The Surface Biology and Geology (SBG) mission is one of the core missions of NASA’s Earth System Observatory (ESO). SBG will acquire high resolution solar-reflected spectroscopy and thermal infrared observations at a data rate of ~10 TB/day and generate products at ~75 TB/day. As the per-day volume is greater than NASA’s total extant airborne hyperspectral data collection, collecting, processing/re- processing, disseminating, and exploiting the SBG data presents new challenges. To address these challenges, we are developing a prototype science pipeline and a full-volume global hyperspectral synthetic data set to help prepare for SBG’s flight. Our science pipeline is based on the science processing operations technology developed for the Kepler and TESS planet-hunting missions. The pipeline infrastructure, Ziggy, provides a scalable architecture for robust, repeatable, and replicable science and application products that can be run on a range of systems from a laptop to the cloud or an on-site supercomputer. Our effort began by ingesting data and applying workflows from the EO- 1/Hyperion 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. We have fully implemented the first stage of processing, from the raw data (Level 0) to top-of-the-atmosphere radiance (Level 1R). We plan to begin reprocessing the entire 55 TB Hyperion data set by the end of 2021. Work to implement an atmospheric correction module to convert the L1R data to surface reflectance (Level 2) is also underway. Additionally, an effort to develop a hybrid High Performance Computing (HPC)/cloud processing framework has been started to help optimize the cost, processing throughput and overall system resiliency for SBG’s science data system (SDS). Separately, we have developed a method for generating full-volume synthetic data sets for SBG based on MODIS data and have made the first version of this data set available to the community on the data portal of NASA’s Advanced Supercomputing Division at NASA Ames Research Center. The synthetic data will make it possible to test parts of the pipeline infrastructure and other software to be applied for product generation.

In recent decades, major growth is observed in wildfire incidents across the globe. These ecological disasters, triggered by natural and/or anthropogenic factors, can have long-lasting effects on the environment, ecosystems, and biodiversity. Advancements in remote sensing technology provided an impetus to forest fires research, enabling precise determination of the geographical locations susceptible to fire and assess fire risk. This study focuses on India, whose total forest cover (71.22 Mha) is about 21.67% of the country’s total geographical area. Around 90% of the forest fires in India are attributable to anthropogenic factors. Therefore, the generation of a forest fire risk map (FRM) is essential for devising strategies to mitigate/manage forest fires and avert their disastrous impacts. An attempt is made to develop FRM for the Budhabalanga river basin, which contains the Similipal national park, a part of the UNESCO World Network of Biosphere Reserves. For this purpose, covariates affecting forest fire are identified viz. fuel (vegetation), topographic features (elevation, aspect, and slope), and human activities. The covariates are assigned weights, depicting their relative importance in influencing the fire, byusing Analytical Hierarchical Process (AHP). The AHP is a widely used technique in multi-criteria decision-making models. The Similipal national park has experienced a prolonged dry spell and below-average monsoon in 2020. The consequent dry conditions led to a significant forest fire event in the last week of February 2021, which lasted nearly three weeks. Fine (30m) resolution satellite data (Landsat-8) are used to calculate the Normalized Difference Vegetation Index (NDVI) corresponding to the study area to assess vegetation conditions before the fire event. Furthermore, Cartosat-1 Digital Elevation Model (24m resolution) is used to extract topographic-related information. The FRM generated for the basin using the assigned weights was 80% accurate when validated with 375m resolution NASA’s VIIRS (Visible Infrared Imaging Radiometer Suite) fire point data for the analyzed fire event. Hence, the methodology considered for developing FRM appears promising. It can be extended to other river basins for identifying fire risk zones and devise timely strategies to mitigate fire risk.

