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