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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. Plain Language Summary: 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. Key Points: We developed an interpolation algorithm to correct noise in near‐infrared bands that improved wetland aboveground biomass (AGB) estimatesWe assessed model approaches and mapped wetland AGB in coastal Louisiana with a machine learning algorithm for March and August 2021The active Atchafalaya and inactive Terrebonne Basins' AGB increased from means of 4.3–9.4 and 4.6–8.9 Mg/ha, respectively [ABSTRACT FROM AUTHOR]

Water movement in coastal wetlands is affected by spatial differences in topography and vegetation characteristics as well as by complex hydrological processes operating at different time scales. Traditionally, numerical models have been used to explore the hydrodynamics of these valuable ecosystems. However, we still do not know how well such models simulate water‐level fluctuations beneath the vegetation canopy since we lack extensive field data to test the model results against observations. This study utilizes remotely sensed images of sub‐canopy water‐level change to understand how marshes drain water during falling tides. We employ rapid repeat interferometric observations from the NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument to analyze the spatial variability in water‐level change within a complex of marshes in Terrebonne Bay, Louisiana. We also used maps of herbaceous aboveground biomass derived from the Airborne Visible/Infrared Imaging Spectrometer‐Next Generation to evaluate vegetation contribution to such variability. This study reveals that the distribution of water‐level change under salt marsh canopies is strongly influenced by the presence of small geomorphic features (<10 m) in the marsh landscape (i.e., levees, tidal channels), whereas vegetation plays a minor role in retaining water on the platform. This new type of high‐resolution remote sensing data offers the opportunity to study the feedback between hydrodynamics, topography and biology throughout wetlands at an unprecedented spatial resolution and test the capability of numerical models to reproduce such patterns. Our results are essential for predicting the vulnerability of these delicate environments to climate change. Plain Language Summary: Salt marshes are vegetated coastal ecosystems particularly susceptible to lateral erosion and sea‐level rise. Hydrodynamic models are crucial to forecasting the vulnerability of these delicate environments to climate change, but we lack extensive field data to assess the accuracy of such models in vegetated flooded areas. Here, we utilize measurements collected from a NASA radar instrument flown on an airplane to determine how water moves beneath the vegetation canopy within a group of salt marshes located in the Mississippi River delta plain in Louisiana. Plant structure in the selected marshes was estimated from an imaging spectrometer which flew near‐simultaneously with the radar instrument. Our study shows the utility of remotely sensed data to comprehend wetland hydrodynamics at a very high spatiotemporal resolution and improve the reliability of models that are needed to predict their fate. Key Points: We utilize a new generation of remote sensing data to study water movement beneath the vegetation canopy in salt marshesSmall morphological features in marsh landscapes affect the spatial variability in water drainage within vegetated areasSalt marshes located at the same elevation within the local tidal range drain water differently [ABSTRACT FROM AUTHOR]

Deltas are fragile ecosystems threatened by sea‐level rise, sediment starvation, and subsidence. Erosional/depositional processes in these systems mainly depend on the sediment supply and the spatial divergence in bed shear stress induced by hydrodynamic forces. Thus, quantifying the spatiotemporal variability of the flow velocity field is essential for forecasting their fate. To calibrate/validate models, field measurements alone are not sufficient because such data only characterize the hydrodynamic conditions in localized areas. Remote sensing has potential to fill this data gap. We developed a methodology to estimate flow velocities from a map of suspended sediment concentration (SSC) measured by the NASA airborne spectrometer AVIRIS‐NG within the Wax Lake Delta, Louisiana. We extracted streaklines from remotely sensed SSC estimates, and quantified water fluxes and velocities based on the distance between them. Our study demonstrates that the velocity field in deltas can be estimated by leveraging the synoptic information offered by remote sensing. Plain Language Summary: River deltas are coastal environments particularly susceptible to sea‐level rise. Sediment deposition can counteract land loss from sea‐level rise, but we have a poor understanding of where and when land is built through sediment accretion. Improving the accuracy of hydrodynamic models is pivotal to predicting the fate of sediment that rebuilds sinking land, but we lack methods to test these models against observations over large areas. Here, we introduce a new method to measure water discharge, flow direction and speed from remotely sensed data. The method takes advantage of water flow patterns revealed in data collected from a NASA instrument flown on an airplane. Our study shows the utility of remotely sensed data to improve the performance and reliability of models that are needed to predict the fate of river deltas. Key Points: We propose a novel methodology to compute water fluxes and flow velocities in deltas from maps of suspended sediment concentrationOur method quantifies flow velocities based on the distance between streaklines, under the assumption that streaklines act as streamlinesFlow velocities obtained via our methodology match well with those derived from a numerical model in a sub‐region of the Wax Lake Delta [ABSTRACT FROM AUTHOR]

