Soil Moisture Ocean Salinity Soil Moisture Research Articles

Performance of SMOS Soil Moisture Products Over Core Validation Sites

The European Space Agency (ESA) launched the SMOS (Soil Moisture Ocean Salinity) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and Near Real Time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics (unbiased root mean square error (ubRMSE) and correlation (R), and anomaly R) with negligible uncertainty and for bias-sensitive metrics (mean difference (MD) and root mean square difference or RMSD) with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, R, and anomaly R of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, R, and anomaly R performance of the IC product and the SMAP product (0.039 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> vs. 0.041 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , 0.80 vs. 0.81, and 0.75 vs. 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.

Self-Correction of Soil Moisture Ocean Salinity (SMOS) Soil Moisture Dry Bias

Satellites produce global monitoring data, while field measurements are made at a local station over the land. Due to difference in scale, it has been a challenge how to define and correct the satellite retrieval biases. Although the relative approach of cumulative distribution functions (CDF) matching compares a long-term climatology of reference data with that of satellite data, it does not mitigate the retrieval biases generated from Instantaneous Field of View (IFOV) measurements over short timescales. As an alternative, we suggest stochastic retrievals (using probabilistic distribution function) to reduce the dry bias in soil moisture retrievals from the satellite SMOS (Soil Moisture and Ocean Salinity) that occurs at the time scale of several days. Rank Probability Skill Score (RPSS) is also proposed as non-local Root Mean Square Errors (RMSEs) of a probabilistic version to optimize stochastic retrievals. With this approach, the time-averaged RMSEs of retrieved SMOS soil moisture is reduced from 0.072 to 0.035 m3/m3. Dry bias also decreases from −0.055 to −0.020 m3/m3. As the proposed approach does not rely on local field measurements, it has a potential as a global operational scheme.

Canola yield sensitivity to climate indicators and passive microwave-derived soil moisture estimates in Saskatchewan, Canada

Canola production is primarily attributed to variations in precipitation and temperature, however the direct relationship between soil moisture and canola yield has yet to be fully evaluated. Recent advancements in microwave remote sensing offer the potential for timely and accurate acquisitions of large-scale soil moisture data; however, the short temporal record of these data sets presents a challenge for defining extreme soil moisture events, and satellite limitations surrounding the detection of surface conditions prevents a direct agronomic link between satellite soil moisture and plant available water. This study examines the potential of passive microwave surface soil moisture observations from the Soil Moisture Ocean Salinity (SMOS) mission satellite as an indicator of canola yields across Saskatchewan over a 6-year period from 2010–2015. An iterative chi-square analysis was employed to investigate the association between SMOS soil moisture and canola yield data from Saskatchewan’s 20 Census Agricultural Regions (CARs). Associations between canola yield and traditionally used climate indicators (temperature and rainfall) were compared to assess the relative effectiveness of SMOS soil moisture as an indicator of yield. Cooler-than-average temperatures (Tmax ≤ 26 °C and Tmin ≤ 12 °C) at the beginning of August were shown to have a positive impact on canola yield, while high nocturnal temperatures (Tmin ≥ 15 °C) in mid-July had a negative effect. Precipitation was found to have a minimal impact on canola production due to greater-than-average rainfall that was observed during the period of study. The strongest associations were observed between soil moisture and canola yield, particularly in low-yielding years with excess soil moisture exhibiting significant associations (p < 0.01, df = 1) throughout the growing season. The beginning of June, coinciding with the stand establishment stage, was identified as a critical time period in which excess soil moisture (≥26.6%) resulted in yield losses. The findings of this study suggest that soil moisture observations obtained by passive microwave satellites, such as SMOS, may provide an effective indicator of crop yields, particularly in areas experiencing excess soil moisture conditions.

