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Accurate large-area Digital Terrain Models (DTMs) are crucial for many science applications. Spaceborne Synthetic Aperture Radar (SAR) platforms are often used to create these DTMs as they provide an effective tool to collect surface elevations across a wide extent. However, SAR-derived digital elevation models (DEMs) cannot accurately measure ground elevations in the presence of forests. This work demonstrates an approach to estimate terrain elevations from 12 m TanDEM-X by using a convolutional neural network (CNN) trained with ground elevations from ICESat-2 – a spaceborne laser altimeter. This approach demonstrated the ability to estimate terrain elevations from TanDEM-X DEMs for the greater North Carolina area. The CNN estimated terrain saw an improvement in RMSE from 11.28 m to 4.42 m within the entire area of interest, and a focused improvement in RMSE from 12.78 m to 4.95 m in forested areas when compared to ICESat-2. The CNN model outperformed linear, random forest, and gradient boosted regression models using comparable model inputs. This work combines 12-m TanDEM-X data with ICESat-2 profiles, resulting in a new DTM product with accuracy approaching that of reference elevations obtained from satellite laser altimetry in the southeastern United States. • Investigates use of CNN to create DTM with TanDEM-X and ICESat-2 in North Carolina. • CNN outperformed linear, random forest, and gradient boosted regression models tested. • CNN reduced RMSE from 11.28 m to 4.42 m with convolutional neural network. • Establishes possible framework for creating global DTMs. [ABSTRACT FROM AUTHOR]

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g. , AMSR-E), current (e.g. , AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers. [Display omitted] • A multi-sensor semi-physical algorithm is applied to retrieve vegetation moisture. • The water component in vegetation optical depth is disentangled from dry biomass. • Vegetation moisture retrievals at X- and Ku-bands capture the in situ dynamics. • L-band has poorer performance due to lack of in situ samples in wood canopy layers. [ABSTRACT FROM AUTHOR]

• Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. • Noncontact radar-based discharge estimates can be as accurate as in-situ, conventional streamgauges. • There was a notable phase sequencing between the surface velocity and stage, indicative of unsteady flows. • Real-time alerting capabilities of an impending flash flood are demonstrated in a vulnerable, ungauged basin. Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams. [ABSTRACT FROM AUTHOR]

• Mesic vegetation is a necessary indicator of water availability in semi-arid regions. • Sentinel data improve detection of water at meaningful temporal and spatial scales. • We capture intra-seasonal dynamics that existing mesic vegetation products do not. • We present a replicable workflow with open data on an open-access platform. Semi-arid and arid systems cover one third of the earth's land surface, and are becoming increasingly drier, but existing datasets do not capture all of the types of water resources that sustain these systems. In semi-arid environments, small surface water bodies and areas of mesic vegetation (wetlands, wet meadows, riparian zones) function as critical water resources. However, the most commonly-used maps of water resources are derived from the Landsat time series or single date aerial photographs, and are too coarse either spatially or temporally to effectively monitor water resource dynamics. In this study, we produced a Sentinel Fusion (SF) water resources product for a semi-arid mountainous region of the western United States, which includes monthly maps of both a) surface water and b) mesic vegetation at 10 m spatial resolution using freely available Earth observation data on an open access platform. We applied random forest classifiers to optical data from the Sentinel-2 time series, synthetic aperture radar (SAR) data from the Sentinel-1 time series, and topographic variables. We compared our SF product with three commonly used and publicly available datasets in the western U.S. We found that our surface water class contained fewer omission errors than a leading global surface water product in (94 % producer's accuracy (PA) vs 84 %) and comparable user's accuracy (UA) (91 % vs 97 %) with commission errors occurring largely in mixed water pixels. Our mesic vegetation class had up to 43 % higher PAs compared to the National Wetlands Inventory (NWI) estimates and up to 78 % higher UAs over the Sage Grouse Initiative mesic resources maps during the most critical part of the water year. We found that while inclusion of SAR data from the C-band Sentinel-1 sensor consistently improved estimates of water resources in each of the last four months of the 2021 water year when compared to optical-only + topographic variables, only in September did those improvements lie outside of the 95 % confidence interval. With nine times finer spatial resolution and more frequent image collection, our SF maps characterize intra-annual dynamics of smaller water bodies (<30 m wide) and mesic vegetation integral to ecosystem functioning in semi-arid systems compared to leading Landsat-derived products. Further, our workflow is easily reproducible using freely available data on an open access platform, and can be adopted to help guide land use decisions related to water resources by farmers, ranchers, and conservationists in semi-arid environments. [ABSTRACT FROM AUTHOR]

