Monitoring soil organic carbon under coastal restoration using time series Sentinel‐1
Bibliographic Data
| ID | 21647780 |
|---|---|
| Authors | Ren‐Min Yang (0000-0002-5788-1625, School of Earth System Science Tianjin University Tianjin China), Lai‐Ming Huang (Yellow River Delta Modern Agricultural Engineering Laboratory, Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China), Laiming Huang (0009-0008-8505-9461, Chinese Academy of Sciences, corresponding author) |
| Year | 2024 |
| Volume | 35 |
| Issue | 8 |
| Pages | 2913-2925 |
| Publication date | 2024-05-15 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Land Degradation and Development (JOURNAL) |
| Journal identifiers | ISSN: 1085-3278 • E-ISSN: 1099-145X |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/ldr.5105 |
| OpenAlex | W4400065309 |
| Language | EN |
| Citations received | 1 |
| References cited | 50 |
Salt marsh soils are key reservoirs of organic carbon. Although the potential for using Sentinel‐1 data to estimate organic carbon in marsh soils has been recognized, there is still a need to find suitable proxies and effective algorithms that enhance predictions. In this study, we assessed the effectiveness of Sentinel‐1 data for predicting soil organic carbon (SOC) stocks in salt marshes located on the coast of eastern‐central China. We defined the relations between SOC stocks and remotely sensed data using a knowledge‐based approach (i.e., structural equation modeling (SEM)), and the results were compared with those obtained using a common data‐driven approach (i.e., random forest (RF)). Predictive models were developed using (1) refined images associated with phenological stages (phenological variables), (2) refined images that were classified as statistically important using Monte Carlo feature selection (MCFS‐based variables), and (3) images from the combination of these refined datasets. The predictions were validated using a leave‐one‐out cross‐validation approach. The results showed that SEM was more accurate than RF when predicting SOC stocks using the same type of predictor variables. Models using phenological variables alone yielded the least accurate predictions. Adding phenological variables to the structural equation model with MCFS‐based variables increased the prediction accuracy by 36% in terms of the R 2 . The results of the case study suggest that images related to phenological stages are good predictors for mapping SOC stocks in marshes. This study highlights the superiority of SEM over RF for developing effective remote sensing‐based models to quantify and map SOC stocks in salt marshes
Geography · Machine learning · Marsh · Phenology · Random forest · Remote sensing · Salt marsh · Soil carbon · Soil water · Computer Science · Environmental Science · Land Use and Ecosystem Services · Remote Sensing in Agriculture · Soil Geostatistics and Mapping · Ecology · Geology · Oceanography · Soil Science
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |