Soil Moisture And Ocean Salinity Soil Moisture Research Articles

Estimating the uncertainties of satellite derived soil moisture at global scale

This study attempts to derive the uncertainty of the soil moisture estimation from passive microwave satellite mission at global scale. To do so, the approach is based on the sensitivity of the Soil Moisture and Ocean Salinity (SMOS) soil moisture retrieval quality to the land surface characteristics within its footprint (presence of forest, topography, open water bodies, sand, clay, bulk density and soil organic carbon content). First, we performed a global assessment of SMOS using in situ measurements from the International Soil Moisture Network (ISMN) as reference, with more than 1900 ISMN stations and 10 years of SMOS data. This assessment shows that the ubRMSD scores vary greatly between locations (with a mean of 0.074 m3m−3 and an interquartile range of 0.030 m3m−3). Second, the scores are analyzed for different surface conditions within the satellite footprint. The best agreement between the ground measurement and SMOS time series are obtained for low forest cover, low topographic complexity, and marginal presence of open water bodies within the SMOS footprint. Soil parameters also have an impact, with better scores for sandier soils with a high bulk-density and low soil organic carbon content. Finally, we propose to extrapolate the obtained relationships, using a multiple linear regression, in order to derive a global map of SMOS uncertainties based on surface conditions. This map of predicted uncertainties show a diverse range of ubRMSD values across the globe (with a mean of 0.076 m3m−3 and an interquartile range of 0.031 m3m−3) depending on the surface characteristics. At the ISMN site location, the predicted ubRMSD shows similar results than the comparison between SMOS and the in situ measurements. The map of predicted SMOS ubRMSD represents an upper bound estimate of the SMOS uncertainty, as it includes the uncertainties of the in situ sensor measurements and the scale mismatch. Further investigations will focus on the different components of this uncertainty budget to obtain a better assessment of the absolute uncertainties of SMOS soil moisture retrievals across the globe.

True global error maps for SMAP, SMOS, and ASCAT soil moisture data based on machine learning and triple collocation analysis

Quantifying the accuracy of the satellite-based soil moisture (SM) data is important for a number of key applications, such as: combining satellite-based SM products for long-term SM analyses, assimilating SM data into land surface models, and providing quality flags to mask bad quality SM data.A range of statistical methods have been proposed to estimate error statistics for large-scale SM datasets including the: instrumental variable (IV) method, triple collocation analysis (TCA), and quadruple collocation analysis (QCDA). While requiring only two input products, the IV method also imposes an additional assumption that one input product possesses serially uncorrelated errors - thus limiting its scope compared to TC. Likewise, QCDA requires four independent SM data products that are difficult to obtain and may not always be available for analysis. Nonetheless, TCA-based methods still cannot provide truly global error maps for satellite SM products due to the limited number of independent SM products and difficulties with baseline TCA assumptions. Moreover, temporal sampling requirements for TCA are often impractical because of low SM retrieval skill in forested and arid areas – as well as in regions prone to radio frequency interference.Here, we seek to fill significant spatial gaps in TCA results using machine learning (ML) and therefore provide spatially complete error maps for the satellite-based SM data products derived from the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) systems. Furthermore, we use SHapley Additive exPlanations (SHAP) values, a model-agnostic technique for interpreting ML models, to examine the impact of various environmental conditions on the quality of satellite-based SM retrievals.Globally, and across all three products, 72.0% of missing error information in a TCA-based analysis, due to either the lack of valid data or the inability of TCA to provide reliable results, can be reconstructed from the ensemble prediction mean of the ML models. Overall, we found that 22.7% (a.m.) and 34.2% (p.m.) of the Earth'sSM dynamics (between 60°S to 60°N) have not been investigated properly across all three satellite missions.

