The efficient use of water requires understanding its spatial and temporal availability and pattern of use. However, in-situ measurements of the components of the hydrological cycle are often unavailable. This is particularly the case for precipitation. In this respect, Public-domain Precipitation Products (PPs) represent an alternative source of information. Nonetheless, precipitation estimates by PPs show discrepancies in spatial and temporal domains; thus, in- depth analyses of similarities and differences of these products is imperative to provide accurate precipitation estimations for water applications. We introduce and test a novel approach for evaluating the performance of PPs. This approach couples traditional evaluation methods (pixel-to-point and pixel-to-pixel) with data mining techniques (Hierarchical Clustering and Principal Component Analyses). It was used to assess the performance of 17 PPs over the Blue Nile Basin (BNB) for the period 2001-2005 on monthly and annual scales. A sensitivity analysis was carried out to test the affinity of the studied PPs. The analysis results were used to guide assimilating several PPs to create Merged Precipitation Products (MPPs). Results exhibit considerable differences between the studied PPs. Noticeable spatial and temporal discrepancies were found between the 17 PPs on the one hand and between PPs and rain gauge data on the other hand. Data mining techniques proved to be useful in detecting similar and dissimilar PPs. Given their advantages over traditional methods, these techniques should be used routinely in PPs assessment. The findings of the current research provide helpful insights to advance the use of PPs in water resources applications.
– Snow and permafrost: terrestrial snow and frozen soils represent an important yet poorly represented compo- nent of the global watercycle. While the retrieval of 2- D snow cover extent is a mature research field (Hall et al., 1995), the retrieval of snow depth, density, or wa- ter equivalent (SWE) is usually of greater interest to hydrologists, since these form key elements of model initialization and forecasting of runoff, drought and flood prediction (Bormann et al., 2013). Unfortunately, retrieving these and related cryospheric variables re- mains a major challenge, particularly for mountainous regions, where spatial variability is high and seasonal snow depth may reach tens of metres. Microwave sen- sors can be used for SWE and snow depth observations, but current systems lack optimal combinations of fre- quencies and resolutions. Although active microwave sensors can improve retrieval resolution and may be better suited for snow monitoring in mountainous re- gions, the maturity of active-based products has not reached the same level as passive approaches. However, even passive microwave-based SWE retrieval can suf- fer from signal saturation due to a deep snowpack, with commonly used Ku- and Ka-band microwave emission signals saturating at around 200 mm SWE. Given the importance of monitoring wet-snow properties for hy- drology, synthetic aperture radar (SAR) retrieval ap- proaches have been proposed, since passive approaches are not sensitive to dry snow parameters. Gravimetric techniques represent another alternative to microwave- based measurement of snow depth (Baur et al., 2009), but the approach is limited by the large spatial and coarse temporal characteristics of such sensors (Niu et al., 2007). In the light of the non-selection of ESA’s CoreH2O as an Earth Explore mission, there remains a need for high-resolution active microwave sensors with high revisit times to more effectively capture the dy- namics of wet-snow in diverse terrain. Apart from snow covered surfaces, understanding the dynamics of frozen soils has become an increasingly important topic in hy- drology, given the observed warming in many cold re- gions and the role that permafrost may play in changing river discharges, particularly in boreal areas (Woo et al., 2008). Permafrost properties include key state variables such as ground temperature, as well as thickness of the
In this paper, instead, we are interested in studying a platform that offers high temporal resolution, offering a service that is complementing low and medium Earth orbit spacecraft. The reason for this interest originates in a number of potential applications that could be enabled by a platform that can continuously cover the poles. Although high- bandwidth telecommunications and high-resolution imagery are difficult due to the large Earth-spacecraft distance, a number of novel potential applications are enabled, both in the fields of observation and telecommunications. It was shown that spatial resolution in the visible wavelength in the range 10-40 km should be sufficient for real-time, continuous views of dynamic phenomena and large-scale polar weather systems . The creation of atmospheric motion vectors (AMV) would also make use of the stationary location of the platform, avoiding gap problems related to geo-location and inter-calibration that composite images introduce . Glaciology and ice-pack monitoring would also benefit from continuous, but low resolution polar observation . Ultraviolet imagery of the polar night regions at 100 km resolution or better would enable real-time monitoring of rapidly-changing hot spots in the aurora that can affect high frequency communications and radar . The platform could also be used as a continuous data-relay for key Antarctic research activities, in particular for scientific experiments, links to automated weather stations, emergency airfields and telemedicine. Ship tracking was also proposed, to support future high-latitude oil and gas exploration .
