Cadmium (Cd) is a naturally occurring toxic heavy metal found in trace concentrations in most soils. However, some soils have high levels of Cd due to parent materials or anthropogenic factors such as industrial waste and legacy fertilizers contaminated with Cd. Complications arise when Cd bioaccumulates in soil and plant tissues with carcinogenic effects in humans such as kidney and skeletal dysfunction. Leafy greens including spinach and kale are especially susceptible to Cd accumulation due to high translocation of the metal into the edible leaf tissues of the crop. However, while high concentrations of Cd in soil are generally toxic to plants, unlike other plant stresses, Cd toxicity has no distinguishing visual features that indicate stress or uptake of the metal in the plant, making it difficult to determine if remediation strategies are effective. In this study, emergent plant sensing tools such as Hyperspectral Imaging (HSI) and red-green-blue (RGB) wavelengths were utilized as high-throughput screening techniques to quickly distinguish wavelengths reflected by spinach plants grown in Cd contaminated soil. Feature selection of the HSI and RGB bands were used to identify regions of interest that exhibited significant differences of Cd uptake within multiple spinach genotypes previously shown to vary in Cd uptake. Applications of these technologies provide nondestructive sampling of crops to identify wavelengths sensitive to Cd stress and uptake, with potential applications using machine learning to provide a framework to model these interactions, informing plant breeding programs, and ultimately reducing human health risks.

We developed a novel Bayesian Hierarchical Network Model (BHNM) for daily streamflow, which uses the spatial dependence induced by the river network topology, and average daily precipitation from the upstream contributing area between station gauges. In this, daily streamflow at each station is assumed to be distributed as Gamma distribution with temporal non-stationary parameters. The mean and standard deviation of the Gamma distribution for each day are modeled as a linear function of suitable covariates. The covariates include daily streamflow from upstream gauges or from the gauge above of the upstream gauges depending on the travel times, and daily, 2-day, or 3-day precipitation from the area between two stations that attempts to reflect the antecedent land conditions. Intercepts and slopes of the mean and standard deviation parameters are modeled as a Multivariate Normal distribution (MVN) to capture their dependence structure. To ensure that the covariance matrix of MVN is positive definite, it is model as an Inverse Wishart distribution. Non-informative priors for each parameter were considered. Using the network structure in incorporating flow information from upstream gauges and precipitation from the immediate contributing area as covariates, enables to capture the spatial correlation of flows simultaneously and parsimoniously. The posterior distribution of the model parameters and, consequently, the predictive posterior Gamma distribution of the daily streamflow at each station and for each day are obtained. The model is demonstrated by its application to daily summer (July-August) streamflow at 4 gauges in the Narmada basin network in central India for the period 1978 – 2014. The skill of the probabilistic forecast is carried out by rank histograms and the Continuous Ranked Probability Score (CRPS). The model validation indicates that the model is highly skillful relative to climatology and relative to a null-model of linear regression. The forecasts present an adequate spread of uncertainty and non-bias. Since flooding is of major concern in this basin, we applied the BHNM in a cross-validated mode on two high flooding years – in that, the model was fitted on other years, and forecasts were made for the dropped-out high flooding year. The skill of the model in forecasting the high flood events was very good across the network – in both the timing and magnitude of the events. The model will be of immense help to policy makers in risk-based flood mitigation. The BHNM framework is general in nature and can be applied to any river network with other covariates as appropriate.

