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The soil moisture products derived from the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions have garnered widespread adoption in drought surveillance, meteorological/climatic forecasting, and hydrological investigations. Nevertheless, satellite platforms inherently suffer from coarse spatial resolutions (approximately 43 km), stemming from constraints on antenna dimensions, thereby posing challenges in leveraging their data for agricultural and ecological endeavors that necessitate meter-scale soil moisture maps. This research endeavor innovatively employed a Portable L-band Radiometer (PoLRa), leveraging microstrip patch array antenna technology, to facilitate portable and cost-effective soil moisture monitoring within the context of the Hulunbeier Prairie Experiments in China. Three distinct soil moisture datasets were procured utilizing Scheme I, a PoLRa-based default iteration of the tau-omega semi-empirical model; Scheme II, a brightness temperature forward simulation akin to the CMEM (Community Microwave Emission Model) framework; and Scheme III, a regression-based approach. The findings are: 1. Employing the brightness temperature data as input, the CMEM-inspired schemes achieve soil moisture retrieval with an RMSE (Root Mean Square Error) of approximately 0.06 cm3/cm3, whereas the regression model between retrieved and observed data manifests a linear bias. 2. The CMEM forward simulation scheme outperforms the tau-omega model but necessitates more intricate modules for the retrieval process. 3. Both in terms of linear bias and RMSE, the regression-based scheme exhibits the poorest performance. This study anticipates enhancing the applicability of soil moisture remote sensing devices and methodologies across diverse disciplines, including agriculture, meteorology, and soil science, by advancing the precision and accessibility of soil moisture monitoring at finer scales.

The Soil Moisture Active Passive (SMAP) product, which delivers spatially continuous soil moisture (SM) data with reliable accuracy, has been widely used in ecologically and hydrologically related studies. However, the active radar of SMAP failed after operation for approximate three months, leading to the loss of the original 3 km (called A3) and 9 km (called AP9) SMAP SM dataset afterwards. Since merely the 36 km SMAP dataset (called P36 in this research) is available up to now, the relatively coarse spatial resolution is difficult to meet the requirement for monitoring at the regional scale. In this paper, the 36 km SMAP SM dataset in the conterminous United States (CONUS) was downscaled to 3 km after the failure of the active radar. To reduce the great uncertainty in downscaling (i.e., a process from 36 km to 3 km directly), a stepwise strategy was proposed. Specifically, our currently published 9 km SMAP SM dataset (called VIP9, from April 2015 to December 2019) was used as input for downscaling, which was downscaled to 3 km by fusion with auxiliary data (e.g., Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI)) at fine spatial resolution based on area-to-point regression kriging (ATPRK). Furthermore, a random forest (RF)-based correction process was developed to enhance the quality of the downscaled SM dataset by fusing with the ground-based SM data (i.e., in-situ data). As a result, a 16-day composited SM dataset at 3 km spatial resolution was produced from 2015 to 2019, with a mean correlation coefficient (CC) and unbiased root mean square error (ubRMSE) in temporal (spatial) validation of 0.888 (0.912) and 0.021 (0.023), respectively. Experimental results demonstrated that the AP9 dataset (although only available in the three months) is helpful to increase the accuracy of downscaling. Moreover, the difference between satellite- and ground-based SM data can be further reduced through the correction process. The produced 3 km SMAP SM dataset has great potential to extend the defunct A3 data and support related studies. The contributions of the paper are twofold, including the development of the stepwise method for downscaling and the generation of the 3 km SMAP SM dataset in the CONUS during 2015–2019.

Fractional water (FW) correction of satellite microwave brightness temperature (Tb) observations is a prerequisite for accurate soil moisture (SM) mapping over mixed land and water areas. Here, we evaluated the FW impacts on NASA Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) SM retrievals using two water masks including (a) the NASA Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Water Mask version 6 (MOD44W) multi-year (2015–2019) water record and (b) the Ocean Discipline Processing System (ODPS) water mask previously used for SMAP global operational Tb and SM processing. The MOD44W and ODPS data were first compared with the European Commission's Joint Research Centre (JRC) Landsat-based water record. MOD44W showed major improvements in land/water classifications relative to the ODPS, with producer accuracy increasing from 50.02% to 95.02%, and user accuracy from 53.93% to 91.73% for water pixels. For assessing the FW impacts on SM retrievals, the same single channel V-polarization (SCA-V) algorithm was applied to SMAP Tb datasets corrected using ODPS and MOD44W water masks separately. MOD44W showed overall greater FW values (mean increase of 0.006) relative to the ODPS, leading to relatively drier SM retrievals (mean decrease: −0.012 m3/m3). Additional comparisons with globally distributed SM measurements confirmed consistently lower SM retrieval biases (mean decrease 0.04 m3/m3) and higher correlations (mean increase 0.06) of the MOD44W-based results relative to those based on the ODPS. Our results revealed non-negligible SM retrieval uncertainty introduced from the underlying ancillary FW data for areas with substantial water presence (e.g. FW>0.01).

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. [ABSTRACT FROM AUTHOR]

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 soil moisture and ocean salinity (SMOS) and soil moisture active passive (SMAP) surface soil moisture (SSM) data. This letter demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated areas 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 with the rice cover fraction within $25\times25$ km2 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 (Tbs) in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential variable for understanding water and heat exchanges between land and the atmosphere. Presently, L-band remote sensing technology has been widely employed for routine measurement of soil moisture from space. However, the spatial resolution of L-band soil moisture products obtained from microwave radiometers is too low (dozens of kilometres) to meet the needs of practical applications, such as hydrology modeling, weather forecasts, agricultural applications and water resource management. Therefore, this article proposes a new concept to downscale the soil moisture active passive (SMAP) L-band brightness temperature by using advanced microwave scanning radiometer-2 (AMSR2) X-band TB data, including the time-series regression (TSR) algorithm and two-dimensional discrete wavelet transform algorithm. An intercomparison study was conducted over a semiarid area located in the Shandian river basin with the other two algorithms of the Backus–Gilbert (BG) optimal interpolation and natural neighbor interpolation without using the X-band TB data. The results revealed that the BG algorithm outperformed the NNI, 2D-DWT, and TSR algorithms compared with the original 36-km SMAP TB and airborne 1-km TB data. However, the soil moisture retrievals within one 9-km pixel with 8 soil moisture stations showed that the downscaled L-band TB with X-band data are reliable with lower unbiased root-mean-squared errors compared with resolution-enhanced TB without AMSR2 data.

Groundwater is the most significant freshwater source and plays a critical role in the earth's water and energy balance. The lack of groundwater observations with a high spatiotemporal resolution at a global scale hinders our ability to study and model the environment when shallow groundwater has a direct impact on surface soil moisture. This study aims to estimate the spatial and temporal distributions of shallow groundwater-influenced areas at a global scale. We trained an ensemble machine learning algorithm, using outputs from a variably saturated soil moisture flux model, to identify the shallow groundwater occurrence. Model simulations spanned various climate zones and soil types across the globe. The overall accuracy of the algorithm in reproducing the soil moisture flux model results was 95.5%. We applied the algorithm to spaceborne soil moisture observations retrieved by NASA's SMAP satellite and present a global-scale shallow groundwater map derived from the SMAP observations. The derived global distribution of shallow groundwater identifies wetlands, large riparian corridors, and seasonally inundated lowlands. The results showed that 19% of terrestrial land cover had been influenced by shallow groundwater at some point in time during the period of interest (2015–2018). Temporally, shallow groundwater follows an annual cyclic pattern with 2% to 6% of the land surface being influenced globally. This study shows that SMAP observations could be used in estimating shallow groundwater in high spatiotemporal resolution at a global scale, potentially providing invaluable inputs for modeling and environmental monitoring studies.

