to make a business out of Earth Observation: keep contact with science

5 September 2005
Heike Bach is one of a select group of
scientists taking Earth Observation techniques out of the research lab
and applying them to the real world.
A decade ago she founded the company
VISTA ?± Remote Sensing Applications in Geosciences, with the motivation
of transferring scientific results in the field of Earth Observation
into hydrological and agricultural applications.
Author of more than 60 journal and conference research papers, Bach
currently serves as Executive Director of VISTA and is managing a large
number of activities including operational flood forecasting and
agricultural monitoring projects.

Flood forecasting and agricultural monitoring seem quite separate fields ?± what is the link between them?
The link between them is water – my
background being a hydrologist. Flood forecasting requires accurate
hydrological models, so you have to understand the whole water cycle to
predict when water is going to appear on the surface and produce floods.
Plants meanwhile need water very urgently. They transpire it through
their leaves. By using satellite data we can tell the water content and
health of plant leaves, based on their spectral characteristics, and on
the other hand we can measure soil moisture to help estimate the
capacity of the soil to absorb rainfall or whether there will be
surface run-off.
How did you get active in this field?
– the science of water – fascinated me from the early beginning because
of its relevance both for people and the environment. It is a very
broad subject which integrates knowledge on soil, plants, meteorology
and engineering.
My interest in remote sensing started at
university. In my department there was a strong group in remote sensing
in the 1980s, one of the first groups in Germany, exploiting data from
the Landsat Thematic Mapper among other satellites.
From the beginning of our research we
treated remote sensing data as quantitative data, meaning a result of a
physical measurement and expressible in numbers. So already at that
time we were working at atmospheric correction of Landsat data, and the
retrieval of parameters correlated to biophysical meanings, such as
canopy reflectance, plant biomass and leaf area index. In 1989 we
started working with hyperspectral images.
What does ‘hyperspectral’ mean?