The Characterization of the Vertical Distribution of Surface Soil Moisture Using ISMN Multilayer In Situ Data and Their Comparison with SMOS and SMAP Soil Moisture Products

In this paper, we investigated the vertical distribution characteristics of surface soil moisture based on ISMN (International Soil Moisture Network) multilayer in situ data (5, 10, and 20 cm; 2, 4, and 8 in) and performed comparisons between the in situ data and four microwave satellite remote sensing products (SMOS L2, SMOS-IC, SMAP L2, and SMAP L4). The results showed that the mean soil moisture difference between layers can be −0.042~−0.024 (for the centimeter group)/−0.067~−0.044 (for the inch group) m3/m3 in negative terms and 0.020~0.028 (for the centimeter group)/0.036~0.040 (for the inch group) m3/m3 in positive terms. The surface soil moisture was found to have very significant stratification characteristics, and the interlayer difference was close to or beyond the SMOS and SMAP 0.04 m3/m3 nominal retrieval accuracy. Comparisons revealed that the satellite retrievals had a higher correlation with the field measurements of 5 cm/2 in, and SMAP L4 had the smallest difference with the in situ data. The mean difference caused by using 10 cm/4 in and 20 cm/8 in in situ data instead of the 5 cm/2 in data could be about −0.019~−0.018/−0.18~−0.015 m3/m3 and −0.026~−0.023/−0.043~−0.039 m3/m3, respectively, meaning that there would be a potential depth mismatch in the data validation.

A New Method for Estimating Irrigation Water Use via Soil Moisture

The ability to obtain an accurate measure of irrigation water use is urgently needed in order to provide further scientific guidance for irrigation practices. This investigation took soil moisture and precipitation as the study objects and quantitatively analyzed their relationship by establishing four models: a linear model, a logarithmic model, a soil water balance model, and a similarity model. The results from building models on every site clearly revealed the relationship between soil moisture and precipitation and confirmed the feasibility of estimating irrigation water use when soil moisture data are known. Four models combined with soil moisture data were used to estimate irrigation water use. First, the 16 sites which monitor soil moisture conditions in Hebi City were identified as study objects, from which everyday meteorological data (temperature, precipitation, atmospheric pressure, wind speed, sunshine duration) and soil moisture data from 2015 to 2020 (totaling six years) were collected. Second, the eligible data from the first four years in the date range were used to create four kinds of models (linear model, logarithmic model, soil water balance model, and similarity model) to estimate the amount of water input to the soil surface based on soil moisture. Third, the eligible data from the last two years in the established date range were used to verify the established models on every site and then judge the accuracy of the models. For example, for site 53990, the RMSE of the linear model, logarithmic model, soil water balance model, and similarity model was 10,547, 10,302, 8619, and 7524, respectively. The results demonstrate that the similarity model proposed in this study can express the quantitative relationship between soil moisture and precipitation more accurately than the other three models. Based on this conclusion, the eligible soil moisture data known in the specific site were ultimately used to estimate the irrigation water use in the field by the relationship expressed in the similarity model. Compared with the amount of irrigation water data recorded, the estimated irrigation water use yielded by the similarity model in this study was 18.11% smaller. In a future study, microwave satellite remote sensing of soil moisture data, such as SMAP and SMOS soil moisture data, will be used to evaluate the performance of estimated regional irrigation water use.

Indicator of Flood-Irrigated Crops From SMOS and SMAP Soil Moisture Products in Southern India

Spaceborne L-band data have the potential to monitor flooded and irrigated areas. However, further studies are needed to assess in real cases the impact of flood-irrigated crops on SMOS and SMAP surface soil moisture (SSM) data. This paper demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated area at the seasonal scale and at a 25 km resolution in the Telangana State in southern India. Over irrigated areas, both SMOS level 3 (L3) SSM and SMAP L3 enhanced SSM products present a bimodal annual cycle, with a peak of SSM during the monsoon (wet) season corresponding to rainfall and irrigation, and a peak during the dry season due to irrigation activities solely. The second peak is absent or has a very small amplitude in areas where rice represents a small fraction (typically below 5-10%). More importantly, the amplitude of the second SSM peak is significantly correlated to the rice cover fraction within 25×25 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> pixels (R=0.81 for SMOS and 0.77 for SMAP), showing its potential to assess crop fraction and hence the water used for irrigation. The SMOS/SMAP L3 SSM peak during the dry period occurs several months before the harvest, constituting an indicator for rice stocks at the end of the season. However the irrigation signature is absent from the SMAP level 4 SSM product derived from the assimilation of SMAP brightness temperatures in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information.

