area of interest; satellite imagery can also be utilized (Figure 12).
Figure 12: Computation of Land Cover using available spatial data and satellite imagery
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6 Final Essential Variables list
The selection of the use cases has a profound influence in the identification of the EV list. We are still in the
course to define the cases to guarantee an optimal uptake of the enviroLENS tools and service.
The next steps will include bilateral meetings with the domain representative to design an implementation
plan for each of the show cases that details the software user requirements to investigate which software
can be employed to serve the user needs in the specific cases as well as the exact data needs and data
services.
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Appendix
Spectral indices for the sensors available within the EO-Toolset
Sentinel-2 indices
Abbreviation Name
ATSAVI Adjusted transformed soil-adjusted VI
AFRI1600 Aerosol free vegetation index 1600
AFRI2100 Aerosol free vegetation index 2100
Alteration
ARI Anthocyanin reflectance index
AVI Ashburn Vegetation Index
ARVI2 Atmospherically Resistant Vegetation Index 2
BWDRVI Blue-wide dynamic range vegetation index
BRI Browning Reflectance Index
CARI2 Chlorophyll Absorption Ratio Index 2
Chlgreen Chlorophyll Green
CIgreen Chlorophyll Index Green
CIrededge Chlorophyll IndexRedEdge
Chlred-edge Chlorophyll Red-Edge
CVI Chlorophyll vegetation index
CI Coloration Index
CTVI Corrected Transformed Vegetation Index
Datt1 Datt1
Datt4 Datt4
Datt6 Datt6
D678/500 Difference 678/500
D800/550 Difference 800/550
D800/680 Difference 800/680
D833/658 Difference 833/658
GDVI Difference NIR/Green Green Difference Vegetation Index
DVIMSS Differenced Vegetation Index MSS
EVI Enhanced Vegetation Index
EVI2 Enhanced Vegetation Index 2
Fe2+ Ferric iron, Fe2+
Fe3+ Ferric iron, Fe3+
Ferric Oxides
Ferrous iron
Ferrous Silicates
GVMI Global Vegetation Moisture Index
Gossan
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Abbreviation Name
GARI Green atmospherically resistant vegetation index
GNDVI Green Normalized Difference Vegetation Index
GBNDVI Green-Blue NDVI
GRNDVI Green-Red NDVI
IPVI Infrared percentage vegetation index
I Intensity
Laterite
LCI Leaf Chlorophyll Index
Maccioni
MCARI/MTVI2 MCARI/MTVI2
MCARI/OSAVI MCARI/OSAVI
MVI Mid-infrared vegetation index
MGVI Misra Green Vegetation Index
MNSI Misra Non Such Index
MSBI Misra Soil Brightness Index
MYVI Misra Yellow Vegetation Index
mND680 mND680
MCARI Modified Chlorophyll Absorption in Reflectance Index
MCARI1 Modified Chlorophyll Absorption in Reflectance Index 1
MCARI2 Modified Chlorophyll Absorption in Reflectance Index 2
mNDVI Modified NDVI
mSR Modified Simple Ratio
MSR670 Modified Simple Ratio 670,800
MSAVI Modified Soil Adjusted Vegetation Index
MSAVIhyper Modified Soil Adjusted Vegetation Index hyper
MTVI1 Modified Triangular Vegetation Index 1
MTVI2 Modified Triangular Vegetation Index 2
Norm G Norm G
Norm NIR Norm NIR
Norm R Norm R
PPR Normalized Difference 550/450 Plant pigment ratio
PVR Normalized Difference 550/650 Photosynthetic vigour ratio
ND774/677 Normalized Difference 774/677
GNDVIhyper Normalized Difference 780/550 Green NDVI hyper
ND782/666 Normalized Difference 782/666
ND790/670 Normalized Difference 790/670
ND800/2170 Normalized Difference 800/2170
PSNDc2 Normalized Difference 800/470 Pigment specific normalised di€fference C2
PSNDc1 Normalized Difference 800/500 Pigment specific normalised di€fference C1
