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Frontiers in land surface Earth observation
Malthus TJ1
Environmental Earth Observation, CSIRO Land and Water, GPO Box 1666, Canberra 2601, Australia,
[email protected].
1
Abstract: Key drivers in Australian land surface observation science include relevant
ecosystem management and reporting develop in sophistication. Meeting these
requirements necessitates increased understanding of water, nutrient and carbon
by land and water management, and climate change and variability. New sensors
offer exciting opportunities for new information and validation through monitoring
new variables. These include soil moisture mapping, and gravity anomalies, as
well as variables at greater spatial and spectral resolutions. The integration of
Earth observation data with models and information from ground–data networks is
the continental and subregional scales. Future challenges lie in integration systems
that can incorporate satellite products, which may be patchy and incomplete in their
spatial and temporal coverage, are independent of particular satellite sensors, and
are based on products adapted to Australian conditions. Furthermore, the greater
reliance on satellite instruments and derived information, which under certain
circumstances may need to be legally defensible, will require improved calibration
generated. This paper builds on the previous presentations in this symposium
and explores these ideas with an emphasis on new developments in model–data
Keywords: Earth observation; land observation; model–data assimilation; new
technologies; opinion
1
INTRODUCTION
Australia has a lengthy history in the successful use of Earth observation (EO) satellite data
and associated technologies for land surface observation. Key factors in the uptake of EO data
have been size (Australia is 7.69 million kilometres2 in area), the harshness and remoteness of
much of the Australian interior, and a generally favourable climate, which has assured a regular
supply of low cloud cover optical data for most areas of the continent. Thus, EO has offered a
practical, synoptic tool upon which to analyse trends in the Australian terrestrial environment
(AAS, 2009). Additional key drivers in the development of Australian land surface observation
science include relevant legislation and policy – which in turn, has driven a high management
as ecosystem management and reporting has developed in sophistication. A recent review
In response to these drivers, remotely sensed electromagnetic radiation in the visible to microwave
domains has provided the basis for a number of unique assimilated EO products generated by a
number of agencies. These include indices of vegetation condition and cover (e.g. Schmidt et al.,
2010; Guerschman et al., 2009; Donohue et al., 2008), precipitation (e.g. Renzullo et al., 2011),
surface temperature and evapotranspiration (e.g. Kalma et al., 2008) and surface soil moisture
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content (e.g. Draper et al., 2011). More recently, a number of these datasets have been further
informative at the continental and subregional scales (e.g. for the water cycle, Van Dijk et al., this
volume). These tools are helping to meet increasingly sophisticated information requirements,
climate change and variability.
Taking a broad perspective, the need for land surface information extends well beyond the needs
of the Water Information Research and Development Alliance. EO has proved fundamental to
Australian land system science, providing inputs to solutions from water problems to ecosystem
science (e.g. productivity, land use change, land availability, climate management, environmental
health, phenology). Yet, this dependency on EO data also takes place in a secondary
dependency: Australia does not own any satellites, and is wholly dependent on the goodwill of
resolution (space/time) dynamics of typically heterogeneous land surface systems at scales
desired by land surface managers. However, largely technical limitations in the design of
resolution, swath width and temporal resolution. Wide swath instruments, for instance, generally
have coarse spatial resolution, but high temporal frequency. An example of a system that delivers
Spectroradiometer (MODIS), which has a daily acquisition rate. With the compounding effects of
to cover most of Australia. However, the coarse spatial resolution of this instrument frequently
fails to meet the ideal needs of land managers.
imager on Landsat 5 and the Enhanced Thematic Mapper+ (ETM+) sensor on Landsat 7, which
development of analytical systems capable of incorporating patchy and incomplete spatial and
temporal satellite data from different sources is a research challenge that needs to be met. The
aim of this paper is to identify further opportunities and challenges in land surface EO. In the
opinion of the author, this will either lead to improved information retrieval from the data, or will
need to be overcome to ensure the continued supply and uptake of EO data. Seven such ‘frontiers’
are discussed: data continuity, model–data integration (MDI), data fusion and downscaling,
legal defensibility.