Deltas are vulnerable landscapes, making it crucial to understand their spatial patterns of deposition/erosion. Here, we used patterns in suspended sediment concentration (SSC) measured by a NASA airborne spectrometer, AVIRIS‐NG, to infer deposition/erosion within Wax Lake Delta, Louisiana. Conceptually, change in SSC within a fluid parcel traveling downstream reflects settling and/or entrainment from the bed. We found that remotely‐sensed SSC displays curvilinear features, which we interpret as streaklines. We developed a semi‐automated technique for extracting streaklines using a cost function based on SSC and its geometric curvature. We measured SSC change along streaklines, which when combined with flow velocities obtained from a hydrodynamic model, allowed us to infer instantaneous deposition/erosion rates. These rates are realistic in magnitude and record coherent spatial patterns across the delta. Our novel method provides a promising avenue for relating spatial patterns of land change to flow conditions over wide areas in vulnerable deltas. Plain Language Summary: River deltas are naturally low‐lying, making them vulnerable to flooding due to sea‐level rise. Predicting which regions of a river delta will be most vulnerable in the future requires understanding of how their elevation will change due to deposition and erosion of sediment. Deposition and erosion are difficult to measure, and depend on factors such as sediment supply and flow conditions. Here, we developed a technique to use suspended sediment concentration measured from airborne imagery to infer deposition/erosion. We find coherent spatial patterns, which we relate to flow conditions and sediment supply. Our novel method provides a promising avenue for relating spatial patterns of land change to flow conditions over wide areas in vulnerable river deltas. Future airborne and satellite‐based measurements will provide new opportunities to apply these methods to other river deltas globally. Key Points: We used change in concentration along streaklines extracted from remote‐sensed imagery to infer instantaneous rates of deposition/erosionOur method accurately inverts for deposition rates when applied to synthetic data from a 2D advection‐settling modelWe obtained coherent patterns of deposition and erosion based on remote‐sensed imagery of Wax Lake Delta, Louisiana [ABSTRACT FROM AUTHOR]

This paper presents the adaptive reflectance geometric correction (ARGC), a bidirectional reflectance distribution function (BRDF) correction algorithm to address intensity gradients across remotely sensed images. The ARGC is developed and tested on data from the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) collected over Louisiana's Atchafalaya River Delta, an area of complex wetland vegetation and waterbodies suited to AVIRIS-NG's fine spatial and spectral resolutions. Changing view and solar geometry, in conjunction with surfaces' anisotropic properties, impact a scene's observed reflectance. As traditional BRDF corrections may not be appropriate for wetland environments that have distinctive vegetation and hydrologic structures, more flexible functional corrections are shown to improve results. We compared two existing methods and the ARGC. The first method fits a quadratic function over image column averages, and the second is based on the inversion of the Ross Thick and Li Sparse kernels. Building upon the principles of these methods, the ARGC uses a multiple regression-based BRDF correction whereby the image's solar and view geometric descriptors form the independent variables. Each BRDF correction method was applied to the set of six partially overlapping AVIRIS-NG scenes. Assuming the actual surface reflectance of a given land cover type is independent of geometry, we used adjacent images' overlapping regions to quantitatively assess each correction method's efficacy. The ARGC produced the lowest overall root-mean-square difference and the lowest overlap mean absolute difference across the vast majority of bands. The ARGC is proposed as a practical new BRDF correction option for investigators using AVIRIS-NG data. [ABSTRACT FROM AUTHOR]

