An Improvement to the Three-Cornered Hat Method: A Case Study in Applying to Soil Moisture Datasets

Due to the lack of in-situ measurements, direct validations can only be carried out at limited pixels and cannot be expanded to a regional or global scale. The three-cornered hat (TCH) method is essentially an optimal solution based on the numerical method, allowing an arbitrary number of datasets (at least three) to simultaneously participate in the calculation, with a tolerance to the error cross-correlation to a certain extent. However, in TCH, only a basic error model is adopted, which describes the products as mutually differing only in an additive random error. This violates our common recognition of the error model (i.e., affine error model) applied to describing products like soil moisture (SM). This work aims to improve the TCH method by updating the basic error model with the affine error model, and the reliability of the improved TCH method is evaluated using simulated soil moisture datasets and multisource soil moisture products against in-situ SM measurements.