Richard de Jeu

Multisensor historical climatology of satellite‐derived global land surface moisture

global product and is consistent in its retrieval approach for the entire period of data record. The moisture retrievals are made with a radiative transfer-based land parameter retrieval model. The various sensors have different technical specifications, including primary wavelength, spatial resolution, and temporal frequency of coverage. These sensor specifications and their effect on the data retrievals are discussed. The model is described in detail, and the quality of the data with respect to the different sensors is discussed as well. Examples of the different sensor retrievals illustrating global patterns are presented. Additional validation studies were performed with large-scale observational soil moisture data sets and are also presented. The data will be made available for use by the general science community.

Afternoon rain more likely over drier soils

Land surface properties, such as vegetation cover and soil moisture, influence the partitioning of radiative energy between latent and sensible heat fluxes in daytime hours. During dry periods, soil-water deficit can limit evapotranspiration, leading to warmer and drier conditions in the lower atmosphere. Soil moisture can influence the development of convective storms through such modifications of low-level atmospheric temperature and humidity, which in turn feeds back on soil moisture. Yet there is considerable uncertainty in how soil moisture affects convective storms across the world, owing to a lack of observational evidence and uncertainty in large-scale models. Here we present a global-scale observational analysis of the coupling between soil moisture and precipitation. We show that across all six continents studied, afternoon rain falls preferentially over soils that are relatively dry compared to the surrounding area. The signal emerges most clearly in the observations over semi-arid regions, where surface fluxes are sensitive to soil moisture, and convective events are frequent. Mechanistically, our results are consistent with enhanced afternoon moist convection driven by increased sensible heat flux over drier soils, and/or mesoscale variability in soil moisture. We find no evidence in our analysis of a positive feedback—that is, a preference for rain over wetter soils—at the spatial scale (50–100 kilometres) studied. In contrast, we find that a positive feedback of soil moisture on simulated precipitation does dominate in six state-of-the-art global weather and climate models—a difference that may contribute to excessive simulated droughts in large-scale models.

The Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

AbstractThe contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satel...

Short communication: A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations

The presence of data gaps is always a concern in geophysical records, creating not only difficulty in interpretation but, more importantly, also a large source of uncertainty in data analysis. Filling the data gaps is a necessity for use in statistical modeling. There are numerous approaches for this purpose. However, particularly challenging are the increasing number of very large spatio-temporal datasets such as those from Earth observations satellites. Here we introduce an efficient three-dimensional method based on discrete cosine transforms, which explicitly utilizes information from both time and space to predict the missing values. To analyze its performance, the method was applied to a global soil moisture product derived from satellite images. We also executed a validation by introducing synthetic gaps. It is shown this method is capable of filling data gaps in the global soil moisture dataset with very high accuracy.

Changing Climate and Overgrazing Are Decimating Mongolian Steppes

Satellite observations identify the Mongolian steppes as a hotspot of global biomass reduction, the extent of which is comparable with tropical rainforest deforestation. To conserve or restore these grasslands, the relative contributions of climate and human activities to degradation need to be understood. Here we use a recently developed 21-year (1988–2008) record of satellite based vegetation optical depth (VOD, a proxy for vegetation water content and aboveground biomass), to show that nearly all steppe grasslands in Mongolia experienced significant decreases in VOD. Approximately 60% of the VOD declines can be directly explained by variations in rainfall and surface temperature. After removing these climate induced influences, a significant decreasing trend still persists in the VOD residuals across regions of Mongolia. Correlations in spatial patterns and temporal trends suggest that a marked increase in goat density with associated grazing pressures and wild fires are the most likely non-climatic factors behind grassland degradation.

Evaluation of soil moisture derived from passive microwave remote sensing over agricultural sites in Canada using ground-based soil moisture monitoring networks

Passive microwave soil moisture datasets can be used as an input to provide an integrated assessment of climate variability as it relates to agricultural production. The objective of this research was to examine three passive microwave derived soil moisture datasets over multiple growing seasons in contrasting Canadian agricultural environments. Absolute and relative soil moisture was evaluated from two globally available datasets from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) sensor using different retrieval algorithms, as well as relative soil wetness at a weekly scale from the Special Sensor Microwave/Imager (SSM/I) sensor. At a daily scale, the Land Parameter Retrieval Model (LPRM) provides a better estimate of surface soil moisture conditions than the National Snow and Ice Data Center (NSIDC) dataset, with root mean squared errors ranging from 5 to 10% for LPRM and 12 to 18% for NSIDC soil moisture when a temporal smoothing is applied to the dataset. Both datasets provided better estimates of soil moisture over the temperate site near Elora, Ontario than the prairie site near Davidson, Saskatchewan. The LPRM dataset tends to overestimate soil moisture conditions at both sites, where the NSIDC dataset tends to underestimate absolute soil moisture. These differences in retrieval methods were independent of radiometric frequency used. At weekly scales, the LPRM dataset provides a better relative estimate of wetness conditions when compared to the NSIDC and the Basist Wetness Index (BWI) from SSM/I data, but the SSM/I dataset did provide a reasonably good relative indicator of moisture conditions. The high variability in accuracy of soil moisture estimation related to retrieval algorithms indicates that consistency is needed in these datasets if they are to be integrated in long term studies for yield estimation or data assimilation.

