With the launch of NISAR, the US community of Earth scientists can turn its attention to the next generation of repeat-pass InSAR observations. This paper considers mission architectures using small satellite platforms that would expand upon the capabilities provided by NISAR, while still providing continuity with those measurements. The architectures developed are the culmination of over six years of study by the Surface Deformation and Change (SDC) mission architecture study team, commissioned by NASA to provide architecture options in response to the 2017 Earth Science Decadal survey. This paper describes the mission architecture finalists from that process and posits a number of scenarios that may lead up to selection of a future mission architecture.

In response to the 2017 Decadal Survey, NASA conducted a five-year study on the Surface Deformation and Change (SDC) designated observable to study potential mission concepts. As part of the SDC mission study, the Commercial Synthetic Aperture Radar (ComSAR) subgroup was tasked with evaluating the current landscape of the SAR and interferometric SAR (InSAR) industry to assess whether NASA could leverage commercial smallsat products to meet the needs of the SDC science mission. The assessment found that although the commercial SAR industry is growing rapidly, off-the-shelf products can currently only make a small-albeit distinct-contribution to SDC mission goals. This gap is due to different design goals between current commercial systems (which prioritize targeted high-resolution, non-interferometric observations at short wavelengths with a daily or faster revisit) and a future SDC architecture (which focuses on broad, moderate-resolution, and interferometric observations at long wavelengths). Even by 2030, planned commercial constellations are expected to only cover similar to ${\sim} $65% of the area needed to match NISAR coverage. Still, high-resolution and rapid-repeat capabilities can augment scientific findings from a future SDC mission, as demonstrated by recent contributions from commercial data to applied sciences, cryosphere, and volcanology. Future innovations on smallsat constellation concepts could further contribute to SDC science and applications. Although current constellation designs are not fully able to satisfy desired SDC science capabilities, initial positive feedback to a request for information indicates a potential future path for a customized SDC commercial architecture; more studies will be needed to determine the feasibility of these approaches.

NISAR, a spaceborne L- and S-band radar mission, is set to launch in 2025. As part of mission testing, airborne L- and S-band radar sensors were employed to obtain dual-frequency quad-polarimetric (LS-ASAR) data in the United States. This study evaluates precursor NISAR data across the polarization channels of both L- and S-band radar frequencies for feature classification in glacierized regions. We use LS-ASAR data from July 2021, collected over Black Rapids Glacier and surrounding areas, to classify the region into snow, water, clean ice, debris-covered surface, rock, and vegetation. A support vector machine classifier is applied to both backscatter-intensity and polarimetric decomposition channels. Polarimetric decomposition improves classification accuracy by 28%, highlighting the influence of surface, volume, and double-bounce scattering mechanisms on feature discrimination. Resampled data at 4-m and 8-m resolutions yield comparable results for intensity-based classification, while the accuracy of polarimetric decomposition-based classification improves by 21.5% at 8 m. Combining all individual L- and S-band channels further enhances accuracy. In addition, our analysis of polarization ratios, particularly the S to L intensity ratio in VH cross-polarization, helps in differentiating landslide-covered ice from the usual debris-covered ice. This study highlights the effectiveness of individual channels and the potential benefits of integrating dual-frequency L- and S-band polarimetric decomposition data to achieve higher accuracy in mapping glacierized regions. The NISAR data is expected to be publicly available, offering the opportunity to study glacierized regions globally at regular intervals, contributing to higher level derived data products from NISAR.

Accurate state estimation in underwater environments requires real-time, high-quality data on bathymetry and water quality due to the complexities of underwater terrains. Gathering this information reliably is a significant challenge. Our primary objective is to improve state estimation and precise positioning of underwater vehicles by incorporating 3D underwater maps generated using satellite-derived water quality estimators. We developed a comprehensive pipeline that collects in-situ data and supplements it with remote sensing images from Sentinel-2 (S2) and Landsat 8-9 (L8-9). Using supervised machine learning methods, we transformed the reflectance (Rrs) indices from these images, augmented with in-situ data, into water quality estimators. This enabled us to predict vital parameters like bathymetry, chlorophyll-a, dissolved oxygen, turbidity from S2 images, and sea surface temperature from L8-9 images. The generated underwater maps were introduced into an Extended Kalman Filter (EKF) for underwater vehicle state estimation, significantly enhancing its capabilities. The effectiveness of our EKF-based approach was validated through computer simulations. Additionally, we introduced a streamlined data management plan to expedite the creation of machine learning datasets and applications as a valuable secondary artifact. Our research primarily contributes to the enhanced state estimation of UUVs, with potential improvements in water quality monitoring and disaster response strategies.