The Wax Lake Delta (WLD) is an actively prograding delta in the Mississippi River Delta Plain that is otherwise experiencing widespread degradation and submergence of its coastal wetlands. The WLD is actively accumulating mineral and organic sediment that increases soil surface elevation, changing emergent wetland communities as the young delta develops. There is uncertainty in how the dynamics of community composition respond to net changes in soil elevation and determine aboveground biomass and carbon storage. This study utilizes high resolution imaging spectrometer data captured on October 17, 2016, to map the delta's dominant vegetation species and wetland types. We validated this vegetation map (overall accuracy = 77.62%, Kappa = 0.72) and compared it with a published species map that used WorldView-2 data collected five years earlier on October 16, 2011. This allowed us to map changes resulting from five years of delta development to determine changes in wetland forest species (Salix nigra), two dominant herbaceous wetland species (Colocasia esculenta and Polygonum punctatum), and various grass species. Results show an increase in C. esculenta and a marginal increase in forested wetlands (S. nigra). C. esculenta 's expansion occurred largely from the delta island heads toward the fringes into wetland area previously occupied by P. punctatum , which saw a corresponding decline. Additionally, this study leveraged these species distributions with a published aboveground biomass (AGB) dataset to examine the dominant plant types' growth patterns across elevational gradients. We characterized variability in AGB by marsh platform elevation and across different elevational zones categorized by hydroperiod, or hydrogeomorphic zones. We found that the herbaceous plant species peak in AGB in the low intertidal zone and decrease with elevation before increasing slightly in the higher elevations, consistent with previous field-based mesocosm experiments for C. esculenta. With vegetation distributions, succession, and growth patterns in the WLD characterized, this study may inform future restoration efforts throughout the Mississippi River Delta Plain regarding the changing vegetation composition that may emerge following changes in surface elevation with sediment deposition. Image 1 • We mapped and validated vegetation species in Louisiana's Wax Lake Delta. • Species extent changes over five years show an increase in Colocasia esculenta. • C. esculenta grew outwards with deltaic accretion, supplanting Polygonum punctatum. • Aboveground biomass for primary herbaceous species peaks in the intertidal zone. • Remote sensing biomass estimates agree with experimentally derived growth rates. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) plays a critical functional role in coastal wetland ecosystem stability, with high biomass vegetation contributing to organic matter production, sediment accretion potential, and the surface elevation's ability to keep pace with relative sea level rise. Many remote sensing studies have employed either imaging spectrometer or synthetic aperture radar (SAR) for AGB estimation in various environments for assessing ecosystem health and carbon storage. This study leverages airborne data from NASA's Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to assess their unique capabilities in combination to estimate AGB in coastal deltaic wetlands. Here we develop AGB models for emergent herbaceous and forested wetland vegetation in coastal Louisiana. In addition to horizontally emitted, vertically received (HV) backscatter, SAR parameters are expressed by the Freeman–Durden polarimetric decomposition components representing volume and double-bounce scattering. The imaging spectrometer parameters include normalized difference vegetation index (NDVI), reflectance from 290 visible-shortwave infrared (VSWIR) bands, the first derivatives from those bands, or partial least squares (PLS) x-scores derived from those data. Model metrics and cross-validation indicate that the integrated models using the Freeman-Durden components and PLS x-scores improve AGB estimates for both wetland vegetation types. In our study domain over Louisiana's Wax Lake Delta (WLD), we estimated a mean herbaceous wetland AGB of 3.58 Megagrams/hectare (Mg/ha) and a total of 3551.31 Mg over 9.92 km2, and a mean forested wetland AGB of 294.78 Mg/ha and a total of 27,499.14 Mg over 0.93 km2. While the addition of SAR-derived values to imaging spectrometer data provides a nominal error decrease for herbaceous wetland AGB, this combination significantly improves forested wetland AGB prediction. This integrative approach is particularly effective in forested wetlands as canopy-level biochemical characteristics are captured by the imaging spectrometer in addition to the variable structural information measured by the SAR. [ABSTRACT FROM AUTHOR]