Since its launch, in September 2018, ICESat-2’s Advanced Topographic Laser Altimeter System (ATLAS) has collected high-resolution measurements of Arctic sea ice by sampling the surface every 70 cm along-track. We utilize the high-resolution capabilities of ATLAS with a novel algorithm called the University of Maryland-Ridge Detection Algorithm (UMD-RDA) to investigate sea ice topography across a range of scales. Applying the UMD-RDA to the ATL03 Global Geolocated Photon product we measure surface roughness and derive the frequency, height, width, and angle of individual pressure ridge sails. Aggregating data from multiple orbit crossings per day we investigate ridge characteristics at length-scales varying from 1 km (individual floes) to the pan-Arctic scale (central Arctic Ocean). Here, we present an evaluation of pressure ridge characteristics during the winter seasons of 2018/19, 2019/20, and 2020/21, comparing results from distinct regions with varying ice conditions. Near-coincident, independent observations of pressure ridges with Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) lidar data, OIB near-coincident Continuous Airborne Mapping By Optical Translator (CAMBOT) high-resolution (~15 cm) optical imagery, and WorldView high-resolution (~30 cm) panchromatic satellite imagery are used to evaluate the accuracy of our ICESat-2 ridge detection scheme. There are many potential use-cases for a high-resolution sea ice topography data product within the community, ranging from navigational hazard mitigation to ecological studies of marine mammal habitats. We discuss plans for releasing these data products and discuss the improvements such data would make within high-resolution sea ice models.

JAMSTEC publicly distributes samples and data obtained by JAMSTEC-AORI-JURCAOS Cooperative Research Cruises [1], as well as those by JAMSTEC private/open-call cruises, through online database (DB) websites, for additional uses with research/educational purposes. These online DBs are, from a long-term perspective, under a process of reorganization and integration: Public-level information systems for rock and sediment core samples were integrated into DARWIN DB [1] in FY2017. As for marine biological samples, whose information has been mainly distributed through “Marine Biological Sample DB [3], a part of information is already shared with DARWIN to utilize its search/map functions. DB integration is in progress for not only public-level but also management-level information system, which are operated by sample holders responsible for storage of physical samples. In FY2018, JSDB (JAMSTEC Sample Management Database) is installed for the management of JAMSTEC rock, sediment core and biological samples, and actual data was transferred into this new system in FY2019. For rock and sediment core samples, information forwarding from management-level system (JSDB) to public-level system (DARWIN) is planned to be established in the near future. JSDB consists of data tables for sample matadata information for onboard sampling activities, sample archive information for status and storage of physical samples, associate data information for various data obtained for samples, literature information for published documents, sample distribution information for service logs for sample requests. User authorization includes group setting, enabling individual sample holders outside Data Management Office to read, create and modify the information in an appropriate manner, although the DB is only accessible from inside JAMSTEC because of security reasons. Associate data information is supposed to include various data types, such as photos/images, text-based descriptions, spread-sheets and URLs etc., and open/close status control and group-setting are available for individual entries. Information for research proposals/projects or other data management issues, which should be associated with multiple samples, can also be managed as associated data information. For the future possibility of involvement with IGSN, sample metadata entries and their vocabulary are reorganized to follow the IGSN Descriptive Metadata [4], as much as possible. Peoples involved in sample and data management tend to spend much effort for dealing with unusual conditions/situations which are not originally expected. Robustness is important for design/operation of information systems, because lack of flexibility and too much complexity of information systems are frequent causes of such kind of problems. [1] https://www.aori.u-tokyo.ac.jp/coop/ [2] http://www.godac.jamstec.go.jp/darwin/ [3] http://www.godac.jamstec.go.jp/bio-sample/ [4] https://www.geosamples.org/metadata

Pressure ridges are deformation features within the sea ice pack created through the collision of sea ice floes. Pressure ridges play an important role in ice drift and influence the mass and energy budgets of the Arctic Ocean. Over the past decade annual airborne surveys over Arctic sea ice have been conducted in late winter (March and April) by NASA’s Operation IceBridge (OIB) mission. A total of 74 OIB flights between 2010 and 2018 surveyed tens of thousands of kilometers of sea ice, providing observations of pressure ridges at a higher spatial and temporal resolution than previous airborne studies. Here we utilize Digital Mapping System (DMS) imagery to identify shadows cast by pressure ridge sails and, then, use these shadows to derive sail height. Over 64,000 DMS images were analyzed, allowing for more than 33 million individual sail height measurements to be calculated. We present the full sail-height distributions of new pressure ridges recently formed across a range of ice conditions on first-year (FYI) and multiyear ice (MYI), and we assess year-to-year variability. We find distinct characteristics depending on the ice type in which the pressure ridge formed. The mean and standard deviation of sail heights on FYI is ~20-30 cm lower than those formed on MYI. Maximum sail heights on FYI are ~1.5 m lower on average. Arctic sea ice is getting younger, shifting from predominantly MYI to predominantly FYI. Our results may inform new model parameterizations of pressure ridges on sea ice in the changing Arctic, thereby supporting advances in sea ice forecasting.

