Error Decomposition of Remote Sensing Soil Moisture Products Based on the Triple-Collocation Method Introducing an Unbiased Reference Dataset: A Case Study on the Tibetan Plateau

Remote sensing (RS) soil moisture (SM) products have been widely used in various environmental studies. Understanding the error structure of data is necessary to properly apply RS SM products in trend and variation analysis and data fusion. However, a spatially continuous assessment of RS SM datasets is impeded by the limited spatial distribution of ground-based observations. As an alternative, the RS apparent thermal inertia (ATI) data related to the SM are transformed into SM values to expand the validation space. To obtain error components, the ATI-based SM along with the Soil Moisture Active Passive Mission (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM are applied with the triple-collocation (TC) method to evaluate the RS SM data regarding random errors and amplitude variances at the regional scale. When the ATI-based SM is regarded as the reference data, the amplitude biases of the other two datasets are determined. The mean bias is also estimated by calculating the mean value difference between the ATI-based and validated RS SM. The results show that the ATI-based SM is a reliable source of reference data that, when combined with the TC method, can correctly estimate the error structure of RS SM datasets in wide space, promoting the reasonable application and calibration of RS SM datasets.