Time-lapse borehole nuclear magnetic resonance (bNMR) relaxation is a promising method for linking water content changes in the unsaturated region of the critical zone with pore-scale properties associated with bedrock weathering. The saturation-dependance of the NMR T2 distribution is strongly controlled by pore-scale material properties and can be linked to hydraulic properties (e.g. the water retention function and hydraulic conductivity). Here, we leverage NMR’s sensitivity to pore-scale properties to investigate material controls on plant-available water storage dynamics in weathered bedrock via time-lapse bNMR relaxation measurements. To overcome slow logging speed and poor signal-to-noise (SNR) ratio typically associated with bNMR measurements in the unsaturated zone, we focus on the sum of echos (SE). We show that the advantage of using SE to characterize NMR relaxation, rather than using the full T2 distribution or the logarithmic mean of the distribution, is that it is easy to calculate, does not require inversion, has enhanced SNR, and is sensitive to both volumetric water content (VWC) and mean T2. This leads to high contrast in SE between time-lapse measurements relative to other metrics of NMR relaxation. At our hillslope study site associated with the Eel River CZO, VWC changes in weathered bedrock driven by deeply-rooted trees allow us to create “NMR characteristic curves” for different regions of the weathering profile. Analogous to a water retention function, the NMR characteristic curves describe NMR relaxation times of a material at a given VWC, and can be used to identify differences in pore-scale properties. We show that mean T2 times are typically shorter in bedrock that is more weathered for the same VWC, which is consistent with smaller pore-sizes and higher surface relaxivities associated with weathering products such as secondary clays and oxides. Our well logging indicates that changes in pore structure associated with bedrock weathering control plant-available water supply within the bedrock weathering profile. While these results illustrate the utility of bNMR, further studies that quantitatively link NMR measurements to flow properties via pore or empirical models will benefit mechanistic understanding of plant available water in the critical zone.

Documented trends in the timing and intensity of precipitation events have been met with associated hydrologic changes in water-balance partitioning. Changes in land use, initiation and length of the growing season and associated irrigation practices, timing of autumn leaf senescence, snow accumulation and melt, flooding, and drought all affect the hydrological processes of infiltration, evapotranspiration, and runoff. This study is an investigation of trends in groundwater recharge over space and time in the temperate climate of the Midwest USA. It tests the hypothesis that increasingly intense precipitation events resulting in extreme runoff events might be short-circuiting processes of infiltration and groundwater recharge by lowering the residence time of water in upland landscapes, while increasing infiltration in lowlands and riparian corridors. The climate-driven changes in precipitation events can be more easily linked to the documented increasing stream discharge trends than trends in groundwater levels. In this ongoing study, multiple methods were utilized to calculate annual and monthly, or seasonal, potential aquifer recharge amounts and trends in unconsolidated aquifers in Indiana and surrounding watersheds. Because observation wells in Indiana are sparsely distributed, estimates of groundwater recharge for selected wells were used for calibration and validation of a 40-year spatially continuous water balance (U.S. Geological Survey Soil Water Balance v 2.0, SWB2) model that solved for potential groundwater recharge (net infiltration) at a daily time step and was analyzed at annual and monthly intervals. Using estimates of annual net infiltration from the SWB2 model, 40-year temporal trends calculated on space-time cubes suggest that over 50% of the model domain is experiencing statistically significant increases (p < 0.006) in potential groundwater recharge. Trend analyses of unconfined water levels from 1980-2019 were conducted, finding that water levels in 8 (out of 9) climate divisions in the state are increasing (rising) in 6 of the 8 climate divisions. The distribution of increasing water tables reflects the same spatial distribution as the annual estimates of recharge generated by the SWB2 model.

Crop yield is sensitive to climate change and has been projected to be negatively affected by future climate. To reduce yield loss and ensure food security in the context of climate change, it is critical to understand how climate variables interact with crop growth in agroecosystems. One important and widely used tool to study yield responses to climate is process-based modeling. However, using process-based models to simulate the climate impacts on crops is becoming challengeable as the future climate is characterized by more and more frequent extreme events, such as heatwaves, unpredictable rainfall, and droughts. Most existing crop models may not be capable of characterizing the impacts of such extreme events on crops simply because they usually do not simulate some critical processes that climate variables directly affect crop growth such as photosynthesis. Instead, they use a simplified approach–radiation-use efficiency (RUE) which is a coefficient to describe empirical relationships between intercepted radiation and biomass. The usage of RUE has simplified computation but also limited our understanding of interactions between climate variables (e.g., temperature, CO2, rainfall) and crop growth. Thus, we developed a module combining processes of radiative transfer and photosynthesis (RP) within the canopy to account for the impacts of climate variables on crop growth dynamically. Then, we integrated the RP module into a popular agricultural system model—the Environmental Policy Integrated Climate (EPIC) to assess its performance. The results show that its capabilities of predicting crop yield are comparable to the traditional RUE method. The correlation between observed and simulated biomass is 0.77 for the RUE method, while 0.76 for the RP method. But the RP method could show responses of biomass accumulation to changes in climate factors, which is almost overwhelming for RUE. For instance, the RP module could simulate how extremely high temperatures (which usually last several hours during a day) affect crop growth and also allow the EPIC to distinguish elevated CO2 impacts on C3 and C4 crops, while the default RUE method could not. Therefore, the RP module is promising to improve capabilities and extend functionalities of current process-based models, which is not only beneficial to the community of crop modeling but also enhances our ability to evaluate the impacts of climate change on the agroecosystem.

