Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China

Wetland ecosystems contain large amounts of soil organic carbon. Their natural environment is often both at the junction of land and water with good conditions for carbon sequestration. Therefore, the study of accurate prediction of soil organic carbon (SOC) density in coastal wetland ecosystems of flat terrain areas is the key to understanding their carbon cycling. This study used remote sensing data to study SOC density potentials of coastal wetland ecosystems in Northeast China. Eleven environmental variables including normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil adjusted vegetation index (SAVI), renormalization difference vegetation index (RDVI), ratio vegetation index (RVI), topographic wetness index (TWI), elevation, slope aspect (SA), slope gradient (SG), mean annual temperature (MAT), and mean annual precipitation (MAP) were selected to predict SOC density. A total of 193 soil samples (0–30 cm) were divided into two parts, 70% of the sampling sites data were used to construct the boosted regression tree (BRT) model containing three different combinations of environmental variables, and the remaining 30% were used to test the predictive performance of the model. The results show that the full variable model is better than the other two models. Adding remote sensing-related variables significantly improved the model prediction. This study revealed that SAVI, NDVI and DVI were the main environmental factors affecting the spatial variation of topsoil SOC density of coastal wetlands in flat terrain areas. The mean (±SD) SOC density of full variable models was 18.78 (±1.95) kg m−2, which gradually decreased from northeast to southwest. We suggest that remote sensing-related environmental variables should be selected as the main environmental variables when predicting topsoil SOC density of coastal wetland ecosystems in flat terrain areas. Accurate prediction of topsoil SOC density distribution will help to formulate soil management policies and enhance soil carbon sequestration.

[1]  Dominique Arrouays,et al.  Spatial distribution of soil organic carbon stocks in France , 2010 .

[2]  Michael F. Hutchinson,et al.  Interpolation of Rainfall Data with Thin Plate Smoothing Splines - Part I: Two Dimensional Smoothing of Data with Short Range Correlation , 1998 .

[3]  Jerome H Friedman,et al.  Multiple additive regression trees with application in epidemiology , 2003, Statistics in medicine.

[4]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[5]  Emmanuelle Vaudour,et al.  Potential of SPOT Multispectral Satellite Images for Mapping Topsoil Organic Carbon Content over Peri‐Urban Croplands , 2013 .

[6]  Qianlai Zhuang,et al.  Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China , 2017 .

[7]  Hossein Asadi,et al.  Spatial variability of soil organic matter using remote sensing data , 2016 .

[8]  Fei Zhang,et al.  New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China , 2018, Remote Sensing of Environment.

[9]  Janine B. Adams,et al.  Wetland carbon storage controlled by millennial-scale variation in relative sea-level rise , 2019, Nature.

[10]  M. Tsukahara,et al.  Efficient folding/assembly in Chinese hamster ovary cells is critical for high quality (low aggregate content) of secreted trastuzumab as well as for high production: stepwise multivariate regression analyses. , 2014, Journal of bioscience and bioengineering.

[11]  Chenghu Zhou,et al.  Purposive Sampling for Digital Soil Mapping for Areas with Limited Data , 2008 .

[12]  José O. Payero,et al.  COMPARISON OF ELEVEN VEGETATION INDICES FOR ESTIMATING PLANT HEIGHT OF ALFALFA AND GRASS , 2004 .

[13]  D. Sullivan,et al.  IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies , 2005 .

[14]  H. Steinmuller,et al.  Characterization of coastal wetland soil organic matter: Implications for wetland submergence. , 2019, The Science of the total environment.

[15]  Zhou Shi,et al.  Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China , 2019, Geoderma.

[16]  J. Farifteh.  Interference of salt and moisture on soil reflectance spectra , 2011 .

[17]  T. L. Coleman,et al.  SPECTRAL DIFFERENTIATION OF SURFACE SOILS AND SOIL PROPERTIES: IS IT POSSIBLE FROM SPACE PLATFORMS? , 1993 .

[18]  Budiman Minasny,et al.  Mapping continuous depth functions of soil carbon storage and available water capacity , 2009 .

[19]  Guirui Yu,et al.  Increased soil organic carbon storage in Chinese terrestrial ecosystems from the 1980s to the 2010s , 2019, Journal of Geographical Sciences.

[20]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[21]  Patrick M. Crill,et al.  Freshwater Methane Emissions Offset the Continental Carbon Sink , 2011, Science.

[22]  Gary A. Peterson,et al.  Soil Attribute Prediction Using Terrain Analysis , 1993 .

[23]  Budiman Minasny,et al.  Digital soil mapping: A brief history and some lessons , 2016 .

[24]  Soil Organic Carbon Stocks in a Large Eutrophic Floodplain Forest of the Southeastern Atlantic Coastal Plain, USA , 2015, Wetlands.

[25]  N. Batjes,et al.  Total carbon and nitrogen in the soils of the world , 1996 .

[26]  Alexander Berk,et al.  Retrieval of atmospheric properties from hyper and multispectral imagery with the FLAASH atmospheric correction algorithm , 2005, SPIE Remote Sensing.

[27]  Sabine Grunwald,et al.  Assessment of Carbon Stocks in the Topsoil Using Random Forest and Remote Sensing Images. , 2016, Journal of environmental quality.

[28]  V. L. Mulder,et al.  The use of remote sensing in soil and terrain mapping — A review , 2011 .

[29]  Hao Wu,et al.  Soil organic carbon storage changes in coastal wetlands of the modern Yellow River Delta from 2000 to 2009. , 2012 .

[30]  P. Leitão,et al.  Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees. , 2014, Spatial and spatio-temporal epidemiology.

[31]  D. W. Nelson,et al.  Total Carbon, Organic Carbon, and Organic Matter , 1983, SSSA Book Series.

[32]  Shuai Wang,et al.  Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China , 2020, Remote. Sens..

[33]  Yiyun Chen,et al.  Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China , 2019, Remote. Sens..

[34]  F. Baret,et al.  A ratio vegetation index adjusted for soil brightness , 1990 .

[35]  Budiman Minasny,et al.  Digital mapping of soil carbon , 2013 .

[36]  O. Mutanga,et al.  Predicting soil organic carbon stocks under commercial forest plantations in KwaZulu-Natal province, South Africa using remotely sensed data , 2020, GIScience & Remote Sensing.

[37]  R. V. Rossel,et al.  Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study , 2008 .

[38]  A. J. Richardsons,et al.  DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION , 1977 .

[39]  H. Elsenbeer,et al.  Soil organic carbon concentrations and stocks on Barro Colorado Island — Digital soil mapping using Random Forests analysis , 2008 .

[40]  Q. Zhuang,et al.  Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data , 2019, Sustainability.