An RFI-suppressed SMOS L-band multi-angular brightness temperature dataset spanning over a decade (since 2010)

The Soil Moisture Ocean Salinity (SMOS) was the first mission providing L-band multi-angular brightness temperature (TB) at the global scale. However, radio frequency interferences (RFI) and aliasing effects degrade, when present SMOS TBs, and thus affect the retrieval of land parameters. To alleviate this, a refined SMOS multi-angular TB dataset was generated based on a two-step regression approach. This approach smooths the TBs and reconstructs data at the incidence angle with large TB uncertainties. Compared with Centre Aval de Traitement des Données SMOS (CATDS) TB product, this dataset shows a better relationship with the Soil Moisture Active Passive (SMAP) TB and enhanced correlation with in-situ measured soil moisture. This RFI-suppressed SMOS TB dataset, spanning more than a decade (since 2010), is expected to provide opportunities for better retrieval of land parameters and scientific applications.

River flow prediction in data scarce regions: soil moisture integrated satellite rainfall products outperform rain gauge observations in West Africa

Satellite precipitation products have been largely improved in the recent years particularly with the launch of the global precipitation measurement (GPM) core satellite. Moreover, the development of techniques for exploiting the information provided by satellite soil moisture to complement/enhance precipitation products have improved the accuracy of accumulated rainfall estimates over land. Such satellite enhanced precipitation products, available with a short latency (< 1 day), represent an important and new source of information for river flow prediction and water resources management, particularly in developing countries in which ground observations are scarcely available and the access to such data is not always ensured. In this study, three recently developed rainfall products obtained from the integration of GPM rainfall and satellite soil moisture products have been used; namely GPM+SM2RAIN, PRISM-SMOS, and PRISM-SMAP. The prediction of observed daily river discharge at 10 basins located in Europe (4), West Africa (3) and South Africa (3) is carried out. For comparison, we have also considered three rainfall products based on: (1) GPM only, i.e., the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (GPM-ER), (2) rain gauges, i.e., the Global Precipitation Climatology Centre, and (3) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. Three different conceptual and lumped rainfall-runoff models are employed to obtain robust and reliable results over the 3-year data period 2015–2017. Results indicate that, particularly over scarcely gauged areas (West Africa), the integrated products outperform both ground- and reanalysis-based rainfall estimates. For all basins, the GPM+SM2RAIN product is performing the best among the short latency products with mean Kling–Gupta Efficiency (KGE) equal to 0.87, and significantly better than GPM-ER (mean KGE = 0.77). The integrated products are found to reproduce particularly well the high flows. These results highlight the strong need to disseminate such integrated satellite rainfall products for hydrological (and agricultural) applications in poorly gauged areas such as Africa and South America.

Multi-time scale analysis of the spatial representativeness of in situ soil moisture data within satellite footprints

We conduct a novel comprehensive investigation that seeks to prove the connection between spatial and time scales in surface soil moisture (SM) within the satellite footprint (~50 km). Modeled and measured point series at Yanco and Little Washita in situ networks are first decomposed into anomalies at time scales ranging from 0.5 to 128 days, using wavelet transforms. Then, their degree of spatial representativeness is evaluated on a per time-scale basis by comparison to large-spatial scale datasets (the in situ spatial average, SMOS, AMSR2 and ECMWF). Four methods are used for this: temporal stability analysis (TStab), triple collocation (TC), the percentage of correlated areas (CArea) and a new proposed approach that uses wavelet-based correlations (WCor). We found that the mean of the spatial representativeness values tends to increase with the time scale but so does their dispersion. Locations exhibit poor spatial representativeness at scales below 4 days, while either very good or poor representativeness at seasonal scales. Regarding the methods, TStab cannot be applied to the anomaly series due to their multiple zero-crossings and TC is suitable for week and month scales but not for other scales where datasets cross-correlations are found low. In contrast, WCor and CArea give consistent results at all time-scales. WCor is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (1 station per footprint). These results are promising to improve the validation and downscaling of satellite SM series and the optimization of SM networks.

A Comparative Study of the SMAP Passive Soil Moisture Product With Existing Satellite-Based Soil Moisture Products

The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP.

Radiative transfer solution for rugged and heterogeneous scene observations

A physical algorithm is developed to solve the radiative transfer problem in the solar reflective spectral domain. This new code, Advanced Modeling of the Atmospheric Radiative Transfer for Inhomogeneous Surfaces (AMARTIS), takes into account the relief, the spatial heterogeneity, and the bidirectional reflectances of ground surfaces. The resolution method consists of first identifying the irradiance and radiance components at ground and sensor levels and then modeling these components separately, the rationale being to find the optimal trade off between accuracy and computation times. The validity of the various assumptions introduced in the AMARTIS model are checked through comparisons with a reference Monte Carlo radiative transfer code for various ground scenes: flat ground with two surface types, a linear sand dune landscape, and an extreme mountainous configuration. The results show a divergence of less than 2% between the AMARTIS code and the Monte Carlo reference code for the total signals received at satellite level. In particular, it is demonstrated that the environmental and topographic effects are properly assessed by the AMARTIS model even for situations in which the effects become dominant.

Monte Carlo approach for solving the radiative transfer equation over mountainous and heterogeneous areas

An algorithm based on the Monte Carlo method is developed to solve the radiative transfer equation in the reflective domain (0.4-4 microm) of the solar spectrum over rugged terrain. This algorithm takes into account relief, spatial heterogeneity, and ground bidirectional reflectance. The method permits the computation of irradiance components at ground level and radiance terms reaching an airborne or satelliteborne sensor. The Monte Carlo method consists of statistically simulating the paths of photons inside the Earth-atmosphere system to reproduce physical phenomena while introducing neither analytical modeling nor assumption. The potentialities of the code are then depicted over different types of landscape, including a seashore, a desert region, and a steep mountainous valley.