This study integrates high resolution remote sensing data from NASA's Delta-X mission with a process based hydrodynamic and sediment transport model to improve predictions of water levels and suspended sediment dynamics in the Mississippi River Delta, in coastal Louisiana, USA, focusing on Atchafalaya and Terrebonne basins. A two dimensional Delft3D Flexible Mesh model was implemented using spatially variable bottom friction maps derived from optical imagery (AVIRIS-NG and Sentinel-2) to represent vegetation heterogeneity. Hydrodynamic calibration leveraged airborne interferometric radar measurements (Airborne Surface Water and Ocean Topography (AirSWOT) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR)) of water surface elevations and water level changes, while validation used in situ tide gauge records from spring and fall 2021. Model comparison with AirSWOT measurements in channels showed that spatially explicit roughness parameterizations substantially improved performance. Results were notably better for the spring acquisitions (root mean square error = 0.09 m; R 2 = 0.82), likely due to higher wind speeds and steeper water surface slopes that increased surface roughness and improved radar retrieval performance. UAVSAR provided additional spatial constraints on transient water level changes across wetlands at similar to 30-min intervals, further informing roughness calibration. Model deviation from UAVSAR were typically within 4 cm, although performance degraded in forested and densely vegetated areas due to reduced radar coherence. Validation with in situ tide gauges confirmed model performance for tidal and subtidal variability. Sediment transport calibration using AVIRIS-NG total suspended solids allowed refinement of settling velocity and critical shear stress. Overall, the integration of remote sensing data into model calibration and parameterization led to measurable improvements in hydrodynamic and sediment predictions.

Coastal river deltas are highly dynamic regions with hydrological processes that vary on hourly, daily, and seasonal timescales. Soil formation in deltas relies on the balance between mineral sediment deposition, erosion, and organic matter production, which are intricately controlled by vegetation and hydrodynamic conditions. The spatial complexity and rapid variations in flow, particularly due to tides, present a major challenge to spaceborne remote sensing achieving the required spatial resolution and temporal sampling. Here, we present an airborne remote sensing and in situ framework that measures parameters that are critical to calibrate and validate hydrodynamic, sediment transport, morphodynamic, and ecogeomorphic models. We discuss the measurements and models within the context of the NASA Earth Venture-Suborbital Delta-X mission, which implemented the framework in two deltaic regions of the Mississippi River Delta with contrasting hydrological regimes, namely the Atchafalaya (i.e., active, river-dominated) and Terrebonne (inactive, river-abandoned) basins that are undergoing land gain and land loss, respectively. The Delta-X framework uses two airborne radar instruments to monitor hydrodynamic processes, measuring water surface level and slope within channels, and tide-induced water level change within wetlands. In addition, an airborne imaging spectrometer provides estimates of suspended sediment concentrations in open water as well as vegetation type and aboveground biomass. We also discuss how the data are used to calibrate and validate the models that estimate sediment deposition and organic soil production, which build land to offset subsidence and sea level rise.

Interferometric Synthetic Aperture Radar (InSAR) is a powerful tool for monitoring surface deformation with high precision. However, low Signal-to-Noise Ratio (SNR) conditions, common in regions with low backscatter, can degrade phase coherence and compromise displacement accuracy. In this study, we quantify the impact of low-SNR conditions on InSAR-derived displacement using L-band UAVSAR data collected over the San Andreas Fault and Greenland ice sheet. We simulate low-SNR conditions by degrading the Noise-Equivalent Sigma Zero (NESZ) to -15 dB and assess the resulting effects on interferometric coherence, phase unwrapping, and time series inversion. The displacement accuracy of 4mm in single interferogram can be achieved by taking looks for the signal decorrelation of 0.6 and SNR between -9dB to -10dB. Our findings indicate that even under low-SNR conditions, a velocity precision of 0.5 cm/yr can be achieved in comparison to high-SNR conditions. By applying multilooking with an 8x8 window, we significantly improve coherence and eliminate this bias, demonstrating that low-SNR systems can achieve comparable precision to high-SNR systems at the expense of spatial resolution. These results have important implications for the design of future cost-effective SAR missions, such as Surface Deformation and Change (SDC), and the optimization of InSAR processing techniques in challenging environments.

The Earth's rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Here, we use monthly simulations of river stores and fluxes to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes. By filtering reaches by estimated river widths, we quantify the relative contribution of global river reaches by size and estimate that over 17% of global discharge to ocean and nearly 9% of the world's river storage lies within rivers smaller than 100 m-hence revealing both strengths and limitations of current observational capabilities.

