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.

Terrestrial sources of carbon and nutrients drive biogeochemical cycles in coastal regions and in the global ocean. Quantifying their impact on the spatiotemporal variability of the ocean carbon cycle is pivotal to understanding the distinctive characteristics of ocean basins dominated by riverine inflow. ECCO-Darwin is a data-constrained, global-ocean biogeochemistry model that has heretofore lacked lateral inputs of carbon and nutrients. The objective of this study is to add this new capability to ECCO-Darwin and to carry out a suite of sensitivity experiments in order to quantify the impact of these lateral fluxes on coastal- and open-ocean biogeochemistry. In this work, we use an optimized version of the data-assimilative global-ocean biogeochemistry ECCO-Darwin model to perform a sensitivity analysis of the ocean to lateral inputs of carbon and nutrients. We generate riverine inputs by combining daily point-source freshwater discharge from JRA55-do with the Global NEWS 2 watershed model, accounting for lateral inputs from 5171 watersheds worldwide. The addition of riverine inputs drives a small CO2 outgassing (+0.02 Pg C yr-1) due to compensating processes at regional scales. In basins dominated by carbon runoff, such as the Tropical Atlantic and Arctic Oceans, the addition of riverine inputs increases CO2 outgassing (+13 % and +9 %, respectively). In contrast, runoff in nutrient-dominated Southeast Asia leads to increased CO2 uptake (+9 %). This new riverine biogeochemical input capability will enable future ECCO-Darwin solutions to better capture key processes that occur along coastal margins in global oceans.

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.

Understanding the relationship between vegetation productivity and soil accretion is critical for assessing the resilience of deltaic wetlands to relative sea-level rise. This study integrates airborne imaging spectroscopy from NASA's AVIRIS-NG sensor with field measurements from the Delta-X campaign to quantify herbaceous vegetation contributions to vertical accretion processes in the Mississippi River Delta's coastal wetlands. We developed imaging spectroscopy-based products for live above- and belowground carbon (AGC, BGC), and aboveground necromass (AGN; or non-photosynthetic vegetation), the latter being derived using a novel combination of spectral unmixing and Random Forest regression. These vegetation maps were evaluated alongside in situ soil accretion measurements-including surface sediment accretion, soil bulk density, organic matter content, and soil organic carbon density-derived from feldspar marker horizons. These data were sampled concurrently with airborne campaigns conducted in April and August 2021 to coincide with the early- and peak-biomass season conditions. While vegetation metrics explained a limited fraction of variability in total vertical accretion, AGC, AGN, and BGC together accounted for a substantial portion of the variance in soil bulk density and organic carbon density, indicating that vegetation-driven organic matter inputs exert a stronger control on soil mass and carbon storage than on short-term elevation gain. These results demonstrate that airborne imaging spectroscopy can resolve distinct vegetation pools that differentially influence wetland soil properties, providing new insight into the coupled vegetation-soil processes underpinning blue carbon dynamics. This highlights imaging spectroscopy's value for advancing landscape-scale assessments of wetland resilience and informing management strategies in sediment-deprived coastal basins.

Coastal wetlands are highly vulnerable to climate change and sea level rise. Hydroperiod, defined as the duration of flooding, is a key indicator of salt marsh resilience, influencing vegetation zonation and health, sediment deposition, and overall ecosystem stability. This study uses Synthetic Aperture Radar (SAR) time series analysis to map hydroperiod in the salt marshes of Plum Island Sound, Massachusetts, USA. We integrate in situ water level measurements to overcome the limited temporal sampling of SAR observations. SAR-derived hydroperiod was evaluated against a 'bathtub' model that simulates flooding by filling a LiDAR-derived digital terrain model (DTM) including bathymetry and topography. The method shows strong agreement in hydroperiod estimates (R2 = 0.92, RMSE approximate to 12.3%). These findings demonstrate the capability of SAR time series to provide high-resolution, spatially extensive estimates of hydroperiod. We anticipate that this method will enable large-scale monitoring of seasonal and interannual variations in saltmarsh hydrology, supporting assessments of wetland vulnerability and resilience in the face of accelerating sea-level rise.