GNDVIhyper2 Normalized Difference 800/550 Green NDVI hyper 2
PSNDb1 Normalized Difference 800/650 Pigment specific normalised di€fference B1
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Abbreviation Name
PSNDa1 Normalized Difference 800/675 Pigment specific normalised di€fference A1
NDII Normalized Difference 819/1600 NDII
NDII2 Normalized Difference 819/1649 NDII 2
NDMI Normalized Difference 820/1600 Normalized Difference Moisture Index
ND827/668 Normalized Difference 827/668
ND833/1649 Normalized Difference 833/1649 Infrared Index
ND833/658 Normalized Difference 833/658
SIWSI Normalized Difference 860/1640
ND895/675 Normalized Difference 895/675
NGRDI Normalized Difference Green/Red Normalized green red difference index, Visible Atmospherically Resistant Indices Green (VIgreen)
NDVI Normalized Difference MIR/NIR Normalized Difference Vegetation Index (in case of strong atmospheric disturbances)
BNDVI Normalized Difference NIR/Blue Blue-normalized difference vegetation index
GNDVI Normalized Difference NIR/Green Green NDVI
MNDVI Normalized Difference NIR/MIR Modified Normalized Difference Vegetation Index
NDVI Normalized Difference NIR/Red Normalized Difference Vegetation Index, Calibrated NDVI - CDVI
NDRE Normalized Difference NIR/Rededge Normalized Difference Red-Edge
NBR Normalized Difference NIR/SWIR Normalized Burn Ratio
RI Normalized Difference Red/Green Redness Index
NDSI Normalized Difference Salinity Index
NDVI690-710 Normalized Difference Vegetation Index 690-710
OSAVI Optimized Soil Adjusted Vegetation Index
PNDVI Pan NDVI
RDVI RDVI
RDVI2 RDVI2
Rededge1 Red edge 1
Rededge2 Red edge 2
RBNDVI Red-Blue NDVI
REP Red-Edge Position Linear Interpolation
Rre Reflectance at the inflexion point
RDVI Renormalized Difference Vegetation Index
IF Shape Index
MSI2 Simple Ratio 1599/819 Moisture Stress Index 2
MSI Simple Ratio 1600/820 Moisture Stress Index
TM5/TM7 Simple Ratio 1650/2218
SR440/740 Simple Ratio 440/740
BGI Simple Ratio 450/550 Blue green pigment index
SR520/670 Simple Ratio 520/670
SR550/670 Simple Ratio 550/670
DSWI-4 Simple Ratio 550/680 Disease-Water Stress Index 4
SR550/800 Simple Ratio 550/800
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Abbreviation Name
GI Simple Ratio 554/677 Greenness Index
SR560/658 Simple Ratio 560/658 GRVIhyper
SR672/550 Simple Ratio 672/550 Datt5
SR672/708 Simple Ratio 672/708
SR674/553 Simple Ratio 674/553
SR675/555 Simple Ratio 675/555
SR675/700 Simple Ratio 675/700
SR675/705 Simple Ratio 675/705
SR700 Simple Ratio 700
SR700/670 Simple Ratio 700/670
SR710/670 Simple Ratio 710/670
SR735/710 Simple Ratio 735/710
SR774/677 Simple Ratio 774/677
SR800/2170 Simple Ratio 800/2170
PSSRc2 Simple Ratio 800/470 Pigment specific simple ratio C2
PSSRc1 Simple Ratio 800/500 Pigment specific simple ratio C1
SR800/550 Simple Ratio 800/550
PSSRb1 Simple Ratio 800/650 Pigment specific simple ratio B1
RVI Simple Ratio 800/670 Ratio Vegetation Index
PSSRa1 Simple Ratio 800/675 Pigment specific simple ratio A1
SR800/680 Simple Ratio 800/680 Pigment Specific Simple Ratio (Cholophyll a) (PSSRa)
SR801/550 Simple Ratio 801/550 NIR/Green
SR801/670 Simple Ratio 801/670 NIR/Red
PBI Simple Ratio 810/560 Plant biochemical index
SR833/1649 Simple Ratio 833/1649 MSIhyper
SR833/658 Simple Ratio 833/658
Datt2 Simple Ratio 850/710 Datt2
SR860/550 Simple Ratio 860/550
SR860/708 Simple Ratio 860/708
RDI Simple Ratio MIR/NIR Ratio Drought Index
SRMIR/Red Simple Ratio MIR/Red Eisenhydroxid-Index