Deltas are important landforms on Mars because they indicate past fluvial activity, contain a sedimentary record amenable to study, and have the potential to store past signs of life. The sedimentary records that are visible today on satellite and rover images are used to deduce the fluvial and climate history of Mars. To do this we use our understanding of deltas on Earth, as deltas are well-studied landforms. However, when making interpretations it is very important to understand the differences in delta morphodynamics and stratigraphy between Earth and Mars. Even though the processes on Mars are similar, the water discharge, sediment flux, and grain size sorting can be significantly different due to for example gravity, sediment density, presence of ice, and lack of ecology. In this research, we focus on the effect of gravity and its net effect on delta morphology and stratigraphy. From preliminary experiments, we expect a significant effect of gravity on grain size sorting, because suspended sediment (fine grains) is affected more by gravity than bedload transport (bigger grains). In addition, we expect bigger grains to travel in suspension on Mars under the same boundary conditions. We will study the net effect of these water and sediment fluxes on morphology and stratigraphy by numerical modelling in Delft3D. This software is typically used for river and coastal systems on Earth, but we adapted the software by identifying all explicit and implicit dependencies on gravity, so it can also be used for deltas on Mars. We developed a 2DH hydro-morphodynamic model of a simplified delta to test different scenarios. The model consists of a straight channel flowing into a sloping basin. In the comparison between our Earth and Mars scenarios, gravity is the only parameter varied. We are currently experimenting with different upstream boundary conditions that we keep equal between the Earth and Mars scenario. We are testing equal discharge, water level, and sediment input. Equal discharge reveals the effect of gravity on sediment flux, equal water level the effect on water and net sediment flux, and equal sediment supply focusses on the sediment sorting that is mostly overshadowed in the other scenarios by the sediment flux difference.

The COVID-19 pandemic resulted in reduced carbon dioxide (CO2) emissions in 2020 in large parts of the world. We have analysed an ensemble of satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2, to find out if the COVID-19 related regional-scale reduction of anthropogenic CO2 emissions can be detected from space. We focus on East China and analysed a set of latest version XCO2 data products from the satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse gases Observing SATellite (GOSAT). We use a data-driven approach, which is based on the computation of XCO2 anomalies using a method called DAM. Via DAM, trends and seasonal variations are largely filtered out and resulting positive values of the XCO2 anomalies correlate with the location of major CO2 source regions such as East China after spatio-temporal averaging. We analysed satellite data between January 2015 to May 2020 and compared monthly XCO2 anomalies in the time period January to May 2020 with corresponding monthly XCO2 anomalies from previous years. In order to link the satellite-derived XCO2 anomalies to East China fossil fuel (FF) emissions, we used target region XCO2 and corresponding FF emissions from a model.

Global environmental science challenges in the limnological research and applications communities can only be advanced when harnessing the collective expertise and capabilities of the satellite remote sensing community and well-established in situ communities such as the Global Lake Ecological Observatory Network (GLEON). At first glance, the groups seem wildly divergent: GLEON is a grass-roots effort which has been active since 2005 and connects researchers and practitioners from around the world to ask and answer questions about lake ecosystems. Earth observing missions can take a decade to plan, build, and launch. NASA and ESA have different missions as space agencies: one primarily focused on exploration and basic research with a year-to-year appropriations cycle, while the other presents a long-term commitment to address societal needs through the Copernicus program Sentinel satellite series. The Surface Biology and Geology (SBG) mission is a future NASA satellite that will launch toward the end of this decade as part of the Earth Systems Observatory. Working together to advance the science of lake ecosystem response to climate change, each group brings different complementary strengths and assets to this societal challenge. Increasing access through open science and cloud computing are creating opportunities for better collaboration. We describe our strategy for international engagement between these groups – cultural and methodological differences aside – to derive new information, learn new insights, and expand the body of knowledge around these unique natural resources.

