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

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm3/cm3, 0.019 cm3/cm3, and 0.034 cm3/cm3, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm3/cm3 (ASCAT) and 0.0662 cm3/cm3 (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product.

Soil moisture (SM) is a key variable in hydrological processes, bio-ecological processes, and biogeochemical processes. Long-term observations of soil moisture over large areas are critical to research on flooding and drought monitoring, water resource management, and crop yield forecasts. In this paper, Fengyun (FY3B and FY3C) SM products, Japan Aerospace Exploration Agency (JAXA) SM products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Land Parameter Retrieval Model (LPRM) AMSR2 L3 SM products, the Version 2 (v2) global land parameter data record (LPDR) of SM products, Soil Moisture Ocean Salinity (SMOS) Centre Aval de Traitement des Donnees SMOS (CATDS) L3 SM products, the Soil Moisture Active Passive (SMAP) passive L3 SM products and the European Space Agency(ESA) Climate Change Initiative (CCI) SM products were evaluated using the ground-based observations in the Little Washita, Fort Cobb and Yanco networks. Long-time series comparison between measured and satellite products was conducted to evaluate the overall performance of FY3 series satellites SM products. Bias (mean bias), R (correlation coefficient), RMSE (root mean square error) and ubRMSE (unbiased root mean square error) were calculated to explore the agreement between satellite products and in-situ measurements. Taylor diagrams were used to compare the performance of various satellite products. The result showed that (1) FY3B, FY3C and LPRM AMSR2 ascending and descending products had an obvious overestimate with in-situ soil moisture in three networks. (2) JAXA AMSR2 ascending and descending products had considerable underestimation in three networks. (3) The validation result of SMOS ascending and descending products over the three networks was satisfactory, with a rather high correlation than other X-band products. (4) The validation result of SMAP ascending and descending products outperformed the other products over the Little Washita and Fort Cobb networks. (5) The ESA CCI product had the lowest values of RMSE and ubRMSE than the other products in the Yanco network, revealing the effectiveness of merging active and passive soil moisture products. (6)The LPDR descending SM products had better performance than the LPDR ascending SM products in the LW and FC networks.

Improving surface soil moisture estimates in West Africa through GRACE data assimilation

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.

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation.&amp;#160; The product is based on the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observations-based precipitation forcing.&amp;#160;In this presentation, we describe three recent improvements in the L4_SM algorithm.&amp;#160; First, satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) are used in two ways: (i) The climatology to which all L4_SM precipitation forcing inputs are rescaled is based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project v2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions.&amp;#160; Second, the Catchment model now includes the recently developed PEATCLSM hydrology module for peatlands and uses an updated global map of peatlands.&amp;#160; Third, revised parameters are used in the L-band radiative transfer model that converts the simulated soil moisture and temperature estimates into Tb predictions for use in the radiance-based L4_SM analysis.&amp;#160; Specifically, climatological parameters for the scattering albedo, soil roughness, and (seasonally-varying) vegetation opacity were derived from the SMAP Level-2 radiometer soil moisture retrieval product.&amp;#160; &amp;#160;The revised precipitation inputs result in considerably improved anomaly time series correlation skill of L4_SM surface soil moisture in South America, Africa, Australia, and parts of East Asia.&amp;#160; Particularly large improvements are seen in central Australia and Myanmar, where the quality of the gauge-only precipitation product used in earlier L4_SM versions was particularly poor. &amp;#160;In peatlands, the dynamics of water table depth, surface soil moisture and evapotranspiration are considerably improved when evaluated against in situ measurements.&amp;#160; Moreover, the time series correlation of surface and root-zone soil moisture vs. in situ measurements is slightly improved, owing to the improved annual cycle phasing of the Level-2 derived vegetation opacity parameters.&amp;#160; Collectively, these improvements are also manifested in smaller Tb observation-minus-forecast residuals.

In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASA's Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESA's 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.

