Investigating Spatial and Vertical Patterns of Wetland Soil Organic Carbon Concentrations in China’s Western Songnen Plain by Comparing Different Algorithms

Ren, Yongxing; Li, Xiaoyan; Mao, Dehua; Wang, Zongming; Jia, Mingming; Lin, Chunye

doi:10.3390/su12030932

Cited by 14 publications

(17 citation statements)

References 51 publications

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“…These values are very low compared to what has been reported in other wetland studies. [ 44,45 ] Available P content in the soils fluctuated with depth ( Figure 5 a) and from upstream to downstream of the wetland ( Figure 6 a) ( p = 0.02). Highest mean values of available P were recorded midstream of the wetland.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Soil Degradation in a Palustrine Wetland and the Implication on Its Water Purification Potential

George

Ngole‐Jeme

2021

CLEAN Soil Air Water

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This study determines the extent of degradation of soils in Khubelu wetland in Lesotho and the impact this may have on its water purification potential. 72 soil samples are collected at different sites and depths around the wetland and characterized for different properties. The values of these properties are then used to determine the chemical degradation index (CDI) of the wetland soils. The soils are non‐saline (electrical conductivity < 0.25 mS cm−1), mostly acidic (4.5 < pH < 5.22) with moderate organic matter content (2.06–3.9%). Soil properties vary with depth and from upstream to downstream of the wetland. Values for soil CDI are 3.42, 3.25, and 3.06 for soils from upstream, midstream, and downstream of the wetland respectively, indicating a decrease in soil degradation from upstream to downstream. The dominance of emergent vegetation in the wetland indicates a potential to get rid of sediments in wetland influents but the soil characteristics may present a challenge in the removal of nutrients and organic and inorganic pollutants from these influents. The characteristics of the soils and the extent of soil degradation present some challenges in the wetland’s ability to filter out nutrients and organic and inorganic pollutants.

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Section: Resultsmentioning

confidence: 99%

Assessment of Soil Degradation in a Palustrine Wetland and the Implication on Its Water Purification Potential

George

Ngole‐Jeme

2021

CLEAN Soil Air Water

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“…Different from wetlands in the tropics and subtropics and the arid zone, wetlands in the LRB have high SOCc due to the small decomposition rate caused by low temperature [41,42]. Compared with other wetland distribution regions in Northeast China such as the Greater Khingan Mountains [43] and Western Songnen Plain [44], the values of wetland SOCc in the LRB are relatively low, mostly due to warmer temperature and more decomposition of organic matter in the LRB located in southern Northeast China. The soil microbial activity decreases at low temperatures and high precipitation, which is conducive to stabilizing soil organic carbon storage.…”

Section: Patterns Of Wetland Socc In the Lrbmentioning

confidence: 93%

Combining Artificial Neural Network and Ordinary Kriging to Predict Wetland Soil Organic Carbon Concentration in China’s Liao River Basin

Kang

Mao

et al. 2020

Sensors

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Accurate prediction of wetland soil organic carbon concentration and an understanding of its controlling factors are important for studying regional climate change and wetland carbon cycles; with that knowledge mechanisms can be put in place that are conducive to sustainable ecosystem management for environmental health. In this study, a hybrid approach combining an artificial neural network and ordinary kriging and 103 soil samples at three soil depth ranges (0–30, 30–60, and 60–100 cm) were used to predict wetland soil organic carbon concentration in China’s Liao River Basin. The model evaluation indicated that a combination of artificial neural network and ordinary kriging and limited soil samples achieved good performance in predicting wetland soil organic carbon concentration. Wetland soil organic carbon concentration in the Liao River Basin has apparent spatial and vertical heterogeneities with values decreasing from southeast to northwest and concentrates present mainly in the topsoil (0–30 cm). Mean wetland soil organic carbon concentration values at the three soil depths were 10.43 ± 0.38, 7.93 ± 0.25, and 7.61 ± 0.22 g/kg, respectively, which are smaller than those over other wetland regions in Northeast China. Terrain aspect contributed the most in predicting wetland soil organic carbon concentration at each of the three soil depths, followed by normalized difference vegetation index at 0–30 cm and mean annual precipitation at 30–60 and 60–100 cm. This study provides a framework method and baseline to quantify the soil organic carbon concentration dynamics in response to climatic and anthropogenic drivers.

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“…The main goal of these studies has been to compare carbon stocks between different types of wetlands and find factors or drivers that affect SOC the most. There have been some efforts to predict SOC using a digital soil mapping (DSM) approach in wetlands [6,32,53]. One study used VIS/NIR spectroscopy and EO-1 Hyperion data to compare different machine learning techniques for the estimation of SOC [6].…”

Section: Status Of Soc Modeling In Tidal Wetlandsmentioning

confidence: 99%

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

et al. 2022

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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.

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Investigating Spatial and Vertical Patterns of Wetland Soil Organic Carbon Concentrations in China’s Western Songnen Plain by Comparing Different Algorithms

Cited by 14 publications

References 51 publications

Assessment of Soil Degradation in a Palustrine Wetland and the Implication on Its Water Purification Potential

Assessment of Soil Degradation in a Palustrine Wetland and the Implication on Its Water Purification Potential

Combining Artificial Neural Network and Ordinary Kriging to Predict Wetland Soil Organic Carbon Concentration in China’s Liao River Basin

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

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