Hydrodynamic models in coastal and estuarine systems are typically constrained by sparse bathymetry, boundary, and validation data, especially in regions where field campaigns are costly or impractical. Here we develop and test a fully satellite-driven framework for hydrodynamic modeling in South Africa’s Langebaan Lagoon without using any local in situ measurements. Bathymetry is derived by training multispectral Sentinel-2 reflectance against ICESat-2 ATL24 photon-derived depths using an XGBoost model optimized with Bayesian search. The final satellite derived bathymetry reproduces independent ATL24 points with RMSE = 0.45 m and R2 = 0.97. This bathymetry was used in a depth-averaged Delft3D Flexible Mesh model driven at the open boundary by TPXO tidal harmonics and by ERA5 winds. We validate modeled water surface elevation against 16 SWOT low-rate (250 m, unsmoothed) passes in 2023. SWOT–model comparisons yield an overall RMSE of 0.11 m and R2 = 0.61, with typical point differences <0.10 m (∼5% of the 2 m tidal range), and showed consistent spatial gradients in water level from the offshore boundary, through Saldanha Bay, and into the lagoon. At the offshore boundary, TPXO and SWOT sea surface heights agree closely (R2 = 0.86). A ∼26 min phase lag, determined using a lag-correlation analysis, reduces the TPXO–SWOT RMSE from 0.18 m to 0.11 m, indicating that phase differences explain some of the mismatch, with remaining differences likely linked to non-tidal signals. Our results demonstrate that combining passive optical, photon-counting LiDAR, radar interferometry, and global tidal/atmospheric models enables robust, transferrable hydrodynamic modeling in data-scarce coastal systems, offering a cost-effective pathway for monitoring.

Deltas and estuaries represent some of the most dynamic and complex transition zones on Earth, serving as vital ecological hubs that are increasingly vulnerable to the dual pressures of accelerated sea-level rise and anthropogenic modification. The NASA-CNES Surface Water and Ocean Topography (SWOT) mission provides a transformative capability to observe these environments by providing high spatial-resolution, wide-swath measurements of Water Surface Elevation (WSE). Unlike traditional nadir altimetry, SWOT’s interferometric SAR technology allows for the capture of two-dimensional water level gradients across scales of estuarine reaches. This study explores the utility of SWOT time-series measurements in resolving complex tidal components within intricate coastal geometries and assesses how these physical measurements can be translated into critical parameters for evaluating the health and long-term vulnerability of coastal ecosystems. In this presentation, we focus on hydroperiod and salinity.By performing harmonic analysis on SWOT-derived WSE time-series, we demonstrate the ability to effectively resolve previously unknown but major tidal constituents in estuarine channels. These tidal components allow for the precise derivation of the hydroperiod—the frequency, duration, and depth of tidal inundation. Hydroperiod is the primary environmental driver of vegetation zonation, nutrient cycling, and carbon sequestration potential in mangroves and saltmarshes. Consequently, accurate SWOT-based mapping of inundation patterns offers a new lens through which to view coastal resilience and the potential for "blue carbon" sequestration. Another driver of ecosystem gradation and vulnerability is salinity, which distribution is a non-linear product of the interaction between tidal forcing, freshwater discharge, and complex bathymetry, making it far more difficult to resolve than surface height alone. Indeed, it requires numerical modeling, which presents significant technical hurdles.We tested the implementation of numerical hydrodynamic models in several distinct geographical settings to evaluate the limits of SWOT-informed simulations. These sites included the high-discharge, tide-dominated Guayas Estuary in Ecuador, the marine-dominated and ecologically sensitive Langebaan Lagoon in South Africa, and the complex, Knysna Estuary, also in South Africa. Our results indicate that while SWOT provides unprecedented boundary conditions for water levels, the ability to simulate salinity and transport remains heavily constrained by a persistent lack of high-resolution bathymetry and other in situ measurements. To improve these simulations, there is an urgent need for better bathymetric data derived from bathymetric lidar for shallow sub-tidal zones and in-situ sonar transects for deeper primary channels. Secondly, the common lack of in situ data along the salinity gradient inhibit robust assessment of the methods. This work highlights the necessity of a synergetic approach, combining SWOT’s wide-swath observations with targeted bathymetric mapping and in situ data assimilation to provide the predictive accuracy required for effective coastal management and ecosystem conservation in a rapidly changing climate.