Validation of NASA SMAP Satellite Soil Moisture Products over the Desert of Kuwait

The goal of this study is to validate and analyze NASA’s Soil Moisture Active Passive (SMAP) products over the desert of Kuwait. The study period was between April 2015 and April 2020. The study domain includes a mission candidate calibration/validation (Cal/Val) site that comprises six permanent soil moisture stations used to verify SMAP estimates. In addition, intensive field campaigns were conducted within and around the candidate Cal/Val site during the study period to collect additional thermogravimetric samples. The mean difference (MD), root mean squared difference (RMSD), unbiased root mean square difference (ubRMSD), and correlation coefficient (R) were computed to assess the agreement between SMAP SM products and in situ observations. The comparison of the six ground station sensors’ observations with the thermogravimetric samples led to an absolute mean bias (AMB) of 0.034 m3 m−3, which was then used to calibrate the sensors and bias-correct their measurements. The temporal consistency of the readings from the test site and calibrated sensors was assessed using the mean relative difference (MRD) and its standard deviation of relative difference (SDRD). Using a sampling density analysis, it was determined that a minimum of four ground stations would be required to validate the test site. Furthermore, the consistency between SMAP satellite soil moisture data and those derived from the Soil Moisture and Ocean Salinity (SMOS) satellite operated by the European Space Agency, and their agreement with in situ samples, was analyzed. The comparison of SMAP and SMOS soil moisture data with in situ observations showed that both satellites successfully captured the spatial and temporal distribution of soil moisture. For SMAP and SMOS, the lowest ubRMSE statistics were 0.043 m3 m−3 and 0.045 m3 m−3, respectively, which are slightly higher than the mission target of 0.04 m3 m−3.

Estimation of high-resolution precipitation using downscaled satellite soil moisture and SM2RAIN approach

Soil moisture (SM) and precipitation play an essential part in land surface and atmospheric processes. Availability of high-resolution SM and precipitation data are quintessential for regional and global studies in hydro climatology. There exists a strong interconnection between the varying amounts of SM and the precipitation over a given area, giving rise to the bottom-up approach of estimating precipitation from SM2RAIN. This study examines precipitation derived from SM, downscaled using a unique cognation of land surface temperature (LST), vegetation index (VI), and SM. For this work, MODIS optical data having one km spatial resolution has been used to downscale the SMOS (25 km) and ESA CCI (25 km) SM data to generate four fine-resolution SM products SMOS-LST-NDVI, SMOS-LST-EVI, ESA CCI-LST-NDVI, and ESA CCI-LST-EVI, respectively. In this study, the Ganga basin in India is used as a case study area to assess the bottom-up SM2RAIN approach. The correlation analysis of SM products with LST, NDVI, and EVI, shows that LST has a robust negative correlation, whereas NDVI and EVI have a significant positive correlation. Results obtained from the SM2RAIN simulation from downscaled SM managed to capture the spatial variation of the high precipitation event. Validation with IMERG GPM precipitation data shows that the SMOS-LST-NDVI precipitation product best captures the spatial variation of observed precipitation. The present study results provide convincing evidence that high-resolution precipitation can be estimated using satellite-derived SM data at basin level, as long as better calibration and validation data are available.

Evaluation of SMOS, SMAP, AMSR2 and FY-3C soil moisture products over China

Microwave remote sensing can provide long-term near-surface soil moisture data on regional and global scales. Conducting standardized authenticity tests is critical to the effective use of observed data products in models, data assimilation, and various terminal scenarios. Global Land Data Assimilation System (GLDAS) soil moisture data were used as a reference for comparative analysis, and triple collocation analysis was used to validate data from four mainstream passive microwave remote sensing soil moisture products: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active and Passive (SMAP), Global Change Observation Mission–Water using the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Fengyun-3C (FY-3C). The effects of topography, land cover, and meteorological factors on the accuracy of soil moisture observation data were determined. The results show that SMAP had the best overall performance and AMSR2 the worst. Passive microwave detection technology can accurately capture soil moisture data in areas at high altitude with uniform terrain, particularly if the underlying surface is soil, and in areas with low average temperatures and little precipitation, such as the Qinghai–Tibet Plateau. FY-3C performed in the middle of the group and was relatively optimal in northeast China but showed poor data integrity. Variation in accuracy between products, together with other factors identified in the study, provides a baseline reference for the improvement of the retrieval algorithm, and the research results provide a quantitative basis for developing better use of passive microwave soil moisture products.

