An Improved Scheme for Correcting Remote Spectral Surface Reflectance Simultaneously for Terrestrial BRDF and Water‐Surface Sunglint in Coastal Environments

Greenberg, Evan; Thompson, David R.; Jensen, Daniel; Townsend, Philip A.; Queally, Natalie; Chlus, Adam; Fichot, Cédric G.; Harringmeyer, Joshua P.; Simard, Marc

doi:10.1029/2021jg006712

Cited by 10 publications

(7 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequent to each AVIRIS-NG flight line, the data underwent atmospheric correction to generate Hemispherical-Directional surface reflectance datasets [58]. Further adjustments were made to account for bidirectional reflectance distribution function (BRDF) effects and sun-glint across land and water pixels, respectively [59,60].…”

Section: Field and Aerial Datasetmentioning

confidence: 99%

Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach

Hemati,

Mahdianpari,

Shiri

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Coastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed in terrestrial forests. The application of remote sensing technologies offers a promising means of monitoring aboveground biomass (AGB) in wetland environments. However, the scarcity of field data poses a significant challenge to the utilization of spaceborne data for accurate estimation of AGB in coastal wetlands. To address this limitation, this study presents a novel multi-scale approach that integrates field data, aerial imaging, and satellite platforms to generate high-quality biomass maps across varying scales. At the fine scale level, the AVIRIS-NG hyperspectral data were employed to develop a model for estimating AGB with an exceptional spatial resolution of 5 m. Subsequently, at a broader scale, large-scale and multitemporal models were constructed using spaceborne Sentinel-1 and Sentinel-2 data collected in 2021. The Random Forest (RF) algorithm was utilized to train spring, fall and multi-temporal models using 70% of the available reference data. Using the remaining 30% of untouched data for model validation, Root Mean Square Errors (RMSE) of 0.97, 0.98, and 1.61 Mg ha−1 was achieved for the spring, fall, and multi-temporal models, respectively. The highest R-squared value of 0.65 was achieved for the multi-temporal model. Additionally, the analysis highlighted the importance of various features in biomass estimation, indicating the contribution of different bands and indices. By leveraging the wetland inventory classification map, a comprehensive temporal analysis was conducted to examine the average and total AGB dynamics across various wetland classes. This analysis elucidated the patterns and fluctuations in AGB over time, providing valuable insights into the temporal dynamics of these wetland ecosystems.

show abstract

Section: Field and Aerial Datasetmentioning

confidence: 99%

Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach

Hemati,

Mahdianpari,

Shiri

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…This process incorporates a radiometric correction for vicarious adjustment to mitigate minor differences between laboratory calibration and actual flight conditions. Details regarding the radiometric correction, which utilizes in-situ references from the Delta-X flight campaign, as well as the sunglint correction, are docu-mented in Bruegge et al (2021), Li (2021), andGreenberg et al (2022). These corrections yield water pixel values that closely approximate the accurate normalized waterleaving reflectance (ρ w ).…”

Section: Aviris-ngmentioning

confidence: 99%

Depth Mapping in Turbid and Deep Waters Using AVIRIS-NG Imagery: A Study in Wax Lake Delta, Louisiana, USA

Kwon,

Passalacqua,

Soloy

et al. 2024

Preprint

View full text Add to dashboard Cite

Remote sensing has been widely applied to investigate fluvial processes, but depth retrievals face significant constraints in deep and turbid conditions. This study evaluates the potential for depth retrievals under such challenging conditions using NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery. We employ interpretable machine learning to construct a hyperspectral regressor for water depth and explore the spectral characteristics of deep and turbid waters in Wax Lake Delta (WLD), LA. The reflectance spectra of WLD show minor effects from depth differences due to turbidity. Nevertheless, a Random Forest with Recursive Feature Elimination (RF-RFE) effectively generalizes high and low turbid cases in a single model, achieving a R² of 0.94 ± 0.005. Moreover, this model shows a maximum detectable depth of approximately 30 m, outperforming other methods. A spectral analysis using Shapley additive explanations (SHAP) points out the importance of learning various spectral bands and non-linear relationships between depth and reflectance. Specifically, the short blue and Near-InfraRed (NIR) bands, with high attenuation coefficients, play a crucial role. This finding highlights the attenuation as the key process for deep-depth retrievals. The depth maps of WLD captured by this model distinctly represent the spatial distribution of deep river and shallow delta regions. However, the high dependency on short blue and NIR bands leads to discontinuous areas due to the noise sensitivity of these bands. This result highlights a drawback of remote sensing using empirical models. Future research will focus on correcting such discontinuities by integrating data from multiple remote sensing sources.

show abstract

“…AVIRIS-NG is an imaging spectrometer that measures radiance at 5 nm sampling across 425 bands in the Visible to Short-Wave Infrared (VSWIR) spectral range (380-2,500 nm). The L1 radiance data were processed to L2 surface reflectance products with atmospheric correction (Thompson et al, 2019), with further corrections applied to adjust for Bidirectional Reflectance Distribution Function effects (Greenberg et al, 2022;Thompson et al, 2022). AGB, quantified as dry biomass in grams per square meter (g/m 2 ), was estimated from the BRDF-adjusted surface reflectance and a coincident AGB field survey (Castañeda-Moya & Solohin, 2022).…”

Section: Above Ground Biomass (Agb)mentioning

confidence: 99%

Spatial Variability in Salt Marsh Drainage Controlled by Small Scale Topography

Donatelli,

Passalacqua,

Jensen

et al. 2023

JGR Earth Surface

Self Cite

View full text Add to dashboard Cite

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.

show abstract

An Improved Scheme for Correcting Remote Spectral Surface Reflectance Simultaneously for Terrestrial BRDF and Water‐Surface Sunglint in Coastal Environments

Abstract: The growing availability and coverage of imaging spectroscopy data has led to its increasing use by Earth and biological sciences at local to regional scales (

Cited by 10 publications

References 61 publications

Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach

Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach

Depth Mapping in Turbid and Deep Waters Using AVIRIS-NG Imagery: A Study in Wax Lake Delta, Louisiana, USA

Spatial Variability in Salt Marsh Drainage Controlled by Small Scale Topography

Contact Info

Product

Resources

About