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and, hence, flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity and two distributed hydrologic models with measurements from in situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with Moderate Resolution Imaging Spectroradiometer vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model, and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflowpredictions.

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (SM), soil temperature, soil salinity, and vegetation, using L-band microwave brightness temperature (TB) observations and retrievals from the soil moisture ocean salinity (SMOS) and soil moisture active passive satellite missions, Catchment land surface model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based SM outperformed CLSM SM when evaluated against field data, and the forward L-band TB simulations derived from in situ SM and soil temperature performed better than those derived from CLSM estimates when evaluated against SMOS TB observations. The surface salinity for the sampled pixels was on average only 4 mg/g and only locally influenced the TB simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize TB simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multiangular SMOS TB data and constraints of CLSM SM and soil temperature. However, the latter modeled SM was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo, and surface roughness resulted in realistic values.

Wildfires are a major concern around the globe because of the immediate impact they have on people's lives, local ecosystems, and the environment. Soil moisture is one of the most important factors that influence wildfire occurrences and spread. However, it is also one of the most challenging hydrological variables to measure routinely and accurately. Therefore, soil moisture is significantly underutilized in operational wildfire risk applications. Thus, the aim here is to use a well-established operational soil moisture product to isolate the soil moisture-fire relationship and assess the utility of using soil moisture as a leading indicator of potential fire risk. We evaluated the value of remotely-sensed soil moisture observations from the soil moisture active passive sensor for monitoring and predicting fire risk in Australia and California. We quantified the relationship between observed fire activity and soil moisture conditions and analyzed the soil moisture conditions for two extreme fire events. Our findings show that fire activity is strongly associated with soil moisture anomalies. Lagged correlation analysis demonstrated that a remote-sensing based soil moisture product could predict fire activity with a 1–2 month lead-time. Soil moisture anomalies consistently decreased in the months preceding fire occurrence, often from normal to drier conditions, according to a spatiotemporal analysis of soil moisture in two extreme fire events. Overall, our findings indicate that soil moisture conditions prior to large wildfires can aid in their prediction and operational satellite-based soil moisture products such as the one used here have real value for supporting wildfire susceptibility and impacts.

Soil Moisture Active Passive (SMAP) mission brightness temperature ($T_{b}$) observations are assimilated into NASA's Catchment Land Surface Model using an ensemble Kalman filter to update simulations of surface and root-zone soil moisture. Different time-series components of the $T_{b}$ observations are assimilated, including anomalies, interannual variations, and high-frequency variations. To optimize the weights that the data assimilation (DA) puts on the observations, the ratio between the uncertainties of modeled and observed $T_{b}$ is approximated using modeled and observed soil moisture uncertainties estimated using triple collocation analysis. In a benchmark experiment, $T_{b}$ observations are assimilated using a spatially constant 4-K observation uncertainty, as in the operational SMAP Level-4 algorithm. All the DA experiments exhibit notable skill improvements in most regions. Improvements are largest for the interannual variations in the simulations of both surface and root-zone soil moisture (mean improvements in terms of Pearson correlation (–) are 0.08 and 0.06, respectively). Anomaly simulations improve similarly (0.07), and improvements in the high-frequency variations are only observed for surface soil moisture simulations (0.06). No notable difference in skill—neither improvement nor deterioration—is observed between the experiments that use optimized observation uncertainty parameters and the 4-K benchmark experiment. This may be explained by the presence of large observation operator errors, which are analytically shown to have the potential to render postupdate uncertainty insensitive to inaccuracies in estimates of the Kalman gain. These results have important implications for the design of soil moisture DA systems, in particular for parameterizing model and observation uncertainties.

A thermal hydraulic disaggregation of soil moisture (THySM) algorithm was implemented to downscale NASA's soil moisture active passive (SMAP) enhanced soil moisture (SM) product to 1 km over the continental United States (CONUS). This algorithm was developed by combining thermal inertia theory with a soil hydraulic-based approach that considers fine-scale SM spatial distribution driven by both heat fluxes and hydraulic conductivity in soils. Relative soil wetness values were estimated using land surface temperature and normalized difference vegetation index for the thermal inertia model and using soil properties for the hydraulic model. The relative soil wetness values at 1 km from both models were then combined by using weighting functions whereby the spatial distribution of SM was governed more by thermal fluxes during times of strong heat transport and infiltration during moisture abundant soil conditions. THySM values were evaluated using in situ SM measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network, and the National Oceanic and Atmospheric Administration Climate Reference Network over CONUS. THySM shows higher accuracy than the SMAP / Sentinel-1 (SPL2SMAP_S) 1 km SM product when compared to in situ measurements. The accuracy of THySM is 0.048 m3/m3 based on unbiased root mean square error (ubRMSE), outperforming SPL2SMAP_S by 0.01–0.02 m3/m3. The ubRMSE of THySM 1 km SM over the SMAP grassland/rangeland-dominated CVS sites is better than 0.04 m3/m3, which meets the SMAP mission SM accuracy requirement applied at 9 and 36 km.

Microwave vegetation optical depth (VOD) and soil moisture (SM) can be simultaneously retrieved based on L-band radiometry with polarization information. VOD is indicative of the vegetation water content (VWC) because it captures the extinction of land surface emission. If the connectivity of VOD to VWC is robust, the pair of VWC-SM observations can be viable bases for understanding soil–plant–atmosphere water relations, providing new perspectives on ecosystem science. Simultaneous SM–VOD retrievals are feasible by inverting the $\tau -\omega $ model with two independent datasets in dual-channel algorithms. However, given correlated satellite vertical and horizontal brightness temperatures (TBs; TBv and ${{\mathrm {TB}}}_{h}$), an ill-posed inverse problem arises where TB errors result in high uncertainties of retrievals. In this study, we apply the degrees-of-information (DoI) metric and propose a signal-to-noise ratio (SNR) metric to assess the “retrievability” of VOD given the Soil Moisture Active Passive (SMAP) TBv–TBh linear dependence. The application of these metrics allows determining where the VOD retrievals are robust and reliable. This is a necessary step in supporting the applications of VOD in ecology and hydrology. Results show that regions with mainly nonwoody vegetation have the best potential for VOD retrievals, though regularization is necessary. We then assess VOD time variations from two regularization products that reduce the impact of underdetermined inversions: the L3 dual-channel algorithm (L3-DCA) and the multitemporal dual-channel algorithm (MTDCA), which constrain VOD time dynamics with and without using a priori VOD climatology, respectively. Though they both reduce noise, especially in the VOD retrievals, they result in differences in VOD seasonal amplitude and coupling to SM at high frequencies as we outline here. [ABSTRACT FROM AUTHOR]

The 2020 'Elucidating the role of clouds-circulation coupling in climate-Ocean-Atmosphere' (EUREC4A-OA) and the 'Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign' (ATOMIC) campaigns focused on improving our understanding of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign utilized many data collection technologies, some of which are relatively new. In this study, we used saildrone uncrewed surface vehicles, one of the newer cutting edge technologies available for marine data collection, to validate Level 2 and Level 3 Soil Moisture Active Passive (SMAP) satellite and Hybrid Coordinate Ocean Model (HYCOM) sea surface salinity (SSS) products in the Western Tropical Atlantic. The saildrones observed fine-scale salinity variability not present in the lower-spatial resolution satellite and model products. In regions that lacked significant small-scale salinity variability, the satellite and model salinities performed well. However, SMAP Remote Sensing Systems (RSS) 70 km generally outperformed its counterparts outside of areas with submesoscale SSS variation, whereas RSS 40 km performed better within freshening events such as a fresh tongue. HYCOM failed to detect the fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product and its drawbacks for their applications in this region and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models. [ABSTRACT FROM AUTHOR]

Errors in soil moisture adversely impact the modeling of land–atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This article investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly coupled land–atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture versus in situ measurements by ∼0.1–0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002–0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2mmax is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ∼700 mb), with relative improvements in bias of 15–25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root mean square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ∼0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land–atmosphere coupling.