An accurate assessment of hydrometeorological variables/ observations over an urban area is crucial to policy-makers and civic bodies to address an extensive range of water resources and environmental problems for informed decision-making related to the water distribution system and drainage networks. This necessitates the establishment of hydrometeorological monitoring networks that can efficiently obtain consistent and reliable information about the spatiotemporal variability of multiple hydrometeorological observations while being economically sustainable. However, the urban catchments especially in underdeveloped and developing countries are often subjected to spatial, environmental as well as monetary limitations which hinders the application of conventional approaches followed to set up the hydrometeorological networks. With this context, we propose a novel rationalization framework to record numerous hydrometeorological variables and acquire maximum information at an optimal cost. We have attempted to combine a multivariate statistical technique, Principal Component Analysis (PCA) with a multi-attribute decision-making method, Technique for Order of Preference by Similarity to Ideal Solutions (TOPSIS) to rank the significant hydrometeorological stations of an existing Automatic Weather Stations (AWS) network. It is observed that the set of rationalized AWS network obtained from this framework can capture the spatiotemporal information of the hydrometeorological variables considered in this study as efficiently as the entire AWS network. Additionally, the comparison of flood inundation and hazard maps derived from a 3-way coupled hydrodynamic flood modeling framework for the rationalized and original network also reflects its credibility to capture the flooding characteristics for the catchment. This proposed framework has been applied over Mumbai city, India, a major flood-prone area, and is characterized by high spatiotemporal variability of hydro-meteorological observations and space constraints due to dense population. This framework is generic and can be employed to reevaluate the prevailing hydro-meteorological networks in other catchments and help in the reduction of the maintenance cost while efficiently capturing the variability of observations.

Episodic volcanic eruptions are often either not considered in future climate projections, or represented in terms of a constant volcanic forcing. This conventional representation of volcanic eruptions in climate models does not account for how climate change might affect the dynamics of volcanic plumes and the stratospheric sulfate aerosol lifecycle and, ultimately, volcanic radiative forcing. The height of eruptive plumes is indeed sensitive to atmospheric conditions such as stratification and the strength of wind. In addition, climate change will affect tropopause height, the Brewer Dobson circulation and stratospheric temperatures which all govern volcanic sulfate aerosol cycle. A recent study showed that for tropical eruptions, these changes would either lead to a dampening or an amplification of volcanic forcing depending on the eruption intensity. In this study, to account for volcano-climate interactions in future climate projections, we present a new modelling approach through coupling a 1-D plume-rise model (Plumeria) with an Earth System model (UKESM). In this approach, each time a volcanic eruption of prescribed intensity (i.e., mass eruption rate) and SO2 mass occur, atmospheric conditions simulated by UKESM are passed interactively to Plumeria which then computes the corresponding height of eruptive plumes. Volcanic SO2 is then injected at the same height in UKESM stratospheric aerosol module. With this new methodology, plume heights are consistent with the climate conditions simulated by UKESM. Our study thus represents a first attempt to consider the impacts of climate change on volcanic eruptions in an Earth System model, which allows us to better evaluate the climate impacts of volcanoes under global warming.

Satellite observations are widely used to investigate, monitor, and forecast volcanic activity. Spaceborne thermal infrared (TIR) measurements of high-temperature volcanic features improve our understanding of the underlying processes and our ability to identify reactivation of activity, forecast eruptions, and assess hazards. In particular, thermal changes, indicative of subtle pre-eruptive volcanic thermal activity, have been observed. Over the last several decades, different approaches have been explored to detect and estimate the temperature above background of these thermal anomalies. The most common approach relies on a spatial statistical analysis based on a scene by scene choice of the background temperature region. Satisfactory results have also been shown by using time series anomaly detection algorithms based on statistical profiling approach. Artificial intelligence (AI) is growing sharply in different remote sensing fields because of its capability to automatically learn patterns from the data. Here, we develop an AI approach to automatically detect volcanic thermal features by using spatiotemporal information from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), LANDSAT-8 Thermal Infrared Sensor (TIRS) and MODerate-resolution Imaging Spectroradiometer (MODIS) data acquired over several decades. Our goal is to exploit AI techniques using a combination of high temporal and high spatial resolution satellite data to improve thermal volcanic monitoring and detect very low-level anomalies caused by pre-eruptive activity. We use both low-spatial, high-temporal resolution MODIS data to detect hotter thermal features at short time scales; as well as high-spatial low-temporal resolution ASTER TIR and TIRS data to detect more subtle thermal changes otherwise missed by MODIS. Our analysis is conducted in Google Earth Engine (GEE), a cloud computing platform with fast access and processing of satellite data. The comparison with a new statistical algorithm is documented in a companion abstract in this session.