The northeastern U.S. experienced land use/land cover (LULC) changes over the last 300 years, including widespread deforestation during the 17th and 19th centuries, followed by reforestation in the 20th century. Reconstructing the extent of historic land use is crucial to understanding longer term impacts of anthropogenic activity. However, LULC datasets around the globe are typically only available for the last 50 years. This study aims to reconstruct the spatial distribution of cleared land with area estimation at two time periods based on stone walls using light detection and ranging (lidar), historic aerial photography, and deep learning. The two time periods are (1) the peak of agricultural activity period (mid-19th century) and (2) early-20th century. We utilized deep learning methods for (1) mapping the distribution of stone walls from lidar derivatives and 1934 aerial photography and (2) mapping historic land use from pre-satellite era aerial photography. The results of area estimation demonstrate strong consistency with the area indicated in the census record for both time periods. Notably, the incorporation of stone walls from 1934 orthomosaic plays a key role in significantly increasing the agreement of cleared area in the mid-19th century, as it compensated for the absence of walls that have been removed. By adding new historical time intervals of cleared land with our method, it allows for investigating the duration of land used for agricultural activities and the spatial pattern of LULC transition over the last 300 years. • Spatial reconstruction of historic land use in the northeastern U.S. using deep learning. • Filling the gap of pre-satellite period using anthropogenic landforms through lidar. • Potential to monitoring of long-term land use change. [ABSTRACT FROM AUTHOR]

Seasonal snow-derived water is a critical component of the water supply in the mountains and downstream regions, and the accurate characterization of available water in the form of snow-water-equivalent (SWE), peak SWE, and snowmelt onset are essential inputs for water management efforts. Arising from recent advancements in artificial intelligence (AI) and machine learning (ML), we introduce a large-scale ML SWE model leveraging publicly available data sources and open-source software. The model demonstrates the application of a limited feature space in a relatively simple ML architecture without the need for process-based formulations to effectively estimate spatially continuous SWE at a daily temporal resolution. Beginning with in situ SWE measurements (i.e., SNOTEL), lidar-derived terrain features, and temporal variables, we employ localized feature engineering and optimization via gradient-boosting decision trees to identify regionally unique drivers of snowpack dynamics and use the optimal features to train regionally independent artificial neural networks to estimate regional SWE at a 1 km spatial resolution. The model results yield respectable skill in reconstructed 1 km gridded SWE magnitudes in a hindcast simulation of the 2019 water year that is independent of the training and testing data. Comparing model estimates to over 6200 observations, the model demonstrates a weighted RMSE of 15.4 cm, Kling-Gupta Efficiency metric of 0.86, and a percent bias of 0.71% across 23 snow-influenced regions in the western U.S. The model simulation produces peak SWE estimates within 10 cm for twenty of the twenty-three regions, demonstrating capability in effectively capturing regional snow accumulation processes. The demonstration of low-error ML workflows capable of providing near-real-time, spatially continuous SWE estimates at a high spatial resolution provides proof-of-concept and a foundation to effectively update snow state variables that drive water supply forecasts in snow-dominated regions. • A new Machine Learning-based SWE estimation model for the Western United States. • Utilization of open-source tools and up-to-date in-situ observations to estimate SWE. • Reconstructed SWE magnitudes exhibit respectable skill in a hindcast simulation. • Output may serve as a supplemental state-of-snowpack input to hydrologic models. [ABSTRACT FROM AUTHOR]

