Satellite-based Earth Observation Research Articles

Using resampled nSight-2 hyperspectral data and various machine learning classifiers for discriminating wetland plant species in a Ramsar Wetland site, South Africa

AbstractMapping wetland ecosystems at the species level provides critical information for understanding the nutrient cycle, carbon sequestration, retention and purification of water, waste treatment and pollution control. However, wetland ecosystems are threatened by climate variability and change and anthropogenic activities; thus, their assessment and monitoring have become critical to inform proper management interventions. Contemporary studies show that satellite-based Earth observation (EO) has significant potential for achieving this task. While many multispectral EO data are freely and readily available, its broad spectral bands limit its utility in differentiating subtle differences among similar plant species. In contrast, hyperspectral data has a high spectral resolution, which is superior in discerning minute differences in similar plant species. However, this data is associated with high dimensionality and multicollinearity, which negatively affect the performance of traditional, parametric classification algorithms. To this end, machine algorithms are often preferred to classify hyperspectral data due to their robustness to various data distributions and noise. The current study compared the performance of three advanced machine learning classifiers, i.e., Support Vector Machine (SVM), Random Forest (RF), and Partial Least Squares Discriminant Analysis (PLS-DA), in discriminating four dominant wetland plant species, i.e., Crocosmia sp., Grasses, Agapanthus sp. and Cyperus sp. using simulated hyperspectral data from an upcoming sensor, i.e., nSight-2. The results revealed that SVM is superior, with an overall accuracy of 93.18% (and class-wise accuracies > 85%). In comparison, there were minor differences in the performances of RF and PLS-DA, i.e., 84.09% and 83.63%, respectively. Overall, the results demonstrated that all the evaluated classifiers could achieve acceptable mapping accuracies. However, SVM is more robust, providing exceptional accuracies, and should be considered for operational mapping once the sensor is in space.

Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

Accurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge, the integration of satellite-based Earth Observation (EO) data with advanced machine learning algorithms offers promising prospects to monitor land and crop phenology over time. However, achieving accurate AGB maps in small crop fields and complex landscapes is still an ongoing challenge. In this study, the AGB was estimated for small sugarcane fields (<1 ha) located in the Kumphawapi district of Udon Thani province, Thailand. Specifically, in order to explore, estimate, and map sugarcane AGB and carbon stock for the 2018 and 2021 years, ground measurements and time series of Sentinel-1 (S1) and Sentinel-2 (S2) data were used and random forest regression (RFR) and support vector regression (SVR) applied. Subsequently, optimized predictive models used to generate large-scale maps were adapted. The RFR models demonstrated high efficiency and consistency when compared to the SVR models for the two years considered. Specifically, the resulting AGB maps displayed noteworthy accuracy, with the coefficient of determination (R2) as 0.85 and 0.86 with a root mean square error (RMSE) of 8.84 and 9.61 t/ha for the years 2018 and 2021, respectively. In addition, mapping sugarcane AGB and carbon stock across a large scale showed high spatial variability within fields for both base years. These results exhibited a high potential for effectively depicting the spatial distribution of AGB densities. Finally, it was shown how these highly accurate maps can support, as valuable tools, sustainable agricultural practices, government policy, and decision-making processes.

Strategies for improving the communication of satellite-derived InSAR data for geohazards through the analysis of Twitter and online data portals

Abstract. Satellite-based earth observation sensors are increasingly able to monitor geophysical signals related to natural hazards, and many groups are working on rapid data acquisition, processing, and dissemination to data users with a wide range of expertise and goals. A particular challenge in the meaningful dissemination of Interferometric Synthetic Aperture Radar (InSAR) data to non-expert users is its unique differential data structure and sometimes low signal-to-noise ratio. In this study, we evaluate the online dissemination of ground deformation measurements from InSAR through Twitter, alongside the provision of open-access InSAR data from the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) processing system. Our aim is to evaluate (1) who interacts with disseminated InSAR data, (2) how the data are used, and (3) to discuss strategies for meaningful communication and dissemination of open InSAR data. We found that the InSAR Twitter community was primarily composed of non-scientists (62 %), although this grouping included earth observation experts in applications such as commercial industries. Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where users disseminated InSAR measurements of ground deformation, often using wrapped and unwrapped interferograms. For earthquake events, Sentinel-1 data were acquired, processed, and tweeted within 4.7±2.8 d (the shortest was 1 d). Open-access Sentinel-1 data dominated the InSAR tweets and were applied to volcanic and earthquake events in the most engaged-with (retweeted) content. Open-access InSAR data provided by LiCSAR were widely accessed, including automatically processed and tweeted interferograms and interactive event pages revealing ground deformation following earthquake events. The further work required to integrate dissemination of InSAR data into longer-term disaster risk-reduction strategies is highly specific, to both hazard type and international community of practice, as well as to local political setting and civil protection mandates. Notably, communication of uncertainties and processing methodologies are still lacking. We conclude by outlining the future direction of COMET LiCSAR products to maximize their useability.