Soil moisture ocean salinity (SMOS) mission has been providing L-band multiangular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations also allow for the retrieval of additional parameters beyond soil moisture, but these are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio-frequency interference (RFI) issues and aliasing at lower look angles increase the uncertainty of observations and thereby affect the soil moisture retrieval that utilizes observations at specific angles. In this study, we proposed a two-step regression approach that uses a mixed objective function based on SMOS L1c data products to refine characteristics of multiangular observations. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS. In addition, refined brightness temperatures were analyzed over three external targets: Antarctic ice sheet, Amazon rainforest, and Sahara desert, by comparing with WindSat observations. These results provide insights for selecting and utilizing external targets as part of the upcoming soil moisture active passive (SMAP) mission.

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

High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms.

&amp;lt;p&amp;gt;Merging data from different instruments is required to construct long time data records of soil moisture (SM). This is the goal of projects such as the ESA Climate Change Initiative (CCI) for SM (Gruber et al., 2019), which uses both active and passive microwave sensors. Currently, the GLDAS v2.1 model is used as reference to re-scale active and passive time series by matching their Cumulative Density Function (CDF) to that of the model. Removing the dependency on models is important, in particular for data assimilation applications into hydrological or climate models, and it has been proposed (Van der Schalie et al., 2018) to use L-band data from one of the two instruments specifically designed to measure SM, ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, as reference to re-scale other time series.&amp;lt;br&amp;gt;To investigate this approach, AMSR-2 SM time series obtained from C1-, C2- and X-band observations using LPRM (Land Parameter Retrieval Model) were re-scaled by CDF-matching (Brocca et al., 2011) using different SMAP and SMOS official (SMAP L2 V005, SMOS L3 V300, SMOS NRT V100&amp;amp;V200) and research (SMOS IC V103) SM products as well as the SMAP and SMOS LPRM v6 SM data used by the ESA CCI. The time series re-scaled using L-band remote sensing data were compared to those re-scaled using GLDAS and were evaluated against in situ measurements at several hundred sites retrieved from the International Soil Moisture Network (Dorigo et al., 2011). The results were analyzed as a function of the land cover class and the Koppen-Geiger climate classification.&amp;lt;br&amp;gt;Overall, AMSR-2 time series re-scaled using SMAP L2, SMAP LPRM and SMOS IC data sets as reference gave the best correlations with respect to in situ measurements, similar to those obtained by the time series re-scaled using GLDAS and slightly better than those of the original AMSR-2 time series. These results imply that different SMAP and SMOS products could actually be used to replace GLDAS as reference for the re-scaling of other sensors time series within the ESA CCI. However, one must bear in mind that this study is limited to the re-scaling of AMSR-2 data at a few hundred sites.&amp;lt;br&amp;gt;For a more detailed assessment of the L-band data set to be used for a global re-scaling, it is necessary to investigate other effects such as the spatial coverage or the time series length. SMAP spatial coverage is better than that of SMOS in regions affected by radio frequency interference. In contrast, the length of SMAP time series can be too short to capture the long term SM variability for climate applications in some regions. The CDF of SMOS time series computed from the date of SMAP launch is significantly different to those of the full length SMOS time series in some regions of the Globe. Possible ways of using a coherent SMAP/SMOS L-band data set will be discussed.&amp;lt;/p&amp;gt;

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.

South America (SA) features diverse land cover types and varied climate conditions, both of which significantly influence the variability of soil moisture (SMO). Obtaining ground-truth measurements for SMO is often costly and labor-intensive, and the limited number of ground SMO stations in SA further complicates the evaluation of satellite-derived SMO products. In this work, we proposed an approach that integrates some statistical methods to assess the reliability of Soil Moisture Active Passive (SMAP), the H113 dataset from the Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite-derived SMO products in SA from 14 May 2015 to 31 December 2016. The integrated methods are error metrics (correlation (R), bias, and ubiased root mean square error (ubRMSE)), Triple Collocation Method (TCM), and Hovmöller diagrams. ERA5 and GLDAS-Noah SM products were used as references for validation. The quality of SMO products was assessed by considering environmental variables, including land cover, vegetation density, and precipitation, within the different climate zones of SA. The results presented that SMAP overall outperforms SMOS and ASCAT, with the highest average correlation (0.55 with GLDAS and 0.61 with ERA5), slight average bias (−0.058 with GLDAS and −0.014 with ERA5), and lowest average ubRMSE (0.045 with GLDAS and 0.041 with ERA5). In arid, semi-arid, and moderate vegetation regions, the SMAP satellite outperforms SMOS and ASCAT, achieving better statistics values with GLDAS and ERA5 datasets, and achieving low error variance and high S/N in the TCM analysis. While the ASCAT H113 product showed good performance, which makes it a good alternative to SMAP, it still has limitations in more dense vegetation regions. SMOS showed the lowest performance across SA, especially in the Amazon basin. The Amazon basin emerges as a critical region where all SMO products displayed a significant SMO variability; however, SMAP showed slightly better results than ASCAT and SMOS. In the absence of ground truths, the proposed approach provides a better evaluation of satellite SMO products. Meanwhile, it provides new spatiotemporal statistical insights into satellite SMO retrieval performance evaluation within diverse climate zones of SA. This research provides valuable guidance for improving SMO monitoring and agricultural management in tropical and semi-arid ecosystems.

