Autocorrelation Metrics to Estimate Soil Moisture Persistence From Satellite Time Series: Application to Semiarid Regions

Satellite-derived soil moisture (SM) products have become an important information source for the study of land surface processes in hydrology and land monitoring. Characterizing and estimating soil memory and persistence from satellite observations is of paramount relevance, and has deep implications in ecology, water management, and climate modeling. In this work, we address the problem of SM persistence estimation from microwave sensors using several autocorrelation metrics that, unlike traditional approaches, build on accurate estimates of the autocorrelation function from nonuniformly sampled time series. We show how the choice of the autocorrelation estimator can have a dramatic impact on the SM persistence metrics derived thereof, particularly given the nonuniform nature of satellite observations, yet this fact has been overlooked to a large extent in literature. We give empirical evidence of performance using ground-based SM measurements, as well as L-band Soil Moisture and Ocean Salinity (SMOS) and C-band [Advanced Microwave Scanning Radiometer-2 (AMSR2), Advanced Scatterometer (ASCAT)] remotely sensed SM data. Experiments along transects allow us to scrutinize the inter-method consistency and the spatial–temporal characteristics of autocorrelation estimators. This motivates the introduction of novel measures of spatial–temporal autocorrelation and allows us to retrieve improved persistence estimates. Results over the Iberian Peninsula indicate the SM persistence patterns captured by L- and C-band microwave sensors over semiarid regions exhibit spatially concurrent patterns of persistence and support their combination in long-term data records. We conclude that accounting for the nonuniform nature of the satellite time series using robust autocorrelation estimations allows providing improved measures and spatial descriptions of SM persistence.