consumption are expected to be low for many periods to come may not outweigh the bene¯t of reducing labor costs (in terms of leisure) associated with a cutback in production. Therefore, it may be in the ¯rm's (or planner's) best interest to have ¯xed investment move together with (rather than against) consumption demand, making production more volatile rather than being smooth. 24 Consequently, with serially correlated demand shocks production may cease to be downward sticky, unlike the case in the previous model without capital. To con¯rm this intuition, table 4 reports predicted moments for di®erent values of the persistence parameter ½. It shows that the more persistent of the shocks, the larger the correlation between consumption and capital investment, hence the more likely for the variance of production to exceed the variance of sales. For example, when ½ = 0:5; capital investment and consumption are negatively correlated ( ¡0:6), hence the variance of production is less than the variance of sales at both the high frequencies and the business-cycle frequencies. But when ½ = 0:99, for example, capital investment is highly positively correlated with consumption (0:85), hence production becomes more volatile than sales at both the high frequencies and the business-cycle frequencies (this perhaps explains why some countries in table 1, such as the US and Finland, have larger variances of production relative to sales at both high and low frequency intervals). When ½ takes values in between, we obtain the normal cases where production is more variable than sales only at the business-cycle frequencies. Quantitatively speaking, however, the model has two obvious shortcomings (obvious in both table 3 and table 4). It tends to generate too strong a negative correlation between inventory investment and sales at the high frequencies compared to what is in the data (e.g., ¡0:86 versus ¡0:4). And it generates a volatility ratio between output and sales that is not large enough at the business cycle frequencies compared to what is in the data (e.g., 1:07 versus 1:38). These two shortcomings are quite robust to parameter values. Considering the simplicity of the model, however, its performance is extraordinary.
decision-making. Earthobservation from space sharpens our sensitivity to the natural environment and thus stimulates our willingness to learn of its relevance to everyday life conditions. It also make us aware of the need to use science and technology methods to obtain information for environmental monitoring. Private consultancies are developing courses on awareness of the potential of Earthobservation and geo-information in both the public and private sectors and for the general public. The accessibility and knowledge of Earthobservation improves the decision support and provides key information needed to promote economic vitality and environmental stewardship. Earthobservation contributes to solutions that result in socio-economic benefits to society.
The trends have fundamentally altered citizen science. Most of the public in the early twentieth century could not be relied upon to identify and report the scientific names of species (though some expert amateur naturalist has done so) and were not equipped with scientific understanding; nor were they carrying around powerful scientific instruments in their pockets. In contrast, today, hundreds of millions of people have such abilities, and therefore the potential for participation is much higher. Yet, it is important to note how the multiple underlying trends are also defining the demographics of those who participate in citizen science. Participants in citizen science activities are typically well educated, working in a job that provides enough income and working conditions for ample leisure, and have access to the Internet as well as own a smartphone. Not surprisingly, because of the imbalances in care responsibilities, science education and income, men are overrepresented in citizen science. For example, a study found that 87% of the participants in a volunteer computing project (see the next section) were men (Krebs 2010), while a similar bias was identified in ecological observations of birds (Cooper and Smith 2010). Internationally, citizen science is concentrated in advanced economies, especially the USA and northern Europe. The need to access the Internet still presents an obstacle, with level of access ranging from 87% in the UK, to 81% in the USA, and only 65% in European countries such as Poland or Portugal (ITU 2013). At the more local level, even for those who have access to a smartphone, many of the software applications (apps) that support citizen science assume continuous and seamless Web connectivity, even though 3G and 4G coverage is partial in highly urbanised environments such as London or New York City, let alone in remote nature reserves. Language can also present a barrier. As the background material and the apps are being developed by scientists, the amount of discipline- specific jargon and the level of understanding that is needed to get involved in a project can exclude many people. Finally, since English is the main language of scientific papers and of science more generally, many of the tools and technologies that support citizen science activities rely on knowledge of English, and are not available in local languages, especially in areas of high cultural heterogeneity such as Europe.
