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

Abstract

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 non-uniformly sampled time series. We show how the choice of the autocorrelation estimator can have a dramatic impact in the SM persistence metrics derived thereof, particularly given the non-uniform nature of satellite observations, yet this fact has been overlooked at a large extent in literature. We give empirical evidence of performance using ground-based SM measurements, as well as L-band (SMOS) and C-band (AMSR2, ASCAT) remotely sensed SM data. Experiments along transects allow 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 semi-arid regions exhibit spatially concurrent patterns of persistence and support their combination in long-term data records. We conclude that accounting for the non-uniform nature of the satellite time series using robust autocorrelation estimations allows providing improved measures and spatial descriptions of soil moisture persistence.

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

“…The relative and standalone tests were also used for this purpose. Autocorrelation analysis was applied in precipitation time series to remove any autocorrelation before trend analysis [43][44][45][46], as autocorrelation affects the trend results. Autocorrelation values were calculated with a 95% confidence interval for precipitation data.…”

Persistence in complex systems

“…The estimation of soil moisture persistence from Earth observation-based products is a promising area of intense current research (e.g. [427,428,429,430]). The motivation behind these works is that learning soil moisture persistence parameters from observations may reduce the sources of uncertainties in Earth system and climate models, where complex dynamics and memory effects are currently poorly represented, or even not considered at all.…”

“…Subsequently, we show an example of soil moisture persistence estimation using groundbased measurements from the REMEDHUS network (Salamanca, Spain), as well as Earth Observation data over Europe. Following the approaches proposed in [430], we will tackle the problem of robust autocorrelation estimation for non-uniform time series and will then provide spatial-temporal descriptions of soil moisture persistence using e-folding times.…”

“…To cope with the non-uniform sampling of the observations, we estimated the autocorrelation function of each SM time series with the Lomb-Scargle periodogram and then applied an spatial-temporal denoising of the autocorrelation tensor before obtaining the e-folding time scale, following [430]. Prior to analysis, we detrended the time series to avoid examining artificial memory associated with the climatological seasonal cycle of soil moisture.…”