The forthcoming Surface Water and Ocean Topography (SWOT) satellite and AirSWOT airborne instrument are the first imaging radar-altimeters designed with near-nadir, 35.75 GHz Ka-band InSAR for mapping terrestrial water storage variability. Remotely sensed surface water extents are crucial for assessing such variability, but are confounded by emergent and inundated vegetation along shorelines. However, because SWOT-like measurements are novel, there remains some uncertainty in the ability to detect certain land and water classes. We study the likelihood of misclassification between 15 land cover types and develop the Ka-band Phenomenology Scattering (KaPS) scattering model to simulate changes to radar backscatter as a result of changing surface water fraction and roughness. Using a separability metric, we find that water is five times more distinct compared with dry land classes, but has the potential to be confused with littoral zone and wet soil cover types. The KaPS scattering model simulates AirSWOT backscatter for incidence angles 1-27°, identifying the conditions under which open water is likely to be confused with littoral zone and wet soil cover types. A comparison of KaPS simulated backscatter with AirSWOT observed backscatter shows good overall agreement across the 15 classes (median r2=0.76). KaPS characterization of the sensitivity of near-nadir, Ka-band SAR to small changes in both wet area fraction and surface roughness enables more nuanced classification of inundation area. These results provide additional confidence in the ability of SWOT to classify water inundation extent, and open the door for novel hydrological and ecological applications of future Ka-band SAR missions.

Coastal marsh survival relies on the ability to increase elevation and offset sea level rise. It is therefore important to realistically model sediment fluxes between marshes, tidal channels, and bays as sediment availability controls accretion. Traditionally, numerical models have been calibrated and validated using in situ measurements at a few locations within the domain of interest. These datasets typically provide temporal information but lack spatial variability. This paper explores the potential of coupling numerical models with high-resolution remote sensing imagery. Products from three sensors from the NASA Delta-X airborne mission are used. Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) provides vertical water level change on the marshland and was used to adjust the bathymetry and calibrate water fluxes over the marsh. AirSWOT yields water surface elevation within bays, lakes, and channels, and was used to calibrate the Chezy bottom friction coefficient. Finally, imagery from AVIRIS-NG provides maps of total suspended solids (TSS) concentration that were used to calibrate sediment parameters of settling velocity and critical shear stress for erosion. Three numerical models were developed at different locations along coastal Louisiana using Delft3D. The coupling enabled a spatial evaluation of model performance that was not possible using simple point measurements. Overall, the study shows that calibration of numerical models and their general performance will greatly benefit from remote sensing.

The NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument has performed tomographic SAR experiments over a number of study areas, including Rabi Forest in Gabon in 2016 and Sierra National Forest in California, USA in 2021. Tomographic SAR, or TomoSAR, is a technique enabling 3-D radar imaging with diverse applications including mapping of vegetation structure. Convolutional neural networks (CNNs) have shown widespread potential for many image processing and computer vision tasks such as image segmentation, classification, and object recognition. By using 3-D CNNs rather than 2-D CNNs, the filters can be applied to all three dimensions of a forest volume imaged by TomoSAR. We have trained 3-D CNN-based deep learning models to estimate canopy height and canopy cover from fully polarimetric UAVSAR TomoSAR images using lidar data as training and validation. When applied to canopy height estimation in the Rabi Forest study area, a trained network had root mean square error (RMSE) of 3.6 m (11%) compared to the validation dataset. For canopy cover estimation in the Sierra National Forest study area, the RMSE was 12%. Further work can be done to optimize the network architecture, improve the output spatial resolution, and to check if these methods can be applied to other study areas or to other vegetation structure parameters such as above-ground biomass. The results show the strong potential of 3-D CNNs for mapping wall-to-wall vegetation structure from tomographic SAR imagery using lidar training data.

