Ozone pollution during the COVID-19 lockdown in the spring 2020 over Europe analysed from satellite observations, in situ measurements and models

Abstract. We present a comprehensive study integrating satellite observations of ozone pollution, in situ measurements and chemistry transport model simulations for quantifying the role of anthropogenic emission reductions during the COVID-19 lockdown in spring 2020 over Europe. Satellite observations are derived from the IASI+GOME2 multispectral synergism, which provides particularly enhanced sensitivity to near-surface ozone pollution. These observations are first analysed in terms of differences between the average on 1–15 April 2020, when the strictest lockdown restrictions took place, and the same period in 2019. They show clear enhancements of near-surface ozone in Central Europe and Northern Italy, and some other hotspots, which are typically characterized by VOC-limited chemical regimes. An overall reduction of ozone is observed elsewhere, where ozone chemistry is limited by the abundance of NOx. The spatial distribution of positive and negative ozone concentration anomalies observed from space is in relatively good quantitative agreement with surface in situ measurements over the continent (a correlation coefficient of 0.55, a root-mean-squared difference of 11 ppb and the same standard deviation and range of variability). An average bias of ∼8 ppb between the two observational datasets is remarked, which can partly be explained by the fact the satellite approach retrieves partial columns of ozone with a peak sensitivity above the surface (near 2 km of altitude). For assessing the impact of the reduction of anthropogenic emissions during the lockdown, we adjust the satellite and in situ surface observations for withdrawing the influence of meteorological conditions in 2020 and 2019. This adjustment is derived from the chemistry transport model simulations using the meteorological fields of each year and identical emission inventories. This observational estimate of the influence of lockdown emission reduction is consistent for both datasets. They both show lockdown-associated ozone enhancements in hotspots over Central Europe and Northern Italy, with a reduced amplitude with respect to the total changes observed between the two years, and an overall reduction elsewhere over Europe and the ocean. Satellite observations additionally highlight the ozone anomalies in the regions remote from in situ sensors, an enhancement over the Mediterranean likely associated with maritime traffic emissions and a marked large-scale reduction of ozone elsewhere over ocean (particularly over the North Sea), in consistency with previous assessments done with ozonesondes measurements in the free troposphere. These observational assessments are compared with model-only estimations, using the CHIMERE chemistry transport model. For analysing the uncertainty of the model estimates, we perform two sets of simulations with different setups, differing in the emission inventories, their modifications to account for changes in anthropogenic activities during the lockdown and the meteorological fields. Whereas a general qualitative consistency of positive and negative ozone anomalies is remarked between all model and observational estimates, significant changes are seen in their amplitudes. Models underestimate the range of variability of the ozone changes by at least a factor 2 with respect to the two observational data sets, both for enhancements and decreases of ozone, while the large-scale ozone decrease is not simulated. With one of the setups, the model simulates ozone enhancements a factor 3 to 6 smaller than with the other configuration. This is partly linked to the emission inventories of ozone precursors (at least a 30 % difference), but mainly to differences in vertical mixing of atmospheric constituents depending on the choice of the meteorological model.

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Simulating Secondary Inorganic Aerosols using the chemistry transport model MOCAGE version R2.15.0

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-3593-2015 ◽

2015 ◽

Vol 8 (4) ◽

pp. 3593-3651 ◽

Cited By ~ 1

Author(s):

J. Guth ◽

B. Josse ◽

V. Marécal ◽

M. Joly

Keyword(s):