Statistical Correlation Between DEMETER Satellite Electronic Perturbations and Global Earthquakes With M ≥ 4.8

Upon obtaining a relatively low false discovery rate (FDR) of alarms and a low false negative rate (FNR) of earthquakes, several previous long-term statistical researches concluded that ionospheric perturbations recorded by satellites are statistically related to earthquakes. However, overly large time-space windows for correlating perturbations with earthquakes will also contribute to low FDR and FNR. In this study, a new score - the number of non-randomly successful alarms - is used to quantitatively describe the sensitivity of Electron Density Perturbations (EDPs) recorded by the DEMETER satellite to global earthquakes with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> ≥4.8. Results show that the EDPs are significantly related to global medium-to-strong earthquakes and that optimal parameters for removing EDPs which are non-related to earthquakes and the optimal time-space windows for correlating earthquakes and EDPs are variable in space. Moreover, our results show that the intensity of EDPs makes little contribution to distinguishing the perturbations related to earthquakes with different magnitudes and perturbations non-related to earthquakes, while the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K_p</i> index is effective for improving the Signal/Noise ratio of our model, where Signal/Noise refers to the EDPs related/non-related to earthquakes. Finally, using the optimal time-space windows for correlating EDPs and earthquake, we construct several earthquake prediction models and quantitatively evaluate their power. We find that these EDP-based earthquake predictions are better than the spatially variable Poisson model showing the great potential of predicting earthquakes based on satellite-based Earth observation techniques. However, the spatio-temporal accuracy of these models for predicting earthquakes is not satisfactory, as the alerted time-space volume is big.

Multi-Sensor Data Analysis of an Intense Weather Event: The July 2021 Lake Como Case Study

A comprehensive analysis of the July 2021 event that occurred on Lake Como (Italy), during which heavy hailstorms and floods affected the surroundings of Lake, is presented. The study provides a detailed analysis of the event using different observation sources currently available. The employed techniques include both conventional (rain gauges, radar, atmospheric sounding) and non-conventional (satellite-based Earth observation products, GNSS, and lightning detection network) observations for hydro-meteorological analysis. The study is split in three main topics: event description by satellite-based observations; long-term analysis by the ERA5 model and ASCAT soil water index; and short-term analysis by lightning data, GNSS delays and radar-VIL. The added value of the work is the near-real-time analysis of some of the datasets used, which opens up the potential for use in alerting systems, showing considerable application possibilities in NWP modeling, where it can also be useful for the implementation of early warning systems. The results highlight the validity of the different techniques and the consistency among the observations. This result, therefore, leads to the conclusion that a joint use of the innovative techniques with the operational ones can bring reliability in the description of events.

