Search Results (19)

On the Korean Peninsula, the Imjin River is a transboundary river that flows from North Korea into South Korea. Therefore, human intervention activities in the upstream region can have a substantial impact on the downstream region of South Korea. In addition to climate impacts, there are increasing concerns regarding upstream man-made activities, particularly the operation of the Hwanggang dam located in the territory of North Korea. This study explored the feasibility of using the publicly available global hydrological model and satellite remote sensing imagery for monitoring reservoir dynamics and assessing their impacts on downstream hydrology. “Naturalized” streamflow simulation was obtained from the Group on Earth Observation (GEO) Global Water Sustainability (GEOGloWS) European Centre for Medium-Range Weather Forecasts (ECMWF) Streamflow Services (GESS) model. To correct the biases of the GESS-based streamflow simulations, we employed quantile mapping using the observed streamflow from a nearby location. This method significantly reduced volume and variability biases by up to 5 times on both daily and monthly scales. Nevertheless, its effectiveness in improving temporal correlation on a daily scale in small catchments remained constrained. For the reservoir storage changes in the Hwanggang dam, we combined multiple remote sensing imagery, particularly cloud-free optical images of Landsat-8, Sentinel-2, and snow-free Sentinel-1, with the area–elevation–volume (AEV) curves derived from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). In assessing its hydrological impacts, the study found that overall impacts within the downstream catchment in Pilseung bridge of South Korea were generally less significant compared to the upstream Hwanggang catchment. However, there was a higher probability of experiencing water shortages during wet months due to the upstream dam’s operations. The study highlights the potential benefits of utilizing the publicly available hydrological model and satellite remote sensing imagery to supplement decision makers with important information for the effective management of the transboundary river basin in ungauged regions. Full article

In the field of Earth observation, the importance of in situ data was recognized by the Group on Earth Observations (GEO) in the Canberra Declaration in 2019. The GEO community focuses on three global priority engagement areas: the United Nations 2030 Agenda for Sustainable Development, the Paris Agreement, and the Sendai Framework for Disaster Risk Reduction. While efforts have been made by GEO to open and disseminate in situ data, GEO did not have a general way to capture in situ data user requirements and drive the data provider efforts to meet the goals of its three global priorities. We present a requirements data model that first formalizes the collection of user requirements motivated by user-driven needs. Then, the user requirements can be grouped by essential variable and an analysis can derive product requirements and parameters for new or existing products. The work was inspired by thematic initiatives, such as OSCAR, from WMO, OSAAP (formerly COURL and NOSA) from NOAA, and the Copernicus In Situ Component Information System. The presented solution focuses on requirements for all applications of Earth observation in situ data. We present initial developments and testing of the data model and discuss the steps that GEO should take to implement a requirements database that is connected to actual data in the GEOSS platform and propose some recommendations on how to articulate it. Full article

The unprecedented availability of petascale analysis-ready earth observation data has given rise to a remarkable surge in demand for regional to global environmental studies, which exploit tons of data for temporal–spatial analysis at a much larger scale than ever. Imagery mosaicking, which is critical for forming “One Map” with a continuous view for large-scale climate research, has drawn significant concern. However, despite employing distributed data processing engines such as Spark, large-scale data mosaicking still significantly suffers from a staggering number of remote sensing images which could inevitably lead to discouraging performance. The main ill-posed problem of traditional parallel mosaicking algorithms is inherent in the huge computation demand and incredible heavy data I/O burden resulting from intensively shifting tremendous RS data back and forth between limited local memory and bulk external storage throughout the multiple processing stages. To address these issues, we propose an in-memory Spark-enabled distributed data mosaicking at a large scale with geo-gridded data staging accelerated by Alluxio. It organizes enormous “messy” remote sensing datasets into geo-encoded gird groups and indexes them with multi-dimensional space-filling curves geo-encoding assisted by GeoTrellis. All the buckets of geo-grided remote sensing data groups could be loaded directly from Alluxio with data prefetching and expressed as RDDs implemented concurrently as grid tasks of mosaicking on top of the Spark-enabled cluster. It is worth noticing that an in-memory data orchestration is offered to facilitate in-memory big data staging among multiple mosaicking processing stages to eliminate the tremendous data transferring at a great extent while maintaining a better data locality. As a result, benefiting from parallel processing with distributed data prefetching and in-memory data staging, this is a much more effective approach to facilitate large-scale data mosaicking in the context of big data. Experimental results have demonstrated our approach is much more efficient and scalable than the traditional ways of parallel implementing. Full article

