precipitation soil moisture: Topics by Science.gov

June 6, 2021

  • Assessment of the SMAP Passive Soil Moisture Product

    NASA Technical Reports Server (NTRS)

    Chan, Steven K.; Bindlish, Rajat; O’Neill, Peggy E.; Njoku, Eni; Jackson, Tom; Colliander, Andreas; Chen, Fan; Burgin, Mariko; Dunbar, Scott; Piepmeier, Jeffrey; hide hide

    2016-01-01

    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 Level 2 soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 kilometer 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 cubic meter per cubic meter unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 cubic meter per cubic meter.

  • Inter-Comparison of Retrieved and Modelled Soil Moisture and Coherency of Remotely Sensed Hydrology Data

    NASA Astrophysics Data System (ADS)

    Kolassa, Jana; Aires, Filipe

    2013-04-01

    A neural network algorithm has been developed for the retrieval of Soil Moisture (SM) from global satellite observations. The algorithm estimates soil moisture from a synergy of passive and active microwave, infrared and visible satellite observations in order to capture the different SM variabilities that the individual sensors are sensitive to. The advantages and drawbacks of each satellite observation have been analysed and the information type and content carried by each observation have been determined. A global data set of monthly mean soil moisture for the 1993-2000 period has been computed with the neural network algorithm (Kolassa et al., in press, 2012). The resulting soil moisture retrieval product has then been used in an inter-comparison study including soil moisture from (1) the HTESSEL model (Balsamo et al., 2009), (2) the WACMOS satellite product (Liu et al., 2011), and (3) in situ measurements from the International Soil Moisture Network (Dorigo et al., 2011). The analysis showed that the satellite remote sensing products are well-suited to capture the spatial variability of the in situ data and even show the potential to improve the modelled soil moisture. Both satellite retrievals also display a good agreement with the temporal structures of the in situ data, however, HTESSEL appears to be more suitable for capturing the temporal variability (Kolassa et al., in press, 2012). The use of this type of neural network approach is currently being investigated as a retrieval option for the SMOS mission. Our soil moisture retrieval product has also been used in a coherence study with precipitation data from GPCP (Adler et al., 2003) and inundation estimates from GIEMS (Prigent et al., 2007). It was investigated on a global scale whether the three observation-based datasets are coherent with each other and show the expected behaviour. For most regions of the Earth, the datasets were consistent and the behaviour observed could be explained with the known

  • Soil Moisture: The Hydrologic Interface Between Surface and Ground Waters

    NASA Technical Reports Server (NTRS)

    Engman, Edwin T.

    1997-01-01

    A hypothesis is presented that many hydrologic processes display a unique signature that is detectable with microwave remote sensing. These signatures are in the form of the spatial and temporal distributions of surface soil moisture. The specific hydrologic processes that may be detected include groundwater recharge and discharge zones, storm runoff contributing areas, regions of potential and less than potential evapotranspiration (ET), and information about the hydrologic properties of soils. In basin and hillslope hydrology, soil moisture is the interface between surface and ground waters.

  • Assessment of SMOS Soil Moisture Retrieval Parameters Using Tau-Omega Algorithms for Soil Moisture Deficit Estimation

    NASA Technical Reports Server (NTRS)

    Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O’Neill, Peggy; Islam, Tanvir; Gupta, Manika

    2014-01-01

    Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel’s equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.

  • Assessment of soil moisture dynamics on an irrigated maize field using cosmic ray neutron sensing

    NASA Astrophysics Data System (ADS)

    Scheiffele, Lena Maria; Baroni, Gabriele; Oswald, Sascha E.

    2015-04-01

    In recent years cosmic ray neutron sensing (CRS) developed as a valuable, indirect and non-invasive method to estimate soil moisture at a scale of tens of hectares, covering the gap between point scale measurements and large scale remote sensing techniques. The method is particularly promising in cropped and irrigated fields where invasive installation of belowground measurement devices could conflict with the agricultural management. However, CRS is affected by all hydrogen pools in the measurement footprint and a fast growing biomass provides some challenges for the interpretation of the signal and application of the method for detecting soil moisture. For this aim, in this study a cosmic ray probe was installed on a field near Braunschweig (Germany) during one maize growing season (2014). The field was irrigated in stripes of 50 m width using sprinkler devices for a total of seven events. Three soil sampling campaigns were conducted throughout the growing season to assess the effect of different hydrogen pools on calibration results. Additionally, leaf area index and biomass measurements were collected to provide the relative contribution of the biomass on the CRS signal. Calibration results obtained with the different soil sampling campaigns showed some discrepancy well correlated with the biomass growth. However, after the calibration function was adjusted to account also for lattice water and soil organic carbon, thus representing an equivalent water content of the soil, the differences decreased. Soil moisture estimated with CRS responded well to precipitation and irrigation events, confirming also the effective footprint of the method (i.e., radius 300 m) and showing occurring water stress for the crop. Thus, the dynamics are in agreement with the soil moisture determined with point scale measurements but they are less affected by the heterogeneous moisture conditions within the field. For this reason, by applying a detailed calibration, CRS proves to be a