2
DATA CONTINUITY – ASSURING THE TIME SERIES
Australia relies on a small number of key, mainly optical, EO datasets. At the continental scale,
these include the Advanced Very High Resolution Radiometer (AVHRR), and more recently
with poor attention to sensor intercalibration, AVHRR has offered less than optimum data quality,
but has a valuable time series that now spans 30 years. However, continuation of the AVHRR
unclear. Both MODIS instruments (Terra, launched December 1999 and Aqua, launched May
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but with some instrument degradation (Xiong, 2011).
a high priority for the analysis of environmental trends. A long time series is key to the separation
and vegetation dynamics. Furthermore, the lack of an assured supply of satellite data leads to
caution when integrating satellite products with models. Practical operational issues will limit
the uptake of such approaches when the data continuation is not guaranteed, and the missions
themselves seen as somewhat experimental (Van Dijk, this volume).
At higher resolution, thematic mapper imagers aboard Landsat 5 (launched 1984) and Landsat 7
10 days, not withstanding gaps imposed by cloud cover. However, with both instruments failing,
5 acquisitions have been temporarily suspended as a result of image download problems, and
Landsat 7 has provided degraded data since 2003.
While potential future datasets will become available (Table 1), the challenges in the transition
from one sensor source to another are data consistency and cross calibration. Ensuring data
equivalence is a key challenge when sensors differ in their spectral, spatial and radiometric
characteristics. An operational overlap period is critical to characterise differences and to perform
Environmental Satellite System (NPOESS) Preparatory Mission (NPP) is intended to provide
a bridge between MODIS data supply and the operational Joint Polar Satellite System (JPSS)
mission (formerly NPOESS, due for launch in 2016). The instrument is intended to have a design
observations, much of the MODIS capability for land science has been retained. However, there
coverages provided by Landsat 5 and Landsat 7
3
Sentinel-2 mission will provide
MODEL–DATA INTEGRATION
observations) and increasingly where the model is used to reconcile and interpolate between
available observations.
gaps between observations in both space and time (Van Dijk, 2011). Satellite observations provide
extensive spatial and temporal data upon which to base MDI approaches, which can range from
the simple to the complex (Holm, 2003), ultimately with the aim of improving the spatial detail of
spatially explicit environmental models and the accuracy of their predictions. An example of the
use of this approach in the Australian Water Resources Assessment (AWRA) system is outlined
by Van Dijk and Renzullo (2010) and Van Dijk et al. (this volume). However, satellite data can be
patchy as a result of cloud occultation, and models need to be designed to cope with this.
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WIRADA Science Symposium Proceedings, Melbourne, Australia, 1–5 August 2011
Table 1: Characteristics of key planned optical sensors providing data continuity to existing data time series
Sensor (agency)
Spatial
resolution
(m)
Swath
(km)
Spectral
resolution
(bands)
Revisit
frequency
Launch
Coarse resolution
VIIRS on NPP
(NOAA/NASA)
375 or 750a
3000
22
Daily
28 October
2011
VIIRS on JPSS
(NOAA/NASA)
375 or 750a
3000
22
Daily
2016
300
1300
21
3 daysb
2013
30
185
8
16 days
January 2013
10, 20a
290
13
5 daysb
2013
OLCI on Sentinel 3
(ESA)
Medium resolution
OLI on LDCM
(NASA/USGS)
(ESA)
a
b
dependent on spectral band
dependent on pair of missions launched
ESA= European Space Agency; JPSS= Joint Polar Satellite System; LDCM= Landsat Data Continuity Mission; MSI=
Multispectral Imagery; NASA= National Aeronautics and Space Administration; NOAA= National Oceanic and
Mission; OLCI= Ocean and Land Colour Instrument; OLI= Operational Land Imaging; USGS= United States Geological
Survey; VIIRS = Visible/Infrared Imager/Radiometer Suite
Van Dijk and Renzullo (2010) outline the different ways in which satellite observations can be
integrated with model approaches:
1. dynamic forcing – direct use of the satellite observations in model analysis and forecasting,
eg. in numerical weather forecasting
2. a priori parameter estimation – direct estimation on the basis of satellite data to estimate
Opportunities exist for greater use (e.g. vegetation height, water content or roughness,
soil properties). Challenges lie in these ultimately inferred properties being subject to
uncertainties in the retrieval algorithms used
3. evaluation of model performance – ultimately leading to model improvement through
improved model structure and parameterisation where processes or quantities are not
adequately described by the model; e.g. comparisons of model estimates of total water
storage versus those derived from the Gravity Recovery and Climate Experiment (GRACE)
(Van Dijk and Renzullo, 2009). Further work needed to understand the uncertainties
introduced by the retrieval algorithms
4. data assimilation – e.g. tuning and updating methods. A summary of approaches used
in assimilating satellite observations with hydrological models is given in Van Dijk and
development; Renzullo et al., this volume) and scaling mismatches between observations and
model variables. Key challenges remain; for example, developing robust operational systems to
handle and assimilate large volumes of satellite imagery at the continental scale, and ensuring
data continuity when current satellite products are derived from research missions, continuity of
supply is uncertain and errors often poorly understood (Van Dijk et al., this volume).