Tropical forests are of great ecological and climatological importance. Although they only cover about 6% of Earth’s surface, they are home to approx. 50% of the world’s animal and plant species. Their trees store 50% more carbon than trees outside the tropics. At the same time, they are one of the most endangered ecosystems on Earth: about 6 million of hectares per year are felled for timber or cleared for farming. Compared to the other components of the carbon cycle (i.e. the ocean as a sink and the burning of fossil fuels as a source), the uncertainties in the local land carbon stocks and the carbon fluxes are particularly large. This is especially true for tropical forests: more than 98% of the carbon flux generated by changes in land-use may be due to tropical deforestation, which converts carbon stored as biomass into emissions. In this context, the AfriSAR 2015/16 campaign, supported by ESA, was carried out over four forest sites in Gabon by ONERA (July 2015) during the dry season and by DLR (February 2016) during the wet season. From the data collected the innovative techniques applied to estimate forest height and biomass could be improved significantly and are summarized in a special issue ‘Forest Structure Estimation in Remote Sensing’ of IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. The motivation of the AfriSAR campaign was to acquire demonstration data for the soon to be launched ESA BIOMASS mission, that was selected as the 7th Earth Explorer mission in May 2013 in order to meet the pressing need for information on tropical carbon sinks and sources by providing estimates of forest height and biomass. AfriSAR focused on African tropical and savannah forest types (with biomass in the 100-300 t/ha range) and complements previous ESA campaigns over Indonesian and Amazonian forest types in 2004 (INDREX-II) and 2009 (TropiSAR). The present contribution concerns the GABONX campaign, the ESA supported successor to AfriSAR, which took place in May to July 2023. GABONX aims to detect and quantify changes that have occurred since the DLR acquisitions in February 2016. To this end, DLR’s F-SAR sensor acquired interferometric stacks of fully polarimetric L- and P-Band data over the same forest sites in the same flight geometry as in 2016. The results presented give an overview of campaign activities with particular emphasis on the calibration of the SAR instrument as well as the validation of forest parameters derived from polarimetric interferometry. The SAR sensor calibration is based on an innovative approach that leverages state-of-the-art EM simulation to accurately characterize the 5m trihedral reference target deployed for the campaign in Gabon. The validation of derived forest parameters uses lidar measurements obtained in the time frame of the GABONX campaign by NASA’s LVIS sensor. As an outlook, further collaborative calibration and validation activities will hopefully include the cross-calibration of DLR’s F-SAR and NASA’s UAVSAR, which is set to acquire L- and P-Band data over the GABONX sites in 2024.

The exchange of water, sediment, and nutrients in wetlands occurs through a complex network of channels and overbank flow. Although optical sensors can map channels at high resolution, they fail to identify narrow intermittent channels colonized by vegetation. Here we demonstrate an innovative application of rapid-repeat interferometric synthetic aperture radar (InSAR) to study hydrologic connectivity and tidal influences in Louisiana’s coastal wetlands, which can provide valuable insights into water flow dynamics, particularly in vegetation-covered and narrow channels where traditional optical methods struggle. Data used were from the airborne UAVSAR L-band sensor acquired for the Delta-X mission. We applied interferometric techniques to rapid-repeat (~30 min) SAR imagery of the southern Atchafalaya basin acquired during two flights encompassing rising-to-high tides and ebbing-to-low tides. InSAR coherence is used to identify and differentiate permanent open water channels from intermittent channels in which flow occurs underneath the vegetation canopy. The channel networks at rising and ebbing tides show significant differences in the extent of flow, with vegetation-filled small channels more clearly identified at rising-to-high tide. The InSAR phase change is used to identify locations on channel banks where overbank flow occurs, which is a critical component for modeling wetland hydrodynamics. This is the first study to use rapid-repeat InSAR to monitor tidal impacts on water flow dynamics in wetlands. The results show that the InSAR method outperforms traditional optical remote sensing methods in monitoring water flow in vegetation-covered wetlands, providing high-resolution data to support hydrodynamic models and critical support for wetland protection and management.