With recent advances in satellite microwave soil moisture estimation, there is a demand for up-to-date validation of satellite soil moisture products. This article presents a sparse network validation over a humid region within the Laurentian Great Lakes basin for five state-of-the-art satellite soil moisture datasets, including the Soil Moisture and Ocean Salinity (SMOS) Level 2 Soil Moisture User Data Product (MIR_SMUDP2) V650, the Soil Moisture Active Passive (SMAP) Enhanced Level 3 Radiometer Soil Moisture (SPL3SMP_E) Version 4, and the European Space Agency Climate Change Initiative (CCI) Soil Moisture v05.2 (containing the Active, Passive, and Combined sets). Unsurprisingly, the five sets of soil moisture products performed differently. With respect to the unbiased root-mean-squared error (ubRMSE), the CCI Combined product performed best (an average ubRMSE of about 0.04 m3 m−3), whereas the CCI Passive had the lowest performance with an average ubRMSE exceeding 0.10 m3 m−3. Overall, in terms of correlation measure, the SMAP and CCI Combined performed better than other products, with the lowest skill from the SMOS product. The SMAP product performed best in the context of the soil moisture anomaly detection, whereas the SMOS and CCI Passive showed the lowest anomaly correlation with the in situ observations. The validation results provide an important guidance for hydrological and meteorological applications involving satellite soil moisture datasets in the study region or other similar areas.

Remotely sensed soil moisture (SM) with high accuracy and high spatial–temporal resolution is crucial to meteorological, agricultural, hydrological, and environmental applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that uses the L-band signal transmitted by the GNSS satellites to develop daily SM data products. In this study, a physics-based algorithm is proposed to couple CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature estimates for accurate SM retrieval. The algorithm is based on the radiative transfer model and the SMAP data to derive a combined parameter of the vegetation optical depth ($\tau$) and the surface roughness parameter ($h$). The CYGNSS L1 Version 2.1 data of the years 2017–2018 and 2019–2020 are used for calibration and validation, respectively. The SM estimates agree and correlate well with the SMAP SM and in situ SM data on a global scale ($R =0.679$ , RMSE $=0.051\,\,\text{m}^{3}\text{m}^{-3}$ , and MAE $=0.045\,\,\text{m}^{3}\text{m}^{-\mathrm {3}}$ against SMAP SM; $R = 0.729$ against in situ SM). The proposed algorithm makes contributions from two aspects. First, the proposed algorithm provides a physics-based algorithm using SMAP brightness temperature to calibrate the attenuation due to vegetation and surface roughness on the CYGNSS-derived SR. Unlike attenuation models that have been explored previously in the context of CYGNSS, this algorithm executes the calibration without relying on observations of $h$ or vegetation biophysical parameters as inputs but with the SMAP brightness temperature as the only observations. Second, the proposed algorithm provides a new way for the combined usage of CYGNSS and SMAP to improve the temporal and spatial coverages of global SM with temporal coverage increased by 38.2% and spatial coverage increased by 31.6%. [ABSTRACT FROM AUTHOR]

Active and passive microwave characteristics of diurnal soil freeze-thaw transitions and their relationships are crucial for developing retrieval algorithms of the soil liquid water content ($\theta _{\mathrm {liq}}$) and freeze/thaw state, which, however, have been less explored. This study investigates these microwave characteristics and relationships via analysis of ground-based measurements of brightness temperature ($T_{B}$) and backscattering coefficients ($\sigma ^{0}$) in combination with simulations performed with the Tor Vergata discrete radiative transfer model. Both an L-band (1.4 GHz) radiometer ELBARA-III and a wide-band (1–10 GHz) scatterometer are installed in a seasonally frozen Tibetan meadow ecosystem to measure diurnal variations of $T_{B}$ and copolarized $\sigma ^{0}$ at both hh ($\sigma _{\mathrm {hh}}^{0}$) and vv ($\sigma _{\mathrm {vv}}^{0}$) polarizations. Analysis of measurements collected between December 2017 and March 2018 shows that 1) diurnal cycles are observed in both $T_{B}$ and $\sigma ^{0}$ due to the change in surface $\theta _{\mathrm {liq}}$ caused by diurnal soil freeze-thaw transitions; 2) a negatively linear relationship is found between $e$ and $\sigma ^{0}$ regardless of frequency, polarization combinations, and observation angles; 3) slopes ($\beta$) of linearly fit equations between $e^{H}$ and $\sigma _{\mathrm {hh}}^{0}$ decrease with increasing observation angles of ELBARA-III, while the ones between $e^{V}$ and $\sigma _{\mathrm {vv}}^{\mathrm {0 {}}}$ increase with increasing observation angles; and 4) correlations between $e$ and $\sigma ^{0}$ increase with decreasing microwave frequency of $\sigma ^{0}$ measurements and ELBARA-III observation angles, and magnitudes of diurnal $\sigma ^{0}$ cycles also increase with decreasing microwave frequency. Moreover, the calibrated Tor Vergata model shows capability to reproduce both diurnal $e$ and $\sigma ^{\mathrm {0 {}}}$ variations as well as to quantify their relationships at different frequencies and observation angles. [ABSTRACT FROM AUTHOR]

Groundwater is the most significant freshwater source and plays a critical role in the earth's water and energy balance. The lack of groundwater observations with a high spatiotemporal resolution at a global scale hinders our ability to study and model the environment when shallow groundwater has a direct impact on surface soil moisture. This study aims to estimate the spatial and temporal distributions of shallow groundwater-influenced areas at a global scale. We trained an ensemble machine learning algorithm, using outputs from a variably saturated soil moisture flux model, to identify the shallow groundwater occurrence. Model simulations spanned various climate zones and soil types across the globe. The overall accuracy of the algorithm in reproducing the soil moisture flux model results was 95.5%. We applied the algorithm to spaceborne soil moisture observations retrieved by NASA's SMAP satellite and present a global-scale shallow groundwater map derived from the SMAP observations. The derived global distribution of shallow groundwater identifies wetlands, large riparian corridors, and seasonally inundated lowlands. The results showed that 19% of terrestrial land cover had been influenced by shallow groundwater at some point in time during the period of interest (2015–2018). Temporally, shallow groundwater follows an annual cyclic pattern with 2% to 6% of the land surface being influenced globally. This study shows that SMAP observations could be used in estimating shallow groundwater in high spatiotemporal resolution at a global scale, potentially providing invaluable inputs for modeling and environmental monitoring studies. [ABSTRACT FROM AUTHOR]