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4
DATA FUSION
Data fusion and its associated methods (blending, downscaling) can be used to address the
need for better quality data, typically to meet demands for increasingly higher resolution global
datasets, or to overcome patchiness in either in situ observation networks or gaps in time series
(Henderson et al., this volume). Data fusion may be used to combine optical remote sensing
observations. Such methods can be used across temporal and spatial scales, and multiple spatial
can overcome limitations in interpolating rainfall estimates based on the heterogeneously
in interpolations are large in sparsely networked regions (Renzullo et al., 2011; Van Dijk and
Renzullo, 2011). Blending the two improves estimation over satellite data alone.
Similarly, characterising the spatial and temporal dynamics in land surface cover is important
for improved understanding of evapotranspiration (ET, the highest water loss factor in most
Australian catchments) and hence of catchment water balance. This requires estimates of land
resolution. ‘Downscaling’ algorithms are one solution to meet this time–space challenge. They
attempt to blend the higher resolution spatial characteristics of Landsat with the higher temporal
resolution characteristics of MODIS (e.g. Zhu et al., 2010; McVicar et al., this volume). Termed
relies on the assumption of a high degree of correlation between images acquired over the same
areas and on similar dates. Errors are low when prediction is based on a larger number of images
acquired over the growing season (Emelyanova et al., 2011). The methods are an important
development compared within previous image merging methods, by being capable of reasonably
ultimately required (e.g. in the prediction of fractional cover over short time periods at Landsat
resolution). Further investigation and development of downscaling techniques is warranted.
5
NEW SENSOR TECHNOLOGIES
New satellite/sensor concepts are allowing new routine observations of the land surface. These
sensor missions offer exciting opportunities for new information and validation through monitoring
new variables. Examples include soil moisture mapping, through the Soil Moisture and Ocean
GRACE); or variables at greater spatial and spectral resolutions (e.g. Environmental Mapping
footprint (300 kilometres). These estimates have proven effective for the evaluation and ultimate
2008; Van Dijk et al., in press). GRACE is currently operating in a reduced capacity to preserving
proposed launch date of 2020.
Soil moisture is a key variable in the improvement of forecasts from hydrological models and
numerical weather predictions. It is important in both regulating water and energy exchanges
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and heat from the land to the atmosphere. Few reliable global datasets on soil moisture have
previously been generated, similar to plant growth and crop yield. The SMOS mission, launched
numerical modelling techniques, SMOS data can be used to estimate water content in soil down
to depths of 1–2 metres (the root zone). The SMAP mission, due for launch in November 2014,
will combine both active radar and passive microwave radiometry observations to estimate soil
Satellite radar altimeter data have been collected for more than 18 years from the European
Envisat platforms. It can yield useful information on river and lake levels (Berry, 2011, this volume).
discharge and changes in lake volumes in Australia. This would be particularly useful to counter
the declining number of in situ hydrological gauging stations and to extend measurements into
ungauged catchments. The method is potentially limited by the number of target strikes on open
density of surface measurements will increase, particularly for the monitoring of small rivers. The
– will use two radar antennas and radar interferometry to enable better resolution of surface water
heights over existing radar altimetry ranging methods.
There is a key link between vegetation structure (especially of forests) to carbon storage capacity
and to ecological function. Light detection and ranging (LIDAR) technologies have revolutionised
vegetation structural assessment, where vegetation height is a key variable in estimates of
operational use. The Geoscience Laser Altimeter System (GLAS) on ICESat, launched in 2003,
for forested environments, the data have proved useful for aboveground biomass estimation
(Harding and Carajabal, 2005; Duncanson et al., 2010). The instrument is now operating on the
in 2016.
