Alpha radioactivity of the lunar surface at the landing sites of Surveyors 5, 6, and 7
Lunar surface alpha radioactivity at Surveyor 5, 6 and 7 landing sites
Economou, T. E.↗
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Soil Carbonyl Sulfide (OCS) Fluxes in Terrestrial Ecosystems: An Empirical Model
Measurements of carbonyl sulfide (OCS) enable independent estimates of regional stomatal conductance provided that non-stomatal OCS fluxes are well constrained. OCS is taken up through plant leaves, following the same pathway as CO 2 ; in contrast to CO 2 , OCS is irreversibly destroyed in plant leaves and plants do not typically exhibit OCS emissions. Ecosystem uptake of OCS can indicate changes in stomatal opening. Here we present an empirical model to assess the potential impact of soil OCS exchange, the non-Stomatal OCS exchange Empirical Model (SOCSEM, version 0). We created biome-specific response curves characterizing soil OCS exchange and restricted the model design to require only knowledge of soil moisture and surface temperature because remote sensing observations are available for these two features. Comparing the model to field-based chamber observations reveal deviations that can be attributed to missing complexity of the ground surface (having excluded litter and plants without regulated stomata), shortwave radiation, or the soil environment. For agricultural regions with known net emissions, we use remotely-sensed surface temperature data and demonstrate that data resolution can affect anticipated fluxes. We further investigate the influence of regions with unknown soil OCS responses, for example, Arctic tundra. We compare our model to a process-based and respiration-based soil OCS exchange model that has been implemented in a land surface model. Further field study of tropical and arctic ecosystems in conjunction with studies of non-stomatal surfaces in addition to soil (e.g., bryophytes) will increase confidence in applying OCS as a regional tracer for stomatal conductance.
54 ENVIRONMENTAL SCIENCES↗
Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Methods and Applications
Thermal Infrared (TIR) remote sensing data can provide important measurements of surface energy fluxes and temperatures, which are integral to understanding landscape processes and responses. One example of this is the successful application of TIR remote sensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that satellite-based measurements from TIR remote sensing data can lead to more accurate regional-scale estimates of daily evapotranspiration. With further refinement in analytical techniques and models, the use of TIR data from airborne and satellite sensors could be very useful for parameterizing surface moisture conditions and developing better simulations of landscape energy exchange over a variety of conditions and space and time scales. Thus, TIR remote sensing data can significantly contribute to the observation, measurement, and analysis of energy balance characteristics (i.e., the fluxes and redistribution of thermal energy within and across the land surface) as an implicit and important aspect of landscape dynamics and landscape functioning. The application of TIR remote sensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate primarily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references on TIR remote sensing throughout the scientific literature. It is our purpose here to provide evidence from work that has employed TIR remote sensing for analysis of landscape characteristics to illustrate how these data can provide important data for the improved measurement of landscape energy response and energy flux relationships. We examine the direct or indirect use of TIR remote sensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of landscape ecological processes.
Quattrochi, Dale A.↗
Validating Ancient Age of the Buried Floor of the Northern Lowlands, Mars
Hesperian and Amazonian plains units cover the northern lowlands but little is known about what this surface covers. Models for the creation of the lowlands and the dichotomy boundary implement mechanisms which vary from internal processes, such as plate tectonics or first-order mantle convection, to external processes, such as a single large impact or multiple impacts. Different models require different time scales for low-land formation; determining the age of the buried low-land surface would help constrain the formation models. The Mars Orbiting Laser Altimeter (MOLA) has yielded a high-precision, topographic gridded data set that reveals the presence of Quasi-Circular Depressions (QCDs) in both the southern highlands and the northern lowlands. Most of these roughly circular depressions have no corresponding visible structural feature on the surface. It is proposed that these QCDs are the surface representation of buried impact craters. Based on this assumption, cumulative number vs. diameter curves were constructed, which placed the age of the buried surface of the northern lowlands in the Early or pre-Noachian. A Noachian basement is supported by the remnants of large craters and multi-ring basins discovered in earlier research, but the QCDs provide the first evidence of this for the entire lowland. Constraining the age of the basement floor to the earliest Noachian, however, would require that the process that formed the northern lowlands either occurred in the early Noachian or involves removal of material from the bottom of the crust without destroying the previously formed craters to achieve the modeled crustal thinning. But can we establish that the QCDs do in fact represent buried impact craters, and thus validate an Early Noachian age for the buried lowland floor?