Ongoing evaluation of the soil moisture active passive (SMAP) soil moisture products has utilized validation networks distributed in several regions around the world. The in situ reference used for validation of the soil moisture retrieval algorithm is associated with measurements from soil moisture probes typically located at 5 cm beneath the soil surface; however, some networks also consider a vertically oriented probe that measures from 0 to 5 cm. In this study, we compare the correlation and unbiased root mean square error (ubRMSE) from the SMAP L2 radiometer soil moisture product when compared to in situ measurements taken at 5 cm (approximately 3.5–6.5 cm) below the surface and measurements taken as an integrated measure from 0 to 5.7 cm. The data were obtained from two SMAP validation networks in Canada: the Kenaston network in Saskatchewan and Carman network situated in Manitoba. At both sites, correlations between the in situ and the SMAP L2 product were consistently higher with vertically oriented probes following rain events. With respect to the ubRMSE, the vertically oriented probes at the Carman site had lower ubRMSE with the SMAP product than the horizontal probes that are currently used for validation activities. In some cases, vertical probe information should be considered in validation approaches when this data is available and could be considered in the design of in situ calibration/validation networks. These results may be useful in design considerations of networks for upcoming soil moisture product validation. [ABSTRACT FROM AUTHOR]

The different orbit design and launching conditions of Soil Moisture and Ocean Salinity (SMOS, ESA) and Soil Moisture Active Passive (SMAP, NASA) result in different passing time over any point on the ground. The time lag between the two satellites is thought to be one of the reasons to induce uncertainties in soil moisture data comparison and validation. This letter calculates the temporal difference between SMOS and SMAP at first; it is found that their mismatch mainly concentrates within a period of 30–90 min. During such time lag, the change in surface soil moisture (5 cm) and other meteorological variables is analyzed on the basis of the U.S. Climate Reference Network (USCRN) high-frequency (5-min) field observations and Murrumbidgee Soil Moisture Monitoring Network (MSMMN) in situ measurements (20-min). This letter found that in most cases, air temperature, wind, and relative humidity present a moderate change of about 10%–20%, while solar radiation shows very strong variation from tens to hundreds (%). Soil moisture and soil temperature are always stable, the value of soil moisture at the two time points when SMOS and SMAP pass overhead are almost the same, and the averaged minimum and maximum fluctuations of soil moisture are only 0.004/0.003 and 0.007/0.01 $\text{m}^{3}/\text{m}^{3}$ , respectively, which are far less than the nominal accuracy of satellites (0.04 $\text{m}^{3}/\text{m}^{3})$ and probably unrecognizable. Soil moisture experiences a natural fading of very small magnitude during the time intervals of satellites, the temporal mismatch may not induce external uncertainties in soil moisture data comparison and validation, and it is safe to conclude that the impact is negligible. [ABSTRACT FROM AUTHOR]

An active–passive microwave retrieval algorithm for simultaneous determination of soil surface roughness parameters [vertical root-mean-square (RMS) height (${s}$) and horizontal correlation length (${l}$)] is presented for bare soils. The algorithm is based on active–passive microwave covariation, including the improved Integral Equation Method (I2EM), and is tested with global soil moisture active passive (SMAP) observations. The estimated retrieval results for ${s}$ and ${l}$ are overall consistent with values in the literature, indicating the validity of the proposed algorithm. Sensitivity analyses showed that the developed roughness retrieval algorithm is independent of permittivity for ${\varepsilon }_{s} > 10$ [-]. Furthermore, the physical model basis of this approach (I2EM) allows the application of different autocorrelation functions (ACF), such as Gaussian and exponential ACFs. Global roughness retrieval results confirm bare areas in deserts such as Sahara or Gobi. However, the type of ACF used within roughness parameter estimation is important. Retrieval results for the Gaussian ACF describe a rougher surface than retrieval results for the exponential ACF. No correlations were found between roughness results and the amount of precipitation or the soil texture, which could be due to the coarse spatial resolution of the SMAP data. The extension of this approach to vegetated soils is planned as an add-on study. [ABSTRACT FROM AUTHOR]

In order to validate its soil moisture products, the NASA Soil Moisture Active Passive (SMAP) mission utilizes sites with permanent networks of in situ soil moisture sensors maintained by independent calibration and validation partners in a variety of ecosystems around the world. Measurements from each core validation site (CVS) are combined in a weighted average to produce an estimate of soil moisture at a 33-km scale that represents the SMAP's radiometer-based retrievals. Since upscaled estimates produced in this manner are dependent on the weighting scheme applied, an independent method of quantifying their biases is needed. Here, we present one such method that uses soil moisture measurements taken from a dense, but temporary, network of soil moisture sensors deployed at each CVS to train a random forests regression expressing soil moisture in terms of a set of spatial variables. The regression then serves as an independent source of upscaled estimates against which permanent network upscaled estimates can be compared in order to calculate bias statistics. This method, which offers a systematic and unified approach to estimate bias across a variety of validation sites, was applied to estimate biases at four CVSs. The results showed that the magnitude of the uncertainty in the permanent network upscaling bias can sometimes exceed 80% of the upper limit on SMAP's entire allowable unbiased root-mean-square error (ubRMSE). Such large CVS bias uncertainties could make it more difficult to assess biases in soil moisture estimates from SMAP.

Socially and economically costly extreme weather events have become more prevalent in the last decade. Monitoring and early warning systems could help mitigate the impact of such events by allowing people to better prepare themselves to manage their responses to these events. One significant element of an effective warning system is soil moisture because it is a key determinant of the exchange of water and heat energy between the land and atmosphere, the partitioning of precipitation between infiltration and runoff, and therefore has an influence on weather patterns and streamflow. In addition, soil moisture governs plant water availability – the key to crop yield forecasting. For these reasons, a wide range of organizations use soil moisture information to better predict and monitor climate and weather phenomena such as floods and droughts. By improving soil moisture estimates, it may be possible to improve the monitoring and early warning systems upon which these organizations rely, and hence better mitigate the impacts of extreme weather events. Through case studies, this article discusses several uses of soil moisture data products from NASA's Soil Moisture Active Passive (SMAP) mission to help improve soil moisture-related monitoring and early warning systems.

The Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup has been established to coordinate the development of standardized validation across the satellite-derived products from different platforms, sensors, and algorithms with reference measurements from the in situ networks. Soil moisture exhibits a high variability in space that challenges the in situ validation. One of the main drivers for this variability is the characteristic heterogeneity in the soil texture. By the machine learning methods using the soil profile measurements and the remotely sensed predictors, spatially continuous maps of basic soil properties such as soil texture and bulk density are available. Those can be used to estimate soil moisture variability within a satellite product grid cell, here exemplarily shown for the Soil Moisture Active Passive (SMAP) 36-km product. The soil moisture standard deviation is described as a function of the mean soil moisture, whereby the approach needs the mean and standard deviation of the hydraulic parameters as input. The resulting global data set helps identifying the number of in situ stations necessary to validate the coarse soil moisture products. For most SMAP grid cells, three to four stations are adequate to estimate the mean soil moisture for validation; however, also regions were identified where 80 stations are necessary. [ABSTRACT FROM AUTHOR]

Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regional agricultural drought monitoring is one of the most logical application areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimeters of the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entire root zone. The assimilation of remotely sensed soil moisture products into continuous soil water balance models provides a way of addressing this shortcoming. Here, we describe the assimilation of NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impact of SMAP on USDA FAS drought monitoring capabilities. The assimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for random errors inherent in its rainfall forcing data. The performance of this SMAP-based assimilation system is evaluated using two approaches. At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moisture and the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ-based soil moisture estimates is carried out at seven of the SMAP core Cal/Val sites located in the USA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.