SRNir/700-715 Simple Ratio NIR/700-715
GRVI Simple Ratio NIR/G Green Ratio Vegetation Index
SRNIR/MIR Simple Ratio NIR/MIR
DVI Simple Ratio NIR/RED Difference Vegetation Index, Vegetation Index Number (VIN)
RRI1 Simple Ratio NIR/Rededge RedEdge Ratio Index 1
IO Simple Ratio Red/Blue Iron Oxide
RGR Simple Ratio Red/Green Red-Green Ratio
SRRed/NIR Simple Ratio Red/NIR Ratio Vegetation-Index
SB1580 Single Band 1580
SB2100 Single Band 2100
enviroLENS – D2.1: Essential Variable (EV) List
38
Abbreviation Name
SB2130 Single Band 2130
SB2180 Single Band 2180
SB2218 Single Band 2218
SB2240 Single Band 2240
SB2250 Single Band 2250
SB2270 Single Band 2270
SB2280 Single Band 2280
SB460 Single Band 460
BB3 Single Band 470 Blackburn3
SR495 Single Band 495
SB550 Single Band 550
SB555 Single Band 555
SB655 Single Band 655
SB660 Single Band 660
SB670 Single Band 670
SB675 Single Band 675
BB1 Single Band 680 Blackburn1
SB700 Single Band 700
SB703 Single Band 703 Boochs
SB705 Single Band 705
SB735 Single Band 735
SB801 Single Band 801
SB850 Single Band 850
SB885 Single Band 885
SAVI Soil Adjusted Vegetation Index
SARVI2 Soil and Atmospherically Resistant Vegetation Index 2
SAVI3 Soil and Atmospherically Resistant Vegetation Index 3
SBL Soil Background Line
Soil Composition Index
SLAVI Specific Leaf Area Vegetation Index
SIPI1 Structure Intensive Pigment Index 1
SIPI3 Structure Intensive Pigment Index 3
SBI Tasselled Cap - brightness
GVIMSS Tasselled Cap - Green Vegetation Index MSS
NSIMSS Tasselled Cap - Non Such Index MSS
SBIMSS Tasselled Cap - Soil Brightness Index MSS
GVI Tasselled Cap - vegetation
WET Tasselled Cap - wetness
YVIMSS Tasselled Cap - Yellow Vegetation Index MSS
TCARI/OSAVI TCARI/OSAVI
TCARI Transformed Chlorophyll Absorbtion Ratio
enviroLENS – D2.1: Essential Variable (EV) List
39
Abbreviation Name
TVI Transformed Vegetation Index
VI700 Vegetation Index 700
VARIgreen Visible Atmospherically Resistant Index Green
VARI700 Visible Atmospherically Resistant Indices 700
VARIrededge Visible Atmospherically Resistant Indices RedEdge
WDVI Weighted Difference Vegetation Index
WDRVI Wide Dynamic Range Vegetation Index
Landsat-8 spectral indices
Abbreviation
Name
AFRI1600 Aerosol free vegetation index 1600
Alteration
AVI Ashburn Vegetation Index
ARVI2 Atmospherically Resistant Vegetation Index 2
BWDRVI Blue-wide dynamic range vegetation index
Carbonate
CIgreen Chlorophyll Index Green
CVI Chlorophyll vegetation index
CI Coloration Index
CTVI Corrected Transformed Vegetation Index
GDVI Difference NIR/Green Green Difference Vegetation Index
DVIMSS Differenced Vegetation Index MSS
EVI Enhanced Vegetation Index
EVI2 Enhanced Vegetation Index 2
Fe2+ Ferric iron, Fe2+
Fe3+ Ferric iron, Fe3+
Ferric Oxides
Ferrous iron
Ferrous Silicates
GVMI Global Vegetation Moisture Index
Gossan
GNDVI Green Normalized Difference Vegetation Index
GBNDVI Green-Blue NDVI
GRNDVI Green-Red NDVI
IPVI Infrared percentage vegetation index
I Intensity
Laterite
MVI Mid-infrared vegetation index
MSAVI Modified Soil Adjusted Vegetation Index
Norm G Norm G
Norm NIR Norm NIR
Norm R Norm R
PPR Normalized Difference 550/450 Plant pigment ratio
PVR Normalized Difference 550/650 Photosynthetic vigour ratio
SIWSI Normalized Difference 860/1640
enviroLENS – D2.1: Essential Variable (EV) List
40
Abbreviation
Name
NGRDI Normalized Difference Green/Red Normalized green red difference index, Visible Atmospherically Resistant Indices Green (VIgreen)
BNDVI Normalized Difference NIR/Blue Blue-normalized difference vegetation index