Assessment of SMAP and SMOS soil moisture products using triple collocation method over Inner Mongolia

Soil moisture (SM) is an indispensable variable in drought monitoring and weather forecast. L-band is found to be the most suitable band for retrieving surface SM. Here, we evaluate two L-band passive microwave SM products Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) in Inner Mongolia. The collected in-situ data from 24 measured sites and the triple collocation (TC) analysis method (using Global Land Data Assimilation System (GLDAS) Noah and the Advanced Scatterometer (ASCAT) SM data as a reference dataset) are used to evaluate SMAP and SMOS SM products. In the validation based on in-situ SM, SMAP shows the highest correlation with the in-situ SM, followed by GLDAS and SMOS. The root mean square error (RMSE) and bias value of SMAP is the lowest. The validation results of each in-situ station show that the median correlation coefficient of ASCAT is the highest, followed by SMAP, GLDAS, and SMOS. The median RMSE and bias of SMAP are the lowest, while SMOS performs the worst among the three products. TC analysis results show that SMAP has better performance with the majority of the random errors distributed between 0.02 and 0.03 m3/m3. The average random error of SMAP is small, while SMOS has relatively large random errors. Simultaneously, SMAP performs better than GLDAS and SMOS in grassland and low vegetation coverage areas. The research results and analysis of this paper are expected to provide some insights for improving the quality of SM products.

From SMOS Soil Moisture to 3-hour Precipitation Estimates at 0.1° Resolution in Africa

Several recent studies have shown that knowledge of the spatiotemporal dynamics of soil moisture intrinsically contains information on precipitation. In this study, we show how SMOS measurements can be used to generate a near-real-time precipitation product with a spatial resolution of 0.1° and a temporal resolution of 3 h. The principle consists of assimilating the SMOS data into a model that simulates the evolution of soil moisture, which is forced by a satellite precipitation product. The assimilation of SMOS soil moisture leads to an adjustment of the satellite precipitation rates. Using data from more than 200 rain gauges set up in Africa between 2010 and 2021, we show that the PrISM algorithm (for Precipitation Inferred from Soil Moisture) almost systematically improves the initial precipitation product. One of the original features of this study is that we used the IMERG-Early satellite precipitation product, which has a finer spatial resolution (0.1°) than SMOS (~0.25°). Despite this, the methodology reduces both the RMSE and bias of IMERG-Early. The RMSE is reduced from 8.0 to 6.3 mm/day, and the absolute bias is reduced from 0.81 to 0.63 mm/day on average over the 200 rain gauges. PrISM performs even slightly better on average than IMERG-Final in terms of RMSE (6.8 mm/day for IMERG-Final) but better scores are obtained by IMERG-Final in terms of absolute bias (0.35 mm/day), which utilizes a network of field measurements to correct the biases of the IMERG-Early product with a 2.5-month delay. Therefore, the use of SMOS soil moisture measurements for Africa can be an advantageous alternative to the use of gauge measurements for debiasing rainfall satellite products in real time.

Impact of Incidence Angle Diversity on SMOS and Sentinel-1 Soil Moisture Retrievals at Coarse and Fine Scales

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (<i>SM</i>) across spatial scales. In this study, the Single Channel Algorithm (SCA) is first applied to SMOS brightness temperatures at vertical polarization (<i>TB_V</i>) to estimate <i>SM</i> at coarse-resolution (25 km) and develop a land cover-specific and incidence angle (32.5&#x00B0;, 42.5&#x00B0; and 52.5&#x00B0;)-adaptive calibration of single scattering albedo (&#x03C9;) and soil roughness (<i>h_s</i>) parameters. These effective parameters are used together with fine-scale multi-angular Sentinel-1 backscatter in a single-pass active-passive downscaling approach to estimate <i>TB_V</i> at fine-scale (1 km) for each SMOS incidence angle. These <i>TB_V</i> are finally inverted to obtain the corresponding high-resolution <i>SM</i> maps. Results over the Iberian Peninsula for year 2018 show an increasing trend of &#x03C9; and a decreasing trend of <i>h_s</i> with SMOS incidence angle, with almost no variability of &#x03C9; across land cover types. The active-passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fine <i>TB_V</i> maps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolution <i>SM</i> maps have maximum differences in mean and standard deviation of 0.016 and 0.015 m³/m³, respectively, and compare well with <i>in situ</i> measurements. Our results indicate that model-based microwave approaches to estimate <i>SM</i> can be adequately adapted to account for the incidence angle diversity of planned missions such as CIMR, ROSE-L and Sentinel-1 next generation.

