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www.csiro.au/WIRADA-Science-Sympsosium-Proceedings 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 302 Malthus TJ WIRADA Science Symposium Proceedings, Melbourne, Australia, 1–5 August 2011 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 Frontiers in land surface Earth observation 303 www.csiro.au/WIRADA-Science-Sympsosium-Proceedings 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. 304 Malthus TJ 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). Frontiers in land surface Earth observation 305 www.csiro.au/WIRADA-Science-Sympsosium-Proceedings 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 306 Malthus TJ WIRADA Science Symposium Proceedings, Melbourne, Australia, 1–5 August 2011 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). Frontiers in land surface Earth observation 307 www.csiro.au/WIRADA-Science-Sympsosium-Proceedings 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 308 Malthus TJ WIRADA Science Symposium Proceedings, Melbourne, Australia, 1–5 August 2011 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). Frontiers in land surface Earth observation 309 www.csiro.au/WIRADA-Science-Sympsosium-Proceedings 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. 310 Malthus TJ WIRADA Science Symposium Proceedings, Melbourne, Australia, 1–5 August 2011 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. 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