This study evaluates global radar-derived digital elevation models (DEMs), namely the Shuttle Radar Topography Mission (SRTM), NASADEM and GLO-30 DEMs. We evaluate their accuracy over bare-earth terrain and characterize elevation biases induced by forests using global Lidar measurements from the Ice, Cloud, and Land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS), the Global Ecosystem Dynamics Investigation (GEDI) and the ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) instruments collected on locally flat terrain. Our analysis is based on error statistics calculated for each 1 degrees x1 degrees $1{}<^>{\circ}\times 1{}<^>{\circ}$ DEM tile, which are then summarized as global error percentiles, providing a regional characterization of DEM quality. We find NASADEM to be a significant improvement upon the SRTM V3. Over bare ground areas, the mean elevation bias and root mean square error (RMSE) improved from 0.68 to 2.50 m respectively to 0.00 and 1.5 m as compared to ICESat/GLAS. GLO-30 is more accurate with bare ground elevation bias and RMSE were below 0.05 and 0.55 m. Similar improvements were observed when compared to GEDI and ICESat-2 measurements. The DEM biases associated with the presence of vegetation vary linearly with canopy height, and more closely follow the 50th $5{0}<^>{th}$ percentile of Lidar Relative Height (RH50). Other factors such as canopy density, radar frequency and Lidar technology also contribute to observed elevation biases. This global analysis highlights the potential of various technologies for mapping of Earth's topography, and the need for more advanced remote sensing observations that can resolve vegetation structure and sub-canopy ground elevation.

In this work, we demonstrate how harmonized optical and SAR satellite imagery can be utilized for robust identification of open water surfaces at a global scale. We train an image segmentation architecture based convolutional neural network (CNN) to extract the most salient features from the input data and generate a per-pixel water/not-water classification. We find that combining optical and radar imagery helps reduce false positive and false negative inferences, illustrating the effectiveness of this harmonization. The resulting model is able to classify water surfaces at the resolution of the SAR sensor (12.5 meters) with a validation set precision and recall of 0.74 and 0.81 respectively. We also demonstrate that the trained model is capable of generating inferences beyond the geographic bounds of the training data.

The GLISTIN-A instrument was flown on the NASA/AFRC C-20 (Gulfstream III) in December 2022 to observe the Mauna Loa eruption event in Hawaii, USA. As the volcano was actively erupting, several of the swaths were repeated on both the same and successive days to observe changes in the lava flow thicknesses and lava fronts. These repeated swaths provided a unique opportunity to re-address our understanding of the calibration of GLISTIN-A and its ability to precisely and accurately compute the topography of significantly sloped terrains.After calibration with localized troposphere estimates from nearby GNSS sites, adjustments to the roll from post-processed Applanix data, and applying an empirical temperature model, the average slope difference is 4.3 millidegrees and the average RMS difference is 1.7 meters between overlapping swaths. The slope and RMS differences were computed for look angles from 11 to 52 degrees.

AirSWOT is an airborne Ka-band synthetic aperture radar, capable of mapping water surface elevation (WSE) and water surface slope (WSS) using single-pass interferometry. AirSWOT participated in the NASA EVS-3 Delta-X campaign in 2021, which combined remote sensing from multiple instruments with an extensive coincident field data collection in the Mississippi River Delta, Louisiana, USA. As part of Delta-X, AirSWOT flew a greater number of flight lines than in previous AirSWOT campaigns, collecting a significant volume of data which can provide insight into the dynamics and quantity of water in the Atchafalaya and Terrebonne basins of the Mississippi River Delta. AirSWOT data has been processed into publicly available data products at a number of processing levels, depending on user needs and application, including a new Level-3 water surface product developed specifically for Delta-X. The Level-3 water surface product uses water masking and spatial averaging to produce a science-ready point data product, using the Level-2 GeoTIFF raster products as input. The Level-3 data allows profiles of WSE and WSS within designated channels to be easily calculated. AirSWOT estimates of WSE from Delta-X have been compared to in situ water level data with root mean square error (RMSE) of 9 cm, excluding data from two flights in September, 2021 which were adversely affected by poor weather conditions that affected the instrument hardware. Including all data, the RMSE increases to 12 cm. We have also used AirSWOT to help estimate the vertical datum for water level gauges without accurate vertical reference information. AirSWOT is capable of mapping WSE and WSS at high resolution in spatially complex coastal environments, making it a valuable instrument for studying these regions.

In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30,000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1-hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results