Buczkowski, D. L.↗
Bridging Empirical and Physical Approaches for Landslide Monitoring and Early Warning
Rainfall-triggered landslides typically occur and are evaluated at local scales, using slope-stability models to calculate coincident changes in driving and resisting forces at the hillslope level in order to anticipate slope failures. Over larger areas, detailed high resolution landslide modeling is often infeasible due to difficulties in quantifying the complex interaction between rainfall infiltration and surface materials as well as the dearth of available in situ soil and rainfall estimates and accurate landslide validation data. This presentation will discuss how satellite precipitation and surface information can be applied within a landslide hazard assessment framework to improve landslide monitoring and early warning by considering two disparate approaches to landslide hazard assessment: an empirical landslide forecasting algorithm and a physical slope-stability model. The goal of this research is to advance near real-time landslide hazard assessment and early warning at larger spatial scales. This is done by employing high resolution surface and precipitation information within a probabilistic framework to provide more physically-based grounding to empirical landslide triggering thresholds. The empirical landslide forecasting tool, running in near real-time at http://trmm.nasa.gov, considers potential landslide activity at the global scale and relies on Tropical Rainfall Measuring Mission (TRMM) precipitation data and surface products to provide a near real-time picture of where landslides may be triggered. The physical approach considers how rainfall infiltration on a hillslope affects the in situ hydro-mechanical processes that may lead to slope failure. Evaluation of these empirical and physical approaches are performed within the Land Information System (LIS), a high performance land surface model processing and data assimilation system developed within the Hydrological Sciences Branch at NASA's Goddard Space Flight Center. LIS provides the capabilities to quantify uncertainty from model inputs and calculate probabilistic estimates for slope failures. Results indicate that remote sensing data can provide many of the spatiotemporal requirements for accurate landslide monitoring and early warning; however, higher resolution precipitation inputs will help to better identify small-scale precipitation forcings that contribute to significant landslide triggering. Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale, which will serve as key inputs to significantly advance the accuracy of landslide hazard assessment, particularly over larger spatial scales.
Kirschbaum, Dalia↗
NASA's Earth Observing System: The Transition from Climate Monitoring to Climate Change Prediction
Earth's 4.5 billion year history is a study in change. Natural geological forces have been rearranging the surface features and climatic conditions of our planet since its beginning. There is scientific evidence that some of these natural changes have not only led to mass extinctions of species (e.g., dinosaurs), but have also severely impacted human civilizations. For instance, there is evidence that a relatively sudden climate change caused a 300-year drought that contributed to the downfall of Akkadia, one of the most powerful empires in the Middle-East region around 2200 BC. More recently, the "little ice age" from 1200-1400 AD forced the Vikings to abandon Greenland when temperatures there dropped by about 1.5 C, rendering it too difficult to grow enough crops to sustain the population. Today, there is compelling scientific evidence that human activities have attained the magnitude of a geological force and are speeding up the rate of global change. For example, carbon dioxide levels have risen 30 percent since the industrial revolution and about 40 percent of the world's land surface has been transformed by humans. We don't understand the cause-and-effect relationships among Earth's land, ocean, and atmosphere well enough to predict what, if any, impacts these rapid changes will have on future climate conditions. We need to make many measurements all over the world, over a long period of time, in order to assemble the information needed to construct accurate computer models that will enable us to forecast climate change. In 1988, the Earth System Sciences Committee, sponsored by NASA, issued a report calling for an integrated, long-term strategy for measuring the vital signs of Earth's climate system. The report urged that the measurements must all be intimately coupled with focused process studies, they must facilitate development of Earth system models, and they must be stored in an information system that ensures open access to consistent, long-term data. This committee emphasized that the only feasible way to collect these consistent, long-term data is through the use of space-based Earth "remote sensors" (instruments that can measure from a distance things like temperature).
King, Michael D.↗
Summertime Continental Shallow Cumulus Cloud Detection Using GOES‐16 Satellite and Ground‐Based Ceilometer at North Alabama
Abstract Accurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite‐based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground‐based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian et al., 2021, https://doi.org/10.3390/rs13122309 , 2022, https://doi.org/10.1029/2021gl097070 ). To validate this algorithm, the framework employs ground‐based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear‐sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground‐based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground‐based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground‐based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land‐atmosphere interactions in North Alabama.