A dynamic landslide hazard monitoring framework for the Lower Mekong Region

The Lower Mekong region is one of the most landslide-prone areas of the world. Despite the need for dynamic characterization of landslide hazard zones within the region, it is largely understudied for several reasons. Dynamic and integrated understanding of landslide processes requires landslide inventories across the region, which have not been available previously. Computational limitations also hamper regional landslide hazard assessment, including accessing and processing remotely sensed information. Finally, open-source software and modelling packages are required to address regional landslide hazard analysis. Leveraging an open-source data-driven global Landslide Hazard Assessment for Situational Awareness model framework, this study develops a region-specific dynamic landslide hazard system leveraging satellite-based Earth observation data to assess landslide hazards across the lower Mekong region. A set of landslide inventories were prepared from high-resolution optical imagery using advanced image-processing techniques. Several static and dynamic explanatory variables (i.e., rainfall, soil moisture, slope, relief, distance to roads, distance to faults, distance to rivers) were considered during the model development phase. An extreme gradient boosting decision tree model was trained for the monsoon period of 2015–2019 and the model was evaluated with independent inventory information for the 2020 monsoon period. The model performance demonstrated considerable skill using receiver operating characteristic curve statistics, with Area Under the Curve values exceeding 0.95. The model architecture was designed to use near-real-time data, and it can be implemented in a cloud computing environment (i.e., Google Cloud Platform) for the routine assessment of landslide hazards in the Lower Mekong region. This work was developed in collaboration with scientists at the Asian Disaster Preparedness Center as part of the NASA SERVIR Program’s Mekong hub. The goal of this work is to develop a suite of tools and services on accessible open-source platforms that support and enable stakeholder communities to better assess landslide hazard and exposure at local to regional scales for decision making and planning.

A dynamic model for assessing soil denitrification in large-scale natural wetlands driven by Earth Observations.

The Wetland Soils Denitrification Model (WSDM) developed here for natural wetlands, is a physical based model that proposes: (i) the inclusion of soil moisture and temperature from satellite-based Earth Observations at diurnal temporal resolution and (ii) the distinction of soils under different wetland typologies (i.e., flooded forests, freshwater marshes, brackish wetlands, peatlands, and complex wetlands). Despite uncertainties involved, these two features are key to upscale nitrification/denitrification dynamics in natural wetlands at landscape, regional and global scales. In this study, the performance of WSDM was validated with soils in flooded forests and freshwater marshes in the central Amazonian floodplain. WSDM multiannual time series (2012–2019) show that climate anomalies intensify denitrification events. Flooded forests were identified with the highest annual denitrification rates. Annual denitrification and N2O emissions estimated in this study are in line with previous studies.

Satellite-based crop identification and area estimation under the SATMIROL system

One important area where satellite-based Earth observation is applied in is agricultural statistics. The use of new digital technologies and satellite data have contributed to the modernisation of the aforementioned scientific area and is designed to overcome the problems that both agricultural statistics and the recipients of statistical data are faced with. The main aim of the article is to present an innovative system of satellite-based crop identification and crop growth monitoring – SATMIROL. The system was developed by Statistics Poland, the Institute of Geodesy and Cartography and the Space Research Centre of the Polish Academy of Sciences. SATMIROL provides agricultural statistics based on satellite data from the Copernicus programme and administrative data. The article focuses on selected modules of the system which involve the use of satellite remote sensing for crop identification. The study also presents a nationwide crop area estimation performed in three periods: 2019, 2020 and 2021. The implementation of SATMIROL allowed the presentation of statistical data on agriculture in spatial terms, which is not possible when applying traditional methods of data production. This new method of acquiring data enables their presentation at all levels of Poland’s administrative division, i.e. by voivodship, poviat (district) and gmina (commune).

Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations

Evapotranspiration (ET) is an essential ecohydrological process linking the land surface energy, water and carbon cycles, and plays a critical role in the earth system. ET remains one of the most problematic components of the water cycle to be determined due to the heterogeneity of the landscape and the complexity of driving factors. The satellite-based earth observation is expected to provide ET information at large-scales. However, accurate global ET information, with spatially and temporally continuous coverage at moderate-to-high resolution, is still scarce. In this paper, a combined model, called ETMonitor, with multi-process parameterizations, was improved and applied to estimate the global ET, mainly using the biophysical and hydrological parameters/variables retrieved from satellite observations. The ETMonitor model was improved in several aspects in this study to generate the global ET datasets during 2000–2019 at daily/1-km resolution, including: 1) adopting high temporal resolution surface water cover and snow/ice cover as input, to simulate the impact of their seasonal change on ET variation; 2) parameterizing the impact of soil moisture on plant transpiration and soil evaporation using high resolution soil moisture, which was downscaled to 1-km resolution from the coarse resolution data retrieved from microwave remote sensing observation; 3) involving a better soil heat flux estimation to reduce its impact on the uncertainty of estimated ET; 4) being calibrated based on global ground flux observations to achieve better accuracy. The estimated daily ET was validated based on the global in situ observations at site scale across various ecosystems, with overall high correlation (0.75), low bias (0.08 mm d-1), and low root mean square error (0.93 mm d-1). It had good ability to partition total ET to plant transpiration and soil evaporation indicated by the good agreement with the ground isotope measurements in a growing season at one site in the northwest China. The estimated global ET was cross-validated by comparing with other existing ET products, and it showed the global ET estimated by ETMonitor could capture the expected global ET patterns both in space and in time. It also indicated the superiority of the ET product by ETMonitor in the following aspects: capability in capturing the seasonal dynamics of waterbody evaporation and sublimation; better performance in capturing the spatial variation of ET in the irrigated cropland regions and mountain regions with complex terrain than other global ET products, e.g., the GLEAM and MOD16 ET products; capability of ET component partitioning at high spatial (1-km) and high temporal (daily) resolutions with good accuracy. The estimated plant transpiration, soil evaporation, canopy rainfall interception loss, and water body evaporation and snow sublimation accounted for 61.54 % (±0.44 %), 19.08 % (±0.54 %), 13.54 % (±0.49 %), and 5.84 % (±0.24 %), respectively, of the total ET on global average. The ETMonitor global ET dataset could provide important information on studies of global terrestrial water and energy cycles and climate change studies, and water resources management at global and regional scales.