The Neoproterozoic Earth was punctuated by two low-latitude Snowball Earth glaciations. Models permit oceans with either total ice cover or substantial areas of open water. Total ice cover would make an anoxic ocean likely, and would be a formidable barrier to biologic survival. However, there are no direct data constraining either the redox state of the ocean or marine biological productivity during the glacials. Here we present iron-speciation, redox-sensitive trace element, and nitrogen isotope data from a Neoproterozoic (Marinoan) glacial episode. Iron-speciation indicates deeper waters were anoxic and Fe-rich, while trace element concentrations indicate surface waters were in contact with an oxygenated atmosphere. Furthermore, synglacial sedimentary nitrogen is isotopically heavier than the modern atmosphere, requiring a biologic cycle with nitrogen ﬁ xation, nitri ﬁ cation and denitri ﬁ cation. Our results indicate signi ﬁ cant regions of open marine water and active biologic productivity throughout one of the harshest glaciations in Earth history.
Dr. Gilberto Câmara (INPE, Brazil) presented the impressive achievements made in the joint development with China of the constellation of the Chinese-Brazilian Earth Resources Satellite(s) (CBERS). Brazil and China announced at the fourth GEO meeting (November 2007) that they are to distribute free images from CBERS to African countries. This constellation will provide free remote sensing data from a range of platforms and sensors with different spatial resolutions at a high temporal frequency. The long-term programme has been planned until 2020. The distribution of CBERS images to Africa will be possible due to the use of receiving stations located in South Africa, Kenya (where the station belongs to Italy), the Canary Islands and Italy. Distribution will be carried out by means of the
The Techno-Economic Segment approach is justified because the EO domain is enabled and constantly reshaped by technological developments. EO is an area where data, technology and economy are locked in a dynamic relation. The technological infrastructures located in outer space, flown in the air, or ground-based, enable the collection of terabytes of EO data on a daily basis. For example, currently, there are 169 earthobservation missions in orbit and 140 approved future missions, as recorded by the Committee on EarthObservation Satellites (CEOS) (CEOS, 2019). The growing number of satellites, the improved performance of the sensing instruments, and higher resolution of the images have led to a fast-growing volume of data. A single Sentinel-1 satellite (Copernicus constellation) maps the whole world once every 12 days (ESA, 2017) and produces an estimated 1.5 petabytes of raw data per year (OECD, 2016). T he EO dat a constitutes a significant portion of big data and its contribution to data economy is potentially enormous (EC 2017). EO data is a source of wealth of valuable informat ion. However, finding and understanding information in the ever-growing volume of heterogeneous EO data proved the traditional way of analysis obsolete. The last decade's advances in data processing and computing capacity, in particular in AI and cloud computing have enabled advanced data analytics potentially contributing to the increased use of data. 1 EO data, often combined with other types of data, is used t o c reat e dat a-
In this by applying a Meta heuristic approach called Cuckoo Search in the area of image classification. The main advantage of this algorithm over other Meta heuristic approach is that its search space is extensive in nature. The proposed methodology is applied to the Alwar region of Rajasthan. The image used is a 7 band image of 472 X 546 dimensions from Indian Remote Sensing Satellite Resiurcesat. This procedure has taken practically all of the terrain features and showed high degree of efficiency for almost all the regions (water, vegetation, urban, rocky, and barren) with a Kappa coefficient is 0.9465.
In general, we have quite a number of different sensors installed on satellites. These include passive instruments observing the backscattered solar illumination or thermal emissions from the Earth—or active imaging instruments (transmitting and receiv- ing light pulses or radio signals toward and from the target area being observed). For the ease of understanding, we will limit ourselves to optical sensors operating in the visible and infrared spectral ranges and to radar sensors applying synthetic-aperture radar (SAR) concepts [2, 3]. These instruments provide large-scale images with a typi- cal spatial resolution of 1–40 m per pixel. The images can be acquired from spacecraft orbits that cover the Earth completely with well-defined repeat cycles.
If the error distribution is zero-mean Gaussian, then the stan- dard uncertainty fully describes the error distribution aris- ing from the effect. Not all effects cause Gaussian-distributed errors. One example is the logarithmic distribution of radar backscatter errors associated with speckle. Another example is quantization (step 4 in the scenario in Sect. 5.1), as illus- trated by Fig. 3, which shows a simulation of the distribu- tions of brightness temperature for an Advanced Very High Resolution Radiometer (AVHRR) viewing a pixel with true scene temperatures of 230 and 300 K. This distribution was obtained by simulating detector noise, amplifier noise, quan- tization, and ideal (unbiased) onboard calibration. The sepa- rated peaks are the effect of the AVHRR 10-bit digitization of the detector and amplifier noise. Each separated spike has a nearly Gaussian distribution with a spread that arises from errors in the calibration process: the calibration applied for a given observation arises from a finite sample of the calibra- tion target views (an internal black body and a space view), which therefore implies some statistical uncertainty. Cases such as this require a numerical representation of error dis- tributions and a Monte Carlo simulation for the propagation of uncertainty (see the next subsection). When quantization is negligible, which is often the case for contemporary sen- sors, the Gaussian distribution realistically describes the sig- nal noise and should be characterized by the standard devi- ation of the error distribution, which is the standard uncer- tainty.
