Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E_SWI, SMOS_SWI, AMSR2_SWI, and ASCAT_SWI, with the L-band-based SMAPL3E_SWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI_SWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3E_SWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment on a global scale is provided by using the triple-collocation (TC) technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques. The SM2RAIN–ASCAT data record is freely available at https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of August 2019).

Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al., 2014). Daily rainfall estimates are generated for an 18-year long period (1998–2015), with a spatial sampling of 0.25° on a global scale, and are based on the integration of the ACTIVE and the PASSIVE ESA CCI SM data sets.The quality of the SM2RAIN-CCI rainfall data set is evaluated by comparing it with two state-of-the-art rainfall satellite products, i.e. the Tropical Measurement Mission Multi-satellite Precipitation Analysis 3B42 real-time product (TMPA 3B42RT) and the Climate Prediction Center Morphing Technique (CMORPH), and one modeled data set (ERA-Interim). A quality check is carried out on a global scale at 1° of spatial sampling and 5 days of temporal sampling by comparing these products with the gauge-based Global Precipitation Climatology Centre Full Data Daily (GPCC-FDD) product. SM2RAIN-CCI shows relatively good results in terms of correlation coefficient (median value > 0.56), root mean square difference (RMSD, median value < 10.34 mm over 5 days) and bias (median value < −14.44 %) during the evaluation period. The validation has been carried out at original resolution (0.25°) over Europe, Australia and five other areas worldwide to test the capabilities of the data set to correctly identify rainfall events under different climate and precipitation regimes.The SM2RAIN-CCI rainfall data set is freely available at https://doi.org/10.5281/zenodo.846259.

Vegetation products based on microwave remote sensing observations, such as Vegetation Optical Depth (VOD), are increasingly used in a variety of applications. One disadvantage is the often coarse spatial resolution of tens of kilometers of products retrieved from microwave observations from spaceborne radiometers and scatterometers. This can potentially be overcome by using new high-resolution Synthetic Aperture Radar (SAR) observations from Sentinel-1. However, the sensitivity of Sentinel-1 backscatter to vegetation dynamics, or its use in radiative transfer models, such as the water cloud model, has only been tested at field to regional scale. In this study, we compared the cross-polarization ratio (CR) to vegetation dynamics as observed in microwave-based Vegetation Optical Depth from coarse-scale satellites over Europe. CR was obtained from Sentinel-1 VH and VV backscatter observations at 500 m sampling and resampled to the spatial resolution of VOD from the Advanced SCATterometer (ASCAT) on-board the Metop satellite series. Spatial patterns between median CR and ASCAT VOD correspond to each other and to vegetation patterns over Europe. Analysis of temporal correlation between CR and ASCAT VOD shows that high Pearson correlation coefficients ($R_p$) are found over croplands and grasslands (median $R_p$ > 0.75). Over deciduous broadleaf forests, negative correlations are found. This is attributed to the effect of structural changes in the vegetation canopy which affect CR and ASCAT VOD in different ways. Additional analysis comparing CR to passive microwave-based VOD shows similar effects in deciduous broadleaf forests and high correlations over crop- and grasslands. Though the relationship between CR and VOD over deciduous forests is unclear, results suggest that CR is useful for monitoring vegetation dynamics over crop- and grassland and a potential path to high-resolution VOD.

This study investigates the quality of Advanced Scatterometer (ASCAT) surface soil moisture (SSM) retrievals with respect to other SSM products derived from the passive Soil Moisture and Ocean Salinity (SMOS) mission and two reanalysis datasets, i.e., the JRA-55 and the ERA-Interim. In particular, the purposes of this study are to 1) characterize the global error structure of the satellite products, 2) understand the spatiotemporal variability of SSM at global scale, and 3) investigate in which areas the assimilation of satellite data may add value to reanalysis. For these purposes, we applied standard statistical methods as well as triple collocation analysis (TCA) for estimating signal-to-noise ratios (SNR). In line with previous studies, we find large and spatially variable biases between all four datasets, but overall spatiotemporal dynamics as reflected in Hovmöller diagrams agree well. With the exception of arid and semiarid environments, ASCAT performs better than SMOS in terms of both its correlation with the models and the SNR. As a result of TCA, we recognize the potential areas for assimilation of ASCAT data, characterized by a high SNR of the satellite data compared to the models, to be the savanna regions in Africa and Central Asia, southwestern North America, and eastern Australia.

Previous validation studies have demonstrated the accuracy of the Metop-A ASCAT soil moisture (SM) product, although over- and underestimation during different seasons of the year suggest a need for improving the retrieval algorithm. In this study, we analyzed whether adapting the vegetation characterization based on global parameters to regional conditions improves the seasonal representation of SM and vegetation optical depth ( $\tau$ ). SM and $\tau$ are retrieved from ASCAT using both a seasonal (mean climatological) and a dynamic vegetation characterization that allows for year-to-year changes. The retrieved SM and $\tau$ are compared with in situ and satellite SM, and with vegetation products (SMAP, AMSR2, and SPOT-VGT/PROBA-V). The study region is set in an agricultural area of Lower Austria that is characterized by heterogeneous land cover and topography, and features an experimental catchment equipped with a SM network (HOAL SoilNet). We found that a stronger vegetation correction within the SM retrieval improves the SM product considerably (increase of the Spearman correlation coefficient $r_s$ by 0.15 on average, and $r_s$ comparable to SMAP and AMSR2). The vegetation product derived with a dynamic vegetation characterization compares well to the reference datasets and reflects vegetation dynamics such as start and peak of season and harvest. Although some vegetation effects cannot be corrected by the adapted vegetation characterization, our results demonstrate the benefits of a parameterization optimized for regional conditions in this temperate climate zone.