As it is known, Space Weather (SWx) phenomena can have dramatic impact on satellite navigation and HF radio communication systems, being also responsible for increases on radiation levels at flight altitudes. For this reason, in recent years the International Civil Aviation Organization (ICAO) has been showing great interest in operational SWx services for aviation purposes in these three domains. Four global SWx centers have been then appointed since November 2019 by ICAO to provide real-time SWx advisories for aviation users. In particular, HF COM conditions are assessed by monitoring the F2-layer critical frequency foF2 or the MUF(3000) ionospheric characteristic (MUF = Maximum Usable Frequency), the latter representing the highest HF radio frequency that can be used for communications over a standard distance of 3000 km via F2-layer ionospheric reflection. As one of the designed SWx centers, several key operational 24/7 products for HF COM conditions assessment have been developed within PECASUS (Partnership for Excellence in Civil Aviation Space weather User Services). Nowcasting and forecasting (1-24hr) maps over Europe of MUF(3000) and its ratio with respect to a background level are then developed by INGV, as a PECASUS partner. The MUF(3000) nowcasting uses all the available real-time ionosonde measurements in different locations in order to upgrade IRI-CCIR-based background maps, and Ordinary Kriging method for spatial interpolation. The MUF(3000) modeling performance was assessed comparing predicted values to measured ones over two test stations during strong geomagnetic storm periods, obtaining an overall RMSE < 2 MHz at both stations. The MUF(3000) predicted 1-24 hours ahead depends on foF2 and M(3000) ionospheric parameters: EUROMAP forecasting model and IRI model are used for the former and the latter, respectively. The method has been applied to Europe where there are ionospheric stations with long (for some solar cycles) historical data and current real-time foF2 observations. A mapping procedure applied to the European stations provides MUF(3000) short-term prediction over the whole area. The application of these methods to storm events occurred after November 2019 is here presented, in order to study the ionospheric conditions they provide when HF COM advisories are expected to be issued.

The Southwest Watershed Research Center (SWRC) of the United States Department of Agriculture-Agricultural Research Service has been conducting arid and semiarid (dryland) watershed research since 1953. This included establishment and continuous operation of the Walnut Gulch Experimental Watershed (WGEW) in southeast Arizona. The 149 km2 ephemeral watershed is one of the most densely instrumented dryland research catchments in the world with a drainage area greater than 10 km2. This instrumentation captures many aspects of the hydrological cycle including how precipitation is partitioned into soil moisture, runoff and evapotranspiration and its subsequent effects on sediment transport, vegetation productivity, carbon sequestration, and groundwater recharge. The long-term, high-resolution record of observations on the WGEW enables understanding of the mean and variability of the hydrological processes, not well characterized with shorter term records, that fail to capture the large variability common to dryland regions. This presentation will highlight trends in temperature, precipitation, and runoff over the WGEW observation period. Additional research findings made by the SWRC and collaborators on erosion; plant productivity and carbon sequestration; the facilitation of soil water redistribution by plant roots; and groundwater recharge will also be presented.

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the ‘signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. Although this procedure originated in LiDAR-based estimation of mean tree and canopy height, ground noise filtering has remained prevalent in LiDAR pre-processing, even as modelers have shifted focus to forest aboveground biomass (AGB) and related characteristics for which ground returns may actually contain useful information about stand density and openness. In particular, ground returns may be helpful for making accurate biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. We applied several ground noise filtering thresholds while mapping two regions within New York State, one a forest-dominated area and the other a mixed-use landscape. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling. By fitting random forest models to each of these predictor sets, we found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds. The relative inferiority of AGB models based on filtered LiDAR returns was much greater for the mixed land-cover study area than for the contiguously forested study area. Our results suggest that ground filtering should be avoided when mapping biomass, particularly when mapping heterogeneous and highly patchy landscapes, as ground returns are more likely to represent useful ‘signal’ than extraneous ‘noise’ in these cases.