Effects of land use change on population survival of three wild rice species in China since 2001.

Land use change stemming from human activities, particularly cropland expansion, heavily threatens the survival of crop wild relatives that usually occur nearby or scatter in farming systems. Understanding the impacts of land use change on wild populations is critical in forming the conservation decision-making of wild relatives. Based on the investigations on the population survival of three wild rice species (Oryza rufipogon, O. officinalis, and O. granulata) in China over the past 40 years (1978–2019), the effect of land use change during the past 20 years (2001–2019) on the natural populations of the three species was examined using the land use type data of satellite-based Earth observations (data from GlobCover). From 1978 to 2019, the number of populations (distribution sites) of the three wild rice species had decreased by 65–87%, mainly because of the habitat destruction or disappearance caused by human-induced land use change. The three wild rice species display different habitat preferences, resulting in specific land use types surrounding their populations. In the recent 20 years, although the surrounding community composition of the wild rice population has been relatively stable, the surrounding vegetation cover area of the survived populations was significantly more extensive than that of the extinct ones (p < 0.05). These findings suggest that habitat vegetation plays a “biological barrier” role in the survival of wild populations through resisting or mitigating the disturbing impact of land use change on wild populations. This study provides not only direct guidelines for the conservation of wild rice but also new insights into the mechanisms underlying the influence of land use change on wild populations.

High-resolution satellite products improve hydrological modeling in northern Italy

Abstract. Satellite-based Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolutions, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based EO data in hydrological modeling. In a set of six experiments, the distributed hydrological model Continuum is set up for the Po River basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture (SM) and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation, and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling–Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGEmean= 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite data on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications.

The Effects of Meteorological Factors on Dengue Cases in Malaysia.

Dengue is a vector-borne disease affected by meteorological factors and is commonly recorded from ground stations. Data from ground station have limited spatial representation and accuracy, which can be overcome using satellite-based Earth Observation (EO) recordings instead. EO-based meteorological recordings can help to provide a better understanding of the correlations between meteorological variables and dengue cases. This paper aimed to first validate the satellite-based (EO) data of temperature, wind speed, and rainfall using ground station data. Subsequently, we aimed to determine if the spatially matched EO data correlated with dengue fever cases from 2011 to 2019 in Malaysia. EO data were spatially matched with the data from four ground stations located at states and districts in the central (Selangor, Petaling) and east coast (Kelantan, Kota Baharu) geographical regions of Peninsular Malaysia. Spearman’s rank-order correlation coefficient (ρ) was performed to examine the correlation between EO and ground station data. A cross-correlation analysis with an eight-week lag period was performed to examine the magnitude of correlation between EO data and dengue case across the three time periods (2011–2019, 2015–2019, 2011–2014). The highest correlation between the ground-based stations and corresponding EO data were reported for temperature (mean ρ = 0.779), followed by rainfall (mean ρ = 0.687) and wind speed (mean ρ = 0.639). Overall, positive correlations were observed between weekly dengue cases and rainfall for Selangor and Petaling across all time periods with significant correlations being observed for the period from 2011 to 2019 and 2015 to 2019. In addition, positive significant correlations were also observed between weekly dengue cases and temperature for Kelantan and Kota Baharu across all time periods, while negative significant correlations between weekly dengue cases and temperature were observed in Selangor and Petaling across all time periods. Overall negative correlations were observed between weekly dengue cases and wind speed in all areas from 2011 to 2019 and 2015 to 2019, with significant correlations being observed for the period from 2015 to 2019. EO-derived meteorological variables explained 48.2% of the variation in dengue cases in Selangor. Moderate to strong correlations were observed between meteorological variables recorded from EO data derived from satellites and ground stations, thereby justifying the use of EO data as a viable alternative to ground stations for recording meteorological variables. Both rainfall and temperature were found to be positively correlated with weekly dengue cases; however, wind speed was negatively correlated with dengue cases.