To facilitate wind power integration for the electric power grid operated by the Inner Mongolia Electric Power Corporation—a major electric power grid in China—a high-resolution (of 2.7 km grid intervals) mesoscale ensemble prediction system was developed that forecasts winds for 130 wind farms in the Inner Mongolia Autonomous Region. The ensemble system contains 39 forecasting members that are divided into 3 groups; each group is composed of the NCAR (National Center for Atmospheric Research) real-time four-dimensional data assimilation and forecasting model (RTFDDA) with 13 physical perturbation members, but driven by the forecasts of the GFS (Global Forecast System), GEM (Global Environmental Multiscale Model), and GEOS (Goddard Earth Observing System), respectively. The hub-height wind predictions of these three sub-ensemble groups at selected wind turbines across the region were verified against the hub-height wind measurements. The forecast performance and variations with lead time, wind regimes, and diurnal and regional changes were analyzed. The results show that the GFS group outperformed the other two groups with respect to correlation coefficient and mean absolute error. The GFS group had the most accurate forecasts in ~59% of sites, while the GEOS and GEM groups only performed the best on 34% and 2% of occasions, respectively. The wind forecasts were most accurate for wind speeds ranging from 3 to 12 m/s, but with an overestimation for low speeds and an underestimation for high speeds. The GEOS-driven members obtained the least bias error among the three groups. All members performed rather accurately in daytime, but evidently overestimated the winds during nighttime. The GFS group possessed the fewest diurnal errors, and the bias of the GEM group grew significantly during nighttime. The wind speed forecast errors of all three ensemble members increased with the forecast lead time, with the average absolute error increasing by ~0.3 m/s per day during the first 72 h of forecasts. Full article

The EO4Agri Strategic Research Agenda (SRA) is a set of recommendations for future research activities in the area of Earth observation for agriculture. The EO4AGRI project provides support to all agri-food sectors based on new uses of COPERNICUS data. At first, part of the deliverable collected user needs from previous work are summarised including gaps in data, delivery platforms and knowledge management. Another input was an analysis of the current political framework and its influence on future agriculture. The implementation of the European Green Deal and the UN Sustainable Development Goals will require future collaboration of the public and private sectors. The main part of the SRA is a list of recommendations for future activities in the Group on Earth Observations (GEO), Horizon Europe (Annex 4 and Annex 6) and the Digital Europe programmes. It is not a revision of these programmes, but additional recommendations or tasks which are important to consider in updating the future programmes. Full article

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making. Full article

This study aimed to describe the interannual climate variability in the West Antarctic Peninsula (WAP) under austral summer conditions. Time series of January sea-surface temperature (SST) at 1 km spatial resolution from satellite-based multi-sensor data from Moderate Resolution Imaging Spectrometer (MODIS) Terra, MODIS Aqua, and Visible Infrared Imager Radiometer Suite (VIIRS) were compiled between 2001 and 2020 at localities near the Gerlache Strait and the Carlini, Palmer, and Rothera research stations. The results revealed a well-marked spatial-temporal variability in SST at the WAP, with a one-year warm episode followed by a five-year cold episode. Warm waters (SST > 0 °C) reach the coast during warm episodes but remain far from the shore during cold episodes. This behavior of warm waters may be related to the regional variability of the Antarctic Circumpolar Current, particularly when the South Polar Front (carrying warm waters) reaches the WAP coast. The WAP can be divided into two zones representing two distinct ecoregions: the northern zone (including the Carlini and Gerlache stations) corresponds to the South Shetland Islands ecoregion, and the southern zone (including the Palmer and Rothera stations) corresponds to the Antarctic Peninsula ecoregion. The Gerlache Strait is likely situated on the border between the two ecoregions but under a greater influence of the northern zone. Our data showed that the Southern Annular Mode (SAM) is the primary driver of SST variability, while the El Niño Southern Oscillation (ENSO) plays a secondary role. However, further studies are needed to better understand regional climate variability in the WAP and its relation with SAM and ENSO; such studies should use an index that adequately describes the ENSO in these latitudes and addresses the limitations of the databases used for this purpose. Multi-sensor data are useful in describing the complex climate variability resulting from the combination of local and regional processes that elicit different responses across the WAP. It is also essential to continue improving SST approximations at high latitudes. Full article