Xavier University of Louisiana, in collaboration with the Institute of Earth Science Research and Education (IESRE) run the ECoSTEM project. This project aims to improve the quality of undergraduate STEM education by introducing a computational, community-oriented component into STEM programs at Xavier University. Specifically, undergraduate students develop microcontroller-based systems for collecting airborne particulate data. They work with public school teachers (and their students) and Louisiana Department of Environmental Quality (LDEQ) officers to deploy the sensors at locations around New Orleans and to analyze the resulting data. Airborne particulates are often the pollutant of greatest concern in minority and other underserved communities. This project provide undergraduates at Xavier and the local teachers (and their students) with learning experiences that are relevant to them and their communities. ECoSTEM uses a tiered mentoring system in which the project team members mentor undergraduates, who in turn work with public school teachers and mentor their students. Research has shown positive effects from student involvement in such real-world, personally relevant STEM projects, including increases in student interest in STEM-related careers after graduation. As a result, the project has the potential to broaden student interest in STEM and increase pursuit of STEM-related majors.

The initial list of Essential Water Variables (EWVs) evolved from wide meta-surveys of water data needs for research and applications that were carried out in 2010 to support GEO Societal Benefit Areas (SBAs). These EWVs were formalized in the Group on Earth Observations System of Systems (GEOSS) Water Strategy Report (WSR) “From Observations to Decisions”, released in 2014. Subsequently, discussions with additional user communities have augmented the list, for example with Surface Water Extent. Besides “primary” EWVs that identify key water variables, including precipitation, soil moisture, and water quality, a set of “supplementary” EWVs is also needed to complete the information that the formal list of primary EWVs should provide, such as Digital Elevation Models. It is clear that all available observing systems, employing both remote sensing and in situ observing instruments and networks are required to address the range of space/time resolutions, accuracies, and data latencies that the end-user applications require. In fact, there are still gaps in our ability to deliver all variables as required. In some cases this is a technical challenge, such as remote sensing capabilities for some water quality variables, while in many other cases it is a matter of administrative and resource challenges. This paper summarizes EWVs as currently defined and required by key end-user research and applications sectors. As a follow up to the WSR, we highlight the relevance of EWVs to the indicator monitoring objectives of the UN Sustainable Development Goals (SDGs), various international Conventions and Frameworks, and the GEO Global Water Sustainability (GEOGloWS) priority thematic communities.

This study’s objective was to evaluate the potential geospatial relationship between agricultural pesticide use and cancer incidences by using the concept of consolidated environmental burden indices. We conducted two sets of experiments, using pesticide data collected from the U.S. Geological Survey Pesticide National Synthesis Project database and using cancer data collected from the National Cancer Institute State Cancer Profiles and Cancer Data Registry of Idaho. In the first experiment, we analyzed the associations between several potentially carcinogenic pesticides and metals to the pediatric cancer incidence among the counties of Idaho. Principal component analysis was adopted to consolidate the environmental variables. This study identified that the principal components (PCs) were significantly associated with pediatric cancer at P-value < 0.01 and a t-statistic > 3. The PC’s improved model performance substantially, from NSE = 0.22 for the control case to NSE = 0.52 for the treatment case. In the second experiment, we evaluated the associations of pesticides to two cancer metrics (pediatric cancer incidence and total cancer incidence) across each of the 11 contiguous states in the Western U.S. at the state and county spatial scales. A multi-level model was developed using fumigant mass, fumigant mass tertiles as well as county and state boundaries, which strongly predicted total cancer incidence (R-squared = 0.95, NSE = 0.91, and SSR = 8.22). Further, this study also identified significant associations between total fumigant mass, total pesticide mass, medium and high fumigant class (tertiles > 0.33), and high pesticide class (tertile > 0.66), relative to total cancer incidence (P-value < 0.01) in the U.S. west. Fumigant application rate was shown to be significantly related to the incidence of total cancers and pediatric cancer. Moreover, this relationship was maintained regardless of the spatial resolution used in the analysis (county or state). Both studies reinforce inferences from previous studies that pesticides are related to an elevated risk for pediatric cancer. Similarly, among the pesticides, fumigants such as metam were shown to be important relative to the cancer incidences for both experiments, highlighting the significance of further analysis.