HydroSat: geometric quantities of the global water cycle from geodetic satellites

Abstract. Against the backdrop of global change, in terms of both climate and demography, there is a pressing need for monitoring of the global water cycle. The publicly available global database is very limited in its spatial and temporal coverage worldwide. Moreover, the acquisition of in situ data and their delivery to the database have been in decline since the late 1970s, be it for economical or political reasons. Given the insufficient monitoring from in situ gauge networks, and with no outlook for improvement, spaceborne approaches have been under investigation for some years now. Satellite-based Earth observation with its global coverage and homogeneous accuracy has been demonstrated to be a potential alternative to in situ measurements. This paper presents HydroSat as a database containing geometric quantities of the global water cycle from geodetic satellites. HydroSat provides time series and their uncertainty in water level from satellite altimetry, surface water extent from satellite imagery, terrestrial water storage anomaly represented in equivalent water height from satellite gravimetry, lake and reservoir water volume anomaly from a combination of satellite altimetry and imagery, and river discharge from either satellite altimetry or imagery. The spatial and temporal coverage of these datasets varies and depends on the availability of geodetic satellites. These products, which are complementary to existing products, can contribute to our understanding of the global water cycle within the Earth system in several ways. They can be incorporated for hydrological modeling, they can be complementary to current and future spaceborne observations, and they can define indicators of the past and future state of the global freshwater system. HydroSat is publicly available through http://hydrosat.gis.uni-stuttgart.de (last access: 18 May 2022​​​​​​​). Moreover, a snapshot of all the data (taken in April 2021) is available in GFZ Data Services at https://doi.org/10.5880/fidgeo.2021.017 (Tourian et al., 2021).

Spectral invariants in ultra-high spatial resolution hyperspectral images

Current operational optical satellite-based Earth observation methods targeting at vegetation are optimised for the high- and medium-resolution satellites with pixels sizes starting at ten meters. This resolution is coarser than the typical size of a vegetation structural element, such as a tree crown, and much coarser that of scattering elements, such as leaves. For this reason, vegetation can be treated as a continuous medium and the variation in the local illumination conditions on individual leaves or tree crowns can be ignored. This does not hold anymore for very and ultra-high resolution imagery, obtained from new satellite systems or unmanned aerial vehicles, where individual tree crowns or even leaves can be discerned. We tested the applicability of the spectral invariant theory to this type of imagery for characterising the local illumination conditions on plant leaves using Monte Carlo ray tracing simulations. The simulations corroborated the direct link between the spectral invariant parameter ρ and the sunlit fraction of visible leaves, earlier alleged for hyperspectral remote sensing data based on mathematical considerations. The approach allowed us to separate the direct beam and multiple scattering irradiance components and provided intuitive interpretations of the recollision probability and canopy scattering coefficient computed for each image pixel.

A Geospatial Artificial Intelligence and satellite-based earth observation cognitive system in response to COVID-19