Hyperspectral measurements are obtained by sensors capable of measuring in a large number
(>200) of contiguous spectral bands. They can resolve spectral features in land surface targets
to allow a more extensive characterisation of surface properties than otherwise available with
swaths of Earth’s surface in 220 contiguous spectral bands over the 400 to 2500 nanometre
retrieval over a range of Australian ecosystems (e.g. Brando and Dekker, 2003; Guerschman
Hyperion, will be launched in 2014. HYSPIRI, a concept proposed for launch in 2016 and capable
advance land surface observation capabilities over Australia. Tools need to be developed to take
advantage of the availability of these potential new data sources.
In the longer term, combining the proven strengths of multispectral optical remote sensing with
instruments could resolve 3D vegetation structure (where height and cover are key determinants
of vegetation type) and function (e.g. photosynthetic processes) in one data source. This would
traditional spectral imagers can only measure the top layer of the canopy that is visible from above.
The technique may also allow for the separation of understorey from overstorey components and
for the study of their individual seasonal dynamics (Morsdorff et al., 2009).
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6
CALIBRATION AND VALIDATION
their products need to be both well calibrated and validated. The calibration of EO data is critical
if we are to reliably attribute detected changes observed in satellite and aircraft data to real
environmental changes occurring at ground level. Without calibration, we are unable to rule out
by the myriad of sensors operated by multiple countries and organisations. Calibration allows the
traceability of sensor data to the same physical standards and is routinely required as sensors
decay throughout their lifetime in space. Calibration is thus critical to the compilation of reliable
the oceans and land.
improvement of the algorithms used (e.g. for atmospheric correction and vegetation state).
Both calibration and validation provide an independent check on sensor spatial and temporal
performance and processing algorithms (e.g. in quantifying uncertainty) and should ultimately
products still require testing to Australian conditions (e.g. MODIS leaf area index collection 5).
Both are fundamental to further reduce uncertainty in the estimates of the key ecological data
derived from such datasets.
Using its geographical possession of a number of large, relatively stable, ‘natural calibration’ sites,
Australia has a long experience in vicarious calibration, used to establish the absolute radiometric
in Australia have been used, such as Lake Frome, Lake Argyle, Lake Lefroy and Bass Strait.
However, none of these sites have autonomous and continual monitoring implemented. The
To date, calibration effort has been somewhat ad hoc and continues to be uncoordinated.
been a call to establish a National Satellite Calibration Working Group to better coordinate
calibration effort across the country. Such approaches can follow internationally agreed criteria
(Committee on Earth Observation Satellites Working Group on Calibration and Validation).
Successful implementation will need planning of issues such as coordination of activities, selection
and establishment of networks of sites, development and deployment of instrumentation to
support measurement campaigns, and adoption of common measurement and data distribution/
approach to sensor calibration and to be in a position to offer calibration services to other satellite
launching nations – not least to secure access to satellite data and to secure involvement in the
planning of future missions.
(see Section 6 ). Validation has two key unresolved challenges. First is the representivity
area of many metres or even kilometres. Rain gauges, for example, do not actually measure
what the satellite ‘sees’, and rain estimates from both will differ over spatial and temporal scales
(McConnell and Weidman, 2009). Validation can thus not simply be based on the difference
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between the two. This is particularly of interest in dynamic environments where change may be
rapidly occurring. Understanding the effects of the lack of coincidence between image acquisition
autonomous highly instrumented ‘super’ and secondary sites are the emerging trend in Cal/Val
(See: <http://calval.jpl.nasa.gov/>) where suites of smart, highly networked in situ devices can
7
GROUND-BASED OBSERVATION NETWORKS
For accuracy and relevance, models will continue to need real data, derived either from satellite
emphasis on computer modelling over the last few decades – brought about by rapid advances
and widespread availability of computing resources – has perhaps led to the importance of
in some key disciplines (e.g. hydrology) has declined (Vörösmarty, 2010). However, recent
developments in sensors and sensor networks has the potential to reverse this decline and is
leading to changing paradigms of in situ environmental monitoring. Key developments here include
networks of sensors are dispersed over a landscape. Emerging development trends focus on the
multidimensional (in time and space), multimodal sensing networks. Such networks are beginning
to serve as proxy tools in monitoring ecosystem response (e.g. vegetation structure, physiology,
phenology), linked to information on ecosystem composition and function. Examples include use
of optical instrumentation for advancing knowledge on vegetation phenology and its responses
to climate variability and change – ultimately linked to carbon exchange and sequestration. Such
ground and those sampled by a satellite pixel.