GNDVI Normalized Difference NIR/Green Green NDVI
MNDVI Normalized Difference NIR/MIR Modified Normalized Difference Vegetation Index
NBR Normalized Difference NIR/SWIR Normalized Burn Ratio
RI Normalized Difference Red/Green Redness Index
NDSI Normalized Difference Salinity Index
PNDVI Pan NDVI
RBNDVI Red-Blue NDVI
TM5/TM7 Simple Ratio 1650/2218
BGI Simple Ratio 450/550 Blue green pigment index
SR550/670 Simple Ratio 550/670
SR560/658 Simple Ratio 560/658 GRVIhyper
SR860/550 Simple Ratio 860/550
RDI Simple Ratio MIR/NIR Ratio Drought Index
SRMIR/Red Simple Ratio MIR/Red Eisenhydroxid-Index
SI Simple Ratio MIR/SWIR Cley Mineral-Index, Salinity Index
GRVI Simple Ratio NIR/G Green Ratio Vegetation Index
SRNIR/MIR Simple Ratio NIR/MIR
DVI Simple Ratio NIR/RED Difference Vegetation Index, Vegetation Index Number (VIN)
IO Simple Ratio Red/Blue Iron Oxide
RGR Simple Ratio Red/Green Red-Green Ratio
SRRed/NIR Simple Ratio Red/NIR Ratio Vegetation-Index
SB1580 Single Band 1580
SB2130 Single Band 2130
SB2180 Single Band 2180
SB2218 Single Band 2218
SB2240 Single Band 2240
SB2250 Single Band 2250
SB2270 Single Band 2270
SB2280 Single Band 2280
SB460 Single Band 460
BB3 Single Band 470 Blackburn3
SR495 Single Band 495
SB550 Single Band 550
SB555 Single Band 555
SB640 Single Band 640
SB655 Single Band 655
SB660 Single Band 660
MODIS spectral indices
Abbreviation Name
Alteration
Fe2+ Ferric iron, Fe2+
Fe3+ Ferric iron, Fe3+
Ferric Oxides
Ferrous iron
enviroLENS – D2.1: Essential Variable (EV) List
41
Abbreviation Name
Ferrous Silicates
GVMI Global Vegetation Moisture Index
Gossan
GNDVI Green Normalized Difference Vegetation Index
Laterite
NDWI2 Normalized Difference 857/1241 Normalized Difference Water Index
NDWI Normalized Difference 860/1240 Normalized Difference Water Index
SIWSI Normalized Difference 860/1640
MNDVI Normalized Difference NIR/MIR Modified Normalized Difference Vegetation Index
NBR Normalized Difference NIR/SWIR Normalized Burn Ratio
NDSI Normalized Difference Salinity Index
Silica 3
SR560/658 Simple Ratio 560/658 GRVIhyper
SRWI Simple Ratio 860/1240
RDI Simple Ratio MIR/NIR Ratio Drought Index
SI Simple Ratio MIR/SWIR Cley Mineral-Index, Salinity Index
SRNIR/MIR Simple Ratio NIR/MIR
SB2130 Single Band 2130
SB460 Single Band 460
BB3 Single Band 470 Blackburn3
SB555 Single Band 555
BB2 Single Band 635 Blackburn2
SB640 Single Band 640
SB655 Single Band 655
SB660 Single Band 660
SB850 Single Band 850
Soil Composition Index
VARIgreen Visible Atmospherically Resistant Index Green
enviroLENS – D2.1: Essential Variable (EV) List
42
Essential Variables list
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Genetic composition (EBV)
Co-ancestry
Allelic diversity
Population genetic differentiation
Breed and variety diversity
Species populations (EBV)
Species distribution
Population abundance
Population structure by age/size class
Species traits (EBV)
Phenology
Body mass
Natal dispersion distance
Migratory behavior
Demographic traits
Physiological traits
Community composition (EBV)
Taxonomic diversity
Species interactions
Ecosystem function (EBV)
Net primary productivity
Secondary productivity
Nutrient retention
Disturbance regime
enviroLENS – D2.1: Essential Variable (EV) List
43
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Ecosystem structure (EBV)
Habitat structure
Ecosystem extent and fragmentation
Ecosystem composition by functional type
Atmosphere (ECV), Physical surface (EOV), Terrestrial (EREV)
Air temperature
Air Temperature (surface)