The pandemic emergency caused by the spread of COVID-19 has stressed the importance of promptly identifying new epidemic clusters and patterns, to ensure the implementation of local risk containment measures and provide the needed healthcare to the population. In this framework, artificial intelligence, GIS, geospatial analysis and space assets can play a crucial role. Social media analytics can be used to trigger Earth Observation (EO) satellite acquisitions over potential new areas of human aggregation. Similarly, EO satellites can be used jointly with social media analytics to systematically monitor well-known areas of aggregation (green urban areas, public markets, etc.). The information that can be obtained from the Earth Cognitive System 4 COVID-19 (ECO4CO) are both predictive, aiming to identify possible new clusters of outbreaks, and at the same time supervisorial, by monitoring infrastructures (i.e. traffic jams, parking lots) or specific categories (i.e. teenagers, doctors, teachers, etc.). In this perspective, the technologies described in this paper will allow us to detect critical areas where individuals can be involved in risky aggregation clusters. The ECO4CO data lake will be integrated with ad hoc data obtained by health care structures to understand trends and dynamics, to assess criticalities with respect to medical response and supplies, and to test possibilities useful to tackle potential future emergencies. The System will also provide geographical information on the spread of the infection which will allow an appropriate context-specific public health response to the epidemic. This project has been co-funded by the European Space Agency under its Business Applications programme.

Analyzing the role of in situ coal fire in greenhouse gases emission in a coalfield using remote sensing data and their dispersion and source apportionment study.

In situ coal fires significantly pollute the environment in many countries of the world. Monitoring these pollutants is challenging due to extensive area coverage and spatial variations. Thus, the present study demonstrates the method of deriving the spatial and temporal profiles of columnar density of three major greenhouse gases (carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO2)) in an in situ coal fire region (Jharia coalfield (JCF), India) using high-resolution satellite data (TROPOMI) of the European Space Agency (ESA). The study also demonstrates a new methodology for estimating greenhouse gas emissions from in situ coal burning. JCF is one of the significant polluted mining regions with multiple in situ coal fire pockets. The columnar density of the gaseous pollutants in the mining region was compared with the same in the rural, urban, and forest regions to identify the major emission inventories. The study results indicated that coal fire is the major source of CO emission in the region, as the CO was high in the fire regions compared to that of the non-fire regions. But, the major source of NO2 is the traffic, as the NO2 was high in the city area as compared to other regions. The spatial profile of SO2 does not reveal the specific emission sources. The study results indicated that TROPOMI onboard satellite sensors could be effectively used for deriving the spatial profiles of greenhouse gaseous in coal fire regions, which further assist in identifying the emission inventories. Furthermore, the satellite-based Earth observations offer information to understand and manage the greenhouse gas emissions over a large area.

Space for the Sustainable Development Goals: mapping the contributions of space-based projects and technologies to the achievement of the 2030 Agenda for Sustainable Development

Progress towards the achievement of the 17 Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development in many ways presupposes the utilisation of science, technology and innovation. Many sustainability-oriented projects across industries make use of space-based technologies and services to contribute to the Goals. Among others, satellite-based Earth observation, positioning, navigation and communication services are used in an array of sectors ranging from monitoring environmental conditions and changes to supporting search and rescue missions. In order to illustrate contributions to the SDGs, space agencies and other institutions have aligned their projects to the SDG framework. This study attempts a more holistic, aggregate mapping of such alignments to gauge which SDGs benefit the most and from space-based projects and technologies, as opposed to those benefiting the least. The results demonstrate that the number of contributing projects varies significantly across the Goal spectrum, as does the share of the various technologies involved, with particular focus on industrial development, hunger elimination, and improved healthcare. Nevertheless, the range of application of space-based technologies is wide and highlights the relevance of space to support the transition towards a sustainable future.

Deformation monitoring and thematic mapping of the Badaling Great Wall using very high-resolution interferometric synthetic aperture radar data

The preventive monitoring and sustainable conservation of large-scale cultural heritage sites require satellite-based Earth observations. In this study, we present the first monitoring and thematic mapping results of the Badaling Great Wall (Beijing, China) using deformation indicators calculated from very high-resolution (VHR) multi-temporal spotlight TerraSAR-X data. The proposed coarse–fine search algorithm achieved high computational efficiency for calculating the unknown parameters of the two-tier network persistent scatterer synthetic aperture radar (SAR) interferometry (PSInSAR) approach. The spatiotemporal deformation anomalies, characterized by the absolute velocity, deformation deviation and acceleration, are informative to identify suspected hotspots for prioritizing monitoring activities. We provide an understandable method for thematic mapping and subsequent sustainable conservation of heritage sites by synergistically exploiting impacts from natural degradation and the tourism industry. We determine that the optimum tourist capacity of the site could be 1.0 million per month based on comparing pre- and post-COVID data (2019–2020). This study demonstrates the potential and performance of spaceborne PSInSAR tools for the intelligent management of large-scale architectural heritage sites by integrating InSAR deformation products with environmental and social data.