Soil moisture plays a significant role in disciplines such as hydrology, meteorology and agriculture, and passive microwave remote sensing has become a widely used technique for global soil moisture estimation over the past three decades. Several satellite missions carrying radiometers have been launched over the past years. Among them are Japan Aerospace Exploration Agency's (JAXA's) Advanced Microwave Scanning Radiometer-EOS (AMSR-E) launched on NASA's Aqua satellite, European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) mission, JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite, and NASA's Soil Moisture Active Passive (SMAP) mission. Based on the availability of these four missions, there is an opportunity to develop a consistent inter-calibrated longterm soil moisture data record. This study focuses on the parametrization of the tau-omega model for soil moisture retrieval at L-, C- and X-band using brightness temperature observations from the four missions and in-situ soil moisture and soil temperature data from the SMAP core validation sites across various land cover types. The ancillary data sets used in the SMAP baseline algorithm are used for the retrievals at different frequencies. Simultaneous calibrations of the vegetation parameter $b$ and roughness parameter $h$ at both horizontal and vertical polarizations are performed. A set of model parameters to successfully retrieve soil moisture at different validation sites at L-, C- and X -band are presented. A preliminary comparison of SMAP and AMSR2 soil moisture retrievals against in situ observations at the Yanco (Australia) and TxSON (U.S.) sites showed the best accuracy and correlation at L-band (RMSD=0.03-0.05 m3/m3; R=0.92-0.94). The C-/X-band performance was not as satisfactory as L-band (RMSD=0.08-0.17 m3/m3; R=0.24-0.79). This also indicates that retrieval at higher frequencies can be very challenging when dense vegetation is present.

Soil moisture is a crucial component of the water cycle and plays an important role in regional weather and climate. However, owing to the lack of In Situ observations, an accurate understanding of the spatiotemporal variations of soil moisture (SM) on the Tibetan Plateau (TP) is still lacking. In this study, we used three gridded SM products to characterize the spatiotemporal features of SM on the TP during the warm season (May to August). We analyzed the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), Global Land Data Assimilation System (GLDAS), and Soil Moisture Active Passive (SMAP) datasets and used station observation data and triple collocation to quantify product accuracy and consistency. Results of the evaluation based on observation data show that both ERA5 and GLDAS overestimate SM, while the accuracy of SMAP is high. In terms of capturing the temporal variations of SM measured at stations, the performance of ERA5 and that of SMAP are superior to that of GLDAS. According to the evaluation based on triple collocation, SMAP exhibits the smallest random error over the TP and the highest temporal correlation with the unknown true SM in eastern TP. For SMAP, SM variability is the largest in the southern TP. For ERA5 and GLDAS, variability in the western TP is substantially larger than that for SMAP. Low-frequency (30&amp;#8211;90 days) variations are the largest contributor to TP SM intraseasonal variability. Relative to SMAP, the contribution of high-frequency variations is low in ERA5 and GLDAS. Land-atmosphere coupling is stronger (weaker) in the western (southeastern) TP, which is relatively dry (wet). Our evaluation of SM product performance over the TP may facilitate the use of these products for disaster monitoring and climate and hydrological studies.

Comparison of remotely sensed and modelled soil moisture data sets across Australia