Simulating spatial distribution of coastal soil carbon content using a comprehensive land surface factor system based on remote sensing.

Surface soil carbon content (SCC) in coastal area is affected by complex factors, and revealing the SCC spatial distribution is considerably significant for judging the quantity of stored carbon and identifying the driving factors of SCC variation. A comprehensive land surface factor system (CLSFS) was established; it utilized the ecological significances of remote sensing data and included four-class factors, namely, spectrum information, ecological indices, spatial location, and land cover. Different simulation algorithms, including single-factor regression (SFR), multiple-factor regression (MFR), partial least squares regression (PLSR), and back propagation neural network (BPNN), were adopted to conduct the surface (0-30cm) SCC mapping in the Yellow River Delta in China, and a 10-fold cross validation approach was used to validate the uncertainty and accuracy of the algorithms. The results indicated that the mean simulated standard deviations were all <0.5g/kg and thus showed a low uncertainty; the mean root mean squared errors based on the simulated and measured SCC were 3.88g/kg (SFR), 3.85g/kg (PLSR), 3.67g/kg (MFR), and 2.78g/kg (BPNN) with the BPNN exhibiting a high accuracy compared to similar studies. The mean SCC was 17.40g/kg in the Yellow River Delta with distinct spatial heterogeneity; in general, the SCC in the alongshore regions, except for estuaries, was low, and that in the west of the study area was high. The mean SCCs in farmland (18.31g/kg) and wetland vegetation (17.98g/kg) were higher than those in water area (16.07g/kg), saltern (15.61g/kg), and bare land (14.71g/kg). Land-sea interaction and human activity jointly affected the SCC spatial distribution. The CLSFS was proven to have good applicability, and can be widely used in simulating the SCC spatial distribution in coastal areas.