The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global ...mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m 3 /m 3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m 3 /m 3 .
The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using ...an L-band (active) radar and an L-band (passive) radiometer. The Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP.
The lack of proper understanding of multi-layer soil moisture (SM) profile (signals) remains a persistent challenge in sustainable agricultural water management and food security, especially during ...drought conditions. We develop a machine-learning algorithm using the concept of learning from patterns to estimate the multi-layer SM information in ungauged locations firmly based on local knowledge of the climatic and landscape controls. The Contiguous United States (CONUS) is clustered into homogeneous regions based on the association between SM and climate and landscape controls. Extreme Gradient Boosting (XGBoost) algorithm is applied to homogenous regions to capture the complex relationship between appropriate predictor variables and in-situ SM at multiple layers over the CONUS. Soil Moisture Active Passive (SMAP) Level 4 (L4) surface (0–5 cm) and rootzone (0–100 cm) SM along with climate and landscape datasets are used as predictor variables. In-situ multi-layer SM recorded by Soil Climate Analysis Network (SCAN), Snow Telemetry (SNOTEL), and U.S. Climate Reference Network (USCRN) networks are utilized as predictands. XGBoost models are then trained region-wise and layer-wise to estimate multi-layer SM information at 5, 10, 20, 50, and 100 cm depths (five layers) at 1-km spatial resolution. Results indicate that the predictor variables have varying levels of influence on SM with changing soil depth, and meteorological variables have the least importance. Validation at 79 independent locations indicates the multi-layer SM estimates successfully capture temporal dynamics of SM, with most locations achieving ubRMSE less than 0.04 m3/m3. The high-resolution SM estimates offer spatial sub-grid heterogeneity compared to SMAP L4 SM.
•New method proposed to estimate multi-layer daily Soil Moisture (SM) over the CONUS.•SM estimated at 1 km resolution across 5 soil depths 5, 10, 20, 50, and 100 cm.•Machine learning model trained with landscape and climate variables to estimate SM.•Relative importance of predictors assessed across the five soil layers.•Spatio-temporal SM estimates at five layers are produced with reasonable accuracy.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Droughts are among the costliest natural disasters. They affect wide regions and large numbers of people worldwide by tampering with water availability and agricultural production. In this research, ...soil moisture drought trends are assessed for Europe using the Soil Moisture Index (SMI) estimated on Joint UK Land Environment Simulator simulations under two Representative Concentration Pathways, the RCP 2.6 and RCP 6.0 scenarios. Results show that SMI drought conditions are expected to exacerbate in Europe with substantial differences among regions. Eastern Europe and Mediterranean regions are found to be the most affected. Spatially and temporally contiguous regions that exhibit SMI of Severe and Extreme index categories are identified as distinct drought events and are assessed for their characteristics. It is shown that even under strong emissions mitigation, these events are expected to increase in occurrence (22% to 123%), while their characteristics will become more unfavorable. Results indicate increase in their spatial extend (between 23% and 46%) and their duration (between 16% and 48%) depending on the period and the scenario. Additional analysis was performed for the exceptionally wide-area (over 106 km2) severe and extreme soil moisture drought events that are expected to drastically increase comparing to the recent past. Projections show that those events are expected to happen between 11 and 28 times more frequently depending on the scenario and the period with a 59% to 246% larger duration. These findings indicate that even applying strong mitigation measures, agricultural drought risk in Europe is expected to become higher than our present experience.
Display omitted
•This study found indications about reduction in future soil moisture for Europe under RCP2.6 and RCP6.0.•It was found that drought events are expected to increase regardless the emission scenario.•Increase in European severe and extreme soil moisture drought events were projected.•Drastic increase in soil moisture droughts over 106 Km2 under RCP2.6 and RCP6.0.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote ...sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices.
•Evaluation of five soil moisture (SM) data products are performed over India.•MERRA SM used for spatio-temporal characterization.•SM variations during a season are carried forward to the following ...season.•Intraseasonal SM persistence is large during a drought year.•Wavelet transform analysis revealed difference in dominant modes of SM variations.
Soil moisture (SM) is an essential climate variable of greater relevance in the monsoon scenario, hence validation and understanding of its spatio-temporal variability over the Indian region is of high significance. In the present study, five SM products are evaluated against in situ SM measurements conducted by India Meteorological Department and the selected data product is used for spatio-temporal characterization of SM in relation to monsoon rainfall. The data products evaluated are: European Space Agency’s merged satellite SM, Modern-Era Retrospective analysis for Research and Applications (MERRA) Land SM, ECMWF’s ERA interim SM, Climate Forecast System Reanalysis SM, and Global Land Data Assimilation System Noah Land Surface Model SM. Comparisons show that seasonal SM patterns in all products generally follow the characteristics of rainfall, even though there are certain differences in details. The statistical estimates indicate fairly good agreement between in situ and the five products, with some variations among them and over the homogeneous rainfall regions. On comparison, MERRA SM is found appropriate for further analyses on spatio-temporal characteristics, which are then carried out with the 20year (1993–2012) SM data. Stability analyses revealed SM patterns indicative of relative SM variability as well as persistence. The spatial stability analysis depicts dry and wet patterns and their seasonal variations over different geographical locations in relation to all India spatial average. Large temporal variations are found over central, western and northern Indian regions caused by large intraseasonal variability in rainfall. In brief, intraseasonal and interannual soil moisture variations broadly follow the rainfall pattern, with long-term influences attributed to SM memory effects. The soil moisture persistence and dominant scales of variability are explored with autocorrelation and wavelet transform techniques. Seasonal persistence is large over regions receiving excessive rainfall, while drought years are noted to have large intraseasonal SM persistence compared to that of surplus year. Wavelet decomposition demonstrates that low periodicity (2–10days) SM modes match that of rainfall during initial periods of monsoon. The occurrence of significant higher periodicity modes are dominant in drought years compared to surplus, which also vary with geographical locations.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Display omitted
•Compiled a multi-year, multi-layer daily soil moisture record across Anhui Province.•Soil moisture generally increases with depth in Anhui Province.•Soil moisture in the region has ...large seasonal fluctuations, decaying with depth.•Effects of antecedent accumulated rainfall on soil moisture varies over depth.