Multi-Source Hydrological Data Products to Monitor High Asian River Basins and Regional Water Security

This project explored the integrated use of satellite, ground observations and hydrological distributed models to support water resources assessment and monitoring in High Mountain Asia (HMA). Hydrological data products were generated taking advantage of the synergies of European and Chinese data assets and space-borne observation systems. Energy-budget-based glacier mass balance and hydrological models driven by satellite observations were developed. These models can be applied to describe glacier-melt contribution to river flow. Satellite hydrological data products were used for forcing, calibration, validation and data assimilation in distributed river basin models. A pilot study was carried out on the Red River basin. Multiple hydrological data products were generated using the data collected by Chinese satellites. A new Evapo-Transpiration (ET) dataset from 2000 to 2018 was generated, including plant transpiration, soil evaporation, rainfall interception loss, snow/ice sublimation and open water evaporation. Higher resolution data were used to characterize glaciers and their response to environmental forcing. These studies focused on the Parlung Zangbo Basin, where glacier facies were mapped with GaoFeng (GF), Sentinal-2/Multi-Spectral Imager (S2/MSI) and Landsat8/Operational Land Imager (L8/OLI) data. The geodetic mass balance was estimated between 2000 and 2017 with Zi-Yuan (ZY)-3 Stereo Images and the SRTM DEM. Surface velocity was studied with Landsat5/Thematic Mapper (L5/TM), L8/OLI and S2/MSI data over the period 2013–2019. An updated method was developed to improve the retrieval of glacier albedo by correcting glacier reflectance for anisotropy, and a new dataset on glacier albedo was generated for the period 2001–2020. A detailed glacier energy and mass balance model was developed with the support of field experiments at the Parlung No. 4 Glacier and the 24 K Glacier, both in the Tibetan Plateau. Besides meteorological measurements, the field experiments included glaciological and hydrological measurements. The energy balance model was formulated in terms of enthalpy for easier treatment of water phase transitions. The model was applied to assess the spatial variability in glacier melt. In the Parlung No. 4 Glacier, the accumulated glacier melt was between 1.5 and 2.5 m w.e. in the accumulation zone and between 4.5 and 6.0 m w.e. in the ablation zone, reaching 6.5 m w.e. at the terminus. The seasonality in the glacier mass balance was observed by combining intensive field campaigns with continuous automatic observations. The linkage of the glacier and snowpack mass balance with water resources in a river basin was analyzed in the Chiese (Italy) and Heihe (China) basins by developing and applying integrated hydrological models using satellite retrievals in multiple ways. The model FEST-WEB was calibrated using retrievals of Land Surface Temperature (LST) to map soil hydrological properties. A watershed model was developed by coupling ecohydrological and socioeconomic systems. Integrated modeling is supported by an updated and parallelized data assimilation system. The latter exploits retrievals of brightness temperature (Advanced Microwave Scanning Radiometer, AMSR), LST (Moderate Resolution Imaging Spectroradiometer, MODIS), precipitation (Tropical Rainfall Measuring Mission (TRMM) and FengYun (FY)-2D) and in-situ measurements. In the case study on the Red River Basin, a new algorithm has been applied to disaggregate the SMOS (Soil Moisture and Ocean Salinity) soil moisture retrievals by making use of the correlation between evaporative fraction and soil moisture.

Assessment of the Temperature Effects in SMAP Satellite Soil Moisture Products in Oklahoma

Soil moisture is a notably important component in various studies in water sciences, including hydrology, agriculture, and water management. To achieve extensive or global spatial coverage, satellites focusing on soil moisture observation have been launched, and many satellite products, such as SMAP and SMOS soil moisture products, have been provided. Most of these satellite observations are based on the dielectric properties of wet soil, and most soil moisture retrieval algorithms are calibrated or evaluated using in situ soil moisture. While the in situ data observed by dielectric sensors, which are the most widely used, are reported to include errors caused by the so-called “temperature effects” of these sensors, the temperature dependency of bulk soil dielectric constant has rarely been discussed on satellite data. Since both in situ dielectric measurements and satellite observations are based on the same physical variable, the dielectric constant and the dielectrically measured in situ soil moisture data are also used as ground truth, it is necessary to assess the impact of temperature effects on satellite products. In this work, we attempted to identify the existence of the temperature effects and evaluate the consequences of removing these effects on in situ and satellite soil moisture and the relationships between the brightness temperature at the soil surface and the brightness temperature provided by satellite observation. To achieve the goals of this study, we analyzed the temperature effects on surface soil moisture data provided by a SMAP mission in Oklahoma, the United States. The results show that temperature effects exist in SMAP soil moisture products in Oklahoma, and the removal of these effects will potentially improve the accuracy of these products.

Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China

Drought is the costliest disaster around the world and in China as well. Northeastern China is one of China’s most important major grain producing areas. Frequent droughts have harmed the agriculture of this region and further threatened national food security. Therefore, the timely and effective monitoring of drought is extremely important. In this study, the passive microwave remote sensing soil moisture data, i.e., the SMOS soil moisture (SMOS-SM) product, was compared to several in situ meteorological indices through Pearson correlation analysis to assess the performance of SMOS-SM in monitoring drought in northeastern China. Then, maps based on SMOS-SM and in situ indices were created for July from 2010 to 2015 to identify the spatial pattern of drought distributions. Our results showed that the SMOS-SM product had relatively high correlation with in situ indices, especially SPI and SPEI values of a nine-month scale for the growing season. The drought patterns shown on maps generated from SPI-9, SPEI-9 and sc-PDSI were also successfully captured using the SMOS-SM product. We found that the SMOS-SM product effectively monitored drought patterns in northeastern China, and this capacity would be enhanced when field capacity information became available.

Synergistic Calibration of a Hydrological Model Using Discharge and Remotely Sensed Soil Moisture in the Paraná River Basin

Hydrological models are useful tools for water resources studies, yet their calibration is still a challenge, especially if aiming at improved estimates of multiple components of the water cycle. This has led the hydrologic community to look for ways to constrain models with multiple variables. Remote sensing estimates of soil moisture are very promising in this sense, especially in large areas for which field observations may be unevenly distributed. However, the use of such data to calibrate hydrological models in a synergistic way is still not well understood, especially in tropical humid areas such as those found in South America. Here, we perform multiple scenarios of multiobjective model optimization with in situ discharge and the SMOS L4 root zone soil moisture product for the Upper Paraná River Basin in South America (drainage area &gt; 900,000 km²), for which discharge data for 136 river gauges are used. An additional scenario is used to compare the relative impacts of using all river gauges and a small subset containing nine gauges only. Across the basin, the joint calibration (CAL-DS) using discharge and soil moisture leads to improved precision and accuracy for both variables. The discharges estimated by CAL-DS (median KGE improvement for discharge was 0.14) are as accurate as those obtained with the calibration with discharge only (median equal to 0.14), while the CAL-DS soil moisture retrieval is practically as accurate (median KGE improvement for soil moisture was 0.11) as that estimated using the calibration with soil moisture only (median equal to 0.13). Nonetheless, the individual calibration with discharge rates is not able to retrieve satisfactory soil moisture estimates, and vice versa. These results show the complementarity between these two variables in the model calibration and highlight the benefits of considering multiple variables in the calibration framework. It is also shown that, by considering only nine gauges instead of 136 in the model optimization, the model is able to estimate reasonable discharge and soil moisture, although relatively less accurately and with less precision than for the entire dataset. In summary, this study shows that, for poorly gauged tropical basins, the joint calibration of SMOS soil moisture and a few in situ discharge gauges is capable of providing reasonable discharge and soil moisture estimates basin-wide and is more preferable than performing only a discharge-oriented optimization process.

Assessment of SMOS and SMAP soil moisture products against new estimates combining physical model, a statistical model, and in-situ observations: A case study over the Huai River Basin, China