Synthetic Aperture Radar (SAR) pulses undergo variable propagation delays in the atmosphere due to changes in pressure, temperature, and humidity within the troposphere, causing large error in Interferometric SAR (InSAR) measurements of land surface displacement. Wet troposphere delay, resulting from condensed water and water vapor clouds, can introduce delays of tens of centimeters that significantly impact surface displacement estimates. This study provides unequivocal evidence of the wet troposphere's impact on InSAR phase measurements by examining spatial patterns in NOAA NEXRAD weather radar reflectivity and interferometric phase outliers. We utilize a feature‐comparison approach with reflectivity data from NEXRAD radar stations to identify artifacts from wet tropospheric delays in InSAR phase measurements derived from rapid repeat‐pass data acquired by UAVSAR L‐band SAR. NEXRAD's 5‐min scanning interval, compared to UAVSAR's 30‐min revisit time, enabled detection of phase artifacts caused by fast‐moving and developing clouds. We identify regions in InSAR interferograms with troposphere‐induced phase artifacts by matching features common to InSAR phase outlier masks and NEXRAD high reflectivity masks. Matched results between InSAR phase noise and NEXRAD reflectivity show phase delays of up to 25 radians in L‐band, corresponding to 48 cm of delay. Comparison with tropospheric delays calculated using the Generic Atmospheric Correction Online Service for InSAR (GACOS) showed global weather models lack sufficient spatial and temporal resolution to accurately estimate observed wet troposphere delays. While our study focused on UAVSAR, findings apply to other SAR missions, including L‐band NISAR and ALOS2/4, aiding identification and interpretation of InSAR results affected by tropospheric delays.

Cyclones cause major damage to mangrove ecosystems globally. While this damage is projected to increase as storms intensify with climate change, the consequences of changes in cyclone attributes other than wind speed remain largely unexplored. Here, we show that shifts in cyclone travel speed may also dramatically alter the risks and mechanisms of damage. By developing an interpretable machine learning model trained with all cyclones recorded worldwide from 2001 to 2021, we find that fast-moving cyclones tend to be especially destructive on steeply sloping coasts, exacerbating physical damage, whereas slow-moving cyclones induce predominantly hydrological damage. Between 1981-2000 and 2001-2020, exposure of global mangrove ecosystems to cyclones increased by 13%, accompanied by substantial changes in cyclone travel speeds, with exposure to slow- and fast-moving cyclones doubling in, respectively, the Caribbean and East Asia. Our results highlight opportunities to integrate regional shifts in cyclone attributes under a changing climate into mangrove management strategies.

Mangrove ecosystems in the northern Gulf of America are expanding due to climate change, yet their resilience to extreme events and shifting coastal conditions remains uncertain, particularly regarding the understudied role of mangrove roots in sediment stabilization. In Louisiana and Texas, mangrove stands develop under distinct environmental pressures-Louisiana's river-dominated deltaic systems experience high sediment deposition, organic-rich soils, and subsidence, while Texas' tide-dominated lagoons are shaped by hydrodynamic forces and coarser sediments. This study uses direct shear testing (DST) to evaluate belowground biomass (BGB) and necromass ( BGN) effects on soil strength in Louisiana and Texas mangroves. Results indicate that BGB and BGN are higher in Port Fourchon, LA, than in East End Lagoon, Galveston, TX, with root mass decreasing consistently with depth in both basins. DST results revealed that top samples from both basins and mid samples from Port Fourchon, LA, exhibited strain-hardening behavior, where shear resistance increased due to root reinforcement. This effect was absent in mid samples from Galveston, TX, likely due to lower root mass. In contrast, bottom samples showed strain-softening behavior, characterized by a peak followed by a decline in shear strength, reflecting reduced root reinforcement at greater depths. The strong correlation between shear strength and live root mass confirms that root reinforcement is a key factor in soil stability, though sediment composition also plays a role. These findings enhance our understanding of how mangrove root systems contribute to soil stabilization, providing insights for identifying areas more resilient to sea-level rise and supporting a mechanistic framework for the design of conservation and restoration projects in the northern Gulf of America.