On the other hand, ∼ 78% of all studies with focus on surface water parameters analyzed regional and subbasin scale study areas. Here, a number of the reviewed studies investigated inundation dynamics in wetlands (e.g., [ 22 , 47 , 165 ]). For example, Yuan et al. [ 47 ] estimated wetland water level and inundation dynamics for a subset of the Congo river basin using ALOS-PALSAR imagery between the years 2002 and 2011. Since the SAR backscattering signal of ALOS-PALSAR imagery was affected by volumetric scattering caused by vegetation cover, the authors also included ENVISAT-RA2 altimeter data and the MODIS vegetation continuous fields product in their investigation. On the contrary, Wilusz et al. [ 165 ] mapped monthly wetland inundation in the Sudd wetland region. For this purpose, they analyzed monthly time-series of ENVISAT-ASAR data at 1 km spatial resolution over a period of 5 years. In addition, further studies carried out analyses in the context of wetlands using JERS-1 imagery, which were acquired in the framework of the “Global Rain Forest Mapping” (GRFM) project (e.g., [ 166 , 167 ]). Moreover, several studies observed inundation dynamics in delta regions (e.g., [ 41 , 168 – 170 ]). For example, Sakamoto et al. [ 168 ] used MODIS time-series to quantify annual inundation extent over 6 years for the Mekong river delta. Next, Gstaiger et al. [ 169 ] developed a threshold-based approach to generate a binary water mask using SAR imagery at different spatial resolutions from the TerraSAR-X, RADARSAT-2, and COSMO-SkyMed sensors. Following this, Kuenzer et al. [ 170 ] analyzed patterns of inundation in the Mekong river delta using a time-series of 60 ENVISAT-ASAR scenes at a spatial resolution of 150 m and covering the period between 2007 and 2011. In another study, the effectiveness of daily MODIS time-series for intra-annual monitoring of inundation dynamics was evaluated for the Mekong, Ganges–Brahmaputra, Mackenzie, and Yellow river deltas [ 41 ]. Moreover
Abstract The volume of civil high resolution EarthObservation (EO) images has steeply in- creased during the past decade due to numerous advances in airborne and spaceborne imaging technologies and has already leveraged a number of new applications. On the other hand, the large quantity of available images has extremely increased the challenge of exploring and understanding the full content of the image (i.e., their semantics). Therefore, the development of new image mining systems providing satisfactory results with reasonable computational effort, became highly demanded. The existing EO image mining systems are usually based on extracted image features provided by various feature descriptors which can represent either pixel level patterns or the higher level semantics of images. Thus, developing feature descriptors which are able to represent the content of images relevant to the users’ requirements helps to improve the accuracy and efficiency of image mining systems. As a consequence, this dissertation introduces new approaches based on Latent Dirichlet Allocation (LDA), a topic model for low and high level image feature de- scriptions. Moreover, the dissertation proposes novel methods based on LDA and information theory for evaluating various image feature descriptors independent of their application case. Since users usually evaluate image mining results based on their semantics, we conducted user studies for assessing the issues such as the sensory and the semantic gaps which affect the user acceptance of the results. Furthermore, this dissertation shows the importance of prior knowledge about the semantic struc- ture of images in shortening the semantic gap between users and computers.
