Bias Correction in Monthly Records of Satellite Soil Moisture Using Nonuniform CDFs

Abstract

It is important to eliminate systematic biases in the field of soil moisture data assimilation. One simple method for bias removal is to match cumulative distribution functions (CDFs) of modeled soil moisture data to satellite soil moisture data. Traditional methods approximate numerical CDFs using 12 or 20 uniformly spaced samples. In this paper, we applied the Douglas–Peucker curve approximation algorithm to approximate the CDFs and found that three nonuniformly spaced samples can achieve the same reduction in standard deviation. Meanwhile, the matching results are always closely related to the temporal and spatial availability of soil moisture observed by automatic soil moisture station (ASM). We also applied the new nonuniformly spaced sampling method to a shorter time series. Instead of processing a whole year of data at once, we divided it into 12 datasets and used three nonuniformly spaced samples to approximate the model data’s CDF for each month. The matching results demonstrate that NU-CDF3 reduced the SD, improved R, and reduced the RMSD in over 70% of the stations, when compared with U-CDF12. Additionally, the SD and RMSD have been reduced by over 4% with R improved by more than 9%.