54 ENVIRONMENTAL SCIENCES↗
Summertime Near-Surface Temperature Biases Over the Central United States in Convection-Permitting Simulations
Convection-Permitting Model (CPM) simulations of the Central United States climate for the summer of 2011 are studied to understand the causes of warm biases in 2-m air temperature (T 2m ) and related underestimates of precipitation including that from mesoscale convective systems (MCSs). Based on 10 CPM simulations and 9 coarser-resolution model simulations, we quantify contributions from evaporative fraction (EF) and radiation to the T 2m bias with both types of models overestimating T 2m largely because they underestimate EF. The performance of CPMs in capturing MCS characteristics (frequency, rainfall, propagation) varies. The pre-summer precipitation bias has large correlation with mean summertime T 2m bias but the relationship between summertime MCS mean rainfall bias and T 2m bias is non-monotonic. Analysis of lifting condensation level deficit and convective available potential energy suggests that models with T 2m warm biases and low EF have too dry and stable boundary layers, inhibiting the formation of clouds, precipitation and MCSs. Among the CPMs with differing model formulations (e.g., transpiration, infiltration, cloud macrophysics and microphysics), evidence suggests that altering the land-surface model is more effective than altering the atmospheric model in reducing T 2m biases. In conclusion, these results demonstrate that land-atmosphere interactions play a very important role in determining the summertime climate of the Central United States.
2-m air temperature↗
Quantifying the long-term changes of terrestrial water storage and their driving factors
Global warming is expected to cause changes in terrestrial water storage (TWS) across the land surface, with widespread impacts on ecosystems and society. Although extensive research has been performed to analyze TWS changes and possible drivers during the post-2000 period, longer-term evolution of TWS and associated environmental forcings remain relatively unexplored. In this study, we evaluated the performance of the Energy Exascale Earth System model (E3SM) land model ELM version 1 (ELM v1) in simulating global TWS, and used factorial simulations of ELMv1 to quantify global TWS changes and their drivers during 1948–2012. We found that ELM’s agreed best with existing satellites and reconstruction datasets in temperate regions unaffected by irrigation. Biome- and climate zone-averaged TWS mainly increased at rates between 0 and 10 mm/year over 1948–2012, but the second half of that period saw smaller positive trends than the first half or even negative trends. Climate change explained >80 % of the TWS trends across most biomes and climate zones, followed by land use and land cover change. The physiological and phenological effects of CO2 primarily induced noticeable TWS trends in the more humid biomes and climate zones across different latitudes. In contrast, nitrogen deposition and aerosol deposition generally had smaller and negative impacts across the biomes and climate regions. Among the meteorological drivers analyzed, the long-term average imbalance between precipitation (P), evapotranspiration (E), and runoff (Q) contributed >50 % of the TWS trends in most biomes and climate zones, with nonlinearity being induced by spatially heterogenous changes in E/P and Q/P ratios. The accumulated detrended anomalies in P, E, and Q also often contributed substantially, while the trends difference between P, E, and Q contributed little. Together, these findings unveiled an intensification of the global TWS and its diverse patterns of climate change and different non-withdrawal human-induced alterations, contributing to a more comprehensive understanding and projection of the global water cycle.
54 ENVIRONMENTAL SCIENCES↗
Climate cooling benefits of cellulosic bioenergy crops from elevated albedo
Abstract Changes in land surface albedo can alter ecosystem energy balance and potentially influence climate. We examined the albedo of six bioenergy cropping systems in southwest Michigan USA: monocultures of energy sorghum ( Sorghum bicolor ), switchgrass ( Panicum virgatum L.), and giant miscanthus ( Miscanthus × giganteus) , and polycultures of native grasses, early successional vegetation, and restored prairie. Direct field measurements of surface albedo ( α s ) from May 2018 through December 2020 at half‐hourly intervals in each system quantified the magnitudes and seasonal differences in albedo (∆ α ) and albedo‐induced radiative forcing (RF ∆ α ). We used a nearby forest as a historical native cover type to estimate reference albedo and RF ∆ α change upon original land use conversion, and a continuous no‐till maize ( Zea mays L .) system as a contemporary reference to estimate change upon conversion from annual row crops. Annually, α s differed significantly ( p < 0.05) among crops in the order: early successional (0.288 ± 0.012SE) >> miscanthus (0.271 ± 0.009) ≈ energy sorghum (0.270 ± 0.010) ≥ switchgrass (0.265 ± 0.009) ≈ restored prairie (0.264 ± 0.012) > native grasses (0.259 ± 0.010) > maize (0.247 ± 0.010). Reference forest had the lowest annual α s (0.134 ± 0.003). Albedo differences among crops during the growing season were also statistically significant, with growing season α s in perennial crops and energy sorghum on average ~20% higher (0.206 ± 0.003) than in no‐till maize (0.184 ± 0.002). Average non‐growing season (NGS) α s (0.370 ± 0.020) was much higher than growing season α s (0.203 ± 0.003) but these NGS differences were not significant. Overall, the original conversion of reference forest and maize landscapes to perennials provided a cooling effect on the local climate (RF αMAIZE : −3.83 ± 1.00 W m −2 ; RF αFOREST : −16.75 ± 3.01 W m −2 ). Significant differences among cropping systems suggest an additional management intervention for maximizing the positive climate benefit of bioenergy crops, with cellulosic crops on average ~9.1% more reflective than no‐till maize, which itself was about twice as reflective as the reference forest.
