Presentation by VISTA on techniques for snow monitoring to create water availability and irrigation models for crops.

1. © VISTA 2020 www.vista-geo.de No. 1
Snow Monitoring
for Water Availability and Irrigation
contributions within
H2020 Extreme Earth
Florian Appel, Silke Migdall, Markus Muerth, Heike Bach ….
and the Extreme Earth Team…
Extreme Earth receives funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No 825258.

2. © VISTA 2020 www.vista-geo.de No. 2
VISTA Remote Sensing in Geosciences GmbH
Founded: 1995 in Munich, currently 30 Employees
From R&D activities developed operational services:
VISTA provides satellite and modell based applications for agriculture and hydrology
Agriculture
• Yield forecast
• Precision Farming
• Irrigation Management
• Organic Certification
Data Assimilation
Snow Cover
Snow Water Equivalent
Snow & Water Balance
Modeling
Hydrology
• Snow monitoring
• Run-off forecast
• Hydropower production
• FloodsSnowSense ®
In 2017:
BayWa AG
acquired 51% of
VISTA’s shares

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Motivation & Background
H2020 Extreme Earth
Copernicus
the most important digital big data
resource: Volume – Velocity – Variety –
Veracity - Value
ESA thematic exploitation platforms
virtual environments for user relevant
EO data and applications
advantage of available computing
resources
DIASs
Data and Information Access
Service (DIAS) platforms
computing power close to
the data
AI & Deep Learning
deep neural network architectures for satellite data
training data, plattforms and GPU technologies
Applications / Use Cases
• Food Security - irrigation support for agricultural areas
• Polar - maritime safety for traffic in critical polar environments
Copernicus
TEPs DIASs
Applications
& Innovations
Big Data
linked geospatial data
software stack with tools that
scale to PBs of data
Big Data
AI / ML / DL

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Extreme Earth
Overview & Work packages
Use Cases
WP4 and WP5
Copernicus and DIASs
WP1
Impact
WP6
Thematic Exploitation Platforms
Food Security TEP
Polar TEP
WP2 and WP3
Deep Learning &
Linked Open Data
WP3 and WP2

5. © VISTA 2020 www.vista-geo.de No. 5
o ExtremeEarth is a H2020 project that aims at developing
o Extreme Analytics techniques and technologies using big Copernicus data,
o applying these technologies in two of the ESA TEPs (Food Security and Polar)
o demonstrating two highly societal and environmental relevant use cases.
1. National and Kapodistrian University of Athens (GR)
2. VISTA Geowissenschaftliche Fernerkundung GmbH (DE)
3. UiT - The Arctic University of Norway (NO)
4. University of Trento (IT)
5. Royal Institute of Technology (SE)
6. National Center for Scientific Research - Demokritos (GR)
7. German Aerospace Center DLR (DE)
8. Polar View Earth Observation Ltd. (UK/DK)
9. Meteorologisk Institutt Norway (NO)
10. LogicalClocks (SE)
11. British Antarctic Survey (UK)
Start:
1.1.2019
Duration:
36 Months
Budget:
~ 6. Mio Euro
Funded by call:
ICT-12-2018-2020
Project Number:
825258
http://earthanalytics.eu
Objectives and Team
H2020 Extreme Earth

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Extreme Earth = Extreme Analytics
innovative AI players in the team
Deep neural networks
technique for optical
multispectral Sentinel
2 images
Scalable
deep
learning and
big data
Deep neural network
techniques for SAR
data (DLR IMF OP)
Remote sensing for the
Arctic, AI for sea ice
monitoring
Developers of HOPS
and Hopsworks

7. © VISTA 2020 www.vista-geo.de No. 7
Extreme Earth and Applications
The Food Security Use Case
o FOOD SECURITY IS ONE
OF THE MOST
CHALLENGING ISSUES OF
THIS CENTURY
(ESPECIALLY IN A
CHANGING EARTH
ENVIRONMENT)
o POPULATION GROWTH,
INCREASED FOOD
CONSUMPTION AND
CHALLENGES OF CLIMATE
CHANGE AND INCREASED
VARIABILITIES WILL
EXPAND OVER THE NEXT
DECADES
• Biomass production and yield will need
to be increased
• Risks of yield loss even under extreme
environmental conditions need to be
minimized
• Mitigations: Plant Protection,
Fertilization, Irrigation and Management
• Irrigation requires reliable water
resources either from ground water or
surface water
• Large portion fresh water is linked to
snowfall, snow/ice storage and the
seasonal release of the water

8. © VISTA 2020 www.vista-geo.de No. 8
Need of Water
Polar
TEP
EO Processing
Sentinel-1
Snow
Parameters
Medium Resolution
Modelling:
Water Balance
Parameters
High Resolution
Modelling:
Crop Growth
Plant Water
Demand Food Security
TEP
EO Processing
Sentinel-2
Crop
Parameters
… for secure food productionWater Availability for Irrigation
• Surface Water
• Soil Moisture
• Groundwater
Origin of the Water
Water from seasonal snow …
Extreme Earth - Food Security Use Case
Concept & Design
„Water – Energy – Food Security Nexus“