Soil moisture often governs the exchange of water between the land surface and the atmosphere and, as a result, has a profound effect on the global water and energy cycles. With the launch of the two new L-band satellite missions tasked with retrieving surface soil moisture (i.e., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active/Passive (SMAP) missions), new possibilities exist for quasi-global soil moisture monitoring. Here, new soil moisture estimates (CLDAS-BPNN), generated by fusing a land surface model soil moisture product (CLDAS) using in-situ observations via a Back Propagation Neural Network (BPNN) method, are used to evaluate four satellite-derived soil moisture products (SMOS-L3, SMOS-IC, SMAP_L3_SM_P, and SMAP_L3_SM_P_E) over the highly irrigated Huai River Basin in China. Results indicate that the post-processing of CLDAS (to generate CLDAS-BPNN) reduces soil moisture errors (particularly bias) and preserves temporal correlations with in-situ observations. Due to extensive irrigation present in the basin, CLDAS-BPNN soil moisture time series have weak seasonal differences and the spatial distribution of their means does not reflect known precipitation patterns. Based on the validation of SMOS and SMAP soil moisture retrievals against CLDAS-BPNN, several key findings can be obtained. First, SMAP retrievals are recommended for locations possessing both valid SMOS and SMAP retrievals. However, SMOS provides generally better spatial support than SMAP. Second, the four satellite retrievals present no significant seasonal variation in time-varying errors, and larger soil moisture underestimation is observed in winter than in summer. Finally, all four satellite products exhibit degraded accuracy in mountainous and forested areas of the Huai River Basin relative to flatter terrain in the Huaibei plain.

Blending Noah, SMOS and In-Situ Soil Moisture Using Multiple Weighting and Sampling Schemes

AbstractSoil moisture can be obtained from in-situ measurements, satellite observations, and model simulations. This study evaluates the importance of in-situ observations in soil moisture blending, and compares different weighting and sampling methods for combining model, satellite, and in-situ soil moisture data to generate an accurate and spatially-continuous soil moisture product at 4-km resolution. Four different datasets are used: Antecedent Precipitation Index (API), KAPI, which incorporates in-situ soil moisture observations with the API using regression kriging, SMOS L3 soil moisture, and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System (NLDAS). Triple collocation, least square weighting, and equal weighting are used to generate blended soil moisture products. An enumerated weighting scheme is designed to investigate the impact of different weighting schemes. The sensitivity of the blended soil moisture products to sampling schemes, station density and data formats (absolute, anomalies and percentiles) are also investigated. The results reveal KAPI outperforms API. This indicates that incorporating in-situ soil moisture improves the accuracy of the blended soil moisture products. There are no statistically significant (p&gt;0.05) differences between blended soil moisture using triple collocation and equal weighting approaches, and both methods provide sub-optimal weighting. Optimal weighting is achieved by assigning larger weights to KAPI and smaller weights to SMOS. Using multiple sources of soil moisture is helpful for reducing uncertainty and improving accuracy, especially when the sampling density is low, or the sampling stations are less representative. These results are consistent regardless of how soil moisture is represented (absolute, anomalies or percentiles).

Soil Moisture Estimation Synergy Using GNSS-R and L-Band Microwave Radiometry Data from FSSCat/FMPL-2

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.

Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment

Global Navigation Satellite System—Reflectometry (GNSS-R) has already proven its potential for retrieving a number of geophysical parameters, including soil moisture. However, single-pass GNSS-R soil moisture retrieval is still a challenge. This study presents a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument with two 19-element antenna arrays with four electronically steered beams each. The instrument was flown twice over the OzNet soil moisture monitoring network in southern New South Wales (Australia): the first flight was performed after a long period without rain, and the second one just after a rain event. In this work, the impact of surface roughness and vegetation attenuation in the reflectivity of the GNSS-R signal is assessed at both L1 and L5 bands. The work analyzes the reflectivity at different integration times, and finally, an artificial neural network is used to retrieve soil moisture from the reflectivity values. The algorithm is trained and compared to a 20-m resolution downscaled soil moisture estimate derived from SMOS soil moisture, Sentinel-2 normalized difference vegetation index (NDVI) data, and ECMWF Land Surface Temperature.

Study of a Strong L-Band RFI Source

CN006 is the name given by the European Space Agency (ESA) in their reports on radio frequency interference (RFI) to an instance of RFI in the L-band spectral window at 1.413 GHz protected for passive use only. The source of the interference is located east of the Chinese city of Hangzhou in an area with radar installations. Its effect was initially indistinguishable from that of many such RFI sources observed by the ESA's SMOS and NASA's Soil Moisture Active Passive (SMAP) radiometers over China. However, in July 2020, the level of radiation increased dramatically reaching levels exceeding 1700000 K as reported by Soil Moisture and Ocean Salinity (SMOS) becoming the strongest source observed by SMOS. This article describes an analysis of this very strong source (a radar). It provides information to provide insight into an example of a well-defined source of RFI and illustrates the powerful capability of the SMAP radiometer receiver and RFI processing incorporated in it for identifying and understanding interference.