Hyperspectral satellite sensors are like having spectrometers in space.
They don’t obtain images in the same way as you get images from Landsat
or normal optical satellites. Instead what you get is the reflected
light in various spectral bands extending beyond the range of the human
This combination of spectral bands can for
example enable analysis of the different contents of the atmosphere –
permitting researchers to correct for atmospheric effects ?± as well as
the mineral contents of soil, bio-chemical constituents like
chlorophyll in plants and in water and also biophysical properties such
as biomass and leaf area index. There is an opportunity for added
information all along the spectral reflectance curve, whereas with
conventional images you mostly just interpret what is depicted in the
Hyperspectral sensors are an improvement on their multispectral
counterparts because they use many more spectral bands and so yield
more information.
Back in 1989-1990 we were using airborne
hyperspectral imagers to investigate what they could tell us, but today
we have two such imagers in orbit ?± the Hyperion sensor aboard the US
EO-1 spacecraft, which is comparatively ‘noisy’, and ESA’s Proba
satellite with the Compact High Resolution Imaging Spectrometer (CHRIS)
sensor – the first very well-working orbital sensor of its type.
What are the advantages of CHRIS/Proba?
Besides the atmospheric, biochemical and biophysical parameters I mentioned that can be measured with CHRIS’s hyperspectral capability, Proba additionally performs a roll as it passes a target, to acquire images at five different angles.
This multi-angular capability with high
spatial resolution is absolutely new and has never happened before from
space. It enables the measurement of the behaviour of the reflectance
at different angles ?± what we call the Bidirectional Reflectance
Distribution Function (BRDF) ?± which is very different and
characteristic for each species and for each time of year when the
plants are developing.
BRDF is extremely difficult to measure
from an aircraft. The long distance makes measurements much easier. So
CHRIS gives information on physical parameters such as the canopy
structure and leaf angle that is completely unprecedented.
I was involved in planning a follow-on
hyperspectral multi-angular Earth Explorer mission called SPECTRA,
which was not selected by ESA. We are now proposing a hyperspectral
sensor called ENMAP, still in selection in Germany, to be decided at
the end of the year.
In the meantime, the different angles
acquired by CHRIS present ESA with an ideal opportunity: this hasn’t
been done yet but I would like to see a database put together of PROBA
acquisitions over test sites all over the globe. This database could
enable the parameterisation of the BRDF function of the worldwide land
surface and improve our understanding of the radiative transfer of the
canopy – helping in turn to improve the accuracy of scientists’ climate
What sort of projects does your company, VISTA, work on?
I formed VISTA ten years ago ?± in terms of
what we do, we have a rough split down the middle between the 50% of
projects that are more science-based, where ESA and other organisations
are sponsoring, and the other 50% are from clients for various
operational services, using all kind of satellite as well as airborne
We have a very good collaboration with
Professor Wolfram Mauser and his remote sensing unit at the University
of Munich. I think it’s important that if you want to make a business
out of Earth Observation data you have to keep contact with science and
make some new developments ?± not just relying on the same tools that
are available to everyone. For example, we recently installed the AVIS
imaging spectrometer in the ultralight aircraft of the University of
Munich. This allows us to provide our customers with low cost
hyperspectral data.
Two regional flood forecasting centres in
Germany get our data ?± for the Upper Rhine and Mosel Rivers ?± and we
also are participating in a big national research programme going on in
the area of precision agriculture. We are also involved in various
other activities, both inside and outside Europe.
How can Earth Observation improve our knowledge of floods?
Flooding is the world’s most damaging
natural catastrophe, costing hundreds of millions of euro in damages
annually. This makes it of significant interest for insurance
companies: VISTA has recently completed an analysis of the Elbe flood
of 2002, where we followed the entire event to help with risk
assessment for the reinsurance sector.
monitoring ?± tracking an actual flood event taking place – is
comparatively straightforward because you can use a combination of
optical and microwave data to see the water sitting on top of the soil
?± the two work well in combination, with microwaves able to penetrate
cloud cover, and also give information not just on the top of the
ground but a little deeper.
Flood forecasting is more demanding.
Microwave data in particular can measure actual soil moisture to help
assess how close it is to being waterlogged, likely to result in
run-off. The combination of optical and microwave measurements can
monitor snow cover and snow melt.
The challenging part is how to properly
assimilate this data into the complex flood and hydrological models
used for forecasting. The information retrieved from each sensor has to
have its reliability assessed, so that an uncertainty estimate can be
considered in the model. This is something that has to be worked on in
general ?± not just for flood models, but agricultural yield forecast
models, carbon models?ñ it is the same across many different Earth
Observation applications.
The two flood forecast centres that employ
our data apply their own hydrological models. We provide the Earth
Observation information for them to make their own forecasts. During
the winter that means daily updates for a given watershed, because the
underlying hydrology is very dynamic and can change significantly from
day to day.
How do you employ Earth Observation in the field of agriculture?
Right now in Germany there is a big
precision agriculture research programme going on called PreAgro. And
VISTA is responsible for remote sensing applications within it, working
on two huge test farms in East and North Germany. This is a shared cost
activity, meaning that 50% of funding comes from the German government
and 50% is paid by the company. We are participating because we believe
precision agriculture will be important in the long-term future.
The basic idea behind it is to reduce the
impact of agricultural management on the environment by applying
fertilisers, fungicides and pesticides only in the regions it is really
needed, on an adaptive basis. This is good for the environment ?±
especially groundwater ?± and also saves the farmer money. The accuracy
should be sufficient that chemical application can be reduced with no
disadvantage for the health of individual plants or the overall yield.
We are making a strong use of
hyperspectral data, CHRIS/Proba images as well, but for operational
applications we use the airborne AVIS sensor I mentioned before. The
Earth Observation updates needed for agricultural monitoring are less
frequent than for flood forecasting ?± with new images acquired every
two weeks to one month. The reason is again data assimilation ?± it is
not the images alone that help us, but the information retrieved from
We do not directly know the grain yield ?±
we cannot count individual grains! However we have a model for yield
estimates, and we can use the information Earth Observation gives us
and integrate it in the model. Then we can simulate the grain yield and
plant biomass for every day combined with actual weather inputs. Every
four weeks or less new satellite images enable us to update the model
Applying Earth Observation to precision
agriculture could potentially also be helpful to satisfy the growing
amount of product documentation farmers need to fulfil, and with
GPS-based harvesting machines increasing in popularity, such GPS
instruments could then get added inputs from Earth Observation. There
is also potential in terms of assessing quality parameters but we are
still learning if that is possible.
How is providing an operational service for customers different from doing science?
The big difference is that the customer
just tells you their problem and you have to find a way to solve it.
They don’t care whether it is with optical or microwave imagery, an
airborne sensor or satellite sensor. They just want the final high
quality product to provide them the information they need, without
wanting to know all the background behind it.
Programmes like ESA and the European Commission’s Global Monitoring for
Environment and Security (GMES) are very useful for transferring
scientific methodologies into practical procedures working on a routine
basis. It is important that such procedures get upgraded to provide the
best operational information to users.
It takes good examples and good
experiences before a customer feels this really works, EO provides
relevant information and can be relied on. It can take a long
collaboration with a customer before they say this year they want to
extend this service from this region to other regions ?± it takes time
and hard work to gain trust.
Related links
(Credits ESA
Author: EARSC

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