Atmosphere (ECV) Air Temperature (upper-air)
Atmosphere (ECV) Wind speed and direction
Wind speed and direction (surface)
Atmosphere (ECV) Wind speed and direction (upper-air)
Atmosphere (ECV), Terrestrial (EREV)
Atmospheric Pressure (surface)
Atmosphere (ECV), Primary (EWV), Terrestrial (EREV)
Precipitation
Atmosphere (ECV)
Water Vapour Water varour (surface)
Water varour (upper-air)
Lightning
Cloud properties
Surface Radiation Budget
enviroLENS – D2.1: Essential Variable (EV) List
44
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Earth Radiation budget
Carbon dioxide, Methane, and other long-lived greenhouse gases
Ozone and Aerosol, supported by their precursors Ozone
Atmosphere (ECV), Supplemental (EWV)
Aerosols
Atmosphere (ECV) Ozone and Aerosol Precursors
Ocean (ECV), Physical surface (EOV), Ocean (EREV)
Temperature (sea-surface, sub-surface, deep-sea) Sea Surface Temperature
Ocean (ECV), Physical sub-surface (EOV), Ocean (EREV)
Subsurface Temperature
Ocean (ECV), Physical surface (EOV)
Salinity Sea Surface Salinity
Ocean (ECV), Physical sub-surface (EOV)
Subsurface Salinity
enviroLENS – D2.1: Essential Variable (EV) List
45
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Ocean (ECV), Physical surface (EOV)
Sea Level
Ocean (ECV), Physical surface (EOV)
Sea State
Ocean (ECV) Sea Ice
Ocean (ECV), Physical sub-surface (EOV)
Current Subsurface Currents
Ocean (ECV), Physical surface (EOV)
Surface Currents
Ocean (ECV) Surface Stress
Ocean (ECV), Physical surface (EOV)
Ocean Colour
Ocean (ECV), Physical surface (EOV)
Carbon dioxide partial pressure
Surface Carbon dioxide partial pressure
Ocean (ECV), Physical sub-surface (EOV)
Subsurface Carbon dioxide partial pressure
Ocean (ECV) Ocean Surface Heat Flux
Ocean (ECV), Physical surface (EOV)
Ocean surface acidity
enviroLENS – D2.1: Essential Variable (EV) List
46
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Ocean (ECV)
Marine Habitat Properties
Phytoplankton/Plankton
Nutrients
Inorganic Carbon
Ocean (ECV), Biochemical (EOV)
Nitrous Oxide
Ocean (ECV), Physical surface (EOV)
Ocen Surface Oxygen
Ocean (ECV), Physical surface (EOV)
Tracers
Land (ECV), Primary (EWV)
River discharge
Land (ECV), Primary (EWV)
Water use
Land (ECV), Primary (EWV)
Groundwater
Land (ECV), Primary (EWV)
Lakes
Land (ECV), Primary (EWV)
Snow cover
Land (ECV) Ice Sheets and ice shelves
Land (ECV) Glaciers and ice caps
enviroLENS – D2.1: Essential Variable (EV) List
47
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Land (ECV), Supplemental (EWV)
Permafrost
Land (ECV) Albedo
Land (ECV), Supplemental (EWV), Solar (EREV)
Land cover/land use
Land Cover
Supplemental (EWV), Solar (EREV)
Land Use
Land (ECV)
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)
Leaf Area Index (LAI)
Above-ground biomass
Anthropogenic Greenhouse Gas Fluxes
Soil carbon
Fire disturbance
Latent and Sensible Heat fluxes
Land (ECV), Solar (EREV)
Land Surface Temperature
Land (ECV) , Terrestrial (EREV)
Humidity
Land (ECV), Primary (EWV)
Soil moisture
enviroLENS – D2.1: Essential Variable (EV) List
48
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Primary (EWV) Soil temperature
All Global Numerical Weather Prediction (NWP) variables and others yet to be determined by WMO/GAW
Ocean (EREV)
Ocean, fixed and floating offshore wind, wave, tidal, currents, OTEC
Ocean Floor Type
Ocean Bathymetry
Sea current speed
Tidal (min, max, sea surface elevation)
Wave, height, direction, period
Terrestrial (EREV) Urbanization
Supplemental (EWV), Solar (EREV)