Lei, Cheyenne↗
Improved Peatland Hydrology and L-Band Microwave Radiative Transfer Modeling in Version 7 of the SMAP Level-4 Soil Moisture Data Assimilation Product
The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The L4_SM estimates are derived from the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model. The recently released Version 7 of L4_SM comprises two key advances. First, the Catchment model now includes the recently developed PEATCLSM hydrology module for peatlands and uses an updated global map of peatlands. Second, revised parameters are used in the L-band radiative transfer model that converts the simulated soil moisture and temperature estimates into Tb predictions for use in the radiance-based L4_SM analysis. Specifically, the L-band scattering albedo, soil roughness, and vegetation opacity climatology are obtained from the SMAP Level-2 dual-channel soil moisture retrieval product. In this presentation, we examine the improvements gained with the model and parameter changes. In peatlands, the dynamics of water table depth, surface soil moisture and evapotranspiration are considerably improved in L4_SM Version 7 over Version 6 when evaluated against in situ measurements. These improvements are also manifested in smaller Tb observation-minus-forecast residuals.
SMAP↗
Climate Reanalysis: Progress and Future Prospects
Reanalysis is the process whereby an unchanging data assimilation system is used to provide a consistent reprocessing of observations, typically spanning an extended segment of the historical data record. The process relies on an underlying model to combine often-disparate observations in a physically consistent manner, enabling production of gridded data sets for a broad range of applications including the study of historical weather events, preparation of climatologies, business sector development and, more recently, climate monitoring. Over the last few decades, several generations of reanalyses of the global atmosphere have been produced by various operational and research centers, focusing more or less on the period of regular conventional and satellite observations beginning in the mid to late twentieth century. There have also been successful efforts to extend atmospheric reanalyses back to the late nineteenth and early twentieth centuries, using mostly surface observations. Much progress has resulted from (and contributed to) advancements in numerical weather prediction, especially improved models and data assimilation techniques, increased computing capacity, the availability of new observation types and efforts to recover and improve the quality of historical ones. The recent extension of forecast systems that allow integrated modeling of meteorological, oceanic, land surface, and chemical variables provide the basic elements for coupled data assimilation. This has opened the door to the development of a new generation of coupled reanalyses of the Earth system, or integrated Earth system analyses (IESA). Evidence so far suggests that this approach can improve the analysis of currently uncoupled components of the Earth system, especially at their interface, and lead to increased predictability. However, extensive analysis coupling as envisioned for IESA, while progressing, still presents significant challenges. These include model biases that can be exacerbated when coupled, component systems with different physical characteristics and different spatial and temporal scales, and component observations in different media with different spatial and temporal frequencies and different latencies. Quantification of uncertainty in reanalyses is also a critical challenge and is important for expanding their utility as a tool for climate change assessment. This talk provides a brief overview of the progress of reanalysis development during recent decades, and describes remaining challenges in the progression toward coupled Earth system reanalyses.
IES↗
Application of Satellite Gravimetry for Water Resources Monitoring
Satellite gravimetry is unique in its ability to measure, from space, mass changes in the atmosphere and on and below Earth’s surface. After accounting for atmosphere and ocean mass circulations, scientists use these measurements to estimate changes in terrestrial water storage (TWS), which comprises groundwater, soil moisture, surface waters, snow, and ice. Monthly TWS data from the GRACE and GRACE-FO missions have been available since 2002. This presentation will discuss the application of these data for inferring and attributing regional trends in TWS around the world. It will also describe how the assimilation of GRACE and GRACEFO data into a land surface model enables monitoring and forecasting droughts and flood vulnerability.
Matthew Rodell↗
An Operational Product for Peatland Applications: The Version 7 of the SMAP Level-4 Soil Moisture Data Assimilation Product
The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture estimates from April 2015 to present with a mean latency of 2.5 days from the time of observation. The L4_SM estimates are derived from the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). The recently released Version 7 of the L4_SM product comprises a key advancement that is of interest for peatland applications. CLSM now includes the recently developed PEATCLSM hydrology module for peatlands and uses an updated global peatland distribution. In this presentation, we first give an overview of the operational product for the peatland community. We further show how the incorporation of PEATCLSM considerably improves the dynamics of water table depth, surface soil moisture and evapotranspiration in L4_SM Version 7 over Version 6 when evaluated against in situ measurements in peatlands. These improvements are also manifested in smaller Tb observation-minus-forecast residuals. Eventually, we provide three research examples in which the data assimilation product is used to gain insights into: peatland hydrological processes, the peat moisture dependency of the carbon cycle, and wildfire occurrence in peatlands.