Prediction of large-scale water-related natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks can benefit from the high spatial resolution soil moisture (SM) data of satellite and modeled products because antecedent SM conditions in the topsoil layer govern the partitioning of precipitation into infiltration and runoff. SM data retrieved from Soil Moisture Active Passive (SMAP) have proved to be an effective method of monitoring SM content at different spatial resolutions: 1) radiometer-based product gridded at 36 km; 2) radiometer-only enhanced posting product gridded at 9 km; and 3) SMAP/Sentinel-1A/B products at 3 and 1 km. In this article, we focused on 9-, 3-, and 1-km SM products: three products were validated against in situ data using conventional and triple collocation analysis (TCA) statistics and were then merged with a Noah-Multiparameterization version-3.6 (NoahMP36) land surface model (LSM). An exponential filter and a cumulative density function (CDF) were applied for further evaluation of the three SM products, and the maximize- $R$ method was applied to combine SMAP and NoahMP36 SM data. CDF-matched 9-, 3-, and 1-km SMAP SM data showed reliable performance: $R$ and ubRMSD values of the CDF-matched SMAP products were 0.658, 0.626, and 0.570 and 0.049, 0.053, and 0.055 m3/m3, respectively. When SMAP and NoahMP36 were combined, the $R$ -values for the 9-, 3-, and 1-km SMAP SM data were greatly improved: $R$ -values were 0.825, 0.804, and 0.795, and ubRMSDs were 0.034, 0.036, and 0.037 m3/m3, respectively. These results indicate the potential uses of SMAP/Sentinel data for improving regional-scale SM estimates and for creating further applications of LSMs with improved accuracy. [ABSTRACT FROM AUTHOR]

In this article, we present a multipolarimetric estimation approach for two model-based vegetation structure parameters (shape ${A}_{P}$ and orientation distribution ${\psi }$ of the main canopy elements). The approach is based on a reduced observation set of three incoherent (no phase information) polarimetric backscatter intensities ($| {S}_{\mathrm{ HH}} |^{2}$ , $| {S}_{\mathrm{ HV}} |^{2}$ , and $| {S}_{\mathrm{ VV}} |^{2}$) combined with a two-parameter (${A}_{P}$ and ${\psi }$) discrete scatterer model of vegetation. The objective is to understand whether this confined set of observations contains enough information to estimate the two vegetation structure parameters from the L-band radar signals. In order to disentangle soil and vegetation scattering influences on these signals and ultimately perform a vegetation-only retrieval of vegetation shape ${A}_{P}$ and orientation distribution ${\psi }$ , we use the subpixel spatial heterogeneity expressed by the covariation of co- and cross-polarized backscatter ${\Gamma }_{{{\mathrm{PP-PQ}}}}$ of the neighboring cells and assume it is indicative for the amount of a vegetation-only co-to-cross-polarized backscatter ratio ${\mu }_{{{\mathrm{PP-PQ}}}}$. The ratio-based retrieval approach enables a relative (no absolute backscatter) estimation of the vegetation structure parameters which is more robust compared to retrievals with absolute terms. The application of the developed algorithm on global L-band Soil Moisture Active Passive (SMAP) radar data acquired from April to July 2015 indicates the potential and limitations of estimating these two parameters when no fully polarimetric data are available. A focus study on six different regions of interest, spanning land cover from barren land to tropical rainforest, shows a steady increase in orientation distribution toward randomly oriented volumes and a continuous decrease in shape arriving at dipoles for tropical vegetation. A comparison with independent data sets of vegetation height and above-ground biomass confirms this consistent and meaningful retrieval of ${A}_{P}$ and ${\psi }$. The retrieved shapes and orientation distributions represent the main vegetation elements matching the literature results from model-based decompositions of fully polarimetric L-band data at the SMAP spatial resolution. Based on our findings, ${A}_{P}$ and ${\psi }$ can be directly applied for parameterizing the vegetation scattering component of model-based polarimetric decompositions. This should facilitate decomposition into ground and vegetation scattering components and improve the retrieval of soil parameters (moisture and roughness) under vegetation. [ABSTRACT FROM AUTHOR]

Understanding and reducing the uncertainties in the inversion of the first-order radiative transfer models at the L-band are important for the improved spaceborne retrievals of soil moisture (SM) and vegetation optical depth (VOD) over dense canopy. This article quantifies and compares the sensitivity of dual-channel inversion of the two-stream (2S) and $\tau $ – $\omega $ models and proposes a new inversion approach for simultaneous retrievals of SM, VOD, and vegetation-scattering albedo ($\omega $) from a single satellite overpass. In particular, the inversion algorithm incorporates the information of the nearby spatial observations, assuming that the values of VOD and $\omega $ remain locally invariant, and constrains its solutions to high-resolution a priori physical/climatological knowledge of the retrieval variables. The results demonstrate that the uncertainty in the inversion of 2S model is slightly higher than the $\tau $ – $\omega $ model under noisy observations and remains homoscedastic for SM and $\omega $ , while grows heteroscedastically for higher VOD values due to the shape of the cost function. The results are validated using the SMAP data, the dense Mesonet SM network, the in situ measurements from the International SM Network (ISMN), and the derived VOD from the Moderate Resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) over the state of Oklahoma in the United States. It is shown that the new approach can recover simultaneously high-resolution features of SM, VOD, and $\omega $ only from a single Soil Moisture Active Passive (SMAP) overpass, where the unbiased root-mean-squared error (ubRMSE) of SM and VOD is reduced by 30% and 70%, respectively, when compared with an unconstrained time-windowed inversion approach. [ABSTRACT FROM AUTHOR]

Surface roughness parameterization plays an important role in passive microwave soil moisture (SM) retrieval. This article proposes a new formulation for estimating surface roughness. The proposed model incorporates the field-scale (micro) roughness as well as topographic (macro) roughness. The performance of the model is evaluated by inverting the traditional tau–omega model for retrieving SM. The study focuses on the passive active L-band system (PALS) radiometer data collected as a part of two Soil Moisture Active Passive Validation Experiment (SMAPVEX), i.e., SMAPVEX12 (humid Manitoba, Canada) and SMAPVEX15 (semiarid Arizona, USA) with highly different microroughness and macroroughness. The measured surface roughness is observed to increase exponentially with clay fraction (CF). This behavior is minimized with increase in leaf area index (LAI). In the absence of vegetation, the contribution of topography toward surface roughness increases. A higher surface roughness value is estimated for SMAPVEX12, which positively correlate with LAI and CF and negatively correlate with wetness conditions. On the other hand, due to the high topographic variability in SMAPVEX15 region, the contribution of topography (surface curvature) toward total surface roughness is significant. Also, consistently dry SM resulted in high microroughness for SMAPVEX15. Nevertheless, a total surface roughness estimated for SMAPVEX15 region is less than for SMAPVEX12. The surface roughness formulation presented in this study can be extrapolated to any spatial resolution. [ABSTRACT FROM AUTHOR]

Microwave brightness temperature (Tb) and backscatter ($\sigma ^{0}$) observations from the Soil Moisture and Ocean Salinity (SMOS) mission, Soil Moisture Active Passive (SMAP) mission, Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Advanced Scatterometer (ASCAT) instrument provide a wealth of operationally available satellite data for soil moisture retrieval and data assimilation purposes. To assist the synergistic and efficient use of such techniques, the soil moisture information content in the respective Level-1 observations needs to be determined. Within this context, we compare L-, C-, and X-band Tb and $\sigma ^{0}$ signatures of the above-named sensors to in situ SM observations in Spain, Australia, and USA. We find that L-band Tb observations from SMOS and SMAP show the best overall performance given the considered diagnostics (correlation, anomaly correlation, and sensitivity), while all sensors provide significant soil moisture information. This finding is consistent across the analyzed incidence angle range for SMOS (25°–60°). The results are discussed with respect to physical processes governing the dynamics of Tb and $\sigma ^{0}$ , noting dependencies on vegetation seasonality and land surface temperature. [ABSTRACT FROM AUTHOR]

The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 $\text{m}^{3}/\text{m}^{3}$ volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg/ $\text{m}^{2}$. Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter $h$ and the polarization mixing parameters $Q$ , a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or $\tau$). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms. [ABSTRACT FROM AUTHOR]

Many remote sensing soil moisture (SM) products have been developed with global coverage. However, most of them are derived from passive microwave observations with very coarse resolution, greatly constraining the applications at regional scales. To increase the spatial resolution, a downscaling method is developed to downscale the 36-km Soil Moisture Active Passive L3 SM (SMAP SM) product to 1 km using the Moderate Resolution Imaging Spectroradiometer (MODIS) products (8-d land surface temperature, LST, and 16-d normalized difference vegetation index, NDVI). In this method, a linking model is first established between SM and LST and NDVI, and a self-adaptive window method is applied with the use of the geographically weighted regression (GWR) method to obtain an optimal local regression. Then, the uncertainty of the linking model, expressed as the regression residual, is redistributed to fine-resolution pixels to analyze the consistency before and after downscaling. The method was applied to the Iberian Peninsula to produce the 8-d downscaled SM product in 2016. The downscaled SM was validated with the in-situ SM network (REMEDHUS). A good agreement was found between the two data sets, with a correlation coefficient ($R$) of 0.87 and an unbiased root-mean-squared error (ubRMSE) of 0.043 m3/m3 at a network level. At station level, the $R$ is larger than 0.6 for all the REMEDHUS stations, with an ubRMSE smaller than 0.06 m3/m3. The evaluation indicates the good potential of the proposed method in the SM downscaling, which achieves a robust consistency and provides rich spatial information while maintaining good accuracy. [ABSTRACT FROM AUTHOR]

The Soil Moisture Active Passive (SMAP) mission currently provides three surface soil moisture products based solely on instrument measurements. The three soil moisture products are: 1) the radiometer-only 36 km gridded; 2) a radiometer-only enhanced product gridded at 9 km; and 3) a high-resolution (3 km) SMAP-Sentinel active–passive product. It is important to validate these released SMAP soil moisture products over various land covers and hydroclimatic domains before they are routinely used in scientific research and applications. This paper evaluates SMAP-based soil moisture products for typical Indian conditions of extreme seasonal variability that leads to changes from very wet to dry soil, especially for the paddy dominated region. The assessment metrics indicate that the enhanced passive-only soil moisture product meets the SMAP accuracy requirement of 0.04 m3/m3 during the nongrowing season (NGS) with unbiased root-mean-square error (ubRMSE) values ranging between 0.025 and 0.036 m3/m3. However, this product underperformed during the paddy growing season (GS) with ubRMSE values ranging between 0.063 and 0.097 m3/m3. In addition, the SMAP-Sentinel active–passive soil moisture product shows satisfactory performance during the NGS (ubRMSE, 0.017–0.051 m3/m3), but during the GS, ubRMSE ranged between 0.089 and 0.104 m3/m3. Use of the vegetation water content climatology and low clay fraction in SMAP baseline algorithm (auxiliary database) that mismatched with the actual values may be the possible source of errors and biases in the SMAP soil moisture products. The reported study provides guidelines for the application of enhanced SMAP soil moisture products in India, especially for the tropical region, and provides information that can be used to improve the retrieval algorithm. [ABSTRACT FROM AUTHOR]

Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regional agricultural drought monitoring is one of the most logical application areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimeters of the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entire root zone. The assimilation of remotely sensed soil moisture products into continuous soil water balance models provides a way of addressing this shortcoming. Here, we describe the assimilation of NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impact of SMAP on USDA FAS drought monitoring capabilities. The assimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for random errors inherent in its rainfall forcing data. The performance of this SMAP-based assimilation system is evaluated using two approaches. At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moisture and the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ-based soil moisture estimates is carried out at seven of the SMAP core Cal/Val sites located in the USA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information. [ABSTRACT FROM AUTHOR]

Data from the CYGNSS mission, originally conceived to monitor tropical cyclones, are being investigated here for land applications as well. In this paper, a methodology for soil moisture (SM) retrieval from CYGNSS data is presented. The approach derives Level 3 gridded daily SM estimations, over the latitudinal band covered by CYGNSS, at a resolution of 36 km × 36 km, using the CYGNSS reflectivity over land, coupled with ancillary vegetation and roughness information from the SMAP mission. The results are compared globally with SM measurements from SMAP, which are assumed to be ground truth, showing a good agreement, and a global root-mean-square difference of 0.07 cm3/cm3. A more extensive comparison is performed over two test regions—Texas in the United States and New South Wales in Australia—where reference data from SMAP are complemented with measurements from the SMOS mission. The results over both regions are generally consistent with the global results, and a good agreement is observed between CYGNSS and reference SM measurements from SMAP and SMOS. The study demonstrates that SM can be successfully retrieved from the CYGNSS mission on a global scale and using ancillary information about the overlying vegetation and the characteristics of the soil. The results open up further future perspectives for global, high-resolution SM products from spaceborne Global Navigation Satellite System-Reflectometry data. [ABSTRACT FROM AUTHOR]

Abstract Knowing the exact sampling depth of microwave radiometry is essential for quantifying the performance and appreciation of the applicability of satellite soil moisture products. We investigate in this study the sampling depth (δ SM) of the L-band microwave emission under frozen and thawed soil conditions on the Tibetan Plateau. Two years of diurnal brightness temperature (T B p ) measurements at a time interval of 30 min are collected by the ELBARA-III radiometer deployed at a Tibetan meadow site. Vertical profiles of soil temperature and volumetric liquid water content (θ liq) are measured simultaneously at soil depths up to 1 m below the surface. The impact of the θ liq measured at different depths on the microwave emission simulations is assessed using the τ - ω emission model, whereby the permittivity of frozen and thawed soil is estimated by the four-phase dielectric mixing model. It is found that: 1) the sampling depth for the effective temperature depends on the magnitude of θ liq , and is estimated to be, on average, about 50 and 15 cm for the cold dry and wet warm period, respectively, because of the seasonality in θ liq ; 2) the δ SM is determined at 2.5 cm for both frozen and thawed soil conditions during both cold and warm periods, which is shallower than the commonly used θ liq measurement depth (i.e. 5 cm) adopted for the in-situ monitoring networks across the globe; 3) the T B p simulations performed with the θ liq measurements taken at the estimated δ SM of 2.5 cm result in lower unbiased root mean squared errors, about 14% (3.16 K) and 22% (3.36 K) for the horizontal and vertical polarizations respectively, in comparison to the simulations with the θ liq measurements taken from 5 cm soil depth; and 4) the θ liq retrieved with the single channel algorithm from the ELBARA-III measured vertically polarized T B p are in better agreement with the θ liq measured at 2.5 cm than the one measured at 5 cm. These findings are crucial for developing strategies for the calibration/validation as well as the application of satellite based soil moisture products relying on the L-band radiometry. Highlights • Sampling depth is about 2.5 cm for L-band radiometry of frozen and thawed soils. • Sampling depth for effective temperature depends on liquid water contents. • Sampling depth of liquid water content is smaller than that for effective temperature. • Emission simulation is improved with the liquid water content taken at sampling depth. • Retrievals of liquid water content are closest to these measured at a depth of 2.5 cm. [ABSTRACT FROM AUTHOR]

The Soil Moisture Active Passive (SMAP) mission measures important soil moisture data globally. SMAP’s products might not always perform better than land surface models (LSM) when evaluated against in situ measurements. However, we hypothesize that SMAP presents added value for long-term soil moisture estimation in a data fusion setting as evaluated by in situ data. Here, with the help of a time series deep learning (DL) method, we created a seamlessly extended SMAP data set to test this hypothesis and, importantly, gauge whether such benefits extend to years beyond SMAP’s limited lifespan. We first show that the DL model, called long short-term memory (LSTM), can extrapolate SMAP for several years and the results are similar to the training period. We obtained prolongation results with low-performance degradation where SMAP itself matches well with in situ data. Interannual trends of root-zone soil moisture are surprisingly well captured by LSTM. In some cases, LSTM’s performance is limited by SMAP, whose main issue appears to be its shallow sensing depth. Despite this limitation, a simple average between LSTM and an LSM Noah frequently outperforms Noah alone. Moreover, Noah combined with LSTM is more skillful than when it is combined with another LSM. Over sparsely instrumented sites, the Noah–LSTM combination shows a stronger edge. Our results verified the value of LSTM-extended SMAP data. Moreover, DL is completely data driven and does not require structural assumptions. As such, it has its unique potential for long-term projections and may be applied synergistically with other model-data integration techniques. [ABSTRACT FROM AUTHOR]

Downscaling of microwave remotely sensed that soil moisture content (SMC) is an efficient way to obtain spatial continuous SMC at a finer resolution. However, the classical optical/thermal and microwave fusion, and the active and passive microwave fusion cannot work under all-weather conditions because of contamination of clouds or the lack of suitable radar data source. In this study, a microwave and meteorological fusion (MMF) is provided. The MMF method is based on a complementary relationship hypothesis assuming SMC is reflected in the adjacent surface atmospheric moisture under midday conditions. By this method, daily passive SMC products from Soil Moisture Active Passive (SMAP) mission with 36-km resolution were disaggregated using a daily gridded meteorological data with nominal 4-km resolution. The original and downscaled SMCs were evaluated by comparing with in situ SMC obtained from three core validation sites and three sparse networks. The experiment was conducted in the central part of the U.S. from April 2015 to June 2018. Results demonstrated that the downscaled SMC maintained the dynamic range of original SMC product and energy was conserved. Furthermore, the downscaled SMC showed good agreement with and slightly outperformed the original SMC as compared with in situ SMC. The downscaling method is shown to capture higher resolution SMC spatial variability while preserving the quality of original SMC. However, because of the complexity of soil moisture–atmosphere interactions, the actual contributing domain of downscaled SMC may be greater than 4 km. The MMF method is suggested as a supplementary for all-weather downscaling coarse-resolution SMC. [ABSTRACT FROM AUTHOR]

An improved method for retrieving Above-ground Biomass (AGB) and Canopy Height (CH) based on an observable from Cyclone Global Navigation Satellite System (CYGNSS), soil moisture from Soil Moisture Active Passive (SMAP) and location is proposed. The observable derived from CYGNSS is more sensitive to vegetation. The CYGNSS observable, soil moisture and the location are used as the input features of an Artificial Neural Network (ANN) to retrieve AGB and CH. The sensitivity analysis of the CYGNSS observable to target parameters shows that the proposed observable is more sensitive to AGB/CH than the conventional observable. The AGB/CH retrievals of the improved method show that it has better performance than that of the traditional method, especially in the areas with AGB in the range of 0 to100 Mg/ha and CH in the range of 0 to10 m. For AGB retrievals, the root mean square error (RMSE) and correlation coefficient are 64.84 Mg/ha and 0.80 in the range of 0 to 550 Mg/ha. Compared with the traditional method, the RMSE is decreased by 11.63%, while the correlation coefficient is increased by 5.26%. For CH retrievals, the RMSE and correlation coefficient are 5.97 m and 0.83 in the range of 0 to 45 m. The RMSE is decreased by 12.59%, while the correlation coefficient is increased by 5.06%. The analysis of the improved method in different areas shows that the performance of the improved method over the area with high vegetation is better than the area with low vegetation. The results obtained here further strengthens the capability of GNSS-R for global AGB/CH retrievals as well as different land cover areas.

Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes. The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands. The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters. To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LAI) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used. Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters. Only the most sensitive parameters are defined as free variables, to separately calibrate the active-only model (TVG-A), the passive-only model (TVG-P), and the active and passive combined model (TVG-AP). The accuracy of the calibrated models is evaluated by comparing the SMAP observations and the model simulations. The results show that TVG-AP can well reproduce the backscattering coefficient and brightness temperature, with correlation coefficients of 0.87, 0.89, 0.78, and 0.43 and root-mean-square errors of 0.49 dB, 0.52 dB, 7.20 K, and 10.47 K for $\sigma _{\mathrm{ HH}}^{o} $ , $\sigma _{\mathrm{ VV}}^{o} $ , $T_{\mathrm {BH}}$ , and $T_{\mathrm {BV}}$ , respectively. In contrast, TVG-A and TVG-P can only accurately model the backscattering coefficient and brightness temperature, respectively. Without any modifications of the calibrated parameters, the error metrics computed from the validation data are slightly worse than those of the calibration data. These results demonstrate the feasibility of the synergistic use of SMAP active radar and passive radiometer observations under the unified framework of a physical model. In addition, the results demonstrate the necessity and effectiveness of applying GSA in model optimization. It is expected that these findings can contribute to the development of model-based soil moisture retrieval methods using active and passive microwave remote sensing data. [ABSTRACT FROM AUTHOR]

Active and passive low-frequency microwave measurements from a number of space- and airborne instruments are used to estimate soil moisture. Each of the sensing approaches has distinct advantages and disadvantages. There is increasing interest in combining active and passive measurements in order to realize the advantages and alleviate the disadvantages. In order to combine active and passive measurements, their covariations with respect to soil moisture need to be known. The covariation is dependent on how the active and passive microwaves interact with vegetation canopy and soil surface. In this paper, we introduce a physics-based model for the covariation of active and passive microwaves over soil surfaces with vegetation cover. The analytical form for a covariation function is derived which depends on the scattering and absorption of microwaves by soil and vegetation with different orientations, structures, and water contents. The main finding is that the covariation function $\beta $ is related to the roughness and vegetation losses in the two measurements. An increase in soil roughness or in vegetation cover leads to less negative values of $\beta $ , which is pronounced for dense and moist vegetation. Both the soil and vegetation components introduce a polarization dependence of $\beta $ that is caused by polarization-induced differences in soil scattering and oriented plant structures. The forward modeled covariations are plotted together with statistically derived covariation estimates from two months of global active and passive L-band observations of the Soil Moisture Active Passive mission. The physically modeled and statistically derived estimates of covariation are comparable in magnitude and scale. [ABSTRACT FROM AUTHOR]