improved information on vegetation structure and function. There is a growing body of research on
querying. However, much less has been done on the integration of the data obtained into model–
data integration frameworks, data analysis and visualisation to ultimately support policy and
sites also warrants attention. Such hierarchies range from highly instrumented, networked
supersites to more loosely coupled networks of spatially extensive traditional monitoring plots and
emerging observations made by volunteers using smartphones (e.g. ‘citizen science’). Issues of
data quality remain, and there is widespread lack of standardisation (e.g. in terms of instrument
The lack of standardisation is also hampering the building of effective networks of sites globally
terrestrial vegetated environments, where the relationships between electromagnetic radiation,
et al., 2006).
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8
LEGAL DEFENSIBILITY
A number of factors contribute to increasing interest in the use of EO data as a tool to monitor
compliance with legislation and management guidelines in both terrestrial and aquatic
environments. These include:
increased understanding of the technology and products produced, across a broader base
of potential users
increased sophistication and accuracy of satellite algorithms and products and their
associated uncertainties
more widespread availability and access to data at relatively low cost
the existence of an extensive historical archive upon which to base comparisons with
contemporary images and to analyse trends
the wide spatial coverage of data and the apparent timeliness of its acquisition (Purdy,
2010).
alternative to traditional methods of ensuring compliance based on licensing and physical
inspection regimes, which can increasingly be seen as blunt and resource intensive (Chen et al.,
2004; De Leeuw et al., 2010). Through change detection, satellite data can be used as a tool to
quality and water use/abstraction guidelines; Lien, 2009). Driven to its logical conclusion, the
question then arises – can EO data provide direct admissible evidence in judicial proceedings?
Australian states have incorporated satellite surveillance of tree clearing within the policing
strategies of their relevant legislation: one of the few examples internationally in which satellites
have been used to monitor and enforce an environmental law. A number of court prosecutions
and other sanctions have used EO data to demonstrate vegetation clearance (Purdy, 2010).
There has also been considerable growth in the use of satellite data for land use evaluation and
the extent/absence of ground cover (bare soil), which increasingly feeds into legislation that also
has compliance associated aspects (ACIL Tasman, 2010). An additional application may be in
the context of water drawdown and its consistency with allocations, which may become more
important in future with water permits or markets taking shape (ACIL Tasman, 2010).
Purdy (2010) questioned the vulnerability of satellite data in courts under existing evidential rules.
Key issues of concern included the accuracy and precision (and hence robustness) of quantitative
must be well known and well characterised. That the technology only provides a surface view
(e.g. from a water quality monitoring perspective, in particular) may also be a challenge point.
Any increased emphasis of the use of EO data as a compliance and monitoring tool will require
products generated.
surveillance technology (Purdy, 2010). Before satellite data can be used in an environmental
compliance context, better understanding and communication is needed regarding whether this
data can achieve enforcement and monitoring outcomes desired by policy managers. Legislation
may need to be amended such that EO data is admissible and allowable in certain compliance
monitoring situations.
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9
CONCLUSIONS
EO plays a fundamental role in Australian ecosystem science across a range of scales, from the
plot to the continent. This paper has outlined a number of continuing and emerging trends in land
to key change drivers (e.g. land management decisions, climate change and variability), ii)
expand the range of variables we can currently detect from space, or iii) represent areas where
satellite observations beyond the lifetime of individual sensors. We need to be ready to adapt
from one sensor to the next. This will be supported by innovative developments in new satellite
of key ecosystem variables at higher spatial and temporal resolutions. Similarly, we need to adopt
newly emerging trends in model–satellite/ground data assimilation methods and to the calibration
and validation of satellite raw data and products. Much of this effort should focus on quantifying
and reducing uncertainty in our models, and our raw satellite data and derived products.
ACKNOWLEDGEMENTS
I thank colleagues from the Environmental Earth Observation Program, CSIRO Land and Water
for stimulating discussions and comments around the topic of this paper.
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