  • Surface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active Passive satellite and evaluation at core validation sites

    USDA-ARS?s Scientific Manuscript database

    This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture …

  • Trends in soil moisture and real evapotranspiration in Douro River for the period 1980-2010

    NASA Astrophysics Data System (ADS)

    García-Valdecasas-Ojeda, Matilde; de Franciscis, Sebastiano; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

    2017-04-01

    This study analyzes the evolution of different hydrological variables, such as soil moisture and real evapotranspiration, for the last 30 years, in the Douro Basin, the most extensive basin in the Iberian Peninsula. The different components of the real evaporation, connected to the soil moisture content, can be important when analyzing the intensity of droughts and heat waves, and particularly relevant for the study of the climate change impacts. The real evapotranspiration and soil moisture data are provided by simulations obtained using the Variable Infiltration Capacity (VIC) hydrological model. This model is a large-scale hydrologic model and allows estimates of different variables in the hydrological system of a basin. Land surface is modeled as a grid of large and uniform cells with sub-grid heterogeneity (e.g. land cover), while water influx is local, only depending from the interaction between grid cells and local atmosphere environment. Observational data of temperature and precipitation from Spain02 dataset are used as input variables for VIC model. The simulations have a spatial resolution of about 9 km, and the analysis is carried out on a seasonal time-scale. Additionally, we compare these results with those obtained from a dynamical downscaling driven by ERA-Interim data using the Weather Research and Forecasting (WRF) model, with the same spatial resolution. The results obtained from Spain02 data show a decrease in soil moisture at different parts of the basin during spring and summer, meanwhile soil moisture seems to be increased for autumn. No significant changes are found for real evapotranspiration. Keywords: real evapotranspiration, soil moisture, Douro Basin, trends, VIC, WRF. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

  • Soil Moisture Sensing Using Reflected GPS Signals: Description of the GPS Soil Moisture Product.

    NASA Astrophysics Data System (ADS)

    Larson, Kristine; Small, Eric; Chew, Clara

    2015-04-01

    As first demonstrated by the GPS reflections group in 2008, data from GPS networks can be used to monitor multiple parameters of the terrestrial water cycle. The GPS L-band signals take two paths: (1) the “direct” signal travels from the satellite to the antenna, which is typically located 2-3 meters above the ground; (2) the reflected signal interacts with the Earth’s surface before traveling to the antenna. The direct signal is used by geophysicists and surveyors to measure the position of the antenna, while the effects of reflected signals are a source of error. If one focuses on the reflected signal rather than the positioning observables, one has a method that is sensitive to surface soil moisture (top 5 cm), vegetation water content, and snow depth. This method – known as GPS Interferometric Reflectometry (GPS-IR) – has a footprint of ~1000 m2 for most GPS sites. This is intermediate in scale to most in situ and satellite observations. A significant advantage of GPS-IR is that data from existing GPS networks can be used without any changes to the instrumentation. This means that there is a new source of cost-effective instrumentation for satellite validation and climate studies. This presentation will provide an overview of the GPS-IR methodology with an emphasis on the soil moisture product. GPS water cycle products are currently produced on a daily basis for a network of ~500 sites in the western United States; results are freely available at http://xenon.colorado.edu/portal. Plans to expand the GPS-IR method to the network of international GPS sites will also be discussed.

