Time series soil moisture retrieval from SAR data: Multi-temporal constraints and a global validation

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Time series algorithms for soil moisture retrieval from synthetic aperture radar (SAR) data have steadily increased in popularity over the past decade due to the feasibility of decoupling the effect of other surface variables, and the increasing availability of dense time series SAR data. While soil moisture inversion from time series data can utilize more independent observations, the value of further constraints on the inversion process are widely acknowledged. However, how to constrain a time series retrieval for global soil moisture mapping is still unresolved. In this study, three kinds of time series constraints were further developed and evaluated, including the use of 1) temporal behavior of soil moisture and soil moisture bounds; 2) temporal behavior of vegetation or time-invariant vegetation; and 3) time series ensemble skill. The effect of these constraints was investigated using 4 years (2016–2019) C-band Sentinel-1 data collected over 547 worldwide stations from 17 networks available on the international soil moisture network (ISMN) and intensive ground samples collected during the Fifth Soil Moisture Active and Passive Experiment (SMAPEx-5). While the effect of these temporal retrieval skills varies in time and space, the global validation yielded four general suggestions: 1) the assumption of time-invariant vegetation contributed negatively even for a short retrieval period of ≤12 days; 2) reliable soil moisture bounds of each retrieval period can substantially improve the retrieval statistics at the cost of an underestimated soil moisture range; 3) the temporal constraints of soil moisture and vegetation need to be used together with the soil moisture bounds for reliable estimation; 4) the use of an ensemble retrieval could partly remove the retrieval uncertainties at the expense of underestimating soil moisture variation. The use of these constraints resulted in a competitive correlation coefficient (R: 0.64), root mean square error (RMSE: 0.072 m3/m3) and unbiased RMSE (ubRMSE: 0.052 m3/m3) at a spatial grid of 100 m, with similar performance achieved across a retrieval window up to 132 days.