In 2015, at the United Nations Conference of the Parties in Paris, France, countries agreed to limit the global mean surface temperature (GMST) increase to 2°C above preindustrial levels, and to pursue efforts to limit it to 1.5°C. However, risks from sea level rise are not well encapsulated by temperature targets. Near term emissions will dictate long term sea level rise responses, but the tendency for policy and negotiations to concentrate on the year 2100 can limit our understanding of intergenerational justice concerns arising from this commitment. Here we present an analysis of the long term spatial variability of sea level rise, and an interdisciplinary review of associated justice considerations from across a wide range of literatures. We center the positioning of the Alliance of Small Island States (AOSIS) to show that AOSIS nations are disproportionately impacted by sea level rise, and that ice sheet instabilities, which could dominate the long term trend in sea level, are associated with feedbacks which can potentially exacerbate climate justice implications.

With the urgent necessity of geo-energy resources to achieve carbon neutrality, fluid injection and production in the fractured media will significantly increase. Applications such as enhanced geothermal systems, geologic carbon storage, and subsurface energy storage involve pressure, temperature, and stress changes that affect fracture stability and may induce microseismicity. To eventually have the ability to control induced seismicity, it is first necessary to understand its triggering mechanisms. To this end, we perform coupled thermo-hydro-mechanical (THM) simulations of cold water injection and production into a rock containing two fracture sets perpendicular between them. The permeability of fractures being four orders of magnitude higher than the one of the rock matrix leads to preferential pressure and cooling advancement, which induce stress changes that affect fracture stability. We find that the fracture set that is oriented favorably to undergo shear slip in the considered stress regime becomes critically stressed, inducing microseismicity. In contrast, the fracture set that is not favorably oriented for shear remains stable. These results contrast with those obtained for an equivalent porous media that does not explicitly include fractures in the model, which fails to reproduce the direction-dependent stability of fractures present in the subsurface. We contend that fractures should be directly embedded in the numerical models when inhomogeneities are of the spatial scale of the reservoir to enable reproducing the THM coupled processes that may lead to induced microseismicity.

We report on the annual variation of quiet-time suprathermal ion composition and spectral properties for C-Fe using Advanced Composition Explorer (ACE)/Ultra-Low Energy Ion Spectrometer (ULEIS) over the energy range 0.3 MeV/nuc to 1.28 MeV/nuc from 1998 through 2020. This extends the work of Desai et al. (2006) and Dayeh et al. (2009, 2017) to cover Solar Cycle 23’s rising phase through Solar Cycle 24’s declining phase. With 5 additional years of data, we show that the number of quiet-time hours strongly anti-correlates with the Sunspot Number (SSN) at better than the -0.9 level. We also show (1) a clear ordering of the cross correlation between abundance (normalized to O) and SSN as a function of solar wind M/Q; (2) the slope of X/O’s abundance as a function of Fe/C decreases with increasing M/Q; and (3) discuss the trend of annual spectral indicies with respect to Oxygen’s spectral index as a function of solar cycle and M/Q. The contrast between our abundance and spectral index results suggests that the source from which suprathermal ions are drawn or accelerated varies with solar activity and is tied to each element’s chemistry, but he acceleration mechanism that governs the spectral shape does not.

Continuous observations from geostationary satellites have been utilized to understand land surface seasonal dynamics and fill data gaps caused by clouds. However, the limited spatial resolution of geostationary satellite products constrained the processes of detecting the terrestrial changes in landscape scale, particularly over heterogeneous areas. Moreover, the variation of sun-target-sensor geometry in geostationary satellites results in diurnal changes in surface reflectance products. To overcome the limitations of geostationary satellite products over heterogeneous areas, we conducted the series of processes: 1) characterizing the effect of the solar and viewing geometry in surface reflectance using the bidirectional reflectance distribution function (BRDF), 2) harmonizing the satellite products from different platforms into a seamless product, and 3) fusing the different satellite products to enhance the spatial resolution of geostationary satellite products. Finally, we adopted spatial and temporal gap-filling methods to achieve daily gap-filled surface reflectance products. For robust application, the results from the integrated process were evaluated in both space and time using hyperspectral maps derived from unmanned aerial vehicle (UAV) and in situ tower-based continuous spectral measurements. We expect the geostationary satellite products with high spatial resolution to uncover the cloud-bound areas towards sensing the changes of the Earth in space and time.