Elevation/topography and geological stratification
Solar (EREV)
Solar Surface Irradiance and its components (global, direct, diffuse)
Solar (EREV)
Sunshine duration (demand in energy)
Crop area
enviroLENS – D2.1: Essential Variable (EV) List
49
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Crop type
Crop condition
Crop phenology
Crop yield (current and forecast)
Crop management and agricultural practices
Famine early warning
Short term forecasting of communicable diseases
Primary (EWV)
Evaporation and evapotranspiration
Runoff/streamflow
Lake reservoir levels and aquifer volumetric change
Water quality
Water use/demand (agriculture, hydrology, energy, urbanization)
Supplemental (EWV)
Water surface altimetry
Supplemental (EWV)
Atmospheric radiation budgets
Supplemental (EWV), Terrestrial (EREV)
Cloud Cover (for EREV means demand in energy)
enviroLENS – D2.1: Essential Variable (EV) List
50
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Supplemental (EWV)
Surface meteorology
Physical surface (EOV)
Sea Level Pressure
Physical sub-surface (EOV)
Global Ocean Heat Content
Ocean Subsurface Acidity
Ocean Subsurface Tracers
Ocean Subsurface Oxygen
Biochemical (EOV)
Ocean Biochemical Oxygen
Macro Nutrients: NO3, PO4, Si, NH4, NO2
Carbonate System: DIC, Total Alkalinity, pCO2, pH
Transient Tracers: CFC-12, CFC11, SF6, tritium, 3He, 14C, 39Ar
Suspended particulates (POC, PON or POM) and PIC ++ laboratory, beam attenuation, backscatter, acidlabile, beam attenuation
Particulate Matter Export: POC export, CaCO3 export, BSi export
enviroLENS – D2.1: Essential Variable (EV) List
51
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Carbon-13: 13C/12C of dissolved inorganic carbon
DOM (Dissolved organic matter), DOC, DON, DOP
Biology and Ecosystems (EOV)
Chlorophyll
Coral Cover
Mangrove Area
Harmful Algal Blooms (HABs)
Zooplankton (biomass/abundance)
Zooplankton (Krill)
Salt Marsh Area
Large marine vertebrates: abundance/distribution
Seagrass Area
Tags and Tracking of species of value/large marine vertebrates
Fish stocks
Bacteria concentration
x-component of polar motion (xp)
y-component of polar motion (yp)
enviroLENS – D2.1: Essential Variable (EV) List
52
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
x-component of nutation/precession (X)
y-component of nutation/precession (Y)
spin parameter (UT1)
Level 0 (EGrV)
Reference Frames: e.g. Centerof Mass
Gravity Standards
Level 1 (EGrV)
Gravity Potential (Geoid)
Gravity Acceleration (1stderivative radial)
Deflections of the Vertical (1stderivatives horizontal)
Gravity Gradients (2ndderivatives)
Level 2 (EGrV)
Global Models (Mean and Time-variable)
Global Geoid (Mean)
Regional Geoid (Mean)
Level 3 (EGrV)
Mass Distribution in Earth System
Mass Transport in Earth System
Built-Up Area
Population Density
enviroLENS – D2.1: Essential Variable (EV) List
53
Essential Variable Class/Category
Essential Variable Group
Essential Variable
Bio
div
ers
ity
Clim
ate
We
ath
er
Ene
rgy
Agr
icu
ltu
re
He
alth
Wat
er
Oce
ans
Pro
tect
ed
Are
as
Eart
h R
ota
tio
n
Gra
vim
etr
ic
Soci
eta
l
Urb
an
Vegetation canopy cover
Soil sealing
Area of natural habitats
Reclaimed waste deposits areas
Air quality
Actual evapotranspiration
Spatial structure of urban areas
Trend of total CO2 emissions
Temperature sum totals (for the active growing period)
Solar-energy potential
Urban runoff coefficient
Natural ground water discharge
Risk to soil and atmospheric drought
enviroLENS – D2.1: Essential Variable (EV) List
54