Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r2 = 0.68 and 0.20, respectively, p < 2.2 × 10−16 for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.
Coastal wetlands are nourished by rivers and periodical tidal flows through complex, interconnected channels. However, in hydrodynamic models, channel dimensions with respect to model grid size and uncertainties in topography preclude the correct propagation of tidal and riverine signals. It is therefore crucial to enhance channel geomorphic connectivity and simplify sub‐channel features based on remotely sensed networks for practical computational applications. Here, we utilize channel networks derived from diverse remote sensing imagery as a baseline to build a ∼10 m resolution hydrodynamic model that covers the Wax Lake Delta and adjacent wetlands (∼360 km2) in coastal Louisiana, USA. In this richly gauged system, intensive calibrations are conducted with 18 synchronous field‐observations of water levels taken in 2016, and discharge data taken in 2021. We modify channel geometry, targeting realism in channel connectivity. The results show that a minimum channel depth of 2 m and a width of four grid elements (approximatively 40 m) are required to enable a realistic tidal propagation in wetland channels. The optimal depth for tidal propagation can be determined by a simplified cost function method that evaluates the competition between flow travel time and alteration of the volume of the channels. The integration of high spatial‐resolution models and remote sensing imagery provides a general framework to improve models performance in salt marshes, mangroves, deltaic wetlands, and tidal flats.
more » « less- PAR ID:
- 10369444
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Earth Surface
- Volume:
- 127
- Issue:
- 3
- ISSN:
- 2169-9003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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