A. Al-Yaari

Global maps of roughness parameters from L-band SMOS observations

The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite dedicated to providing global surface soil moisture (SM). SMOS operates at L-band and at this frequency, the signal depends on soil moisture but is also significantly affected by surface soil roughness. Using the Combined soil Roughness & Vegetation Effects (CRVE) method detailed in this paper, the effect of vegetation and soil roughness can be combined using a single parameter, referred to as TR here. SM and TR were retrieved by inverting the SMOS observations using the forward emission model (L-MEB). Assuming a linear relationship between TR and LAI obtained by the MODIS data, an Australian map of soil roughness was computed. This map could lead to improved soil moisture retrievals for present and future microwave remote sensing missions such as SMOS and the Soil Moisture Active Passive (SMAP) scheduled for launch in November 2014.

Evaluating the impact of roughness in soil moisture and optical thickness retrievals over the VAS area

In this paper, roughness parameterizations providing best retrievals of soil moisture (SM) at L-band were evaluated. Different parameterizations were tested to find the best correlation R, bias and ubRMSE when comparing retrieved SM and in situ SM measurements carried out at the VAS (Valencia Anchor Station) over a vineyard field. Roughness measurements were always performed after the agricultural practices in the vineyard. These in situ data was used as input of the L-MEB (L-band Microwave Emission of the Biosphere) model, which permits the retrieval of SM and TAU (vegetation optical depth). In addition, a simplified method consisting on the retrieval of a parameter which combines the effects of roughness and TAU was tested. Significantly higher correlation (R=0.86) for SM was found using this method, while the absolute bias (-0.062) and RMSE (0.069) were slightly higher than for other roughness parameterizations.

Evaluation of the most recent reprocessed SMOS soil moisture products: Comparison between SMOS level 3 V246 and V272

Soil Moisture and Ocean Salinity (SMOS) satellite has been providing surface soil moisture (SSM) and ocean salinity (OS) retrievals at L-band for five years (2010-2014). During these five years, the SSM retrieval algorithm i.e. the L-MEB (L-Band Microwave Emission of the Biosphere [1] model has been progressively improved and hence results in different versions of the SMOS SSM products. This study aims at evaluating the last improvement in the SSM products of the most recent SMOS level 3 (SMOSL3) reprocessing (SMOSL3_2.72) vs. an earlier version (SMOSL3_246). Correlation, bias, Root Mean Square Difference (RMSD) and unbiased RMSD (unbRMSD) were used as performance criteria in this study using the ECMWF SM-DAS-2 product as a reference. Results show that the SMOS SSM estimates have been improved: (i) SMOSL3_272 was closer to SM-DAS-2 over most of the globe-with the exception of arid regions-in terms of unbRMSD (ii) SMOSL3_272 was closer to SM-DAS-2 over Spain, Brazil, parts of Sahel, high latitude and equator regions but comparable with SMOSL3_246 over most of the rest of the globe in terms of correlations.

First application of regression analysis to retrieve Soil Moisture from SMAP brightness temperature observations consistent with SMOS

In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASAs Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESAs Soil Moisture and Ocean Salinity (SMOS) satellite retrieved surface soil moisture. This was achieved by calibrating coefficients of the regression model using SMOS soil moisture and horizontal and vertical brightness temperatures (TB), over the 2013 - 2014 period. Next, this model was applied to recent SMAP TB data from 31/03/2015-08/09/2015. The retrieved surface soil moisture from SMAP (referred here to as SMAP-reg) was compared to the operational SMAP L3 surface soil moisture retrieved using the single channel algorithm. Both exhibit comparable temporal dynamics with a good agreement of correlation (correlation coefficient R mostly > 0.8) between the SMAP-reg and the operational SMAP L3 surface soil moisture products.

Analyzing the impact of using the SRP (Simplified roughness parameterization) method on soil moisture retrieval over different regions of the globe

This paper focuses on a new approach to account for soil roughness effects in the retrieval of soil moisture (SM) at L-band in the framework of the SMOS (Soil Moisture and Ocean Salinity) mission: the Simplified Roughness Parameterization (SRP). While the classical retrieval approach considers SM and τNAD (vegetation optical depth) as retrieved parameters, this approach is based on the retrieval of SM and the TR parameter combining τNAD and soil roughness (TR = τNAD + HR/2). Different roughness parameterizations were tested to find the best correlation (R), bias and unbiased RMSE (ubRMSE) when comparing homogeneous retrievals of SM and in situ SM measurements carried out at the VAS (Valencia Anchor Station) vineyard field. The highest R (0.68) and lowest ubRMSE (0.056 m3 m-3) were found using the SRP method. Using the SMOS observations comparisons against several SM networks were also made: AACES, SCAN, watersheds and SMOSMANIA. SM was retrieved over all these stations. The SRP and another similar approach (SRP2) improved the averaged ubRMSE, while the SRP2 method leaded to higher correlation values (R). A global underestimation of SM was noticed, which may be linked to the differences in the sampling depths of the L-band observations (~ 0-3cm for both Elbara-II and SMOS) and of the in situ measurements (~ 0-5 cm).

Evaluating roughness effects on C-band AMSR-E observations

The usefulness of microwave remote sensing to retrieve near-surface soil moisture has already been demonstrated in many studies. However, obtaining high quality estimates of soil moisture is influenced by many effects from soil, vegetation and atmosphere; one of the key parameters is surface roughness. This research focusses on a semi-empirical method to evaluate the roughness effects from space borne observations. Global maps of roughness effects are evaluated at C-band from AMSR-E measurements.

Compared performances of microwave passive soil moisture retrievals (SMOS) and active soil moisture retrievals (ASCAT) using land surface model estimates (MERRA-LAND)

Performances of two global satellite-based surface soil moisture (SSM) retrievals with respect to model-based SSM derived from the MERRA (Modern-Era Retrospective analysis for Research and Applications) rea-nalysis were explored in this paper: (i) Soil Moisture and Ocean Salinity (SMOS; passive) Level-3 SSM (SMOSL3) and (ii) the Advanced Scatterometer (ASCAT; active) SSM. Temporal correlation was used to investigate the performance of SMOSL3 and ASCAT SSM products during the period 05/2010-2012 on a global basis. Both SMOSL3 and ASCAT (slightly better) captured well (R>0.70) the long-term variability of the modelled SSM, particularly, over the Indian subcontinent, the Great Plains of North America, and the Sahel. However, ASCAT had negative correlations in arid regions, in particular across the Sahara and the Arabian Peninsula. This may be due to complex scattering mechanisms over very dry surfaces. To explore the land cover dependence of the analyzed statistical indicators, the global correlation results were averaged per biome extracted from a global map of biomes. In general, SMOSL3 and ASCAT performances behaved differently from one biome to another. For SMOSL3, the highest average correlation was observed over “tropical semi-arid” (R = ~ 0.5) and “temperate semi-arid” biomes, whereas for ASCAT, the highest correlations were observed over “tropical semi-arid” (R = ~ 0.7) and “tropical humid” biomes. The poorest agreement for both SMOSL3 and ASCAT was generally found over “tundra” and “desert temperate” biomes, particularly for ASCAT. This study showed that the performance of both SMOSL3 and ASCAT is highly dependent on vegetation. We also showed that both of them provide complementary information on SSM, which implies a potential for data fusion which would be pertinent for the ESA climate change initiative (CCI).