  • Statistical analysis of simulated global soil moisture and its memory in an ensemble of CMIP5 general circulation models

    NASA Astrophysics Data System (ADS)

    Wiß, Felix; Stacke, Tobias; Hagemann, Stefan

    2014-05-01

    Soil moisture and its memory can have a strong impact on near surface temperature and precipitation and have the potential to promote severe heat waves, dry spells and floods. To analyze how soil moisture is simulated in recent general circulation models (GCMs), soil moisture data from a 23 model ensemble of Atmospheric Model Intercomparison Project (AMIP) type simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are examined for the period 1979 to 2008 with regard to parameterization and statistical characteristics. With respect to soil moisture processes, the models vary in their maximum soil and root depth, the number of soil layers, the water-holding capacity, and the ability to simulate freezing which all together leads to very different soil moisture characteristics. Differences in the water-holding capacity are resulting in deviations in the global median soil moisture of more than one order of magnitude between the models. In contrast, the variance shows similar absolute values when comparing the models to each other. Thus, the input and output rates by precipitation and evapotranspiration, which are computed by the atmospheric component of the models, have to be in the same range. Most models simulate great variances in the monsoon areas of the tropics and north western U.S., intermediate variances in Europe and eastern U.S., and low variances in the Sahara, continental Asia, and central and western Australia. In general, the variance decreases with latitude over the high northern latitudes. As soil moisture trends in the models were found to be negligible, the soil moisture anomalies were calculated by subtracting the 30 year monthly climatology from the data. The length of the memory is determined from the soil moisture anomalies by calculating the first insignificant autocorrelation for ascending monthly lags (insignificant autocorrelation folding time). The models show a great spread of autocorrelation length from a few months in

  • Soil moisture controls on phenology and productivity in a semi-arid critical zone.

    PubMed

    Cleverly, James; Eamus, Derek; Restrepo Coupe, Natalia; Chen, Chao; Maes, Wouter; Li, Longhui; Faux, Ralph; Santini, Nadia S; Rumman, Rizwana; Yu, Qiang; Huete, Alfredo

    2016-10-15

    The Earth’s Critical Zone, where physical, chemical and biological systems interact, extends from the top of the canopy to the underlying bedrock. In this study, we investigated soil moisture controls on phenology and productivity of an Acacia woodland in semi-arid central Australia. Situated on an extensive sand plain with negligible runoff and drainage, the carry-over of soil moisture content (θ) in the rhizosphere enabled the delay of phenology and productivity across seasons, until conditions were favourable for transpiration of that water to prevent overheating in the canopy. Storage of soil moisture near the surface (in the top few metres) was promoted by a siliceous hardpan. Pulsed recharge of θ above the hardpan was rapid and depended upon precipitation amount: 150mm storm(-1) resulted in saturation of θ above the hardpan (i.e., formation of a temporary, discontinuous perched aquifer above the hardpan in unconsolidated soil) and immediate carbon uptake by the vegetation. During dry and inter-storm periods, we inferred the presence of hydraulic lift from soil storage above the hardpan to the surface due to (i) regular daily drawdown of θ in the reservoir that accumulates above the hardpan in the absence of drainage and evapotranspiration; (ii) the dimorphic root distribution wherein most roots were found in dry soil near the surface, but with significant root just above the hardpan; and (iii) synchronisation of phenology amongst trees and grasses in the dry season. We propose that hydraulic redistribution provides a small amount of moisture that maintains functioning of the shallow roots during long periods when the surface soil layer was dry, thereby enabling Mulga to maintain physiological activity without diminishing phenological and physiological responses to precipitation when conditions were favourable to promote canopy cooling. Copyright © 2016 Elsevier B.V. All rights reserved.

  • Spatial variability of soil moisture retrieved by SMOS satellite

    NASA Astrophysics Data System (ADS)

    Lukowski, Mateusz; Marczewski, Wojciech; Usowicz, Boguslaw; Rojek, Edyta; Slominski, Jan; Lipiec, Jerzy

    2015-04-01

    Standard statistical methods assume that the analysed variables are independent. Since the majority of the processes observed in the nature are continuous in space and time, this assumption introduces a significant limitation for understanding the examined phenomena. In classical approach, valuable information about the locations of examined observations is completely lost. However, there is a branch of statistics, called geostatistics, which is the study of random variables, but taking into account the space where they occur. A common example of so-called “regionalized variable” is soil moisture. Using in situ methods it is difficult to estimate soil moisture distribution because it is often significantly diversified. Thanks to the geostatistical methods, by employing semivariance analysis, it is possible to get the information about the nature of spatial dependences and their lengths. Since the Soil Moisture and Ocean Salinity mission launch in 2009, the estimation of soil moisture spatial distribution for regional up to continental scale started to be much easier. In this study, the SMOS L2 data for Central and Eastern Europe were examined. The statistical and geostatistical features of moisture distributions of this area were studied for selected natural soil phenomena for 2010-2014 including: freezing, thawing, rainfalls (wetting), drying and drought. Those soil water “states” were recognized employing ground data from the agro-meteorological network of ground-based stations SWEX and SMUDP2 data from SMOS. After pixel regularization, without any upscaling, the geostatistical methods were applied directly on Discrete Global Grid (15-km resolution) in ISEA 4H9 projection, on which SMOS observations are reported. Analysis of spatial distribution of SMOS soil moisture, carried out for each data set, in most cases did not show significant trends. It was therefore assumed that each of the examined distributions of soil moisture in the adopted scale satisfies