The Panama Bight eco-region along the Pacific coast of central and South America is considered to have one of the best-preserved mangrove ecosystems in the American continent. The regional climate, with rainfall easily reaching 5–8 m every year and weak wind conditions, contribute to the exceptionally tall mangroves along the southern Colombian and northern Ecuadorian Pacific coasts (Nariño Department and Esmeraldas Province areas). Here we evaluate the use of different methods (ground-based measurements, drone imagery and radar data [Shuttle Radar Topography mission-SRTM and TanDEM-X]) to characterize the structure of the tallest of these forests. In November 2019, three mangrove sites with canopy heights between 50 and 60 m, previously identified with SRTM data, were sampled close to the town of Guapi, Colombia. In addition to in situ field measurements of trees, we conducted airborne drone surveys in order to generate georeferenced orthomosaics and digital surface models (DSMs). We found that the extensive mangrove forests in this area of the Colombian Pacific are almost entirely composed of Rhizophora spp. trees. The tallest mangrove tree measured in the three plots was 57 m. With ca. 900 drone photographs, three orthomosaics (2 cm pixel –1 resolution) and digital surface models (3.5 cm pixel –1 ) with average area of 4,0 ha were generated. The field-measured canopy heights were used to validate the drone-derived and radar-derived data, confirming these mangrove forests as the tallest in the Americas. The drone-derived orthomosaics showed significant patches of the Golden Leather Fern, Acrostichum aureum , an opportunistic species that can be associated to mangrove degradation, indicating that the mangrove forests investigated here may be threatened from increased selective logging requiring improvements and effective implementation of the current mangrove management plans in Colombia. The techniques used here are highly complementary and may represent the three tiers for carbon reporting, whereby the drone-derived canopy height maps, calibrated with local in situ measurements, provides cheap but reliable Tier 3 estimates of carbon stocks at the project level.

Coastal wetlands provide critical ecosystem services, including the enhancement of biodiversity, carbon sequestration, and flood protection. Although these ecosystems have been mapped for country-level biodiversity typing, improved extent mapping is necessary to account for estuarine dynamics and improved reporting to the Kunming-Montreal Global Biodiversity Framework (GBF) by 2030. We achieved an overall coastal wetland accuracy of 90.7% (95% confidence interval: 90.2%-91.4%) utilizing a dense time series of very high spatial resolution (3 m) PlanetScope satellite imagery to map coastal wetlands with a combination of Random Forest to develop training data, U-Net convolutional neural networks, and a final decision tree to determine discrete ecosystem extents. Across the 84 mapped estuaries totaling 67,452 ha and 2,135 images, we mapped 9,131.1 +/- 1,596.9 ha (13.5% of total estuarine functional zone extent) of salt marsh & reed beds and 1,718.6 +/- 234.3 ha (2.5%) of Submerged Aquatic Vegetation (SAV). In addition to our earth observation analysis, we calculated tidal amplitudes and water level trends for 20 water level gauges across the region. We found tidal amplitude was a significant driver of salt marsh extent, explaining 33.6% of the variation (F (1,19) = 9.62, p = 0.005). We demonstrate a repeatable methodology for improved mapping of ecosystem zonation and utilize water level data to explore potential drivers of ecosystem distribution. Our method could be incorporated into a robust earth observation approach for reporting progress toward the goals of the/reporting to the GBF and Sustainable Development Goals (SDGs).