The expansion of satellite constellation applications brings attention to the need for responsive, reliable satellite communication. For preflight technology assessment of missions, a simulation of two spacecraft in low Earth orbit has been created to estimate clock synchronization and precision orbit determination based on measured instrumentation performance. A novel MATLAB-based numerical simulator was developed to model spacecraft-to-spacecraft laser time-transfer and estimate the offset between the spacecraft clocks over time. This simulation includes timing errors associated with laser pulse detection, as well as non-Gaussian clock drift models. Two on-board clocks are modeled: a cesium-based chip-scale atomic clock and a rubidium-based miniature atomic clock. The positions and velocities of the spacecraft at a reference epoch and the constant coefficients of a polynomial clock model are estimated. Results compare the estimated clock model of a mission operation that only uses GPS measurements and one that uses both GPS and laser pulse time-of-flight measurements between spacecraft referenced to their on-board clocks. Including lasing measurements reduces the root-mean-square clock model error to approximately 80% of the RMS of the cases with only GPS measurements. This simulation tool can be used to optimize the lasing operations schedule based on mission timing performance objectives.

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of >90% and <1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh’s agriculture areas were affected.

Path planning is an important primitive in robotics. In this paper, a new Informed Sampling Space (ISS) driven Informative Path Planning (IPP) approach is developed to facilitate autonomous robots to navigate and explore unknown and hazardous environments for in-situ resource utilization efficiently. The developed ISS-driven IPP approach is targeted on multi-objective optimization enabling the robot to plan its path from start to target locations in the environment and simultaneously explore multiple high-interest areas efficiently. The high-interest areas could be locations advised by a human supervisor or from the robot’s prior knowledge of the environment. Typically, a cost function (time, distance, etc.) is used in sampling-based path planners. A new cost function is also developed to incorporate the high-interest spots in this paper, which is based on Multivariate normal (MVN) probability density function (PDF) and a normalization function. Two different IPP models are developed using the new cost function to assist robot navigation. IPP with RRT* is used in the first model with no heuristics, while IPP with RRT* and heuristic ISS is used in the second model. Simulation and comparative analysis substantiate the efficacy and robustness of our approach. The simulation results corroborate that our proposed ISS-driven IPP with RRT* converges rapidly towards the near-optimal solution with respect to both navigation time and environment exploration.

Abstract Land subsidence rates in Mexico City reach 500 mm/year, causing progressive damage to the city’s core infrastructure, including the Metro system. A deadly overpass collapse in 2021, along a Metro line that had operated for less than 10 years, brought subsidence-related structural damage to the attention of the system’s authorities and led to major repairs to two of the twelve Metro lines. Still, the need for quantifying the magnitude and extent of subsidence affecting the Metro system’s widespread infrastructure prevails. Using a wealth of satellite radar interferometry observations, levelling surveys, subsurface profiles, linear gradient and differential displacement analyses, and structural-engineering parameters, we assess the vulnerability of the Metro system’s street-level and elevated segments to land subsidence. Our results reveal that high subsidence velocity gradients occur over sharp transitional zones between stable and fast-subsiding areas, reaching values of 1 $$\times \,10 ^{-3}$$ × 10 - 3 year $$^{-1}$$ - 1 , resulting in slope changes up to 3.5% over a 20-year period and differential displacements between columns. Our findings suggest locations where the consequences of subsidence have compromised the train’s braking safety design, increased railway flooding hazard, produced railway bending, and reduced the conceived 50-year service life of the Metro’s elevated overpasses.

Use of Synthetic Aperture Radar (SAR) data has been steadily increasing over the past decade as data from sensors become freely available. In this article, our focus is on the NASA-ISRO SAR (NISAR), the Observational Products for End-Users from Remote Sensing Analysis (OPERA), and Surface Deformation and Change (SDC), and how their user engagement activities combine to build a cohesive user engagement effort for SAR remote sensing. All of NASA remote sensing data is openly and freely available through its Distributed Active Archive Centers (DAAC), and SAR data is no exception. Data going back to SEASAT in 1978 can be accessed through the Alaska Satellite Facility, NASA’s SAR DAAC. The NISAR mission is expected to significantly increase the volume of SAR data available to the end users after its launch in early 2024. OPERA project is already underway preparing high-level data products suited for end-users and will be leveraging NISAR data when it becomes available. Finally, NASA is already conducting the SDC mission study to define a mission architecture for the next decade.