A model for downscaling SMOS soil moisture using Sentinel-1 SAR data

A model for downscaling SMOS (Soil Moisture Ocean Salinity) soil moisture products is developed by using multi-temporal dual-polarized (HH+HV) C-band SAR data. In this model, the effect of vegetation on soil moisture retrieval from SAR data is minimized by using the water-cloud model (WCM), in which vegetation contribution is quantified using the backscatter coefficient of HV polarization. The wavelet transform is used to fuse high resolution Sentinel-1A SAR backscatter with low resolution SMOS soil moisture, where the difference in spatial heterogeneity between scales is also accounted for. The influence of soil surface roughness is eliminated by using multi-temporal data. The multi-temporal SMOS soil moisture and dual-pol Sentinel-1/SAR data are the only inputs of this downscaling model. The model is tested in southern Ontario, Canada to downscale 40 km resolution SMOS soil moisture to 1.25 km and 2.5 km resolutions. The downscaled results show good agreements with the in-situ soil moisture collected in May and July of 2016 with an unbiased root-mean-square-error (RMSE) of 0.045 m3/m3 and 0.047 m3/m3 and a coefficient of determination (R2) of 0.54 and 0.70 at 1.25 km and 2.5 km resolutions respectively. The results suggest that the model can be applied for C-band at regional scales to provide continuous soil moisture mapping at higher resolutions. The high revisit frequency of the up-coming Radarsat Constellation Mission (RCM) combined with its large areal coverage characteristics are ideal for the generation of downscaled products.

Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation

Global-scale surface soil moisture products are currently available from multiple remote sensing platforms. Footprint-scale assessments of these products are generally restricted to limited number of densely-instrumented validation sites. However, by taking active and passive soil moisture products together with a third independent soil moisture estimates via land surface modeling, triple collocation (TC) can be applied to estimate the correlation metric of satellite soil moisture products (versus an unknown ground truth) over a quasi-global domain. Here, an assessment of Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS) and Advanced SCATterometer (ASCAT) surface soil moisture retrievals via TC is presented. Considering the potential violation of TC error assumptions, the impact of active-passive and satellite-model error cross correlations on the TC-derived inter-comparison results is examined at in situ sites using quadruple collocation analysis. In addition, confidence intervals for the TC-estimated correlation metric are constructed from moving-block bootstrap sampling designed to preserve the temporal persistence of the original (unevenly-sampled) soil moisture time-series. This study is the first to apply TC to obtain a robust global-scale cross-assessment of SMAP, SMOS and ASCAT soil moisture retrieval accuracy in terms of anomaly temporal correlation. Our results confirm the overall advantage of SMAP (with a global average anomaly correlation of 0.76) over SMOS (0.66) and ASCAT (0.63) that has been established in several recent regional, ground-based studies. SMAP is also the best-performing product over the majority of applicable land pixels (52%), although SMOS and ASCAT each shows advantage in distinct geographic regions.

Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX

Global rainfall information is useful for many applications. However, real-time versions of satellite-based rainfall products are known to contain errors. Recent studies have demonstrated how the information about rainfall intrinsically contained in soil moisture data can be utilised for improving rainfall estimates. That is, soil moisture dynamics are impacted for several days by the accumulated amount of rainfall following within a particular event. In this context, soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite is used in this study to correct rainfall accumulation estimates provided by satellite-based real-time precipitation products such as CMORPH, TRMM-3B42RT or PERSIANN. An algorithm based on the SMOS measurements data assimilation is tested in two land-surface models of different complexity: a simple hydrological model (Antecedent Precipitation Index (API)) and a more sophisticated state-of-the-art land-surface model (SURFEX (Surface Externalisée)). We show how the assimilation technique, based on a particle filter method, generally leads to a significant improvement in rainfall estimates, with slightly better results for the simpler (and less computationally demanding) API model. This methodology has been evaluated for six years at ten sites around the world with different land use and climatological features. The results also show the limitations of the methodology in regions highly affected by mountainous terrain, forest or intense radio-frequency interference (RFI), which can notably affect the quality of the retrievals. The satisfactory results shown here invite the future operational application of the methodology in near-real time on a global scale.

Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

A regional scale performance evaluation of SMOS and ESA-CCI soil moisture products over India with simulated soil moisture from MERRA-Land