Using bibliometric analysis techniques, we trace the evolution of climate and climate-change related articles in major oceanographic journals, 1987-2017. We use these bibliometric tools (network mapping, cluster analysis, alluvial analysis, corpus keyword detection) to document trends in growth, integration and centralization of climate-related research within ocean sciences over the past three decades. Such analysis methods offer an objective and complementary methodology, in contrast to the traditional “expert panel” approach, for guiding long-term strategic science planning. But how does the macro trend compare to scientific outputs supported by large ocean observatory facilities? Have scientists making use of these facilities followed, led or diverged from the general trend? We compare the macro trend to corpora of published science from two such facilities, Australia’s Integrated Marine Observing System (IMOS) and Ocean Networks Canada (ONC). The goal is to discern the extent to which these “big science” ocean observatories have been able to support or lead research that helps inform policy, management and the public about critical societal issues such as long term ocean change.

Soil and Water Assessment Tool (SWAT) is one of the widely used hydrological models, especially it has been successfully applied for the assessment of the impact of land use land cover and best management practices scenarios. But it is less applicable for type of research that requires integration or optimization with other models since it cannot update the land-use and best management practice information efficiently and it should be run separately when results for the multiple watersheds are needed. These days, the attention on the water security has been growing and interdisciplinary works desires integration of the hydrological model with other models are being highlighted. Thus, there are needs of development of the surrogate model which is computationally efficient and applicable to multiple watersheds. In this research, we propose the surrogate model of the SWAT with novel machine learning techniques such as random forest model. As a first step, the models are trained with the SWAT data from one watershed, which is a Maumee River Watersheds. Models for flow, mineral phosphorus, total nitrogen, total phosphorus, and sediment transport are built separately, and the model performance was above satisfactory level based on R-squared value, Nash-Sutcliffe efficiency, and percent bias. In addition, the surrogate models were tested for the different best management practices adoption scenarios and were trained additional data to make the model valid for the wide range of the best management practices adoption ratios. Finally, the surrogate models were expanded to multiple watersheds, by training SWAT results from Huron River Watersheds and River Raisin and they evaluated with the R-squared value. High R-squared values indicated that the surrogate model could be used in place of SWAT.

Wireless sensor networks for environmental monitoring are becoming a common tool for researchers across many of the field sciences. However, managing these systems is still an emerging issue. Internet of Things (IoT) technologies have provided many tools for creating big data solutions to these issues, but the industry is at cross-purposes with scientists. The big data approach is to collect massive amounts of data then throw out the anomalous points. For environmental monitoring, we need to archive and curate all the data as a permanent record of rapid environmental change. To achieve this, we need to combine IoT with museum curation sensibility. Dendra is cyberinfrastructure for real-time sensor data storage, retrieval, management, and curation for the field sciences. It is a cloud-based, multi-organizational system, designed to support massive permanent monitoring efforts (https://dendra.science). The name is derived from dendritic networks, such as river networks. Environmental monitoring performs in a similar manner, pulling data from the earth’s surface to a single location. To curate streaming data, we developed a dynamic data versioning system. A field scientist reports invalid data from the field via mobile phone, the annotation is approved by curator, and is instantly applied to all data accessed. Data is only modified on extract. This allows us to pull data from any time in the past with the edits and calibrations of that time. Networked data logger integration works with LoggerNet, GOES satellite, and soon Iridium satellite. Dendra is hosted on NSF’s XSEDE Jetstream cloud service. The system is designed as a set of microservices that interact through as set of persistent ques (NATS). Server-side javascript with Node.js is the primary development language. A data abstraction layer allows for multiple time-series databases (InfluxDB, MySQL, etc) to be accessed, even for a single instrument over time and reassembled as a single datastream. Access is via REST API & website. Dendra is used and supported by: Eel River Critical Zone Observatory (23 stations) in Mendocino, California; the University of California Natural Reserve System (25 stations); the Moore Foundation funded California Heartbeat Initiative (4 stations, 10 mobile, 40 planned).

