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NASA NTRS2019

Passive/active Microwave Soil Moisture Change Disaggregation Using Smapvex12 Data

The SMAPVEX12 (Soil Moisture Active Passive (SMAP) Validation Experiment 2012) experiment was conducted during June-July 2012 in Manitoba, Canada with the goal of collecting remote sensing data and ground measurements for the development and testing of soil moisture retrieval algorithms under varying vegetation and soil conditions for the SMAP satellite. The aircraft based soil moisture data provided by the passive/active microwave sensor PALS (Passive and Active L-band System) has a nominal spatial resolution of 1600 m. However, this resolution is not compatible with agricultural, meteorological and hydrological studies that require high spatial resolutions and this issue can be solved by soil moisture disaggregation. The soil moisture disaggregation algorithm integrates radiometer soil moisture retrievals and high-resolution radar observations and it can provide soil moisture estimates at a finer scale than the radiometer data alone. In this study, a change detection algorithm was used for disaggregation of coarse resolution passive microwave soil moisture retrievals with radar backscatter coefficients obtained from the higher spatial resolution UAVSAR (Unmanned Air Vehicle Synthetic Aperture Radar) at crop field scale. The accuracy of the disaggregated change in soil moisture was evaluated using ground based soil moisture measurements collected during SMAPVEX12 campaign. The results showed that soil moisture spatial variabilities were better characterized by the disaggregated change in soil moisture estimates at 5 m / 800 m resolution as well as good agreement with in situ measurements. It also showed that VWC (Vegetation Water Content) did not have a big impact on disaggregation algorithm performance, with R2 of the disaggregated results ranging 0.628-0.794. The 5 m and 800m resolution disaggregated soil moisture did no show significant difference in statistical performance variables.

Radar Backscatter↗

NASA NTRSDate not supplied

Evaluation of GHGSat Methane Emission Estimations of South Side Landfill in Indianapolis, Indiana

As global methane (CH4) continues to grow, accurate quantification of CH4 emission sources has become a high priority for climate management. Urban CH4 emissions in the US could potentially account close to 20% of the national CH4 anthropogenic emissions. While isolated insitu urban CH4 emissions quantification projects have been successful, they require sophisticated planning and prolonged analyses. Such studies are imperative, but they cannot easily operate in every city. An ability to quantify the magnitude of CH4 point sources in a variety of cities with a satellite could support wide scale urban CH4 emissions quantification. Whole-earth scanning satellite instruments such as the TROPOMI and GOSAT are too coarse and noisy to provide meaningful measurements over cities with mid-strength emissions and most point-source emissions in general. It may be possible to address this deficiency utilizing a product with the high spatial resolution of 30-50 m from a private company GHGSat Inc. These sensors are specifically designed to identify and quantify CH4 emissions from diverse types of point sources of varying intensities. In this work we analyze a year (05/2023-05/2024) of GHGSat scans over South Side Landfill in downtown Indianapolis, Indiana and assess the instruments’ accuracy and precision with respect to prior estimations. The results indicate that the data are helpful for estimating landfill’s emissions (~750 kg/hr ± 200 kg/hr) over months-to-a-year time frame but may not be appropriate for identifying daily or even seasonal variations with any trustable degree of certainty, at least not in an urban environment.

Nikolay Balashov↗

DOE OSTI2024

Multi-functional cementitious materials with ultra-high damage tolerance and self-sensing ability

Cementitious materials having high damage tolerance and self-sensing ability are described herein. These materials may replace conventional concrete to serve as a major material component for infrastructure systems with greatly improved resistance to cracking, reinforcement corrosion, and other common deterioration mechanisms under service conditions, and prevents fracture failure under extreme events. These materials can also be used for the repair, retrofitting or rehabilitation of existing concrete structures or infrastructure systems. Furthermore, these materials may offer capacity for distributed and direct sensing of cracking, straining and deterioration with spatially continuous resolution wherever the material is located, without relying on installation of sensors. The present invention relates to multifunctional cementitious structural or infrastructure materials that integrate self-sensing with damage tolerance for improving safety, extending service life, and health monitoring of structures, components, and infrastructure systems.

