Remote Sensing of Surface and Subsurface Soil Organic Carbon in Tidal Wetlands: A Review and Ideas for Future Research

Abstract

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.

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

“…The spectral transformation method adopted by TK is homogenization after first-order differential, and the model is a linear regression equation. The accuracy exceeds 0.70, and MRE and RMSE are also very low [48]. The distribution of the content of seven mangrove components presents different laws according to the analysis of the calculation results (Figure 4) [16].…”

“…In addition, mangroves have protective functions, such as purifying seawater, preventing wind and waves, and carbon fixation [48]. It should be noted that due to human interference and destruction, the biodiversity of some mangrove areas is threatened, making the protection and restoration of mangroves particularly important [10,46,48]. As far as we know, the current study quantifies the relationship between different LAI and leaf nutrient content of mangrove for the first time [1].…”

“…These animals depend on the environment in which mangroves grow and form a complex food chain, maintaining the balance of the ecosystem. In addition, mangroves have protective functions, such as purifying seawater, preventing wind and waves, and carbon fixation [48]. It should be noted that due to human interference and destruction, the biodiversity of some mangrove areas is threatened, making the protection and restoration of mangroves particularly important [10,46,48].…”

“…The relative amounts of morphology, forest age, canopy coverage, aboveground biomass, and wood debris were extracted from the time series data of spaceborne optical radar and interferometric radar data [45,46]. The best method to interpret the change in mangrove carbon storage using remote sensing data was found through image processing [47,48]. Using remote sensing technology to extract quantitative information on the physiological and biochemical characteristics of mangrove forests is a practical and effective method.…”

“…It will be more difficult to implement effective policies and actions for sustainable protection of mangroves in the context of climate change mitigation and adaptation without effective quantitative methods to monitor the biophysical parameters of mangroves [24]. In this regard, remote sensing is an important tool for monitoring mangroves and determining species and other attributes, and the accurate measurement of species leaf area is crucial for assessing forest growth and health [4,48].…”