The aim of this paper was to test the capabilities of the Sentinel-1 radar data in downscaling Soil Moisture Active Passive (SMAP) radiometer data for high-resolution soil moisture estimation. Three different active–passive downscaling algorithms, including the brightness temperature-based downscaling algorithm (BTBDA), the soil moisture-based downscaling algorithm (SMBDA), and a change detection method (CDM), were analyzed using pairs of Sentinel-1 active and SMAP passive observations collected over a semiarid landscape in southeastern Australia from May 2015 to May 2016. While these algorithms have been tested previously, this is the first study to evaluate the three algorithms using real Sentinel-1 radar and SMAP radiometer data. The SMAP passive observations were disaggregated to 9-, 3-, and 1-km scales and then compared with ground soil moisture measurements. The results suggest that the root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.057, 0.056, and 0.067 cm3/cm3 for the BTBDA, SMBDA, and CDM, respectively. The accuracy of downscaling methods was generally decreased when applied at the finer spatial resolution. The SMBDA had overall better performance in terms of correctly detecting the soil moisture pattern and relatively lower RMSE values, and is, therefore, recommended for the combined Sentinel-1 radar and SMAP radiometer setup for soil moisture monitoring. The influence of incidence angle normalization of Sentinel-1 SAR data on downscaled soil moisture was also investigated and found to be minimal. [ABSTRACT FROM AUTHOR]

The brightness temperature ( T B p ) observed by the Soil Moisture Active Passive (SMAP) satellite mission is significantly affected by the soil permittivity ( ε s ), surface roughness and vegetation opacity ( τ p ). This study assesses the impact of these factors on simulating the SMAP horizontally ( p  = H) and vertically ( p  = V) polarized T B p measurements and retrieving the liquid soil water content ( θ liq ) for both frozen and thawed soils in the typical Tibetan desert and meadow ecosystems. For this investigation, the zero-order approximation of the radiative transfer equations, i.e., τ - ω emission model, is configured with surface roughness and τ p parameterizations adopted by current SMAP soil moisture retrieval algorithms, and the ε s is computed with the four-phase dielectric mixing model that is applicable for both frozen and thawed soils. For the Tibetan desert site, the τ - ω emission model with above configurations underestimates year-round the SMAP T B H measurements (bias > 20 K), while T B V are underestimated during the cold season. Implementation of a new surface roughness parameterization reduces the T B H underestimation, and the improved T B p simulations lead to better θ liq retrievals produced by the single channel algorithm (SCA) using the T B V as well as T B H measurements. The remaining T B H and T B V underestimations are removed by further adopting a new ε s parameterization. For the Tibetan meadow site, the τ - ω emission model overestimates both T B H and T B V during the warm season and underestimates T B H during the cold season when the vegetation is sparse. Implementation of the new surface roughness parameterization reduces the T B H underestimation, and further the T B p overestimation is mitigated by adopting a new τ p parameterization derived from a discrete radiative transfer model previously developed and tested for the same site. The in-situ measured θ liq dynamics are better captured by corresponding retrievals for both frozen and thawed soils with implementation of the new surface roughness and τ p parameterizations, which reduces the unbiased RMSEs by more than 40%. The parameterizations developed in this study are useful to provide consistent and reasonable T B p simulations and θ liq retrievals over the Tibetan Plateau for both frozen and thawed soils based on both SMAP T B H and T B V measurements. [ABSTRACT FROM AUTHOR]

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30−40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active−passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation.

Agricultural and hydrological applications could greatly benefit from soil moisture (SM) information at sub-field resolution and (sub-) daily revisit time. However, current operational satellite missions provide soil moisture information at either lower spatial or temporal resolution. Here, we downscale coarse resolution (25−36 km) satellite SM products with quasi-daily resolution to the field scale (30 m) using the random forest (RF) machine learning algorithm. RF models are trained with remotely sensed SM and ancillary variables on soil texture, topography, and vegetation cover against SM measured in the field. The approach is developed and tested in an agricultural catchment equipped with a high-density network of low-cost SM sensors. Our results show a strong consistency between the downscaled and observed SM spatio-temporal patterns. We found that topography has higher predictive power for downscaling than soil texture, due to the hilly landscape of the study area. Furthermore, including a proxy of vegetation cover results in considerable improvements of the performance. Increasing the training set size leads to significant gain in the model skill and expanding the training set is likely to further enhance the accuracy. When only limited in-situ measurements are available as training data, increasing the number of sensor locations should be favored over expanding the duration of the measurements for improved downscaling performance. In this regard, we show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations. Overall, our findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products.

The newest soil moisture-dedicated satellite, the Soil Moisture Active Passive (SMAP) mission, provides global maps of soil moisture using concurrent L-band radar and radiometer acquisitions. To support the ongoing validation activities of SMAP soil moisture products, in this paper, we examined the retrieval accuracy of four SMAP soil moisture products by using well-calibrated and dense in situ measurements from the Little Washita Watershed network, one of the SMAP core validation sites with intensive ground sampling. The four SMAP products include the active (3 km), passive (36 km), active-passive (9 km), and the enhanced passive product which is a newly released soil moisture data set with a grid resolution of 9 km. Efforts on identifying the possible error sources of these products were also made for the purpose of improving the SMAP soil moisture algorithms. The results show that the passive and active-passive products can well capture the temporal dynamic of ground soil moisture with overall unbiased root-mean-square error (ubRMSE) values of 0.032 and 0.041 m3· m-3, respectively, which generally meet their mission requirement of 0.04 m3· m-3. In contrast, some irregular fluctuations exist in the active product, leading to an overall wet bias, which makes its accuracy a little poorer than its expected retrieval accuracy of 0.06 m3· m-3. The new enhanced passive product shows the lowest ubRMSE value of 0.026 m3 · m-3 though it underestimates in situ measurements with a bias of 0.059 m3 · m-3, revealing its great potential to substitute the active-passive product to provide global soil moisture measurements at a medium resolution of 9 km. The underestimation of SMAP surface temperature data may be one of the reasons that contribute to the dry bias of SMAP passive, active-passive, and enhanced passive products. The microwave polarization difference index and HV-polarized backscatter show good response to in situ soil moisture and may be considered in SMAP algorithms to further improve the accuracy of soil moisture retrievals. We expect that our findings can be fed back to improve the SMAP soil moisture algorithms and thus promote the application of SMAP soil moisture products in terrestrial water, energy, and carbon cycles. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays an important role in determining the antecedent condition of a watershed, while topographic attributes define how and where SM and rainfall interact to create floods. Based on this principle, we present a method to identify flood risk at a location in a watershed by using remotely sensed SM and open-access information on rainfall, soil properties, and topography. The method consists of three hydrologic modules that represent the generation, transfer, and accumulation of direct runoff. To simplify the modeling and provide timely warnings, the flood risk is ascertained based on frequency of exceedance, with warnings issued if above a specified threshold. The simplicity of the method is highlighted by the use of only three parameters for each watershed of interest, with effective regionalization allowing use in ungauged watersheds. For this proof-of-concept study, the proposed model was calibrated and tested for 65 hydrologic reference stations in the Murray–Darling Basin in Australia over a 35-year study period by using satellite-derived surface SM. The three model parameters were first estimated using the first ten-year data and then the model performance was evaluated through flood threshold exceedance analyses over the remaining 25-year study period. The results for estimated parameters and skill scores showed promise. The three model parameters can be regionalized as a function of watershed characteristics, and/or representative values estimated from neighboring watersheds, allowing use in ungauged basins everywhere. [ABSTRACT FROM PUBLISHER]