Li, Mo↗

DOE OSTI2022

Smart sensors using artificial intelligence for on-detector electronics and ASICs

Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated in next-generation experiments. Therefore, the need for near-sensor, at the data source, processing with more powerful algorithms is becoming increasingly important to more efficiently capture the right experimental data, reduce downstream system complexity, and enable faster and lower-power feedback loops. In this paper, we discuss the motivations and potential applications for on-detector AI. Furthermore, the unique requirements of particle physics can uniquely drive the development of novel AI hardware and design tools. We describe existing modern work for particle physics in this area. Finally, we outline a number of areas of opportunity where we can advance machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

iButton snow-ground interface temperature measurements in Los Alamos, New Mexico from 2023-2024

Snow/ground interface temperature measurements were collected at two sites in Los Alamos, New Mexico. Data were collected from November 29, 2023 to April 8, 2024 using iButton Link DS1921G-F5# Thermochron miniature temperature sensors (https://www.ibuttonlink.com/products/ds1921g). These sensors are a cost-efficient way to collect snowpack temperatures at a higher spatial resolution than what is normally achieved. iButton data were collected every 3 hours from a total of 19 iButtons. iButtons were placed in pairs, with one iButton placed at the ground surface and another buried 1 - 5 cm below the ground surface. One buried iButton did not successfully collect data, and therefore was excluded from this dataset. Data were collected throughout the snow cover season so that snowpack characteristics could be derived using the temperature data. Specifically, this dataset was used as a validation source for a novel machine learning approach to estimating snow depth (see related publication). Sensors were placed in areas with bare ground or minimal grass coverage, located away from any large vegetation. At Site A (TA51), manual snow depths were collected as validation data. These measurements were taken next to iButtons periodically throughout the winter, and notes on other precipitation types were also recorded. At Site B (TA6 Meteorological Station), a nearby sensor collected snow depths throughout the winter. This dataset contains one *.csv file of snow/ground interface temperatures at two sites, one *.csv file of manually collected snow depths, and one *.kml file of sensor locations. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Validation and Scaling of Soil Moisture in a Semi-Arid Environment: SMAP Validation Experiment 2015 (SMAPVEX15)

The NASA SMAP (Soil Moisture Active Passive) mission conducted the SMAP Validation Experiment 2015 (SMAPVEX15) in order to support the calibration and validation activities of SMAP soil moisture data products. The main goals of the experiment were to address issues regarding the spatial disaggregation methodologies for improvement of soil moisture products and validation of the in situ measurement upscaling techniques. To support these objectives high-resolution soil moisture maps were acquired with the airborne PALS (Passive Active L-band Sensor) instrument over an area in southeast Arizona that includes the Walnut Gulch Experimental Watershed (WGEW), and intensive ground sampling was carried out to augment the permanent in situ instrumentation. The objective of the paper was to establish the correspondence and relationship between the highly heterogeneous spatial distribution of soil moisture on the ground and the coarse resolution radiometer-based soil moisture retrievals of SMAP. The high-resolution mapping conducted with PALS provided the required connection between the in situ measurements and SMAP retrievals. The in situ measurements were used to validate the PALS soil moisture acquired at 1-km resolution. Based on the information from a dense network of rain gauges in the study area, the in situ soil moisture measurements did not capture all the precipitation events accurately. That is, the PALS and SMAP soil moisture estimates responded to precipitation events detected by rain gauges, which were in some cases not detected by the in situ soil moisture sensors. It was also concluded that the spatial distribution of the soil moisture resulted from the relatively small spatial extents of the typical convective storms in this region was not completely captured with the in situ stations. After removing those cases (approximately10 of the observations) the following metrics were obtained: RMSD (root mean square difference) of0.016m3m3 and correlation of 0.83. The PALS soil moisture was also compared to SMAP and in situ soil moisture at the 36-km scale, which is the SMAP grid size for the standard product. PALS and SMAP soil moistures were found to be very similar owing to the close match of the brightness temperature measurements and the use of a common soil moisture retrieval algorithm. Spatial heterogeneity, which was identified using the high-resolution PALS soil moisture and the intensive ground sampling, also contributed to differences between the soil moisture estimates. In general, discrepancies found between the L-band soil moisture estimates and the 5-cm depth in situ measurements require methodologies to mitigate the impact on their interpretations in soil moisture validation and algorithm development. Specifically, the metrics computed for the SMAP radiometer-based soil moisture product over WGEW will include errors resulting from rainfall, particularly during the monsoon season when the spatial distribution of soil moisture is especially heterogeneous.