AirSWOT is an experimental airborne Ka-band radar interferometer developed by NASA-JPL as a validation instrument for the forthcoming NASA Surface Water and Ocean Topography (SWOT) satellite mission. In 2017, AirSWOT was deployed as part of the NASA Arctic Boreal Vulnerability Experiment (ABoVE) to map surface water elevations across Alaska and western Canada. The result is the most extensive known collection of near-nadir airborne Ka-band interferometric synthetic aperture radar (InSAR) data and derivative high-resolution (3.6 m pixel) digital elevation models to produce water surface elevation (WSE) maps. This research provides a synoptic assessment of the 2017 AirSWOT ABoVE dataset to quantify regional WSE errors relative to coincidentin situfield surveys and LiDAR data acquired from the NASA Land, Vegetation, and Ice Sensor (LVIS) airborne platform. Results show that AirSWOT WSE data can penetrate cloud cover and have nearly twice the swath-width of LVIS as flown for ABoVE (3.2 km vs. 1.8 km nominal swath-width). Despite noise and biases, spatially averaged AirSWOT WSEs can be used to estimate sub-seasonal hydrologic variability, as confirmed with field GPS surveys andin situpressure transducers. This analysis informs AirSWOT ABoVE data users of known sources of measurement error in the WSEs as influenced by radar parameters including incidence angle, magnitude, coherence, and elevation uncertainty. The analysis also provides recommended best practices for extracting information from the dataset by using filters for these four parameters. Improvements to data handing would significantly increase the accuracy and spatial coverage of future AirSWOT WSE data collections, aiding scientific surface water studies, and improving the platform's capability as an airborne validation instrument for SWOT.

Coastal wetlands are productive ecosystems driven by highly dynamic hydrological processes such as tides and river discharge, which operate at daily to seasonal timescales, respectively. The scientific community has been calling for landscape-scale measurements of hydrological variables that could help understand the flow of water and transport of sediment across coastal wetlands. While in situ water level gauge data have enabled significant advances, they are limited in coverage and largely unavailable in many parts of the world. In preparation for the NISAR mission, we investigate the use of spaceborne Interferometric Synthetic Aperture Radar (InSAR) observations of phase and coherence at L-band for landscape-scale monitoring of water level change and vegetation cover in coastal wetlands across seasons. We use L-band SAR images acquired by ALOS/PALSAR from 2007 to 2011 to study the impact of seasonal changes in vegetation cover on InSAR sensitivity to water level change in the wetlands of the Atchafalaya basin located in coastal Louisiana, USA. Seasonal variations are observed in the interferometric coherence ( γ ) time-series over wetlands, with higher coherence during the winter and lower coherence during the summer. We show with InSAR time-series that coherence is inversely correlated with Normalized Difference Vegetation Index (NDVI). Our analysis of polarimetric scattering mechanisms demonstrates that double-bounce is the dominant mechanism in swamps while its weakness in marshes hinders estimation of water level changes. In swamps, water level change maps derived from InSAR are highly correlated (r2 = 0.83) with in situ data from the Coastwide Reference Monitoring System (CRMS). From October to December, we observed that the water level may be below wetland elevation and thus not inundating wetlands significantly. Our analysis shows that water level can only be retrieved when both images used for InSAR are acquired when wetlands are inundated. The L-band derived-maps of water level change show large scale gradients originating from the Gulf Intracoastal Waterway rather than the main delta trunk channel, confirming its significant role as a source of hydrologic connectivity across these coastal wetlands. These results indicate that NISAR, with its InSAR observations every 12 days, will provide the measurements necessary to reveal large scale hydrodynamic processes that occur in swamps across seasons.

We map mangrove extents in Pongara National Park, Gabon using the Freeman-Durden Decomposition and InSAR Coherence derived from ALOS-2 imagery. Specifically, we obtain a land cover map derived from both this polarimetric decomposition and a 14-day repeat-pass coherence. Our classification model and results are highly interpretable based on a depth 2 decision tree. We further illustrate the correlation between InSAR coherence and height obtaining rough mangrove height estimates from TanDEM-X data. From our results, we observe that repeat-pass interferometric coherence provides invaluable information about mangrove extents and coastal forests. The clear identification of mangrove extents presents a significant opportunity for NISAR, which will provide 12-day repeat pass images over coastal areas globally.

We have mapped flooded areas in data collected by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) using two convolutional neural network (CNN) image classifier architectures: U-Net and SegNet. Our study area was a region around Houston, TX, USA affected by widespread flooding in 2017 due to Hurricane Harvey. To train and test the classifiers, we manually labelled over 10000 image segments in two flight lines. Both U-Net and SegNet yielded higher accuracy than a previous non-machine learning classifier we used as a baseline. U-Net had slightly higher accuracy than SegNet. The classifiers performed better in areas with more homogeneous land cover. To independently validate the classifier accuracy we used NOAA aerial imagery, with overall accuracy around 80%. Future work includes assessing the classifier robustness in other study areas, assessing the classifier dependence on UAVSAR incidence angle, particularly for open water and bare ground, and collecting more training data, particularly in urban areas. This study demonstrates the potential of CNN image classifiers for mapping flooded areas in airborne polarimetric SAR imagery, and for land cover classification of polarimetric SAR imagery more generally.