The flow and deformation of glacier ice in response to stress is often described using Glen’s Flow Law, a power-law relation that compactly represents ice rheology with a prefactor, A, and stress exponent, n. For natural ice, these parameters (and the parameters subsumed within them) come with large uncertainties that have not been robustly quantified with observations. Modern remote sensing technologies that collect data with finer resolutions and broader coverage provide us with an opportunity to robustly calibrate these rheological parameters for certain environments. Here, we utilize publicly available observations of ice sheet surface velocity and elevation acquired with remote sensing platforms to calibrate the flow law parameters over select Antarctic ice shelves. We build upon recent work that used remote sensing observations to quantify the relationship between ice stress and strain rate in extensional flow to infer an exponent of n = 4.1 +/- 0.4 for Antarctica. Here, we model two-dimensional flow and perform parameter calibration by constructing and training physics-informed neural networks (PINNs) to learn spatially-varying A and uniform n for each ice shelf. We cast the parameter estimation problem as a neural network optimization problem through minimization of a cost function that includes both data reconstruction errors and momentum balance residuals derived from the 2D shallow-shelf approximation. Additionally, we formulate the networks to predict spatially-varying uncertainties for A by using variational inference techniques, which approximate Bayesian inference (traditionally a computationally-intensive procedure) as an additional optimization objective. Finally, we demonstrate the use of time-dependent surface velocities, which are becoming increasingly more available over the ice sheets, to independently constrain the stress exponent n, confirming the appropriateness of n = 4 derived from previous work. Overall, calibration of these parameters with robust uncertainties are critical for placing observational constraints on prognostic ice flow model parameters and to improve our understanding of flow and fracture processes on ice shelves in Antarctica.

The increasing role of women in leadership roles may be both a cause and effect of public attitudes. Recent politicization of science amidst increasing polarization of American politics juxtaposed with examples of female leadership (Fleadership) throughout the U.S (and abroad) begs the question of how gender impacts crisis response decision-making. Here, we investigate at the state level, female gubernatorial leadership, recent presidential (and VP) voting patterns, climate denial, and COVID/100k. These reveal correlations between climate denial, high COVID/100k, and presidential votes for exclusively male candidates. Americans who embraced federal Fleadership in 2016 and again in 2020 were less likely to deny climate change and spread COVID-19. The results suggest that rather than Fleadership inspiring responsible COVID preventative behavior at state or national levels, or leading to better understanding of climate change, the populations who tend to elect females, also understand science more than those who do not.

We present a fast model for stratospheric ozone chemistry based on a neural network approach. The model is intended to replace the detailed chemistry schemes of chemistry and transport models (CTMs), general circulation models (GCMs) or Earth system models (ESMs), which are computationally very expensive. The neural network (NN) model estimates the rate of change of ozone in 24 hours at a grid point and is trained on data of the detailed full chemistry model of the ATLAS chemistry and transport model (CTM). The benefit of this surrogate models is a much lower computation time (minutes instead of days) while the same level of accuracy is achieved. This represents a necessary step from understanding the chemistry and building sophisticated CTMs towards the usage of this knowledge in climate models, which is only feasible if much lower computation times can be achieved. Modelling of the Earth system is a complex task and models usually contain a large number of sub-modules and parameterizations. This applies for example to the atmosphere, hydrosphere, solid earth and the ice sheets. Atmospheric chemistry is complex and usually involves dozens of species and hundreds of reactions with a wide range of concentrations and lifetimes. This project concentrates on the estimation of the rate of change of ozone in the extrapolar stratosphere. The dynamics from the polar regions and from other layers of the atmosphere regarding the ozone change are not treated within this work. The ATLAS model is a Lagrangian CTM for stratospheric chemistry. It solves a coupled differential equation system using a stiff solver and a variable time-step. The stratospheric chemistry scheme of ATLAS has 46 active species, 171 reactions and heterogeneous chemistry on polar stratospheric clouds. It is not using the concept of chemical families. The application of the ATLAS CTM has high requirements on computational power. This is the reason why the coupling of full chemical models to climate models is generally not feasible with respect to the computation time of a global climate model. However, the incorporation of detailed chemistry is often desirable, in order to account for various feed-backs between chemistry, atmosphere and ocean. These complex chemical models motivate the formulation of faster but still accurate surrogate models, that are tailored to the coupling into earth climate models. This project builds on the SWIFT project, which has a polar and an extrapolar surrogate model for the stratospheric ozone chemistry. We investigate an alternative approach to the polynomial approach used by extrapolar SWIFT by exploiting the improved approximation capability of NN with respect to nonlinear contexts.