Abstract Three multi-decadal satellite soil moisture (SM) products, obtained by merging two active and six passive, all-active-merged (CCI-ACT), all-passive-merged (CCI-PAS) and all-active-passive-merged (CCI-COMBINED), and Level-3 SM retrieved from Soil Moisture Ocean Salinity (SMOS) mission were evaluated over India. The evaluation strategy employed is twofold: (a) time series and correlation analysis of SM datasets with respect to the Modern Era Retrospective-analysis for Research and Applications-Land (MERRA-L) SM simulation and the India Meteorological Department (IMD) gridded rainfall; (b) investigate the spatial distribution of random error of the satellite products using Triple Collocation (TC) approach. The Pearson's correlation analysis showed that the performance of CCI-ACT and CCI-COMBINED are comparable to each other and they agree well with the MERRA-L simulated SM time series. They also had a good rank correlation with rainfall. The random error from TC is represented in terms of fractional Root Mean Square Error ( fRMSE TC ). It also represents the sensitivity of satellite retrievals to changes in true state. The analysis of fRMSE TC showed that descending swath of SMOS SM has a lower error than ascending for 71% of the pixels over India. CCI-ACT was found to have the most number of pixels with the lowest errors, having a mean fRMSE TC of 0.7188, compared to 0.7705 for CCI-COMBINED, 0.7828 for CCI-PAS and 0.8308 for SMOS-D. However, the error in CCI-ACT was highest in arid desert regions of western India. The error in CCI-COMBINED, CCI-PAS and SMOS-D grew with an increase in vegetation density. The fRMSE TC maps were analysed against the maps of the probability of occurrence of Radio Frequency Interference (RFI), Normalized Difference Vegetation Index (NDVI), soil texture (percentage of clay, sand, and silt) and modified Koppen-Geiger climate classification. The climate classification map was used to classify fRMSE TC against the different homogeneous climate classes. The analysis of the maps revealed that the inconsistency in SMOS is because of the RFI events over India. However, a multiple linear regression based attribution study showed that SMOS-D is the least affected by vegetation (4%) and the spatial distribution of CCI-ACT and CCI-COMBINED error showed more affinity towards soil texture than vegetation density.

Assimilation of SMOS soil moisture into a distributed hydrological model and impacts on the water cycle variables over the Ouémé catchment in Benin

Abstract. Precipitation forcing is usually the main source of uncertainty in hydrology. It is of crucial importance to use accurate forcing in order to obtain a good distribution of the water throughout the basin. For real-time applications, satellite observations allow quasi-real-time precipitation monitoring like the products PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, TRMM (Tropical Rainfall Measuring Mission) or CMORPH (CPC (Climate Prediction Center) MORPHing). However, especially in West Africa, these precipitation satellite products are highly inaccurate and the water amount can vary by a factor of 2. A post-adjusted version of these products exists but is available with a 2 to 3 month delay, which is not suitable for real-time hydrologic applications. The purpose of this work is to show the possible synergy between quasi-real-time satellite precipitation and soil moisture by assimilating the latter into a hydrological model. Soil Moisture Ocean Salinity (SMOS) soil moisture is assimilated into the Distributed Hydrology Soil Vegetation Model (DHSVM) model. By adjusting the soil water content, water table depth and streamflow simulations are much improved compared to real-time precipitation without assimilation: soil moisture bias is decreased even at deeper soil layers, correlation of the water table depth is improved from 0.09–0.70 to 0.82–0.87, and the Nash coefficients of the streamflow go from negative to positive. Overall, the statistics tend to get closer to those from the reanalyzed precipitation. Soil moisture assimilation represents a fair alternative to reanalyzed rainfall products, which can take several months before being available, which could lead to a better management of available water resources and extreme events.

Validation of Aquarius Soil Moisture Products Over the Northwest of Spain: A Comparison With SMOS

A validation of the new L2 and L3 soil moisture products from the Aquarius/SAC-D mission from August 2011 to June 2014 using two in situ networks in Spain was conducted. The first network, the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), is considered to be a dense network. The second network (Inforiego) could be considered a sparse or large-scale network. Comparisons of temporal series using different strategies were made. Similar analysis was performed for the same area and period with two soil moisture ocean salinity (SMOS) soil moisture products: SMOS L2 and SMOS Barcelona Expert Center (BEC) L3. The aim of the study was to analyze the performance of the Aquarius soil moisture products and to compare with that of SMOS soil moisture. Results from the area-averaged comparison show that Aquarius products have correlation coefficients (R) between 0.33 and 0.65, and root-mean-square difference (RMSD) and centered RMSD (cRMSD) between 0.046 and $0.111\,\text{m}^3\text{m}^{-3}$ . A better match was found for the L2 ascending series than for the L2 descending and L3 series. A dry bias was found. SMOS products showed better accuracy $(\text{R} > 0.8$ , RMSD and $\text{cRMSD}\sim 0.06 \text{m}^{3}\text{m}^{-3}$ ) than those of Aquarius. The comparison made at point-scale reflected that the size and density of the networks do not influence the validation results at the Aquarius resolution, but it is remarkable at the SMOS resolution. Despite the scale restrictions, the results of this study showed that Aquarius soil moisture products have reasonably good performance.

