Effects of multi‐temporal environmental variables on SOC spatial prediction models in coastal wetlands of a Chinese delta

Bibliographic Data

ID21647725
AuthorsYiming Xu (0000-0001-9812-3970, School of Grassland Science Beijing Forestry University Beijing PR China), Bin Li (0000-0002-9633-0033, School of Ecology and Environment Beijing Technology and Business University Beijing PR China), Junhong Bai (0000-0003-2613-2143, State Key Laboratory of Water Environment Simulation, School of Environment Beijing Normal University Beijing PR China, corresponding author), Guangliang Zhang (0000-0001-9992-1341, State Key Laboratory of Water Environment Simulation, School of Environment Beijing Normal University Beijing PR China), Xin Wang (0000-0003-2833-6940, State Key Laboratory of Water Environment Simulation, School of Environment Beijing Normal University Beijing PR China), Scot E Smith (School of Forest Resources and Conservation ‐ Geomatics Program University of Florida Gainesville Florida USA), Shudong Du (State Key Laboratory of Water Environment Simulation, School of Environment Beijing Normal University Beijing PR China)
Year2022
Volume33
Issue17
Pages3557-3567
Publication date2022-11-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLand Degradation and Development (JOURNAL)
Journal identifiersISSN: 1085-3278 • E-ISSN: 1099-145X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ldr.4408
OpenAlexW4283527423
LanguageEN
Citations received1
References cited43

Mapping soil organic carbon (SOC) distributions in coastal wetlands plays an important role in assessing ecosystem services and investigating the global carbon cycle. Little research has explored the effects of environmental variables with seasonal variations on digital soil mapping (DSM). Our research utilized machine learning methods and established multiple prediction models of SOC based on multi‐temporal data from dry and wet seasons, and mono‐temporal data from April. The results showed that the relationships between SOC and environmental variables in different months varied significantly in coastal wetlands of the Yellow River Delta (YRD). In general, the environmental variables in the wet season showed stronger correlations and higher importance scores with SOC compared with those in the dry season. In addition, SOC prediction models in wet season and April had stronger prediction performance compared with those in the dry season. As a result, data fusion of multi‐temporal data did not necessarily contribute to the model performance enhancement. Relative homogenous soil‐landscape attributes and spectral characteristics in dry season could not accurately explain the strong spatial variation of SOC in this area, and it might be the major reason that caused the stronger model performance of soil prediction models in wet season than those in dry season. Therefore, the accurate spatial prediction of soil properties requires the characterization of the seasonal dynamics of soil‐landscape relationships. In general, the findings of this research demonstrated that seasonal variation of environmental variables should be considered in the establishment of a DSM model in coastal wetland

Cartography · Delta · Dry season · Ecosystem · Geography · Growing season · Physical geography · Seasonality · Soil carbon · Soil water · Spatial variability · Statistics · Wet season · Wetland · Environmental Science · Mathematics · Remote Sensing in Agriculture · Soil Carbon and Nitrogen Dynamics · Soil Geostatistics and Mapping · Ecology · Soil Science

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