This study investigates the main threats related to environmental degradation that affect Amazonian Indigenous Lands (ILs). Through a cluster analysis, we group ILs according to the set of common environmental threats that occur within and outside their limits. The results show that most of the 383 ILs are affected internally by a combination of different environmental threats, namely: deforestation, forest degradation, fires, mining, croplands, pastures, and roads. However, the ILs affected by multiple and relatively severe threats are mainly located in the arc of deforestation and the Roraima state. The threats related to forest loss (deforestation, forest degradation, and fires) are more intense in the ILs’ buffer zones than within, showing that ILs effectively promote environmental preservation. In the cluster analysis, we identified seven clusters that are characterized by common environmental threats within and around their limits, and, based on these results, we have outlined four environmental policy priorities to be strengthened and applied in Amazonian ILs: protecting ILs’ buffer zones; strengthening surveillance actions, and combating illegal deforestation, forest degradation, and mining activities in ILs; preventing and fighting fires; and removing invaders from all ILs in the Amazon. In this study, we warn that the threats presented make the Indigenous peoples in the Amazon more vulnerable. To guarantee indigenous peoples’ rights, illegal actions in these territories and their surroundings must be contained, and quickly. Full article

An in-depth and comprehensive assessment of new observations from BDS-3 satellites is presented, with the main focus on the Carrier-to-Noise density ratio (C/N₀), the quality of code and carrier phase observations for B1C and B2a signal. The signal characteristics of geosynchronous earth orbit (GEO), inclined geosynchronous satellite orbit (IGSO) and medium earth orbit (MEO) satellites of BDS-3 were grouped and compared, respectively. The evaluation results of the new B1C and B2a signals of BDS-3 were compared with the previously B1I/B2I/B3I signals and the interoperable signals of GPS, Galileo and quasi-zenith satellite system (QZSS) were compared simultaneously. As expected, the results clearly show that B1C and B2a have better signal strength and higher accuracy, including code and carrier phase observations. The C/N₀ of the B2a signal is about 3 dB higher than other signals. One exception is the code observation accuracy of B3I, which value is less than 0.15 m. The carrier precision of B1C and B2a is better than that of B1I/B2I/B3I. Despite difference-in-difference (DD) observation quantity or zero-base line evaluation is adopted, while B1C is about 0.3 mm higher carrier precision than B2a. The BDS-3 MEO satellite and GPS, Galileo, and QZSS satellites have the same level of signal strength, code and phase observation accuracy at the interoperable frequency, namely 1575.42 MHz and 1176.45 MHz which are very suitable for the co-position application. Full article

Many of the world’s agricultural frontiers are located in the tropics. Crop and cattle expansion in these regions has a strong environmental impact. This paper examines land use and land cover transformations in Brazil, where large swaths of natural vegetation are being removed to make way for agricultural production. In Brazil, the land use dynamics are of great interest regarding the country’s sustainable development and climate mitigation actions, leading to the formulation and implantation of public policies and supply chain interventions to reduce deforestation. This paper uses temporal trajectory analysis to discuss the patterns of agricultural practices change in the different biomes of Mato Grosso State, one of Brazil’s agricultural frontiers. Taking yearly land use and cover classified images from 2001 to 2017, we identified, quantified, and spatialized areas of stability, intensification, reduction, interchange, and expansion of single and double cropping. The LUC Calculus was used as a tool to extract information about trajectories and trajectories of change. Over two decades, the land use change trajectories uncover the interplay between forest removal, cattle raising, grain production, and secondary vegetation regrowth. We observed a direct relationship between the conversion of forest areas to pasture and of pasture to agriculture areas in the Amazon portion of the Mato Grosso State in different periods. Our results enable a better understanding of trends in agricultural practices. Full article

Infrared (IR) radiometers from geostationary (GEO) satellites have an advantage over low-earth orbiting (LEO) satellites as they provide continuous observations to monitor the diurnal variations in the sea surface temperature (SST), typically better than 30-minute interval. However, GEO satellite observations suffer from significant diurnal and seasonal biases arising due to varying sun-earth-satellite geometry, leading to biases in SST estimates from conventional non-linear regression-based algorithms (NLSST). The midnight calibration issue occurring in GEO sensors poses a different challenge altogether. To mitigate these issues, we propose SST estimation from split-window IR observations of INSAT-3D and 3DR Imagers using One-Dimensional Variational (1DVAR) scheme. Prior to SST estimation, the bias correction in Imager observations is carried out using cumulative density function (CDF) matching. Then NLSST and 1DVAR algorithms were applied on six months of INSAT-3D/3DR observations to retrieve the SST. For the assessment of the developed algorithms, the retrieved SST was validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQuam) for the study period. The quantitative assessment confirms the superiority of the 1DVAR technique over the NLSST algorithm. However, both the schemes under-estimate the SST as compared to in-situ SST, which may be primarily due to the differences in the retrieved skin SST versus bulk in-situ SST. The 1DVAR scheme gives similar accuracy of SST for both INSAT-3D and 3DR with a bias of −0.36 K and standard deviation (Std) of 0.63 K. However, the NLSST algorithm provides slightly less accurate SST with bias (Std) of −0.18 K (0.87 K) for INSAT-3DR and −0.27 K (0.95 K) for INSAT-3D. Both the NLSST and 1DVAR algorithms are capable of producing the accurate thermal gradients from the retrieved SST as compared to the gradients calculated from daily Multiscale Ultrahigh Resolution (MUR) level-4 analysis SST acquired from Group for High-Resolution Sea Surface Temperature (GHRSST). Based on these spatial gradients, thermal fronts can be generated that are very useful for predicting potential fishery zones (PFZ), which is available from GEO satellites, INSAT-3D/3DR, in near real-time at 15-minute intervals. Results from the proposed 1DVAR and NLSST algorithms suggest a marked improvement in the SST estimates with reduced diurnal/seasonal biases as compared to the operational NLSST algorithm. Full article

Our cities are the frontier where the battle to achieve the global sustainable development agenda over the next decade would be won or lost. This requires an evidence-based approach to local decision-making and resource allocation, which can only be possible if current gaps in urban data are bridged. Earth observation (EO) offers opportunities to provide timely, spatially disaggregated information that supports this need. Spatially disaggregated information, which is also demanded by cities for forward planning and land management, has not received much attention largely due to three reasons: (i) the cost of generating this data through traditional methods remains high; (ii) the technical capacity in geospatial sciences in many countries is low due to a shortage of skilled professionals who can find and/or process available data; and (iii) the inertia against disturbing routine workflows and adopting new practices that are not imposed through legal requirements at the country level. In support of overcoming the first two challenges, this paper discusses the importance of EO data in the urban context, how it is already being used by some city leaders for decision making, and what other applications it offers in the realm of urban sustainability monitoring. It also illustrates how the EO community, via the Group on Earth Observations (GEO) and its members, is working to make this data more easily accessible and lower barriers of use by policymakers and urban practitioners that are interested in implementing and tracking sustainable development in their jurisdictions. The paper concludes by shining a light on the challenges that remain to be overcome for better adoption of EO data for urban decision making through better communication between the two groups, to enable a more effective alignment of the produced data with the users’ needs. Full article

Demand for agricultural exports in Brazil has stimulated the expansion of crop production and cattle raising, which has caused environmental impacts. In response, Brazil developed public policies such as the new Forest Code (FC) and supply chain arrangements such the Soy and the Cattle Moratoriums. This paper analyzes the effectiveness of these policies, considering the trajectories of agricultural expansion in the state of Mato Grosso in three years: 2005 (pre-moratorium and before the new FC), 2010 (post-moratorium and before the new FC) and 2017 (post-moratorium and post-new FC). Our analysis uses a detailed land use change data for both the Amazon and Cerrado biomes in Mato Grosso. In all the years considered, soybean expansion occurred in consolidated production areas and by conversion of pastures. Pasture expansion is influenced by existence of pastures nearby, by areas of secondary vegetation and deforestation. Our data and models show the effectiveness of public policies and private arrangements to reduce direct conversion from forests to crop production. However, our results also provide evidence that soybean expansion has caused indirect impacts by replacing pasture areas and causing pasture expansion elsewhere. Evidence from our work indicates that Brazil needs broader-ranging land use policies than what was done in the 2010s to be able to reach the land use goals stated in its Nationally Determined Contribution (NDC) to the Paris Agreement. Full article

The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud–shadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (chicken-and-egg dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as subset-of artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a “universal” AutoCloud+ software system in operating mode, capable of systematic cloud and cloud–shadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO big data cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are both conceptual and pragmatic in the definition of remote sensing best practices, which is the focus of efforts made by intergovernmental organizations such as the Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS). First, the ESA EO Level 2 product definition is recommended for consideration as state-of-the-art EO Analysis Ready Data (ARD) format. Second, systematic multi-sensor ESA EO Level 2 information product generation is regarded as: (a) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes, where SCBIR and SEIKD are part-of the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (b) Horizontal policy, the goal of which is background developments, in a “seamless chain of innovation” needed for a new era of Space Economy 4.0. In the subsequent Part 2 (proposed as Supplementary Materials), the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed. Full article