SMAPVEX15↗

NASA NTRS1985

Position Paper: Workshop on Geologic Applications of Remote Sensing to the Study of Sedimentary Basins

The utility of LANDSAT Multispectral Scanner (MSS) and Thematic Mapper (TM) data for providing regional coverage has been demonstrated. Information derived from LANDSAT spectral data is varied, but has primarily been used for distinguishing materials in a scene. Because of the limited number and width of the MSS and TM bands, LANDSAT data has only limited utility for actual identification of materials. The new advanced sensors (currently airborne, but eventually shuttle-based) have more limited areal extent than LANDSAT, but greater spatial and spectral resolution. The Airborne Imaging Spectrometer (AIS) has spectral resolution of about .01 microns and, typically, has 10m/pixel spatial resolution. This improved spectral resolution means that diagnostic spectral features may now be identified, which in turn means that actual identification of mineral phases present can be addressed (of either monominerallic or composite materials). Another new sensor, the Thermal Infrared Multispectral Scanners (TMS), obtains data in the 8 to 12 micron range. The information from the various sensors (e.g., LANDSAT, AIS, and TIMS) is complementary, covering different wavelength regions and consequently sensitive to different materials or physical properties of materials. In addition, the new sensors also produce a higher volume of data, which must be cleverly and efficiently handled.

Blake, P. L.↗

NASA NTRS1992

Measuring dry plant residues in grasslands: A case study using AVIRIS

Grasslands, savannah, and hardwood rangelands are critical ecosystems and sensitive to disturbance. Approximately 20 percent of the Earth's surface are grasslands and represent 3 million ha. in California alone. Developing a methodology for estimating disturbance and the effects of cumulative impacts on grasslands and rangelands is needed to effectively monitor these ecosystems. Estimating the dry biomass residue remaining on rangelands at the end of the growing season provides a basis for evaluating the effectiveness of land management practices. The residual biomass is indicative of the grazing pressure and provides a measure of the system capacity for nutrient cycling since it represents the maximum organic matter available for decomposition, and finally, provides a measure of the erosion potential for the ecosystem. Remote sensing presents a possible method for measuring dry residue. However, current satellites have had limited application due to the coarse spatial scales (relative to the patch dynamics) and insensitivity of the spectral coverage to resolve dry plant material. Several hypotheses for measuring the biochemical constituents of dry plant material, particularly cellulose and lignin, using high spectral resolution sensors were proposed. The use of Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) to measure dry plant residues over an oak savannah on the eastern slopes of the Coast Range in central California was investigated and it was asked what spatial and spectral resolutions are needed to quantitatively measure dry plant biomass in this ecosystem.

Fitzgerald, Michael↗

NASA NTRS2015

Crosscutting Airborne Remote Sensing Technologies for Oil and Gas and Earth Science Applications

Airborne imaging spectroscopy has evolved dramatically since the 1980s as a robust remote sensing technique used to generate 2-dimensional maps of surface properties over large spatial areas. Traditional applications for passive airborne imaging spectroscopy include interrogation of surface composition, such as mapping of vegetation diversity and surface geological composition. Two recent applications are particularly relevant to the needs of both the oil and gas as well as government sectors: quantification of surficial hydrocarbon thickness in aquatic environments and mapping atmospheric greenhouse gas components. These techniques provide valuable capabilities for petroleum seepage in addition to detection and quantification of fugitive emissions. New empirical data that provides insight into the source strength of anthropogenic methane will be reviewed, with particular emphasis on the evolving constraints enabled by new methane remote sensing techniques. Contemporary studies attribute high-strength point sources as significantly contributing to the national methane inventory and underscore the need for high performance remote sensing technologies that provide quantitative leak detection. Imaging sensors that map spatial distributions of methane anomalies provide effective techniques to detect, localize, and quantify fugitive leaks. Airborne remote sensing instruments provide the unique combination of high spatial resolution (<1 m) and large coverage required to directly attribute methane emissions to individual emission sources. This capability cannot currently be achieved using spaceborne sensors. In this study, results from recent NASA remote sensing field experiments focused on point-source leak detection, will be highlighted. This includes existing quantitative capabilities for oil and methane using state-of-the-art airborne remote sensing instruments. While these capabilities are of interest to NASA for assessment of environmental impact and global climate change, industry similarly seeks to detect and localize leaks of both oil and methane across operating fields. In some cases, higher sensitivities desired for upstream and downstream applications can only be provided by new airborne remote sensing instruments tailored specifically for a given application. There exists a unique opportunity for alignment of efforts between commercial and government sectors to advance the next generation of instruments to provide more sensitive leak detection capabilities, including those for quantitative source strength determination.

hyperspectral↗

NASA NTRS1980

The Space Telescope Observatory

The Space Telescope is an international astronomical observatory which will be placed into a low earth orbit in December 1983. The observatory will provide astronomers the opportunity to use five different instruments (wide field/planetary camera, faint object camera, high-resolution spectrograph, faint-object spectrograph, and high speed photometer), and to do astrometry with the use of the fine guidance sensors used primarily for pointing control. These instruments, the spectral range available above the earth's atmosphere, and the spatial resolution available from the combination of a near-diffraction limited 94-in. primary mirror and the stability provided by the Pointing Control System provide a tremendous potential to the astronomical community.