TRMM-TMI satellite observed soil moisture and vegetation density (1998-2005) show strong connection with El Nino in eastern Australia

Oscillation Index (SOI) in spring (r 2 = 0.90), and to a progressively lesser extent autumn, summer and winter. The Indian Ocean Dipole (IOD) index also explained part of the variation in spring q and t. Correlation analysis suggested that the regions most affected by El Nino are mainly located in eastern Australia. The results suggest that the drought conditions experienced in eastern Australia since 2000 and clearly expressed in these satellite observations have a strong connection with El Nino. Citation: Liu, Y., R. A. M. de Jeu, A. I. J. M. van Dijk, and M. Owe (2007), TRMM-TMI satellite observed soil moisture and vegetation density (1998 - 2005) show strong connection with El Nino in eastern Australia, Geophys. Res. Lett., 34, L15401, doi:10.1029/2007GL030311.

Clarifications on the “Comparison Between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products Over Four Watersheds in U.S.”

In a recent paper, Leroux compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2-3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable.” In this clarification, the analysis of Leroux is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.

Satellite soil moisture for advancing our understanding of earth system processes and climate change

Soil moisture products obtained from active and passive icrowave satellites have reached maturity during the last decade De Jeu and Dorigo, 2016): On the one hand, research algorithms hat were initially applied to sensors designed for other purposes, .g., for measuring wind speed (e.g. the Advanced Scatteromeer (ASCAT)), sea ice, or atmospheric parameters (e.g. the TRMM icrowave Imager (TMI) and the Advanced Microwave Scanning adiometer – Earth Observing System AMSR-E), have developed nto fully operational products. On the other hand, dedicated soil oisture satellite missions were designed and launched by ESA (the oil Moisture Ocean Salinity (SMOS) mission) and NASA (the Soil oisture Active Passive (SMAP) mission). The development of new products and satellite missions was not nly driven by new technological possibilities but possibly even ore by the widespread user recognition of satellite soil moisure products for a wide range of applications. In the end, the ser requirements have driven the design of the SMOS and SMAP oil moisture missions as well as the specifications of soil moisure Earth Observation programmes, e.g., ESA’s Climate Change nitiative for soil moisture, EUMETSAT’s Satellite Application Facilties Support to Operational Hydrology and Water Management H-SAF), and the GMES/Copernicus land services of the European ommission. Throughout the years, the potential of satellite soil moisture roducts has been explored for a wide range of applications, rangng from model validation to improving our understanding of limate variability and change. A selection of applications including eferences is given in Table 1. Traditionally, satellite soil moisture is astly used for validating soil moisture states for a variety of modls, including hydrological, land surface, dynamic global vegetation, nd drought models (Table 1). As a result of their recognised qualty and their increasing operational and near-real-time availability, emotely sensed soil moisture products are increasingly being ssimilated into numerical weather models, e.g. at the UK MetOfce, into reanalysis products, e.g. at ECMWF, and into hydrological odels to update the model soil moisture states and ultimately o improve related analysis products such as air temperature and umidity. But apart from updating model states, the assimilation of atellite soil moisture into hydrological and biogeochemical modls has also has allowed for calibrating the parameterisations of the odels themselves.

Analyzing the Vegetation Parameterization in the TU-Wien ASCAT Soil Moisture Retrieval

In microwave remote sensing of the Earths surface, the satellite signal holds information on both soil moisture and vegetation. This necessitates a correction for vegetation effects when retrieving soil moisture. This paper assesses the strengths and weaknesses of the existing vegetation correction as part of the Vienna University of Technology (TU-Wien) method for soil moisture retrieval from coarse-scale active microwave observations. In this method, vegetation is based on a multiyear climatology of backscatter variations related to phenology. To assess the plausibility of the correction method, we first convert the correction terms for retrievals from the Advanced Scatterometer (ASCAT) into estimates of vegetation optical depth τa using a water-cloud model. The spatial and temporal behaviors of the newly developed τa are compared with the optical depth retrieved from passive microwave observations with the land parameter retrieval model τp. Spatial patterns correspond well, although low values for τa are found over boreal forests. Temporal correlation between the two products is high (R = 0.5), although negative correlations are observed in drylands. This comparison shows that τa and thus the vegetation correction method are sensitive to vegetation dynamics. Effects of the vegetation correction on soil moisture retrievals are investigated by comparing retrieved soil moisture before and after applying the correction term to modeled soil moisture. The vegetation correction increases the quality of the soil moisture product. In areas of high interannual variability in vegetation dynamics, we observed a negative impact of the vegetation correction on the soil moisture, with a decrease in correlation up to 0.4. It emphasizes the need for a dynamic vegetation correction in areas with high interannual variability.