The Everglades Vulnerability Analysis (EVA) is a series of connected, modular Bayesian networks that predict the response of several Everglades indicators of ecosystem health to changes in hydrology, salinity, and the landscape. We created a soil depth layer for use in the sawgrass peat module by using universal kriging to estimate soil depth at a 400-meter resolution for individual management areas across the Greater Everglades footprint. We compiled soil depth data from various sources totaling 687 coordinate point locations across all sampling years which ranged from 1964 to 2018. We calculated mean soil depth when multiple depths were documented at the same location. We examined soil depth within each management area for normality and used a cube root transformation on the data to normalize the distribution where needed. We also observed spatial trends in soil depth, and therefore we examined easting and northing as covariates when kriging. We used the ‘autoKrige’ function from the R package automap to fit variograms with easting and northing and to perform kriging. We selected the best fitting model, back-transformed, and bias corrected interpolated depths and used the kriged depths to create the soil depth layer.