While satellite availability has enabled assessments of significant mangrove loss in several regions, such as Southeast Asia and the Caribbean mangrove forests, there are very few examples of mangrove conservation in communities with strong local organizations and well-developed environmental management. In the Gulf of Tribuga in the northern of Colombia's Pacific coast, mangrove cover has remained relatively stable for decades. This is not only due to factors such as low population, lack of infrastructure expansion, and policies such as collective land titling, but also significantly because of the often-overlooked role of ecosystem management by local communities. We investigate the relationship of mangrove changes observed from multisource remote sensing datasets with management practices by local communities in the Gulf of Tribuga. Results using the Landsat time series suggest that mangrove cover has decreased by 12 % between 2000 and 2020 (0.6 % annually). In the case of Sentinel-1, sub-annual mangrove disturbances account for similar to 30ha from 2018 to 2023. Although the management plan has partially contributed to preventing deforestation, local communities have expressed concerns. There is a need for constant monitoring of coastal variation and assessment of future threats associated with planned infrastructure investment, such as road expansion and construction of a port. This study provides a landscape analysis to help understand mangrove forest change and how local perceptions and management are linked to highlight future negative impacts on mangrove forests and communities' well-being.

Awareness of the significant benefits of mangroves to human lives and their role in regulating environmental processes has increased during the recent decades. Yet there remains significant uncertainty about the mangrove change trajectories and the drivers of change at national scales. In Colombia, the absence of historical satellite imagery and persistent cloud cover have impeded the accurate mapping of mangrove extent and change over time. We create a temporally consistent Landsat-derived dataset using the LandTrendr algorithm to track the historical land cover and mangrove conversion from 1984-2020 across Colombia. Over this period, mangrove extent decreased by ~48.000ha (14% of total mangrove area). We find a gradual reduction of mangrove extent along the Pacific coast since 2004, whereas, in the Caribbean, mangrove cover declined around during 1984-1988 and also after 2012. Our time-series analysis matches with drivers of mangrove change at three local sites. For instance, hydroclimatic events, dredging activities, and high sediment loads transported by the rivers have collectively improved mangrove recovery in some sites. In contrast, human activities pressure linked to agricultural expansion and road construction have degraded mangroves. The transition from dense mangrove to other vegetation types is the most significant conversion affecting mangrove cover in Colombia, impacting an area of 38,469 ± 2,829 ha. We anticipate increased mangrove loss, especially along the Pacific coast, resulting from intensified human activity. Prioritization of conservation areas is needed to support local institutions, maintain currently protected areas, and develop strategies (e.g. payment for ecosystem services) to preserve one of the most pristine mangrove regions in the Western Hemisphere.

Mangrove forests thrive along global tropical coasts, acting as a barrier that protects coastlines against storm surges and as nurseries for an entire food web. They are also known for their high carbon sequestration rates and soil carbon stocks. We introduce a new global mangrove canopy height map generated from TanDEM-X spaceborne elevation measurements collected during the 2011–2013 period with a 12-meter spatial resolution and an accuracy of 2.4 meters (RMSE). Height was calibrated and validated using GEDI mission data and independently verified with airborne Lidar. The tallest mangrove stands reach nearly 60 meters in Colombia and Gabon, and potentially other countries. The map captures a broader range of canopy heights with finer spatial details than other available global products that use optical imagery. This new global mangrove height dataset can aid in evaluating mangrove ecosystem services at local and regional scales, improving our understanding of factors controlling mangrove structure, and supporting conservation, climate mitigation and adaptation strategies.