  • Using the Spatial Persistence of Soil Moisture Patterns to Estimate Catchment Soil Moisture in Semi-arid Areas

    NASA Astrophysics Data System (ADS)

    Willgoose, G. R.

    2006-12-01

    In humid catchments the spatial distribution of soil water is dominated by subsurface lateral fluxes, which leads to a persistent spatial pattern of soil moisture principally described by the topographic index. In contrast, semi-arid, and dryer, catchments are dominated by vertical fluxes (infiltration and evapotranspiration) and persistent spatial patterns, if they exist, are subtler. In the first part of this presentation the results of a reanalysis of a number of catchment-scale long-term spatially-distributed soil moisture data sets are presented. We concentrate on Tarrawarra and SASMAS, both catchments in Australia that are water-limited for at least part of the year and which have been monitored using a variety of technologies. Using the data from permanently installed instruments (neutron probe and reflectometry) both catchments show persistent patterns at the 1-3 year timescale. This persistent pattern is not evident in the field campaign data where field portable instruments (reflectometry) instruments were used. We argue, based on high-resolution soil moisture semivariograms, that high short-distance variability (100mm scale) means that field portable instrument cannot be replaced at the same location with sufficient accuracy to ensure deterministic repeatability of soil moisture measurements from campaign to campaign. The observed temporal persistence of the spatial pattern can be caused by; (1) permanent features of the landscape (e.g. vegetation, soils), or (2) long term memory in the soil moisture store. We argue that it is permanent in which case it is possible to monitor the soil moisture status of a catchment using a single location measurement (continuous in time) of soil moisture using a permanently installed reflectometry instrument. This instrument will need to be calibrated to the catchment averaged soil moisture but the temporal persistence of the spatial pattern of soil moisture will mean that this calibration will be deterministically

  • Use of midlatitude soil moisture and meteorological observations to validate soil moisture simulations with biosphere and bucket models

    NASA Technical Reports Server (NTRS)

    Robock, Alan; Vinnikov, Konstantin YA.; Schlosser, C. Adam; Speranskaya, Nina A.; Xue, Yongkang

    1995-01-01

    Soil moisture observations in sites with natural vegetation were made for several decades in the former Soviet Union at hundreds of stations. In this paper, the authors use data from six of these stations from different climatic regimes, along with ancillary meteorological and actinometric data, to demonstrate a method to validate soil moisture simulations with biosphere and bucket models. Some early and current general circulation models (GCMs) use bucket models for soil hydrology calculations. More recently, the Simple Biosphere Model (SiB) was developed to incorporate the effects of vegetation on fluxes of moisture, momentum, and energy at the earth’s surface into soil hydrology models. Until now, the bucket and SiB have been verified by comparison with actual soil moisture data only on a limited basis. In this study, a Simplified SiB (SSiB) soil hydrology model and a 15-cm bucket model are forced by observed meteorological and actinometric data every 3 h for 6-yr simulations at the six stations. The model calculations of soil moisture are compared to observations of soil moisture, literally ‘ground truth,’ snow cover, surface albedo, and net radiation, and with each other. For three of the stations, the SSiB and 15-cm bucket models produce good simulations of seasonal cycles and interannual variations of soil moisture. For the other three stations, there are large errors in the simulations by both models. Inconsistencies in specification of field capacity may be partly responsible. There is no evidence that the SSiB simulations are superior in simulating soil moisture variations. In fact, the models are quite similar since SSiB implicitly has a bucket embedded in it. One of the main differences between the models is in the treatment of runoff due to melting snow in the spring — SSiB incorrectly puts all the snowmelt into runoff. While producing similar soil moisture simulations, the models produce very different surface latent and sensible heat fluxes, which