Bahcall, J. N.↗

NASA NTRSDate not supplied

Observtion of Carbon Monoxide and Ozone from 2019-2020 Australia Fires Using Thermal Infrred and Near-Infrared Satellite Sensors

The Single Field-of-View (SFOV) retrieval products from CrIS on SNPP have a spatial resolution of 14 km. These products include temperature, water vapor, clouds and trace gases, such as ozone (O3) and carbon monoxide (CO). Such high-resolution sounder products enable us to make process-oriented analysis of CO emission and transport from Australia’s unprecedented wildfires as well as the O3 production along the transport of fire plumes in the end of 2019 to early 2020. Comparisons of CO with other satellite products, such as AIRS, TROPOMI and MOPPIT, show a good agreement of CO from these space-borne observations. The mean difference SFOV total CO with TROPOMI CO is 0.22%14.0% (R=0.92) and 1.1%17.6% (R=0.87) using data on December, 30, 2019 and January 1, 2020, respectively. This study also demonstrates the advantage of the SFOV CO in capturing the feature of CO transport for air quality study.

Xiaozhen Xiong↗

NASA NTRS1983

Electron-bombarded CCD detectors for ultraviolet atmospheric remote sensing

Electronic image sensors based on charge coupled devices operated in electron-bombarded mode, yielding real-time, remote-readout, photon-limited UV imaging capability are being developed. The sensors also incorporate fast-focal-ratio Schmidt optics and opaque photocathodes, giving nearly the ultimate possible diffuse-source sensitivity. They can be used for direct imagery of atmospheric emission phenomena, and for imaging spectrography with moderate spatial and spectral resolution. The current state of instrument development, laboratory results, planned future developments and proposed applications of the sensors in space flight instrumentation is described.

Carruthers, G. R.↗

NASA NTRS1997

Application of High-Resolution Thermal Infrared Remote Sensing and GIS to Assess the Urban Heat Island Effect

Day and night airborne thermal infrared image data at 5 m spatial resolution acquired with the 15-channel (0.45 micron - 12.2 micron) Advanced Thermal and Land Applications Sensor (ATLAS) over Alabama, Huntsville on 7 September, 1994 were used to study changes in the thermal signatures of urban land cover types between day and night. Thermal channel number 13 (9.6 micron - 10.2 micron) data with the best noise-equivalent temperature change (NEAT) of 0.25 C after atmospheric corrections and temperature calibration were selected for use in this analysis. This research also examined the relation between land cover irradiance and vegetation amount, using the Normalized Difference Vegetation Index (NDVI), obtained by ratioing the difference and the sum of the red (channel number 3: 0.60-0.63 micron) and reflected infrared (channel number 6: 0.76-0.90 micron) ATLAS data. Based on the mean radiance values, standard deviations, and NDVI extracted from 351 pairs of polygons of day and night channel number 13 images for the city of Huntsville, a spatial model of warming and cooling characteristics of commercial, residential, agricultural, vegetation, and water features was developed using a GIS approach. There is a strong negative correlation between NDVI and irradiance of residential, agricultural, and vacant/transitional land cover types, indicating that the irradiance of a land cover type is greatly influenced by the amount of vegetation present. The predominance of forests, agricultural, and residential uses associated with varying degrees of tree cover showed great contrasts with commercial and services land cover types in the center of the city, and favors the development of urban heat islands. The high-resolution thermal infrared images match the complexity of the urban environment, and are capable of characterizing accurately the urban land cover types for the spatial modeling of the urban heat island effect using a GIS approach.