The water in Earth's rivers propagates as waves through space and time across hydrographic networks. A detailed understanding of river dynamics globally is essential for achieving accurate knowledge of surface water storage and fluxes to support water resources management and water‐related disaster forecasting and mitigation. Global in situ information on river flows are crucial to support such an investigation but remain difficult to obtain at adequate spatiotemporal scales, if they even exist. Many expectations are placed on remote sensing techniques as key contributors. Despite a rapid expansion of satellite capabilities, however, it remains unclear what temporal revisit, spatial coverage, footprint size, spatial resolution, observation accuracy, latency time, and variables of interest from satellites are best suited to capture the space‐time propagation of water in rivers. Additionally, the ability of numerical models to compensate for data sparsity through model‐data fusion remains elusive. We review recent efforts to identify the type of remote sensing observations that could enhance understanding and representation of river dynamics. Key priorities include: (a) resolving narrow water bodies (finer than 50–100 m), (b) further analysis of signal accuracy versus hydrologic variability and relevant technologies (optical/SAR imagery, altimetry, microwave radiometry), (c) achieving 1–3 days observation intervals, (d) leveraging data assimilation and multi‐satellite approaches using existing constellations, and (e) new variable measurement for accurate water flux and discharge estimates. We recommend a hydrology‐focused, multi‐mission observing system comprising: (a) a cutting‐edge single or dual‐satellite mission for advanced surface water measurements, and (b) a constellation of cost‐effective satellites targeting dynamic processes.

Developing accurate landscape-scale aboveground biomass (AGB) maps is critical to understanding coastal deltaic wetland resilience, as AGB influences stability and elevation dynamics in herbaceous wetlands. Here we used AVIRIS-NG imaging spectrometer (or "hyperspectral") data from NASA's 2021 Delta-X mission in coastal Louisiana to map seasonal changes in herbaceous AGB across two deltaic basins with contrasting sediment delivery and hydrologic regimes: the Atchafalaya (active) and Terrebonne (inactive). We assessed the impact of atmospheric effects on our retrievals, as high water vapor content in August 2021 caused significant noise in the 880-1,000 and 1,080-1,200 nm near-infrared (NIR) wavelengths. We hypothesized that correcting these wavelengths with our conditional Gaussian interpolation algorithm would improve AGB estimates due to their association with plant canopy water content. We empirically assessed the performance of the corrected spectra on AGB estimates using Partial Least Squares Regression (PLSR), finding that the corrected NIR bands attained high variable importance and reduced estimation errors. Our Random Forest regression approach based on the corrected spectra attained equivalent error metrics via leave-one-out-cross-validation as the PLSR models (R2 = 0.43, mean absolute error = 257.3 g/m2) while greatly improving the AGB maps' visual quality, having better captured variability while reducing noise and discontinuities in AGB estimates across flightlines. The maps show differing seasonal growth, with the Atchafalaya and Terrebonne Basins' AGB increasing from means of 4.3-9.4 and 4.6-8.9 Mg/ha, respectively. We demonstrated that imaging spectroscopy can be applied to assess herbaceous biomass stocks, growth patterns, and resilience in coastal ecosystems. Developing accurate landscape-scale aboveground biomass (AGB) maps is critical to understanding the sustainability of coastal deltaic systems, as plant biomass aids in a wetland maintaining its elevation in the face of relative sea level rise. Here we use imaging spectroscopy, or "hyperspectral" remote sensing, to map wetland biomass across seasons in coastal Louisiana. In doing so, we correct for the noise caused by high atmospheric water vapor content in important infrared bands, thereby improving our biomass models. We test empirical and machine learning models for estimating wetland biomass, determining that a machine learning regression model using our corrected data delivers the best model metrics and map quality. We thus map AGB in April and August 2021, across the Atchafalaya and Terrebonne Basins. Our maps show various seasonal growth patterns, with the Atchafalaya and Terrebonne Basins' herbaceous AGB increasing from means of 4.3-9.4 and 4.6-8.9 Mg/ha, respectively. We developed an interpolation algorithm to correct noise in near-infrared bands that improved wetland aboveground biomass (AGB) estimates We assessed model approaches and mapped wetland AGB in coastal Louisiana with a machine learning algorithm for March and August 2021 The active Atchafalaya and inactive Terrebonne Basins' AGB increased from means of 4.3-9.4 and 4.6-8.9 Mg/ha, respectively

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.