Imaging spectroscopy data is becoming more readily available from different satellite and airborne platforms. As this data becomes more prolific, there is a need for shared data tools and code for wrangling, cleaning, and analyzing it. The geospatial Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC) pioneers an on-demand science data processing platform with scalable back-end compute. It considers user experience and facilitates open science. ImgSPEC enables users to create data products in areas of interest using default workflows from registered algorithms, while also enabling users to customize scripts and workflows. ImgSPEC seamlessly interfaces with NASA Earthdata Search and tracks appropriate metadata for reproducibility when generating data products to share with others. Users can work in their preferred workspace (e.g., Rstudio, Jupyterlab, or command line) thereby facilitating use of open science software packages and collaborative coding through Git. ImgSPEC leverages existing NASA-funded information technologies such as the hybrid on-premise/cloud science data system (HySDS) and the Multi-mission Algorithm and Analysis Platform (MAAP). It also creates seamless interfaces with NASA-funded ECOSIS – a crowd-sourced spectral database, and ECOSML – a crowd-sourced model database. We demonstrate ImgSPEC on the Terrestrial Ecosystem use case processing through to foliar traits and fractional cover, thus aligning with driving thrusts for the NASA Surface Biology and Geology (SBG) Science and Applications Communities. As this technology is more widely adopted the interface with Amazon Web Services and NASA Earthdata search will enable broader use of more data (publicly available or loaded by the user) across more domains.

While machine learning (ML) techniques have proven to have exceptional performance in prediction of variables that have long and varied observational records, it is not clear how to use such techniques to learn about intermediate processes which may not be readily observable. We build on previous work that found that encoding either known, or approximated, physical relationships into the machine learning framework can allow the learned model to implicitly represent processes that are not directly observed, but can be related to an observable quantity. Zhao et al. (2019) found that encoding a Penman-Monteith-like equation of latent heat in an artificial neural network could reliably predict the latent heat while providing an estimate of the resistance term, which is not readily observable at the landscape scale. Specifically, we advance this framework in two ways. First, we expand the physics-based layer to include the partitioning of both the latent and sensible heat fluxes among the vegetation and soil domains, each with their own resistance terms. Second, we couple a land-surface model (LSM), which provides information from simulated processes to the ML model. We thus effectively provide the ML model with both physics-informed inputs as well as maintain constraints such as mass and energy balance on outputs of the coupled ML-LSM simulations. Previously we found that coupling a LSM to the ML model could provide good predictions of bulk turbulent heat fluxes, and in this work we explore how incorporating the additional physics-based partitioning allows the model to learn more ecohydrologically-relevant dynamics in diverse biomes. Further, we explore what the model learned in predicting the unobserved resistance terms and what we can learn from the model itself. Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., et al. (2019). Physics-Constrained Machine Learning of Evapotranspiration. Geophysical Research Letters, 46(24), 14496–14507. https://doi.org/10.1029/2019GL085291