Subsurface drainage pipes are commonly used in the Midwestern United States to reduce excess soil moisture and improve crop yields. However, they are the considerable source of nonpoint pollution due to nutrient losses. Detecting the locations of drainage pipes is crucial for water quality management, but information about drainage pipe maps is often privately owned and unavailable. In this study, we propose an ensemble learning approach that uses eight fully convolutional networks (FCNs), including well-known architectures such as Unet, DenseNet, and Wnet, to detect subsurface drainage pipe locations from aerial images. Each FCN model is trained and validated using an aerial image dataset, taking a 256 × 256 × 3 pixel aerial image patch as input and outputting a pixel-wise drainage pipe detection map. Weighted averaging is then applied to the individual FCN outputs to create a unified drain pipe detection map. The performance of the proposed approach is evaluated using large-scale aerial image data that has not been used during the training and validation phases. The results demonstrate that the proposed approach provides accurate and more robust drain pipe detection over the case of using an individual FCN model. We further explore the effects of image resolution for the effective use of the proposed drain pipe detection approach. • Our ensemble learning method detects subsurface pipes accurately and robustly. • The ensemble model outperforms image processing and single machine learning models. • An optimal resolution can enhance the performance of the ensemble learning model. [ABSTRACT FROM AUTHOR]

Smoother pavement provides a comfortable, safe and economical riding experience to the public and prolongs the life of the pavement with low maintenance costs. Hence, obtaining a smooth pavement is considered a necessity by many transportation agencies around the world. In order to achieve a smooth pavement, smoothness specifications are developed. Smoothness specifications mainly consist of four parts: smoothness measuring equipment, smoothness indices and analysis method, acceptance limits, and incentive/disincentive payment schemes. This paper summarizes smoothness specifications in the United States and various countries around the world and provide an overview on the comparison of smoothness measurement. The smoothness measuring devices, indices, limits and payment schemes vary from country-to-country International Roughness Index is the most used smoothness index in the US and around the world. Inertial profilers are used to measure the pavement profile for smoothness. [ABSTRACT FROM AUTHOR]

The Energy Act of 2020 authorizes $1 billion over five years from 2021 to 2025 to support energy storage development in the United States. In addition, the Federal Energy Regulatory Commission (FERC) Orders 841 and 2222 opened the wholesale energy markets for distributed energy resources, including energy storage. The statute and orders pave the way for novel energy storage technologies to participate in electricity markets as the qualifying facilities, thereby expanding opportunities for energy storage development. However, the existing policy, legal, and regulatory regime, including these much-welcomed newcomers, fails to recognize and support the entire spectrum of benefits that some forms of energy storage create. In this paper, we focus on Pumped Underground Storage Hydro (PUSH), a variant of pumped hydro storage (PHS), which currently provides over 90% of the world's energy storage capacity. PUSH operates with the same principle as PHS; however, it is an entirely underground variant of PHS. In addition to becoming competitive in the wholesale electricity market, PUSH facilities can be developed as community infrastructure in the postindustrial landscape, particularly in abandoned underground mines. Given federal energy law and policy development, this paper identifies how communities with abandoned mines, technically feasible for PUSH facilities and operating as municipal-owned utilities or cooperatives, can participate and take advantage of federal legislation. We further look into the implications through the lens of technical, economic, and social aspects of energy justice. We consider energy justice as a conceptual framework that seeks to explain the occurrence of injustice within energy system decisions and outcomes and how policymakers can respond to these injustices. We use it as a conceptual tool for understanding policy formulation and detailing the energy system's missing ethical and justice dimension. We argue that as a technically mature technology, PUSH facilities can act as a potential means to speed up the energy transition in the United States. The federal and state law along with utility market structures are vital in shaping the potential opportunities and barriers for energy storage facilities like PUSH. We show that although it supports PUSH development, there are gaps in the current market structure, specifically in the regulatory framework, when seen through the lens of justice and valuation of just energy services. These gaps limit the realization of utility-scale energy storage technology's potential to fully contribute to a decarbonized energy system that is more resilient and more just than the incumbent system. [ABSTRACT FROM AUTHOR]