9. © VISTA 2020 www.vista-geo.de No. 9
Polar
TEP
EO Processing
Sentinel-1
Snow
Parameters
Medium Resolution
Modelling:
Water Balance
Parameters
Water from seasonal snow …
Building Blocks of Part 1
„Water from seasonal snow storage“:
• In-Situ Snow Measurements
• methods developed within ESA IAP
SnowSense
• Earth Observation
• applied within ESA GSE Polar View
• partly applied on the Polar TEP
• Water Balance Modelling
• Application of PROMET
• tool of VISTAs hydrological activities
• proven in number of studies & services
In-Situ Snow
Measurements
Meteo
Data
and
NWP
Extreme Earth - Food Security Use Case
Tools and Methods

10. © VISTA 2020 www.vista-geo.de No. 10
Building Blocks of Part 2
„Efficient and intelligent use of water“
• Earth Observation
• Sentinel 2 maximum exploitation
• methods applied in various activities of
VISTAs agricultural services
• Leaf Area / Biomass Monitoring
• Plant Dynamics Modelling
• methods applied in various activities of
VISTAs agricultural services
• Soil Moisture Simulations
• Plant water demands
• Irrigation recommendations
High Resolution
Modelling:
Crop Growth
Soil Moisture
Food Security
TEP
EO Processing
Sentinel-2
Crop
Parameters
… for secure food production
New AI Algorithms and Results
Field Boundaries / Crop Type Detection
Extreme Earth - Food Security Use Case
Tools and Methods

11. © VISTA 2020 www.vista-geo.de No. 11
Extreme Earth - Food Security Use Case
Pilot Demonstration Areas
Government of Romania
Department of Sustainable Development
DUERO RIVER BASIN AUTHORITY

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Runoff
Annual variation of
snow storage in the
Upper Danube
The Danube
Catchment – Snow – Runoff – Variations – Irrigation Issues
817.000 km2
20 Countries
▪ Hydropower
▪ Irrigation
▪ Shipping
▪ Water Supply
Inn / Salzach Area
+ 50 %
- 50 %
Shift in Runoff Patterns

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The Danube
Catchment – Snow – Runoff – Variations – Irrigation Issues
Simulated aver. ann. Irrigation Demand 2015-2017
180900
Irrigation Water [mm/year]
Surface water?
Extract irrigation water from
closest extraction point in
the river network
max.10m
Groundwater?
Extract irrigation water
from the groundwater
underneath the pixel
"How does irrigation affect production?"
Regional - Example Danube
• Irrigation can increased water user efficiency by 50%.
• Corresponds to 30 million tons increase in corn
• Corresponds to 5 billion Euro turnover
• Means 5.3 billion m³ water evaporated
• Big impact on ecology

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Functionalimplementation
• Soil Moisture
• Snow Storage
• Run-Off
• Groundwater
• Biomass
• Water
Demand
NWP+
Seasonal
Meteo
Data
• Snow MeltEO S1
In-Situ
• Snow Water
EquivalentCopernicus
• Snow
Covered
Area
Irrigation
Recommendations
• Crop Type
• LAI
• Features
EO S2
AITraining
Agricultural Status
Information
Water Availability
Information

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Pan-European SCA / FCA
products of FP7 „Cryoland“
daily 500m
Anticipating:
EEA/Copernicus HR Snow
Cover Monitoring
Extreme Earth - Food Security Use Case
Snow Monitoring Activities
+ own Sentinel-2 SCA 20m products from VISTA Processing
own Sentinel-1
Snow Melt
products from
VISTA Processing
+ Polar TEP
implementation
*Newfoundland / Canada Illustrations
Our common challenge:
Snow Water Equivalent
Mountainous Areas
+ Model
+ In-Situ
“Water stored in
the snow cover”
*
*
*

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S N
S S
O W
E EN
Extreme Earth - Food Security Use Case
In-Situ Snow SWE Monitoring
ESA IAP Demo Projekt
‚SnowSense‘ (2015-2019)
▪ Use of GNSS signals to measure snow properties
✓ Snow Water Equivalent SWE
✓ Liquid Water Content LWC
▪ No mechanical components, low power
consumption, autonomous operations
▪ No maintenance during operation
▪ Low cost standard electronic components
▪ Daily information gain using satellite communication
Modelled Runoff
vs
Measurements
Significant
improvement on total
water volume released
from the snow cover
Run-off information
can be provided for all
locations, also those
without any
measurements

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Basin-wide information layers:
• Snow storage
• Soil moisture
• River run-off
• Reservoir conditions*
Regional agriculture specific
information layers:
• Crop type maps
• Leaf area developments
• Drought stress*
• Phenological development*
Food Security TEP * Tailored for Involved Users
Extreme Earth - Food Security Use Case
Information Scope
Field-specific delivery for
pilot demo users:
• Recommendation when
and how much to
irrigate
• Yield forecast with and
without optimized
irrigation plan

18. © VISTA 2020 www.vista-geo.de No. 18
Summary
Extreme Earth receives
funding from the
European Union’s
Horizon 2020 research
and innovation
programme under grant
agreement
No 825258.
▪ H2020 Extreme Earth project on developing extreme analytics
technologies that scale to the PBs of big Copernicus data
▪ Application of technologies, information and knowledge in ESA TEPs
(e.g. AI integration)
▪ Application of existing, enhanced and new technologies to proof
benefits within two Use Cases relevant to society
▪ Connecting capabilities and technologies to exploit Innovations
▪ Food Security Use Case connecting Water Availability
(Snow) with Water Demand (Irrigation)
▪ Application of Snow Monitoring (EO and In-Situ) and
Water Balance Modelling to improve water management
▪ Detailing of Pilot Demonstrations in upcoming User
Workshops (> Danube, > Douro) http://earthanalytics.eu