The Space Flight Laboratory (SFL) at the University of Toronto Institute for Aerospace Studies, in collaboration with the Slovenian Centre of Excellence for Space Sciences and Technologies (SPACE-SI), is developing a 40 kg microsatellite for earth monitoring and observation that is capable of resolving a Ground Sampling Distance (GSD) of 2.8 m from a design altitude of 600 km. NEMO-HD (Nanosatellite for Earth Monitoring and Observation - High Definition) is the second spacecraft that is based on SFL's high-performance NEMO bus and builds upon the heritage of SFL's flight-proven Generic Nanosatellite Bus (GNB). NEMO-HD will carry two optical instruments: a narrow-field instrument as well as a wide-field instrument. The narrow-field instrument will be capable of resolving 2.8 m GSD in four channels corresponding to Landsat-1, 2, 3, and 4 spectral channels (450-520 nm, 520-600 nm, 630-690 nm, and 760-900 nm). The wide-field instrument will be capable of resolving 75 m GSD or better. Both instruments are capable of recording High-Definition video at 1920 by 1080 pixels. The spacecraft will be capable of performing global imaging and real-time video streaming over Slovenia and other regions where it will be in view of the ground station. In addition, the spacecraft will also be capable of performing remote observations. NEMO- HD will include the standard complement of subsystems, sensors and actuators that make up a three-axis stabilized NEMO bus. NEMO-HD will be enhanced to include a 50 Mbps X-band downlink, 128 GB of on-board storage, a high-performance instrument computer, and a power system generating 31 W at end-of-life with a 130 W-h Li-ion battery. The paper provides an overview of the NEMO-HD system design.
The mobile application for EOM was developed to provide access to time-series data and derived analyses on mobile devices. Using their current GPS location or a manual set position, users can extract vegetation time- series data, as well as view data plots, trend, and breakpoint analysis plots directly on their mobile devices. An OGC WPS process was developed for the mobile application. This provides all necessary functionalities in a single process, available as web service. This process extracts the data from Google Earth Engine 6 and plots the time-series and decomposition figure. In a second step, time-series analyses for breakpoint detection (BFAST) and trend calculations (greenbrown) are executed and plotted in a figure. The resulting output is a GeoJSON file containing the values of the analysis tools, as well as links to the generated figures. Figure 8 shows screenshots of the mobile app linked with a chart of the OGC WPS process and how they interact.
Land-use and land-cover (LULC) is driven by natural and an- thropogenic activities, which in turn drive changes impacting natural ecosystem (Rawat and Kumar, 2015). Understand- ing the patterns of land cover change and the factors driving spatial and temporal patterns is vital for proper land man- agement and decision use of land for water resources man- agement. There is increasing global stress on land and water resources because of population growth and increased per- capita water, energy and food demand (Pfister et al., 2011). In response, the Water–Energy–Food (WEF) Nexus approach is promoted as a way of understanding the interlinkages be- tween water, energy and food systems. For example, the Bonn 2011 Conference, “The Water Energy and Food Se- curity Nexus – Solutions for the Green Economy” recom- mended that water, energy and food be considered in an inte- grative manner, explicitly identifying the interdependencies in decision making. This is based on the premise that de- cisions made in one sector affect one or more of the other sectors. Integrated decision making is therefore necessary to utilise the synergies and minimise trade-offs. In this study, it is argued water, energy and food rely on the same resource base which is land and ecosystems, and that changes in land cover affect the delivery of water, energy and food ecosystem services. Therefore, land and ecosystems is viewed to be at the centre of the WEF nexus (Fig. 1). The arrows represent the flow of resources from one sector to the other, with land and ecosystems at the centre and supporting all three sectors. Environmental change drivers, such as climate change and population growth are seen to influence decision making to achieve development goals, including Sustainable Develop- ment Goals (SDGs), regional, basin and national goals. Ta- ble 1 outlines some of the water energy and food ecosystem services associated with major land cover types.
from earthobservation satellites is noted. The use of Google and Microsoft Virtual Earth engines can be seen as generic examples of this trend. For hydrology, water resources research and professional practice, we also note a growing availability of earthobservation (EO) data and products, needed for operational land and water management. Many of these data are public domain and made freely available using low-cost global data dissemination infrastructures like GEONETCast, managed by the spaces agencies EUMETSAT and NOAA amongst others, within the context of the GEOSS framework. Also web-based data provision and servicing is increasing. Today, a multitude of software packages can be legally downloaded and used with little restrictions, including source code access. The near real time and open access aspect of many satellite datasets make their use and application for land and water resources motivating. This short paper shows the use of near real time satellite and in-situ data, coupled to an open source geospatial analysis system. The versatility of the open toolbox concept is shown using a number of project-based applications for water resources, food security and weather. The public domain nature of both the EO data and geospatial software permit the water and climate community to develop applications of choice at user-defined spatial scale, ranging from regional and country level to river basin and small catchment or field-scale.