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Satellite-based precipitation products, with simultaneously high spatial and temporal resolutions, are mostly needed to assess climate change repercussions. Previous research used datasets neglecting either good temporal or good spatial resolution, PERSIANN-CCSCDR, ERA5, and SM2RAIN-ASCAT are some of the projects aiming to remedy these limitations. This study's goal is to evaluate the accuracy of the PERSIANN-CCS-CDR, ERA5, and SM2RAIN-ASCAT at a monthly scale and their suitability for drought assessment in a Moroccan semiarid watershed. Several statistical indices were computed, the drought SPI was calculated using PERSIANN-CCS-CDR estimates, ERA5 products, and observed records as an input in the SPI formula using Gamma distribution to simulate drought from 1983 to 2017. The preliminary comparison and evaluation results of PERSIANN-CCS-CDR estimates and ERA5 datasets showed good CC on a basin scale for monthly precipitation, with a slight overestimation of the observed precipitation shown by the PBIAS. The NSE scored 0.41 for PERSIANN-CCS-CDR and 0.72 for ERA5. The results for SM2RAIN-ASCAT showed an overestimation of the observed precipitation data. At the basin scale, the SPI3 correlation coefficients between the PERSIANN-CCS-CDR monthly estimates and observed gauge rainfall data were greater than 0.67, and the RMSE was closer to 0, outperforming ERA5 in the SPI3 evaluation.

Near‐surface soil moisture observations from the active microwave ASCAT and the passive microwave AMSR‐E satellite instruments are assimilated, both separately and together, into the NASA Catchment land surface model over 3.5 years using an ensemble Kalman filter. The impact of each assimilation is evaluated using in situ soil moisture observations from 85 sites in the US and Australia, in terms of the anomaly time series correlation‐coefficient, R. The skill gained by assimilating either ASCAT or AMSR‐E was very similar, even when separated by land cover type. Over all sites, the mean root‐zone R was significantly increased from 0.45 for an open‐loop, to 0.55, 0.54, and 0.56 by the assimilation of ASCAT, AMSR‐E, and both, respectively. Each assimilation also had a positive impact over each land cover type sampled. For maximum accuracy and coverage it is recommended that active and passive microwave observations be assimilated together. Key Points Assimilating ASCAT or AMSR‐E SM significantly improves root‐zone SM skill Assimilating ASCAT or AMSR‐E SM yields similar skill, regardless of land cover The minimum SM observation skill for positive assimilation impact is quantified

This study assesses four Satellite-derived Precipitation Products (SPPs) that are corrected and validated against gauge data such as Soil Moisture to Rain—Advanced SCATterometer V1.5 (SM2RAIN-ASCAT), Multi-Source Weighted-Ensemble Precipitation V2.8 (MSWEP), Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM Final run V6 (GPM IMERGF), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). We evaluate the performance of these SPPs in Nepal’s Myagdi Khola watershed, located in the Kali Gandaki River basin, for the period 2009–2019. The SPPs are evaluated by validating the gridded precipitation products using the hydrological model, Soil and Water Assessment Tool (SWAT). The results of this study show that the SM2RAIN-ASCAT and GPM IMERGF performed better than MSWEP and CHIRPS in accurately simulating daily and monthly streamflow. GPM IMERGF and SM2RAIN-ASCAT are found to be the better-performing models, with higher NSE values (0.63 and 0.61, respectively) compared with CHIRPS and MSWEP (0.45 and 0.41, respectively) after calibrating the model with monthly data. Moreover, SM2RAIN-ASCAT demonstrated the best performance in simulating daily and monthly streamflow, with NSE values of 0.57 and 0.63, respectively, after validation. This study’s findings support the use of satellite-derived precipitation datasets as inputs for hydrological models to address the hydrological complexities of mountainous watersheds.

This study evaluates the accuracy of several recent remote sensing Surface Soil Moisture (SSM) products at sites in southwestern France. The products used are Soil Moisture Active Passive “SMAP” (level 3: 36 km × 36 km, level 3 enhanced: 9 km × 9 km, and Level 2 SMAP/Sentinel-1: 1 km × 1km), Advanced Scatterometer “ASCAT” (level 2 with three spatial resolution 25 km × 25 km, 12.5 km × 12.5 km, and 1 km × 1 km), Soil Moisture and Ocean Salinity “SMOS” (SMOS INRA-CESBIO “SMOS-IC”, SMOS Near-Real-Time “SMOS-NRT”, SMOS Centre Aval de Traitement des Données SMOS level 3 “SMOS-CATDS”, 25 km × 25 km) and Sentinel-1(S1) (25 km × 25 km, 9 km × 9 km, and 1 km × 1 km). The accuracy of SSM products was computed using in situ measurements of SSM observed at a depth of 5 cm. In situ measurements were obtained from the SMOSMANIA ThetaProbe (Time Domaine reflectometry) network (7 stations between 1 January 2016 and 30 June 2017) and additional field campaigns (near Montpellier city in France, between 1 January 2017 and 31 May 2017) in southwestern France. For our study sites, results showed that (i) the accuracy of the Level 2 SMAP/Sentinel-1 was lower than that of SMAP-36 km and SMAP-9 km; (ii) the SMAP-36 km and SMAP-9 km products provide more precise SSM estimates than SMOS products (SMOS-IC, SMOS-NRT, and SMOS-CATDS), mainly due to higher sensitivity of SMOS to RFI (Radio Frequency Interference) noise; and (iii) the accuracy of SMAP-36 km and SMAP-9 km products was similar to that of ASCAT (ASCAT-25 km, ASCAT-12.5 km and ASCAT-1 km) and S1 (S1-25 km, S1-9 km, and S1-1 km) products. The accuracy of SMAP, Sentinel-1 and ASCAT SSM products calculated using the average of statistics obtained on each site is defined by a bias of about −3.2 vol. %, RMSD (Root Mean Square Difference) about 7.6 vol. %, ubRMSD (unbiased Root Mean Square Difference)about 5.6 vol. %, and R coefficient about 0.57. For SMOS products, the station average bias, RMSD, ubRMSD, and R coefficient were about −10.6 vol. %, 12.7 vol. %, 5.9 vol. %, and 0.49, respectively.

The scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as they provide area-average soil moisture within a 150–250 m radius footprint. In this study, we evaluate differences and similarities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). Six CRNPs located on five continents have been selected as test sites: the Rur catchment in Germany, the COSMOS sites in Arizona and California (USA), and Kenya, one CosmOz site in New South Wales (Australia), and a site in Karnataka (India). Standard validation scores as well as the Triple Collocation (TC) method identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs. The potential of CRNPs for satellite soil moisture validation has been proven; however, biomass correction methods should be implemented to improve its application in regions with large vegetation dynamics.

The inter-comparison of different soil moisture (SM) products over the Tibetan Plateau (TP) reveals the inconsistency among different SM products, when compared to in situ measurement. It highlights the need to constrain the model simulated SM with the in situ measured data climatology. In this study, the in situ soil moisture networks, combined with the classification of climate zones over the TP, were used to produce the in situ measured SM climatology at the plateau scale. The generated TP scale in situ SM climatology was then used to scale the model-simulated SM data, which was subsequently used to scale the SM satellite observations. The climatology-scaled satellite and model-simulated SM were then blended objectively, by applying the triple collocation and least squares method. The final blended SM can replicate the SM dynamics across different climatic zones, from sub-humid regions to semi-arid and arid regions over the TP. This demonstrates the need to constrain the model-simulated SM estimates with the in situ measurements before their further applications in scaling climatology of SM satellite products.

Several studies currently strive to improve the spatial resolution of coarse scale high temporal resolution global soil moisture products of SMOS, SMAP, and ASCAT. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. We use this information for the prediction of the sub-grid soil moisture variability for each SMOS, SMAP, and ASCAT grid cell. The approach is based on a method that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean, available at https://doi.org/10.1594/PANGAEA.878889. The resulting data set helps identify adequate regions to validate coarse scale soil moisture products by providing a measure of representativeness of small-scale measurements for the coarse grid cell. Moreover, it contains important information for downscaling coarse soil moisture observations of the SMOS, SMAP, and ASCAT missions. In this study, we present a simple application of the estimated sub-grid soil moisture heterogeneity scaling down SMAP soil moisture to 1 km resolution. Validation results in the TERENO and REMEDHUS soil moisture monitoring networks in Germany and Spain, respectively, indicate a similar or slightly improved accuracy for downscaled and original SMAP soil moisture in the time domain for the year 2016, but with a much higher spatial resolution.

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 (Rp) are found over croplands and grasslands (median Rp > 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.

The accuracy and consistency of a quintuple collocation analysis of ocean surface vector winds from buoys, scatterometers, and NWP forecasts is established. A new solution method is introduced for the general multiple collocation problem formulated in terms of covariance equations. By a logarithmic transformation, the covariance equations reduce to ordinary linear equations that can be handled using standard methods. The method can be applied to each determined or overdetermined subset of the covariance equations. Representativeness errors are estimated from differences in spatial variances. The results are in good agreement with those from quadruple collocation analyses reported elsewhere. The geometric mean of all solutions from determined subsets of the covariance equations equals the least-squares solution of all equations. The accuracy of the solutions is estimated from synthetic data sets with random Gaussian errors that are constructed from the buoy data using the values of the calibration coefficients and error variances from the quintuple collocation analysis. For the calibration coefficients, the spread in the models is smaller than the accuracy, but for the observation error variances, the spread and the accuracy are about equal only for representativeness errors evaluated at a scale of 200 km for u and 100 km for v. Some average error covariances differ significantly from zero, indicating weak inconsistencies in the underlying error model. Possible causes for this are discussed. With a data set of 2454 collocations, the accuracy in the observation error standard deviation is 0.02 to 0.03 m/s at the one-sigma level for all observing systems.

In this study, the performances of four satellite-based precipitation products (IMERG-V06 Final-Run, TRMM-3B42V7, SM2Rain-ASCAT, and PERSIANN-CDR) were assessed with reference to the measurements of in-situ gauges at daily, monthly, seasonal, and annual scales from 2010 to 2017, over the Hindu Kush Mountains of Pakistan. The products were evaluated over the entire domain and at point-to-pixel scales. Different evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias)) and categorical indices (False Alarm Ration (FAR), Critical Success Index (CSI), Success Ratio (SR), and Probability of Detection (POD)) were used to assess the performances of the products considered in this study. Our results indicated the following. (1) IMERG-V06 and PERSIANN capably tracked the spatio-temporal variation of precipitation over the studied region. (2) All satellite-based products were in better agreement with the reference data on the monthly scales than on daily time scales. (3) On seasonal scale, the precipitation detection skills of IMERG-V06 and PERSIANN-CDR were better than those of SM2Rain-ASCAT and TRMM-3B42V7. In all seasons, overall performance of IMERG-V06 and PERSIANN-CDR was better than TRMM-3B42V7 and SM2Rain-ASCAT. (4) However, all products were uncertain in detecting light and moderate precipitation events. Consequently, we recommend the use of IMERG-V06 and PERSIANN-CDR products for subsequent hydro-meteorological studies in the Hindu Kush range.