North America ◽

Transport Model ◽

Regional Scale ◽

In Situ Measurements ◽

Aerosol Composition ◽

Chemistry Transport Model ◽

Inorganic Aerosols ◽

Modis Aod ◽

Inorganic Aerosol

Abstract. In this study we develop a Secondary Inorganic Aerosol (SIA) module for the chemistry transport model MOCAGE developed at CNRM. Based on the thermodynamic equilibrium module ISORROPIA II, the new version of the model is evaluated both at the global scale and at the regional scale. The results show high concentrations of secondary inorganic aerosols in the most polluted regions being Europe, Asia and the eastern part of North America. Asia shows higher sulfate concentrations than other regions thanks to emissions reduction in Europe and North America. Using two simulations, one with and the other without secondary inorganic aerosol formation, the model global outputs are compared to previous studies, to MODIS AOD retrievals, and also to in situ measurements from the HTAP database. The model shows a better agreement in all geographical regions with MODIS AOD retrievals when introducing SIA. It also provides a good statistical agreement with in situ measurements of secondary inorganic aerosol composition: sulfate, nitrate and ammonium. In addition, the simulation with SIA gives generally a better agreement for secondary inorganic aerosols precursors (nitric acid, sulfur dioxide, ammonia) in particular with a reduction of the Modified Normalised Mean Bias (MNMB). At the regional scale, over Europe, the model simulation with SIA are compared to the in situ measurements from the EMEP database and shows a good agreement with secondary inorganic aerosol composition. The results at the regional scale are consistent with those obtained with the global simulations. The AIRBASE database was used to compare the model to regulated air quality pollutants being particulate matter, ozone and nitrogen dioxide concentrations. The introduction of the SIA in MOCAGE provides a reduction of the PM2.5 MNMB of 0.44 on a yearly basis and even 0.52 on a three spring months period (March, April, May) when SIA are maximum.

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Potential of multispectral synergism for observing ozone pollution by combining IASI-NG and UVNS measurements from EPS-SG satellite

10.5194/amt-2016-374 ◽

2016 ◽

Author(s):

Lorenzo Costantino ◽

Juan Cuesta ◽

Emanuele Emili ◽

Adriana Coman ◽

Gilles Foret ◽

...

Keyword(s):

Degrees Of Freedom ◽

Transport Model ◽

Lower Troposphere ◽

Real Data ◽

Satellite Observations ◽

Maximum Sensitivity ◽

Ozone Pollution ◽

Chemical Transport Model ◽

Near Surface ◽

Multispectral Method

Abstract. Present and future satellite observations offer a great potential for monitoring air quality on daily and global basis. However, measurements from currently in orbit satellites do not allow using a single sensor to probe accurately surface concentrations of gaseous pollutants such as tropospheric ozone (Liu et al., 2010). Using single-band approaches based on spaceborne measurements of either thermal infrared radiance (TIR, Eremenko et al., 2008) or ultraviolet reflectance (UV, Liu et al., 2010) only ozone down to the lower troposphere (3 km) may be observed. A recent multispectral method (referred to as IASI+GOME-2) combining the information of IASI and GOME-2 (both onboard MetOp satellites) spectra, respectively from the TIR and UV, has shown enhanced sensitivity for probing ozone at the lowermost troposphere (LMT, below 3 km of altitude) with maximum sensitivity down to 2.20 km a.s.l. over land, while sensitivity for IASI or GOME-2 only peaks at 3 to 4 km at lowest (Cuesta et al., 2013). Future spatial missions will be launched in the upcoming years, such as EPS-SG, carrying new-generation sensors of IASI and GOME-2 (respectively IASI-NG and UVNS) that will enhance the capacity to observe ozone pollution and particularly by synergism of TIR and UV measurements. In this work we develop a pseudo-observation simulator and evaluate the potential of future EPS-SG satellite observations through IASI-NG+UVNS multispectral method to observer near-surface O3. The pseudo-real state of atmosphere (nature run) is provided by the MOCAGE (MOdèle de Chimie Atmosphérique à Grande Échelle) chemical transport model. Simulations are calibrated by careful comparisons with real data, to ensure the best consistency between pseudo-reality and reality, as well as between the pseudo-observation simulator and existing satellite products. We perform full and accurate forward and inverse radiative transfer calculations for a period of 4 days (8–11 July 2010) over Europe. In the LMT, there is a remarkable agreement in the geographical distribution of O3 partial columns, calculated between the surface and 3 km of altitude, between IASI-NG+UVNS pseudo-observations and the corresponding MOCAGE pseudo-reality. With respect to synthetic IASI+GOME-2 products, IASI-NG+UVNS shows a higher correlation between pseudo-observations and pseudo-reality, enhanced by about 11 %. The bias on high ozone retrieval is reduced and the average accuracy increases by 22 %. The sensitivity to LMT ozone is enhanced on average with 154 % (from 0.29 to 0.75, over land) and 208 % (from 0.21 to 0.66, over ocean) higher degrees of freedom. The mean height of maximum sensitivity for the LMT peaks at 1.43 km over land and 2.02 km over ocean, respectively 1.03 km and 1.30 km below that of IASI+GOME-2. IASI-NG+UVNS shows also good retrieval skill in the surface-2 km altitude range with a mean DOF (degree of freedom) of 0.52 (land) and 0.42 (ocean), and an average Hmax (altitude of maximum sensitivity) of 1.29 km (land) and 1.96 km (ocean). Unique of its kind for retrieving ozone layers of 2–3 km thickness, in the first 2–3 km of the atmosphere, IASI-NG+UVNS is expected to largely enhance the capacity to observe ozone pollution from space.