Lo, C. P.↗

NASA NTRS2011

Sensor for Direct Measurement of the Boundary Shear Stress in Fluid Flow

The formation of scour patterns at bridge piers is driven by the forces at the boundary of the water flow. In most experimental scour studies, indirect processes have been applied to estimate the shear and normal stress using measured velocity profiles. The estimations are based on theoretical models and associated assumptions. However, the turbulence flow fields and boundary layer in the pier-scour region are very complex. In addition, available turbulence models cannot account accurately for the bed roughness effect. Direct measurement of the boundary shear and normal stress and their fluctuations are attractive alternatives. However, this approach is a challenging one especially for high spatial resolution and high fidelity measurements. The authors designed and fabricated a prototype miniature shear stress sensor including an EDM machined floating plate and a high-resolution laser optical encoder. Tests were performed both in air as well as operation in water with controlled flow. The sensor sensitivity, stability and signal-to-noise level were measured and evaluated. The detailed test results and a discussion of future work will be presented in this paper.

sheer stress↗

NASA NTRS2011

Sensor for Direct Measurement of the Boundary Shear Stress in Fluid Flow

shear stress↗

NASA NTRS2015

Evaluation of the Surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States

We use surface fine particulate matter (PM2.5) measurements collected by the United States Environmental Protection Agency (US EPA) and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks as independent validation for Version 1 of the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero) developed by the Global Modeling Assimilation Office (GMAO). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of bias corrected Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on both Terra and Aqua satellites. By combining the spatial and temporal coverage of GEOS-5 with observational constraints on AOD, MERRAero has the potential to provide improved estimates of PM2.5 compared to the model alone and with greater coverage than available observations.Importantly, assimilation of AOD data constrains the total column aerosol mass in MERRAero subject to assumptions about optical properties for each of the species represented in GOGART. However, single visible wavelength AOD data does not contain sufficient information content to correct errors in either aerosol vertical placement or composition, critical elements for a proper characterization of surface PM2.5. Despite this, we find that the data-assimilation equipped version of GEOS-5 better represents observed PM2.5 between 2003 and 2012 compared to the same version of the model without AOD assimilation. Compared to measurements from the EPA-AQS network, MERRAero shows better PM2.5 agreement with the IMPROVE network measurements, which are composed essentially of rural stations. Regardless the data network, MERRAero PM2.5 are closer to observation values during the summer while larger discrepancies are observed during the winter. Comparing MERRAero to PM2.5 data collected by the Chemical Speciation Network (CSN) offers greater insight on the species MERRAero predicts well and those for which there are biases relative to the EPA observations. Analysis of this speciated data indicates that the lack of nitrate emissions in MERRAero and an underestimation of carbonaceous emissions in the Western US explains much of the reanalysis bias during the winter. To further understand discrepancies between the reanalysis and observations, we use complimentary data to assess two important aspects of MERRAero that are of relevance to the diagnosis of PM2.5, in particular AOD and vertical structure

Air Pollution↗

NASA NTRS2016

Seahawk: An Advanced Cubesat Mission for Sustained Ocean Color Monitoring

Sustained ocean color monitoring is vital to understanding the marine ecosystem. It has been identified as an Essential Climate Variable (ECV) and is a vital parameter in understanding long-term climate change. Furthermore, observations can be beneficial in observing oil spills, harmful algal blooms and the health of fisheries. Space-based remote sensing, through MERIS, SeaWiFS and MODIS instruments, have provided a means of observing the vast area covered by the ocean which would otherwise be impossible using ships alone. However, the large pixel size makes measurements of lakes, rivers, estuaries and coastal zones difficult. Furthermore, retirement of a number of widely used and relied upon ocean observation instruments, particularly MERIS and SeaWiFS, leaves a significant gap in ocean color observation opportunities. This paper presents an overview of the SeaHawk mission, a collaborative effort between Clyde Space Ltd., the University of North Carolina Wilmington, Cloudland Instruments, and Goddard Spaceflight Center, funded by the Gordon and Betty Moore Foundation. The goal of the project is to enhance the ability to observe ocean color in high temporal and spatial resolution through use of a low-cost, next-generation ocean color sensor flown aboard a CubeSat. The final product will be 530 times smaller (0.0034 vs 1.81cu m) and 115 time less massive (3.4 vs 390.0 kg) but with a ground resolution 10 times better whilst maintaining a signal/noise ratio 50 that of SeaWiFs. This paper will describe the objectives of the mission, outline the payload specification and the spacecraft platform to support it.

SeaHawk↗

DOE OSTI2023

Modeling Global Vegetation Gross Primary Productivity, Transpiration and Hyperspectral Canopy Radiative Transfer Simultaneously Using a Next Generation Land Surface Model—CliMA Land

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400–2,500 nm for reflectance, 640–850 nm for fluorescence) at hourly time step and 1° spatial resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The spatial patterns of modeled GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R 2 = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, for example, providing insights into when GPP, SIF, and NIRv diverge.

54 ENVIRONMENTAL SCIENCES↗