Abstract. During volcanic disasters, the remoteness of the terrains combined with potentially incapacitated lifelines (e.g., disturbed transportation network) prevent ground-based surveys for timely assessment of damage extents. To that effect, we worked to combine satellite optical and Synthetic Aperture Radar (SAR) data to rapidly delineate the areas impacted by fast-moving volcanic flows during an eruption (e.g., Pyroclastic Density Currents (PDCs), lahars), which can in turn be used to target and organize the response efforts. We used the 2015 eruptions of Volcán de Colima (Mexico) and Volcán Calbuco (Chile) volcanoes to calibrate detection thresholds of different types of volcanic flows, from optical and SAR imagery. Optical imagery is used to calculate temporal changes of Normalized Difference Vegetation Index (NDVI) associated with the presence of erupted materials on the surface. SAR amplitude images are used to detect changes in surface roughness (sigma0) attributed to the emplacement of new volcanic flows. Classification of the respective NDVI and SAR amplitude signal changes for different types of volcanic flows is done using very-high-resolution imagery and ground-based data obtained during field work. Linear rescaling of minimal and maximal threshold signals is used to create probability maps of volcanic flow deposits extent, and then combined into a joint probability map to maximize the accuracy of the deposit extents. We tested our ability to generate volcanic flow extent maps during the April 2021 eruption of Soufrière St Vincent, using this detection method and the calibrated threshold values for PDCs and lahars.

Satellite remote sensing data provide key information needed to understand the dynamic behavior of our planet as well as to prepare for, respond to, and recover from disasters. The Observational Products for End-Users from Remote Sensing Analysis (OPERA) project at the Jet Propulsion Laboratory, in partnership with the US Geological Survey and the University of Maryland, starts releasing near-global products that are based on Harmonized Landsat-8 Sentinel-2 A/B (HLS) optical datasets in February 2023: (1) Dynamic Surface Water eXtent (DSWx-HLS) and (2) Land Surface Disturbance (DIST-HLS) product suites. These derived products have applications including monitoring and guiding future hazard management and recovery efforts. While OPERA does not have an urgent response requirement for disasters, the project will process and deliver the data to end-users as soon as possible. HLS has 2-3 day revisit frequency at the equator allowing the potential for OPERA products to help provide analysis ready data from before, during, and after some events to aid disaster response and recovery efforts. All the DSWx and DIST products will be freely available to the public through various Distributed Active Archive Centers (PO.DAAC for DSWx, https://podaac.jpl.nasa.gov/; LPDAAC for DIST, https://lpdaac.usgs.gov/) and NASA’s Earthdata Search platform based on their scheduled operational release.Here, we present applications of the first provisional products from the DSWx-HLS and DIST-HLS suites to monitor changes in water bodies and vegetation cover due to droughts, floods, and wildfires. In particular, we focus our analysis on: (a) drastic extent changes in reservoirs, such as for Lake Mead from 2014-present, (b) mapping flood extents such as the 2020 dam failures in Midland, Michigan, and (c) mapping burned areas due to wildfires such as the 2022 wildfires in New Mexico and in California. We develop open-source tutorials using GIS software and Jupyter Notebooks to visualize and showcase these applications. Both the provisional data and the tutorials are available on the OPERA website (https://www.jpl.nasa.gov/go/opera) to ensure broad access and reproducibility.

The Surface Deformation and Change (SDC) mission study is investigating a synthetic aperture radar (SAR) mission that is expected to launch in the next decade, building on the foundation established by the NASA ISRO Synthetic Aperture Radar (NISAR) mission. Since 2019, the SDC study team has updated the observation needs identified by the 2017 Earth Science Decadal Survey, developing a Science and Applications Traceability Matrix (SATM) that includes an expanded set of geophysical observables (GOs). These needs were further refined by a team of discipline experts, resulting in 48 GOs. For each GO, imaging characteristics such as revisit, accuracy, resolution, polarisation, data latency, are defined in the SATM. This paper describes the benefit assessment methodology, provides an example to generate current commercial feasibility scores for each GO in the SATM, even though the SDC mission will not be launched until the next decade. This methodology generates a quantitative assessment of commercial SAR data in meeting the measurement needs of a GO defined in SDC's SATM. Our assessment suggests that current commercial SAR data are particularly useful for constraining geophysical processes that benefit from short-repeat acquisition times and high spatial resolution.

This paper explores the retrieval of snow water equivalent (SWE) through the use of machine learning techniques and active radar data collected over the 2020 SnowEx campaign. The retrieval makes use of active radar measurements provided by NASA's SWESARR instrument for direct sensing of snowpack sensitivity to SWE. The example results show that an RMSE of 1.93 cm can be obtained through a combined use of SAR data with sufficient ancillary data. Such results may indicate successful SWE estimation by means of pairing spaceborne SAR measurements with sufficient auxiliary information.