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Reanalysis of and attribution to near-surface ozone concentrations in Sweden during 1990–2013

10.5194/acp-2017-338 ◽

2017 ◽

Author(s):

Camilla Andersson ◽

Heléne Alpfjord ◽

Lennart Robertson ◽

Per Erik Karlsson ◽

Magnuz Engardt

Keyword(s):

Transport Model ◽

Linear Trend ◽

Surface Ozone ◽

Vegetation Indices ◽

High Sensitivity ◽

Anthropogenic Emissions ◽

Data Sets ◽

Chemistry Transport Model ◽

Near Surface ◽

Surface O3

Abstract. We have constructed two data sets of hourly resolution reanalyzed near-surface ozone (O3) concentrations for the period 1990–2013 for Sweden. Long-term simulations from a chemistry-transport model (CTM) covering Europe were combined with hourly ozone concentration observations at Swedish and Norwegian background measurement sites using data assimilation. The reanalysis data sets show improved performance than the original CTM when compared to independent observations. In one of the reanalyzes we included all available hourly near-surface O3 observations, whilst in the other we carefully selected time-consistent observations in order to avoid introducing artificial trends. Based on the second reanalysis we investigated statistical aspects of the near-surface O3 concentration, focusing on the linear trend over the 24 year period. We show that high near-surface O3 concentrations are decreasing and low O3 concentrations are increasing, which is mirrored by observed improvement of many health and vegetation indices (apart from those with a low threshold). Using the chemistry-transport model we also conducted sensitivity simulations to quantify the causes of the observed change, focusing on three processes: change in hemispheric background, meteorology and anthropogenic emissions (Swedish and other European). The rising low concentrations of near-surface O3 in Sweden are caused by a combination of all three processes, whilst the decrease in the highest O3 concentrations is caused by O3 precursor emissions reductions. While studying the relative impact of anthropogenic emissions changes, we identified systematic differences in the modelled trend compared to observations that must be caused by incorrect trends in the utilised emissions inventory or by too high sensitivity of our model to emissions changes.

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Evaluation of modeling NO<sub>2</sub> concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-4171-2018 ◽

2018 ◽

Vol 18 (6) ◽

pp. 4171-4186 ◽

Cited By ~ 9

Author(s):

Fei Liu ◽

Ronald J. van der A ◽

Henk Eskes ◽

Jieying Ding ◽

Bas Mijling

Keyword(s):

Spatial Correlation ◽

Rural Areas ◽

Urban Areas ◽

Transport Model ◽

Chemical Transport ◽

In Situ Measurements ◽

Nox Emissions ◽

Emission Inventories ◽

Bottom Up

Abstract. Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope  =  0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope  =  1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10–40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of −30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

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Evaluation of modelling NO<sub>2</sub> concentrations driven by satellite-derived and bottom-up emission inventories using in-situ measurements over China

10.5194/acp-2017-475 ◽

2017 ◽

Author(s):

Fei Liu ◽

Ronald J. van der A ◽

Henk Eskes ◽

Jieying Ding ◽

Bas Mijling

Keyword(s):

Spatial Correlation ◽

Rural Areas ◽

Urban Areas ◽

Transport Model ◽

Chemical Transport ◽

In Situ Measurements ◽

Nox Emissions ◽

Emission Inventories ◽

Bottom Up

Abstract. Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modelling surface NO2 concentrations from the CHIMERE regional chemical-transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modelled ratio of NO2 to NOz. The model accurately reproduces the spatial variability of NO2 from in-situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope = 0.74/0.64 for the daily-mean/daytime only) and the MIX (slope = 1.3/1.1) inventory respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modelled concentrations is reduced with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban/rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10–40 % higher than the corresponding model grid-cell mean. This reduces the estimate of the negative bias of the DECSO based simulation to the range of −30 % to 0 % on average, and establishes more firmly that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer, due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle, but shows more significant disagreement between simulations and measurements during night time, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

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Potential of multispectral synergism for observing ozone pollution by combining IASI-NG and UVNS measurements from the EPS-SG satellite

Atmospheric Measurement Techniques ◽

10.5194/amt-10-1281-2017 ◽

2017 ◽

Vol 10 (4) ◽

pp. 1281-1298 ◽

Cited By ~ 10

Author(s):

Lorenzo Costantino ◽

Juan Cuesta ◽

Emanuele Emili ◽

Adriana Coman ◽

Gilles Foret ◽

...

Keyword(s):

Near Infrared ◽

Transport Model ◽

Satellite Observations ◽

Maximum Sensitivity ◽

Ozone Pollution ◽

Chemical Transport Model ◽

Near Surface ◽

Atmospheric Sounding ◽

New Generation ◽

Multispectral Method

Abstract. Present and future satellite observations offer great potential for monitoring air quality on a daily and global basis. However, measurements from currently orbiting satellites do not allow a single sensor to accurately probe surface concentrations of gaseous pollutants such as tropospheric ozone. Combining information from IASI (Infrared Atmospheric Sounding Interferometer) and GOME-2 (Global Ozone Monitoring Experiment-2) respectively in the TIR and UV spectra, a recent multispectral method (referred to as IASI+GOME-2) has shown enhanced sensitivity for probing ozone in the lowermost troposphere (LMT, below 3 km altitude) with maximum sensitivity down to 2.20 km a.s.l. over land, while sensitivity for IASI or GOME-2 alone only peaks at 3 to 4 km at the lowest.In this work we develop a pseudo-observation simulator and evaluate the potential of future EPS-SG (EUMETSAT Polar System – Second Generation) satellite observations, from new-generation sensors IASI-NG (Infrared Atmospheric Sounding Interferometer – New Generation) and UVNS (Ultraviolet Visible Near-infrared Shortwave-infrared), to observe near-surface O3 through the IASI-NG+UVNS multispectral method. The pseudo-real state of the atmosphere is provided by the MOCAGE (MOdèle de Chimie Atmosphérique à Grande Échelle) chemical transport model. We perform full and accurate forward and inverse radiative transfer calculations for a period of 4 days (8–11 July 2010) over Europe.In the LMT, there is a remarkable agreement in the geographical distribution of O3 partial columns between IASI-NG+UVNS pseudo-observations and the corresponding MOCAGE pseudo-reality. With respect to synthetic IASI+GOME-2 products, IASI-NG+UVNS shows a higher correlation between pseudo-observations and pseudo-reality, which is enhanced by about 12 %. The bias on high ozone retrieval is reduced and the average accuracy increases by 22 %. The sensitivity to LMT ozone is also enhanced. On average, the degree of freedom for signal is higher by 159 % over land (from 0.29 to 0.75) and 214 % over ocean (from 0.21 to 0.66). The mean height of maximum sensitivity for the LMT peaks at 1.43 km over land and 2.02 km over ocean, respectively 1.03 and 1.30 km below that of IASI+GOME-2. IASI-NG+UVNS also shows good retrieval skill in the surface–2 km altitude range. It is one of a kind for retrieving ozone layers of 2–3 km thickness, in the first 2–3 km of the atmosphere. IASI-NG+UVNS is expected to largely enhance the capacity to observe ozone pollution from space.

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First implementation of secondary inorganic aerosols in the MOCAGE version R2.15.0 chemistry transport model

Geoscientific Model Development ◽

10.5194/gmd-9-137-2016 ◽

2016 ◽

Vol 9 (1) ◽

pp. 137-160 ◽

Cited By ~ 26

Author(s):

J. Guth ◽

B. Josse ◽

V. Marécal ◽

M. Joly ◽

P. Hamer

Keyword(s):