Michel Bechtold↗
Mars Smart Lander Simulations for Entry, Descent, and Landing
Two primary simulations have been developed and are being updated for the Mars Smart Lander Entry, Descent, and Landing (EDL). The high fidelity engineering end-to-end EDL simulation that is based on NASA Langley's Program to Optimize Simulated Trajectories (POST) and the end-to-end real-time, hardware-in-the-loop simulation testbed, which is based on NASA JPL's (Jet Propulsion Laboratory) Dynamics Simulator for Entry, Descent and Surface landing (DSENDS). This paper presents the status of these Mars Smart Lander EDL end-to-end simulations at this time. Various models, capabilities, as well as validation and verification for these simulations are discussed.
Striepe, S. A.↗
NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment
Terrestrial hydrologic trends over the conterminous United States are estimated for 1980–2015 using the National Climate Assessment Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah version 3.3 land surface model at 0.125° 3 0.125° forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products. Mean annual trends are reported using the nonparametric Mann–Kendall test at p < 0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the upper Great Plains and Northeast to -1 to -9 mm/yr in the West and South, net radiation flux trends range from +0.05 to +0.20 W/sq. m yr in the East to -0.05 to -0.20 W/sq. m yr in the West, and U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes, and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from west to east. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%. Trends in latent heat flux were hardly affected, with RMSE decreasing only from 0.17 to 0.16 W/sq. m yr, while trend detection increased 2%. NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gauged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.
Michael F. Jasinski↗
Soil Moisture–Atmosphere Feedbacks Dominate Land Carbon Uptake Variability
Year-to-year changes in carbon uptake by terrestrial ecosystems play an essential role in determining atmospheric carbon dioxide concentrations. It remains uncertain to what extent temperature and water availability can explain these variations at the global scale. Here we use factorial climate model simulations and show that variability in soil moisture drives 90% of the inter-annual variability in global land carbon uptake, mainly through its impact on photosynthesis. We find that most of this ecosystem response occurs indirectly as soil moisture–atmosphere feedbacks amplify temperature and humidity anomalies, and enhance the direct effects of soil water stress. The strength of this feedback mechanism explains why coupled climate models indicate a dominant role of soil moisture which is not readily apparent in land surface model simulations and observational analyses. These findings highlight the need to account for feedbacks between soil and atmospheric dryness when estimating the carbon cycle’s response to climatic change globally, as well as when conducting field-scale investigations of the ecosystem response to droughts. Our results show that most of the global variability in modelled land carbon uptake is driven by temperature and vapour pressure deficit effects which are controlled by soil moisture.
Atmospheric dynamics↗
Asheville Urban Development II: Mapping Urban Heat to Support Cooling Initiatives and Climate Resilience Planning in the Greater Asheville Area
Asheville, North Carolina experiences the urban heat island effect, where temperatures in the city are higher than in surrounding rural areas. This effect intensifies with increased urbanization and less vegetative cover. Asheville’s urban heat island was exacerbated by population increases and tree cover decline, escalating the need for heat mitigation. We partnered with the City of Asheville’s Sustainability Department and Asheville GreenWorks whose actions prioritize sustainable city planning and equitable climate resilience. Using NASA Earth observations and ancillary datasets we spatially mapped urban heat, heat vulnerability, and cooling and adaptive capacity from 2019-2023. To map urban heat, we used Landsat 8 and 9 Operational Land Imager and Thermal Infrared Sensor for land surface temperature and albedo data and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station for evapotranspiration data. We assessed heat vulnerability using the urban heat data andthe Centers for Disease Control and Prevention’s Social Vulnerability Index. To evaluate cooling and adaptive capacity we used the InVEST Urban Cooling Model, integrating our heat vulnerability analysis with land use and cover data from Sentinel-1 Synthetic Aperture Rada rand Sentinel-2 Multispectral Instrument. Our results revealed distinct spatial patterns of urban heat, heat vulnerability, and cooling and adaptive capacity in Asheville with downtown as the focal hotspot and an outward decreasing radial pattern. These findings highlight targeted need for interventions to reduce heat impacts, address environmental injustices, and enhance climate resilience. Our project provided research to local organizations that can be used for heat mitigation in the greater Asheville area.
Authors not in NED but are confirmed contractors. ↗