Generating three-dimensional soil organic carbon density dataset by soil depth function and correction methods in Yangtze River Delta, China

Accurate, high resolution, and depth continuous soil organic carbon density (SOCD) dataset is crucial for various research and management practices. Based on 593 soil samples, we tested different soil depth functions and correction methods for generating the 0–1.0 m three-dimensional SOCD dataset with the spatial resolution of 90-m in the Yangtze River Delta region. Depth functions (power function, exponential decay function, logarithmic function and inverse function) were fitted for different samples in the training set, and their obtained parameters were mapped by random forest based on ancillary variables. Then three correction methods, including coefficient scaling, data fusion and residual correction, were applied in the validation set to correct the predictions of depth functions. After correcting, the prediction accuracies have been significantly improved at all depths. Our dataset can generate accurate SOCD maps at any specific depth interval by constructing the vertical continuous distribution of the corrected coefficients.