Little measurement-based soil moisture information is available for the humid to semi-humid transitional zones in China. Anhui Province is a representative area of these zones and spans several hydroclimatic and geomorphological zones. To overcome shortage of observational soil moisture information in this area, we collected and compiled a multi-layer daily soil moisture record from 2012 to 2016 across Anhui Province based on the observations of a soil moisture-observing network composed of 85 sites. We then analyzed the vertical and spatiotemporal variability of soil moisture across this region. Primary findings are: (a) soil moisture in this region has apparent vertical gradients and increases with soil depth; (b) substantial soil moisture spatial variability appears in the shallow soil layers (depth ≤40 cm) with the highest values in the middle part of this region, but soil moisture in the deep layers (depth >40 cm) shows low spatial variability; (c) seasonality of soil moisture across this region is complicated and has fluctuations with July as the wettest month in most of the province; (d) inter-monthly fluctuation of soil moisture is larger in the upper soil layers than in the lower layers; and (e) antecedent accumulated rainfall is a major factor influencing soil moisture throughout this region with soil moisture in the upper layers more sensitive to immediate weather conditions and soil moisture in the lower layers showing lagged responses to the weather conditions. This study can not only reveal the spatiotemporal and vertical characteristics of soil moisture across this region, but also provide a useful reference dataset for the other soil moisture modeling and retrieval studies.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
A land data assimilation system is developed to merge satellite soil moisture retrievals into the Joint U.K. Land Environment Simulator (JULES) land surface model (LSM) using the Local Ensemble ...Transform Kalman Filter (LETKF). The system assimilates microwave soil moisture retrievals from the Soil Moisture Active Passive (SMAP) radiometer and the Advanced Scatterometer (ASCAT) after bias correction based on cumulative distribution function fitting. The soil moisture assimilation estimates are evaluated with ground-based soil moisture measurements over the continental U.S. for five consecutive warm seasons (May–September of 2015–2019). The result shows that both SMAP and ASCAT retrievals improve the accuracy of soil moisture estimates. Especially, the SMAP single-sensor assimilation experiment shows the best performance with the increase of temporal anomaly correlation by ΔR ~ 0.05 for surface soil moisture and ΔR ~ 0.03 for root-zone soil moisture compared with the LSM simulation without satellite data assimilation. SMAP assimilation is more skillful than ASCAT assimilation primarily because of the greater skill of the assimilated SMAP retrievals compared to the ASCAT retrievals. The skill improvement also depends significantly on the region; the higher skill improvement in the western U.S. compared to the eastern U.S. is explained by the Kalman gain in the two experiments. Additionally, the regional skill differences in the single-sensor assimilation experiments are attributed to the number of assimilated observations. Finally, the soil moisture assimilation estimates provide more realistic land surface information than model-only simulations for the 2015 and the 2016 western U.S. droughts, suggesting the advantage of using satellite soil moisture retrievals in the current drought monitoring system.
•Active and passive microwave soil moisture retrievals were assimilated into JULES LSM.•The Local Ensemble Transform Kalman Filter is used in the soil moisture assimilation.•SMAP retrievals are more beneficial than ASCAT for soil moisture skill improvement.•Assimilation metrics are proposed to evaluate results from assimilation experiments.•Assimilated soil moisture estimates may further improve drought monitoring systems.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Cosmic-ray neutron sensing (CRNS) allows for non-invasive soil moisture estimations at the field scale. The derivation of soil moisture generally relies on secondary cosmic-ray neutrons in the ...epithermal to fast energy ranges. Most approaches and processing techniques for observed neutron intensities are based on the assumption of homogeneous site conditions or of soil moisture patterns with correlation lengths shorter than the measurement footprint of the neutron detector. However, in view of the non-linear relationship between neutron intensities and soil moisture, it is questionable whether these assumptions are applicable. In this study, we investigated how a non-uniform soil moisture distribution within the footprint impacts the CRNS soil moisture estimation and how the combined use of epithermal and thermal neutrons can be advantageous in this case. Thermal neutrons have lower energies and a substantially smaller measurement footprint around the sensor than epithermal neutrons. Analyses using the URANOS (Ultra RApid Neutron-Only Simulation) Monte Carlo simulations to investigate the measurement footprint dynamics at a study site in northeastern Germany revealed that the thermal footprint mainly covers mineral soils in the near-field to the sensor while the epithermal footprint also covers large areas with organic soils. We found that either combining the observed thermal and epithermal neutron intensities by a rescaling method developed in this study or adjusting all parameters of the transfer function leads to an improved calibration against the reference soil moisture measurements in the near-field compared to the standard approach and using epithermal neutrons alone. We also found that the relationship between thermal and epithermal neutrons provided an indicator for footprint heterogeneity. We, therefore, suggest that the combined use of thermal and epithermal neutrons offers the potential of a spatial disaggregation of the measurement footprint in terms of near- and far-field soil moisture dynamics.
PDF
