A global-scale intercomparison of Triple Collocation Analysis- and ground-based soil moisture time-variant errors derived from different rescaling techniques

Abstract

Accurate specification of spatiotemporal errors of remotely sensed soil moisture (SM) data is essential for a correct assessment of their utility and optimally integrating multiple SM products or assimilating them into hydrological models. Although Triple Collocation Analysis (TCA) has been widely used to provide SM errors, the impact of rescaling technique on the TCA error estimates has not received major attention, which can lead to biased and inaccurate error estimates. Moreover, current knowledge about time-variant SM errors derived from TCA is still very limited, which hampers the advance of applying time-variant errors in data merging and data assimilation studies efficiently. Based on these considerations, this work aims to advance the use of the TCA for characterizing errors with a focus on the rescaling techniques, and validating TCA-based time-variant errors using global ground measurements in 759 grid cells. To this end, the Advanced Scatterometer (ASCAT) and four passive-based SM products, including Soil Moisture and Ocean Salinity Level-3 (SMOS-L3), SMOS INRA-CESBIO (SMOS-IC), Soil Moisture Active Passive Level-3 (SMAP-L3), and SMAP INRAE BORDEAUX (SMAP-IB) SM products, were considered. The time-variant error term here denotes an aggregate error magnitude over a 101-day moving-time-window. It is found that different selection of the rescaling technique considered in TCA led to TCA error estimates with significantly different accuracy when ground-based errors are regarded as the benchmark. The optimal combination strategy to implement TCA is applying TCA to SM anomalies and rescaling the errors by coefficients derived from the TCA model. Pearson's correlation with ground-based time-variant errors is 0.62, 0.72, 0.83, 0.89, and 0.93 for SMAP-IB, SMAP-L3, SMOS-IC, SMOS-L3, and ASCAT SM, respectively. Considering time-variant errors in applications is necessary since time-variant errors deviate from time-invariant errors by 50% when errors are rescaled by the TCA model parameters. Time-invariant errors are greater than time-variant errors when SM products are rescaled against a reference dataset while the opposite conclusion can be drawn when errors are rescaled by the TCA coefficients. TCA- and ground-based methods provide consistent evaluations in 74.7% (77.3%), 75.8% (79.8%), 79.6% (81.1%), and 78.6% (79.7%) of the analysis period on average (median) for the TCA implementations with SMAP-L3, SMAP-IB, SMOS-L3, and SMOS-IC SM, respectively. The error analysis reveals that TCA typically underestimated ASCAT errors while overestimated passive SM errors when considering ground-based evaluation as the benchmark. Moreover, TCA was found to have relatively less power to efficiently characterize SM errors in croplands when compared with other land cover types. This study validated TCA time-variant errors using ground measurements and compared TCA- and ground-based evaluation performances on a global scale. Our work arouses particular attention to the rescaling technique selection considered in TCA, which is crucial for accurately characterizing SM errors and efficiently using them in various hydrometeorological applications.