Disaggregation of SMOS soil moisture over the Canadian Prairies

In this study, we used the Disaggregation based on Physical And Theoretical scale Change (DISPATCH) algorithm under very wet soil conditions in Western Canada for the disaggregation of coarse resolution 40-km soil moisture derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The algorithm relies on the Soil Evaporative Efficiency (SEE), which was estimated using the 1-km resolution data from the MODerate resolution Imaging Spectoradiometer (MODIS). The study aimed to: (i) evaluate DISPATCH under wet soil conditions, (ii) test the linearity/non-linearity of the relationship between soil moisture and SEE, and (iii) propose a more robust procedure to calibrate the SEE model under very wet soil conditions. The disaggregated soil moisture values were compared to 0–5 cm in situ measurements and the soil moisture derived from the L-MEB (L-band Microwave Emission of the Biosphere) model from airborne brightness temperature at 1.4 GHz collected during the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) field campaign. The results show a correlation between 0.7 and 0.8 and bias values ~ 0 m 3 /m 3 . The DISPATCH algorithm shows better disaggregation results under very wet soil conditions when a non-linear relationship is considered between SEE and soil moisture instead of a linear model. This is mainly due to the small variability of surface temperature inside the area covered by the SMOS pixel under very wet soil conditions, and the difficulty in accurately estimating the maximum soil temperature ( Ts max ), which is a driving factor for SEE. A sensitivity analysis was conducted and it shows that the linear model performs well only if Ts max can be determined more accurately. The possibility to determine Ts max using high resolution MODIS data over a larger area than the SMOS pixel is examined and discussed in the paper. • We validate DISPATCH downscaling algorithm under very wet soil condition. • Data from CanEX-SM10 field campaign were used. • Linear and non-linear models linking SM to SEE are compared. • Better results are found DISPATCH coupled to the non-linear model. • With an improved Ts max value, DISPATCH results are better with the linear model.

Monitoring Agricultural Risk in Canada Using L-Band Passive Microwave Soil Moisture from SMOS

Abstract Soil moisture from Soil Moisture Ocean Salinity (SMOS) passive microwave satellite data was assessed as an information source for identifying regions experiencing climate-related agricultural risk for a period from 2010 to 2013. Both absolute soil moisture and soil moisture anomalies compared to a 4-yr SMOS satellite baseline were used in the assessment. The 4-yr operational period of SMOS was wetter than the 30-yr climate normal in many locations, particularly in the late summer for most regions and in the spring for the province of Manitoba. This leads to a somewhat unrepresentative baseline that skews anomaly measures at different parts of the growing season. SMOS soil moisture does, however, show a clear trend where extremes are present, with drier-than-average conditions during periods that drought and dry soil risks were identified and wetter-than-average conditions when flooding and excess moisture were present. Areas where extreme weather events caused crop losses were identifiable using SMOS soil moisture, both at the provincial and regional scales. The variability in soil moisture between at-risk areas and normal areas is very small but consistent, both geographically and over time, making SMOS a good real-time indicator for risk assessment.

Evaluation of SMOS retrievals of soil moisture over the central United States with currently available in situ observations

The European Space Agency launched the Soil Moisture Ocean Salinity (SMOS) satellite in November 2009. Using SMOS soil moisture retrievals for 2010 processed using algorithm V4.00, we evaluated SMOS retrievals by comparing them to in situ soil moisture observations for the top 5 cm at several stations in the Great Plains of the U.S. A major issue with comparing the satellite data with in situ data is that a SMOS footprint is about 40 km across and we compare to point observations. To address this issue, we chose locations in Oklahoma that have 10 to 25 different in situ observations within each SMOS footprint. The SMOS retrievals have a dry bias when compared to the average of all in situ stations in a footprint. Large differences exist between the in situ observations, even for probes only a few meters apart. Observations from different sensors within a SMOS footprint differ from each other by a larger amount than they differ from the SMOS retrieval. Removing the mean and normalizing the data bring the in situ observations into better agreement with each other and with SMOS but there are still substantial differences. Agricultural Research Service Micronet regions in Oklahoma had highly varying values of soil moisture despite being in close proximity to one another, but when averaged and compared to SMOS they had less of a bias than the other regions. Further north in the Great Plains, SMOS retrievals of top 5 cm soil moisture from descending orbits were consistently about 5% by volume wetter than ascending retrievals.