North America ◽

Transport Model ◽

Regional Scale ◽

In Situ Measurements ◽

Aerosol Composition ◽

Chemistry Transport Model ◽

Inorganic Aerosols ◽

Modis Aod ◽

Inorganic Aerosol

Abstract. In this study we develop a secondary inorganic aerosol (SIA) module for the MOCAGE chemistry transport model developed at CNRM. The aim is to have a module suitable for running at different model resolutions and for operational applications with reasonable computing times. Based on the ISORROPIA II thermodynamic equilibrium module, the new version of the model is presented and evaluated at both the global and regional scales. The results show high concentrations of secondary inorganic aerosols in the most polluted regions: Europe, Asia and the eastern part of North America. Asia shows higher sulfate concentrations than other regions thanks to emission reductions in Europe and North America. Using two simulations, one with and the other without secondary inorganic aerosol formation, the global model outputs are compared to previous studies, to MODIS AOD retrievals, and also to in situ measurements from the HTAP database. The model shows a better agreement with MODIS AOD retrievals in all geographical regions after introducing the new SIA scheme. It also provides a good statistical agreement with in situ measurements of secondary inorganic aerosol composition: sulfate, nitrate and ammonium. In addition, the simulation with SIA generally gives a better agreement with observations for secondary inorganic aerosol precursors (nitric acid, sulfur dioxide, ammonia), in particular with a reduction of the modified normalized mean bias (MNMB). At the regional scale, over Europe, the model simulation with SIA is compared to the in situ measurements from the EMEP database and shows a good agreement with secondary inorganic aerosol composition. The results at the regional scale are consistent with those obtained from the global simulations. The AIRBASE database was used to compare the model to regulated air quality pollutants: particulate matter, ozone and nitrogen dioxide concentrations. Introduction of the SIA in MOCAGE provides a reduction in the PM2.5 MNMB of 0.44 on a yearly basis and up to 0.52 for the 3 spring months (March, April, May) when SIAs are at their maximum.

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The Carbon:²³⁴Thorium ratios of sinking particles in the California Current Ecosystem 2: Examination of a thorium sorption, desorption, and particle transport model

10.26434/chemrxiv.7803698 ◽

2019 ◽

Author(s):

Michael Stukel ◽

Thomas Kelly

Keyword(s):

Organic Carbon ◽

Water Column ◽

Particulate Organic Carbon ◽

Transport Model ◽

California Current ◽

In Situ Measurements ◽

Power Law Distribution ◽

Sinking Particles ◽

Organic Carbon Concentration

Thorium-234 (234Th) is a powerful tracer of particle dynamics and the biological pump in the surface ocean; however, variability in carbon:thorium ratios of sinking particles adds substantial uncertainty to estimates of organic carbon export. We coupled a mechanistic thorium sorption and desorption model to a one-dimensional particle sinking model that uses realistic particle settling velocity spectra. The model generates estimates of 238U-234Th disequilibrium, particulate organic carbon concentration, and the C:234Th ratio of sinking particles, which are then compared to in situ measurements from quasi-Lagrangian studies conducted on six cruises in the California Current Ecosystem. Broad patterns observed in in situ measurements, including decreasing C:234Th ratios with depth and a strong correlation between sinking C:234Th and the ratio of vertically-integrated particulate organic carbon (POC) to vertically-integrated total water column 234Th, were accurately recovered by models assuming either a power law distribution of sinking speeds or a double log normal distribution of sinking speeds. Simulations suggested that the observed decrease in C:234Th with depth may be driven by preferential remineralization of carbon by particle-attached microbes. However, an alternate model structure featuring complete consumption and/or disaggregation of particles by mesozooplankton (e.g. no preferential remineralization of carbon) was also able to simulate decreasing C:234Th with depth (although the decrease was weaker), driven by 234Th adsorption onto slowly sinking particles. Model results also suggest that during bloom decays C:234Th ratios of sinking particles should be higher than expected (based on contemporaneous water column POC), because high settling velocities minimize carbon remineralization during sinking.

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Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

Remote Sensing ◽

10.3390/rs13020228 ◽

2021 ◽

Vol 13 (2) ◽

pp. 228

Author(s):

Jian Kang ◽

Rui Jin ◽

Xin Li ◽

Yang Zhang

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Spatial Scales ◽

Microwave Remote Sensing ◽

In Situ Measurements ◽

Spatial Density ◽

Linear Regression Method ◽

Spatiotemporal Resolution ◽

Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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