Deltaic floodplains are highly vulnerable to relative sea level rise (RSLR) depending on the sediment supply from river channels that provides elevation capital as adaptation mechanism. In river channels where levees have restricted sediment supply to coastal deltaic floodplains, river sediment diversions have been proposed as a restoration strategy to increase elevation allowing for marshes to establish and cope with RSLR. The response of coastal wetlands to surface elevation has been well-defined for estuarine marshes, but models for coastal deltaic floodplain marshes have not been resolved. Here we coupled field observations from biomass plots and a mesocosm experiment ('marsh organ') with remote sensing techniques to assess biomass allocation of tidal freshwater marsh species in response to gradients in hydroperiod in Wax Lake Delta (WLD), coastal Louisiana, U.S.A.. We found that, contrary to salt-tolerant species, Colocasia esculenta aboveground biomass (AGB) is strongly positively correlated with percent inundated time (R2 = 0.79, P < 0.001), increasing from (mean ± 1SE) 186 ± 69 g/m2 in the supratidal zone to 1422 ± 148 g/m2 beyond its natural occurrence range in the lower intertidal zone. Belowground biomass consistently exceeded AGB at 2363 ± 294 g/m2 on average across elevation treatments. We also found that C. esculenta expanded its surface coverage area by 31% in five years consistent with the growth and emergence of WLD's subaqueous platforms, reflecting this species ability to cope with higher inundation time. In contrast to earlier studies conducted in brackish and saline settings, where longer hydroperiods had negative effects on biomass accumulation, our data suggest that tidal freshwater marshes can cope with longer hydroperiods caused by river sediment diversions. • Tidal freshwater marshes are adapted to broader surface elevation ranges. • Hydrogeomorphic zones control tidal freshwater marsh distribution and production. • Colocasia esculenta biomass increased with percent inundation time. • Tidal freshwater marshes track and speed deltaic islands' expansion and elevation gain. • Ecogeomorphic feedbacks following river sediment diversions help sustain stable shorelines. [ABSTRACT FROM AUTHOR]

Coastal ecosystem studies using remote visible/infrared spectroscopy typically invert an atmospheric model to estimate the water-leaving reflectance signal. This inversion is challenging due to the confounding effects of turbid backscatter, atmospheric aerosols, and sun glint. Simultaneous estimation of the surface and atmosphere can resolve the ambiguity enabling spectral reflectance maps with rigorous uncertainty quantification. We demonstrate a simultaneous retrieval method that adapts the Optimal Estimation (OE) formalism of Rodgers (2000) to the coastal domain. We compare two surface representations: a parametric bio-optical model based on Inherent Optical Properties (IOPs); and an expressive statistical model that estimates reflectance in every instrument channel. The latter is suited to both land and water reflectance, enabling a unified analysis of terrestrial and aquatic domains. We test these models with both vector and scalar Radiative Transfer Models (RTMs). We report field experiments by two airborne instruments: NASA's Portable Remote Imaging SpectroMeter (PRISM) in an overflight of Santa Monica, California; and NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) in an overflight of the Wax Lake Delta and lower Atchafalaya River, Louisiana. In both cases, in situ validation measurements match remote water-leaving reflectance estimates to high accuracy. Posterior error predictions demonstrate a closed account of uncertainty in these coastal observations. • We adapt Optimal Estimation (OE) methods to VSWIR coastal imaging spectroscopy. • OE enables simultaneous surface and atmosphere retrieval with uncertainty estimates. • We compare surface parameterizations suited for land, deep water, and turbid water. • We perform field tests with multiple airborne imaging spectrometer field campaigns. • Remote reflectance accurately matches in situ validation measurements. [ABSTRACT FROM AUTHOR]