Scientists continue to study the red tide and fish-kill events happening in Florida. Machine learning applications using remote sensing data on coastal waters to monitor water quality parameters and detect harmful algal blooms are also being studied. Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) are often deployed on data collection and disaster response missions. To enhance study and mitigation efforts, robots must be able to use available data to navigate these underwater environments. In this study, we compute a satellite-derived underwater environment (SDUE) model by implementing a supervised machine learning model where remote sensing reflectance $(R_{rs})$ indices are labeled with in-situ data they correlate with. The models predict bathymetry and water quality parameters given a recent remote sensing image. In our experiment, we use Sentine1-2 (S2) images and in-situ data of the Biscayne Bay to create an SDUE that can be used as a Chlorophyll-a map. The SDUE is then used in an Extended Kalman Filter (EKF) application that solves an underwater vehicle localization and navigation problem.

Many studies suggest that water quality parameters can be estimated by applying statistical and machine learning methods using remote sensing or in-situ data. However, identifying best practices for implementing solutions appears to be done on a case-by-case basis. In our case, we have in-situ data that covers a large period, but only small areas of Biscayne Bay, Florida. In this paper, we combine available in-situ data with remote sensing data captured by Landsat 8 OLI-TIRS Collection 2 Level 2(L8), Sentinel-2 L2A(S2), and Sentinel-3 OLCI L1B(S3). The combined data set is for use in a water quality parameter estimation application. Our contributions are two-fold. First, we present a pipeline for data collection, processing, and co-location that results in a usable data set of combined remote sensing and in-situ data. Second, we propose a classification model using the combined data set to identify areas of interest for future data collection missions based on chlorophyll-a in-situ measurements. To further prove our methodology, we conduct a data collection mission using one of the predicted paths from our model.

The NISAR mission will provide data covering nearly all the Earth's land areas and sea ice designed specifically to meet requirements of the NISAR science disciplines. However, for each science use case, the same data could potentially be used by one or more applications, often with little or no change in the core observables. Here we describe NISAR's capabilities for supporting a wide range of applications and the activities undertaken to inform, engage, and train the community that will increase the mission's societal impact.

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Understanding Active Layer Thickness Variability Under Changing Climatic Conditions Across the North American Taiga-Tundra EcotoneAuthorsBradleyGayiDAmandaArmstrongiDPaulMontesanoBatuhanOsmanogluKevinSchaeferKennethRansonHowardEpsteiniDSee all authors Bradley GayiDCorresponding Author• Submitting AuthorGeorge Mason University FairfaxiDhttps://orcid.org/0000-0003-2617-2559view email addressThe email was not providedcopy email addressAmanda ArmstrongiDUniversities Space Research Association ColumbiaiDhttps://orcid.org/0000-0002-9123-8924view email addressThe email was not providedcopy email addressPaul MontesanoScience Systems and Applications, Inc.view email addressThe email was not providedcopy email addressBatuhan OsmanogluNASA Goddard Space Flight Centerview email addressThe email was not providedcopy email addressKevin SchaeferNational Snow and Ice Data Center, Cooperative Institute for Research in the Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, U.S.Aview email addressThe email was not providedcopy email addressKenneth RansonNASA Goddard Space Flight Centerview email addressThe email was not providedcopy email addressHoward EpsteiniDUniversity of VirginiaiDhttps://orcid.org/0000-0003-2817-4486view email addressThe email was not providedcopy email address

Climate-influenced changes in hydrology affect water-food-energy security that may impact up to two billion people downstream of the High Mountain Asia (HMA) region. Changes in water supply affect energy, industry, transportation, and ecosystems (agriculture, fisheries) and as a result, also affect the region's social, environmental, and economic fabrics. Sustaining the highly interconnected food-energy-water nexus (FEWN) will be a fundamental and increasing challenge under a changing climate regime. High variability in topography and distribution of glaciated and snow-covered areas in the HMA region, and scarcity of high resolution (in-situ) data make it difficult to model and project climate change impacts on individual watersheds. We lack basic understanding of the spatial and temporal variations in climate, surface impurities in snow and ice such as black carbon and dust that alter surface albedo, and glacier mass balance and dynamics. These knowledge gaps create challenges in predicting where and when the impact of changes in river flow will be the most significant economically and ecologically. In response to these challenges, the United States National Aeronautics and Space Administration (NASA) established the High Mountain Asia Team (HiMAT) in 2016 to conduct research to address knowledge gaps. This paper summarizes some of the advances HiMAT made over the past 5 years, highlights the scientific challenges in improving our understanding of the hydrology of the HMA region, and introduces an integrated assessment framework to assess the impacts of climate changes on the FEWN for the HMA region. The framework, developed under a NASA HMA project, links climate models, hydrology, hydropower, fish biology, and economic analysis. The framework could be applied to develop scientific understanding of spatio-temporal variability in water availability and the resultant downstream impacts on the FEWN to support water resource management under a changing climate regime.