A Lithosphere–Atmosphere–Ionosphere Coupling Phenomenon Observed Before M 7.7 Jamaica Earthquake

The satellite-based earth observations have become an appropriate instrumental approach in monitoring the natural hazards among modern-day researchers. This study presents a multi-parameter approach using precursors of different physical nature defining the states of atmosphere and the ionosphere in terms of temporal and spatial variations about 5 days before the impending M 7.7 Jamaican earthquake (EQ). We performed a comprehensive analysis from the surface to the ionosphere at different altitude levels by analyzing the different datasets comprising, surface air temperature, relative humidity, total column water vapor, air pressure, Outgoing Longwave Radiations (OLR), and the total electron content of the global ionosphere maps (GIM-TEC). We observed a sharp increment in the atmospheric chemical potential (ACP) due to the increased radon activity that led to an abrupt decrement in the atmospheric relative humidity and, consequently, increased OLR that provides strong evidence of the air ionization production around the epicenter of M 7.7 EQ. Moreover, to check the periodicity of these atmospheric parameters, we performed a confutation analysis by meticulously analyzing these parameters in the same month and region for the previous 5 years during the non-existence of any major seismicity. This technique confirmed that the simultaneous atmospheric variations observed before the Jamaica EQ are not cyclic in the absence of significant seismic activities. The ionospheric conditions have also shown consistency with atmospheric disturbances, as depletions in GIM-TECs, having an amplitude of 4 TECU, are observed over the epicenter for 6 h (LT = 13–19 (-– UT)) on January 23, 2020. Additionally, the vertical ionospheric and atmospheric profiles from FORMOSAT-7/COSMIC-2 (F7/C2), at different altitudes (75–225 km) over the EQ epicenter, showed significant depletions on January 23, 2020. These TEC variations are observed to be an effect of the vertical seismogenic electric field due to the production of the air ionization at the atmospheric boundary layer by increased radon activity around the seismic preparation zone. The existence of these co-located synchronized atmospheric and ionospheric anomalies is explicitly and persistently local over a small region of the epicenter of Jamaican EQ that could be considered as potential short-term precursors. Also, these multi-observed anomalies will contribute to the physical explanation of the Lithosphere-Atmosphere–Ionosphere Coupling (LAIC) model.

Exploitation of optical and SAR amplitude imagery for landslide identification: a case study from Sikkim, Northeast India

Detection and mapping of landslides is one of the most important techniques used for reducing the impact of natural disasters especially in the Himalaya, owing to its high amount of tectonic deformation, seismicity, and unfavorable climatic conditions. Moreover, the northeastern part of the Himalaya, severely affected by landslides every monsoon, is poorly studied. The information on the inventories is inhomogeneous and lacking. In this context, satellite-based earth observation data, which has significantly advanced in the last decade and often serves as a potential source for data collection, monitoring, and damage assessment for disasters in a short time span, has been implemented. Keeping in mind the above framework, this study aims to exploit the potentials of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery for identifying new landslides in vegetated and hilly areas of the northeastern part of India. In order to assess the potentials of our data and methodology, a landslide event which occurred on 13 August 2016 13:30 h (IST) in North Sikkim, India, triggered due to rainfall has been explored in detail. The landslide also resulted in the formation of a lake, 2.2 km in length and 290 m in width. Difficulty in procurement of cloud-free datasets immediately after the event led us to the use of Sentinel-1 SAR backscatter data, to assess its potential for this purpose. It is observed that the potential of SAR amplitude imagery is limited to different aspects as per the sensor look direction during the mode of acquisition. Furthermore, the present study also incorporates a change detection algorithm to evaluate the performance of the Sudden Landslide Identification Product (SLIP) model to identify new landslides using Sentinel-2 multispectral imagery. Overall, the results exhibit that integrated usage of both optical and SAR amplitude imagery may provide a plethora of information for identification and mapping of new landslides for damage assessment and early warning. All the above results combined together suggest this method for rapid identification of landslides in the Himalayan terrain with special emphasis on the northeastern part of the Himalaya. The automation of this method for future operational usage is also suggested.