Axial Seamount is the most active submarine volcano in the NE Pacific Ocean, and is monitored by instruments connected to a cabled observatory (the US Ocean Observatories Initiative Cabled Array), supplemented by autonomous battery-powered instruments on the seafloor (at ~1500 m depth). Axial is a basaltic hot spot volcano superimposed on the Juan de Fuca spreading ridge, giving it a robust and apparently continuous magma supply. It has had three effusive eruptions in the last 21 years: in 1998, 2011, and 2015. Deformation measurements have been conducted at Axial Seamount since the late 1980’s with bottom pressure recorders (BPRs) that can detect vertical movements of the seafloor with a resolution of ~1 cm. This monitoring has produced a 22+ year time-series including co-eruption rapid deflation events of 2.5-3.2 meters, separated by continuous gradual inter-eruption inflation at variable rates between 15-80 cm/yr. The overall pattern appears to be inflation-predictable, with eruptions triggered at or near a critical level of inflation. Using this pattern, the 2015 eruption was successfully forecast within a one-year time window, 7 months in advance. As of December 2019, Axial Seamount has re-inflated 1.98 m (~78%) of the 2.54 m it deflated during the 2015 eruption. We are exploring several methods to forecast the next eruption, including daily extrapolation of the average rate of inflation from OOI BPR data during the last 3 months forward in time until it intersects the threshold reached before the 2015 eruption. Using this method with the difference in inflation between two OOI BPR instruments located 3.5 km apart removes noise from tidal residuals and oceanographic signals that are common to both instruments. This method suggests the next eruption is likely between 2020 and 2024. However, this simple method is complicated by uncertainties in the next inflation threshold (the volcano inflated 20 cm higher before the 2015 eruption compared to 2011), changes in the rate of inflation with time, and by intermittent pauses in the inflation (and seismicity) observed since 2015 that have lasted from a week to several months.

Multiplicity of open source remote sensing date platforms help in bringing various opportunities. Spatio-temporal analysis ofa region can help in analysing changes in regional climate over different constituent land use/land cover (LU/LC). This studyderives a pattern of Land Surface Temperature (LST) over a period of 10 years in 11 smart cities of Uttar Pradesh using opensource data and software programs only. Smart cities namely Agra, Aligarh, Bareilly, Jhansi, Kanpur, Lucknow, Moradabad,Prayagraj, Rampur, Saharanpur and Varanasi are studied for LST in year 2010, 2015 and 2019 by using data from BHUVAN,NRSC and Copernicus Global Land Service: Land Cover (CGLS: LC-100) products. Boundary of the smart cities aredigitized form maps of various local authorities. Land use maps are obtained as Annual Landuse Land Cover 250k scaleproducts for year 2010 & 2015 from BHUVAN, NRSC but CGLS: LC-100 products are of resolution 100 m for year 2019.Both the Land use products are having 12 classes in region of smart cities which are reclassified into 5 LU classes of Built-up, Vegetation, Crop land, Barren land and Water. USGS Earth Explore is used to generate LST for year 2010 throughLandsat-5 ETM images by At-Surface Brightness Temperature & for year 2015 and 2019 through Landsat-8 TIRS bandimages by Radiative Transfer equation. Analysis of LST over years and LU classes show that smart cities of Aligarh andJhansi are dominantly warm over other smart cities of Uttar Pradesh. Capital city of Lucknow and Moradabad smart city arerelatively cooler than other smart cities. Rampur and Jhansi are having the lowest and highest standard deviation in LSTrespectively. Difference in LST over smart cities can be in range of 10-15 °C. Barren Land in these smart cities is found to behotter than Built-up land use class and vegetation is having lowest LST in all 11 smart cities. Range between LST values indifferent years over different LU classes vary between 28-35 °C. In Year 2019 LST statistics seem to be cooled down afteryear 2015 being worst in terms of LST range, maximum value and standard deviation of 6.12 °C. Percentage of vegetationhelping in reducing LST is surely a motivation to apply concept of Urban Green Space (UGS) in these 11 smart cities.