ABSTRACT
The uncertainty of surface soil moisture (SM) retrievals from satellite brightness temperature (TB) observations depends primarily on the choice of radiative transfer model (RTM) parameters, prior SM information and TB inputs. This paper studies the sensitivity of several established and experimental SM retrieval products from the Soil Moisture Ocean Salinity (SMOS) mission to these choices at 11 reference sites, located in 7 watersheds across the United States (US). Different RTM parameter sets cause large biases between retrievals. Whereas typical RTM parameter sets are calibrated for SM retrievals, it is shown that a parameter set carefully optimized for TB forward modeling can also be used for retrieving SM. It is also shown that the inclusion of dynamic prior SM estimates in a Bayesian retrieval scheme can strongly improve SM retrievals, regardless of the choice of RTM parameters. The second part of this paper evaluates the ensemble uncertainty metrics for SM retrievals obtained by propagating a wide range of RTM parameters through the RTM, and the relationship with time series metrics obtained by comparing SM retrievals with in situ data. As expected for bounded variables, the total spread in the ensemble SM retrievals is smallest for wet and dry SM values and highest for intermediate SM values. After removal of the strong long-term SM bias associated with the RTM parameter values for individual ensemble members, the remaining anomaly ensemble SM spread shows higher values when SM deviates further from its long-term mean SM. This reveals higher-order biases (e.g. differences in variances) in the retrieval error, which should be considered when characterizing retrieval error. The time-average anomaly ensemble SM spread of 0.037 m3/m3 approximates the actual time series unbiased root-mean-square-difference of 0.042 m3/m3 between ensemble mean retrievals and in situ data across the reference sites.
ABSTRACT
Mounds originating from wind-blown sediment accumulation beneath vegetation (nebkhas) often indicate land degradation in dry areas. Thus far, most nebkha research has focused on individual plants. Here, we aimed to explore population-scale processes (up to scales of about 100 m) that might explain an observed nebkha landscape pattern. We mapped the Rhazya stricta Decne. population in a 3 ha study site in a hyper-arid region of Saudi Arabia. We compared the spatial patterns of five different cohorts (age classes) of observed nebkha host plants to those expected under several hypothesized drivers of recruitment and intraspecific interaction. We found that all R. stricta cohorts had a limited fractional vegetation cover and established in large-scale clusters. This clustering weakened with cohort age, possibly indicating merging of neighboring vegetation patches. Different cohort clusters did not spatially overlap in most cases, indicating that recruitment patterns changed position over time. Strong indications were found that the main drivers underlying R. stricta spatial configurations were allogenic (i.e. not driven by vegetation) and dynamic. Most likely these drivers were aeolian-driven sand movement or human disturbance which forced offspring recruitment in spatially dynamic clusters. Competition and facilitation were likely active on the field site too, but apparently had a limited effect on the overall landscape structure.