  • Impact of realistic soil moisture initialization on the representation of extreme events in the western Mediterranean

    NASA Astrophysics Data System (ADS)

    Helgert, Sebastian; Khodayar, Samiro

    2017-04-01

    In a warmer Mediterranean climate an increase in the intensity and frequency of extreme events like floods, droughts and extreme heat is expected. The ability to predict such events is still a great challenge and exhibits many uncertainties in the weather forecast and climate predictions. Thereby the missing knowledge about soil moisture-atmosphere interactions and their representation in models is identified as one of the main sources of uncertainty. In this context the soil moisture(SM) plays an important role in the partitioning of sensible and latent heat fluxes on the surface and consequently influences the boundary-layer stability and the precipitation formation. The aim of this research work is to assess the influence of soil moisture-atmosphere interactions on the initiation and development of extreme events in the western Mediterranean (WMED). In this respect the impact of realistic SM initialization on the model representation of extreme events is investigated. High-resolution simulations of different regions in the WMED, including various climate zones from moderate to arid climate, are conducted with the atmospheric COSMO (Consortium for Small-scale Modeling) model in the numerical weather prediction and climate mode. A multiscale temporal and spatial approach is used (days to years, 7km to 2.8km grid spacing). Observational data provided by the framework of the HYdrological cycle in the Mediterranean EXperiment (HyMeX) as well as satellite data such as precipitation from CMORPH (CPC MORPHing technique), evapotranspiration from Land Surface Analysis Satellite Applications Facility (LSA-SAF) and atmospheric moisture from MODIS (Moderate Resolution Imaging Spectroradiometer) are used for process understanding and model validation. To select extreme dry and wet periods the Effective Drought Index (EDI) is calculated. In these periods sensitivity studies of extreme SM initialization scenarios are performed to prove a possible impact of soil moisture on

  • Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia

    NASA Astrophysics Data System (ADS)

    Brocca, Luca; Pellarin, Thierry; Crow, Wade T.; Ciabatta, Luca; Massari, Christian; Ryu, Dongryeol; Su, Chun-Hsu; Rüdiger, Christoph; Kerr, Yann

    2016-10-01

    Remote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) satellite is used for improving satellite rainfall estimates obtained from the Tropical Rainfall Measuring Mission multisatellite precipitation analysis product (TMPA) using three different “bottom up” techniques: SM2RAIN, Soil Moisture Analysis Rainfall Tool, and Antecedent Precipitation Index Modification. The implementation of these techniques aims at improving the well-known “top down” rainfall estimate derived from TMPA products (version 7) available in near real time. Ground observations provided by the Australian Water Availability Project are considered as a separate validation data set. The three algorithms are calibrated against the gauge-corrected TMPA reanalysis product, 3B42, and used for adjusting the TMPA real-time product, 3B42RT, using SMOS soil moisture data. The study area covers the entire Australian continent, and the analysis period ranges from January 2010 to November 2013. Results show that all the SMOS-based rainfall products improve the performance of 3B42RT, even at daily time scale (differently from previous investigations). The major improvements are obtained in terms of estimation of accumulated rainfall with a reduction of the root-mean-square error of more than 25%. Also, in terms of temporal dynamic (correlation) and rainfall detection (categorical scores) the SMOS-based products provide slightly better results with respect to 3B42RT, even though the relative performance between the methods is not always the same. The strengths and weaknesses of each algorithm and the spatial variability of their performances are identified in order to indicate the ways forward for this promising research activity. Results show that the integration of bottom up and top down approaches

  • Remote sensing of soil moisture with microwave radiometers

    NASA Technical Reports Server (NTRS)

    Schmugge, T.; Wilheit, T.; Webster, W., Jr.; Gloerson, P.

    1976-01-01

    Results are presented that were derived from measurements made by microwave radiometers during the March 1972 and February 1973 flights of National Aeronautics and Space Administration (NASA) Convair-9900 aircraft over agricultural test sites in the southwestern part of United States. The purpose of the missions was to study the use of microwave radiometers for the remote sensing of soil moisture. The microwave radiometers covered the 0.8- to 21-cm wavelength range. The results show a good linear correlation between the observed microwave brightness temperature and moisture content of the 0- to 1-cm layer of the soil. The results at the largest wavelength (21 cm) show the greatest sensitivity to soil moisture variations and indicate the possibility of sensing these variations through a vegetative canopy. The effect of soil texture on the emission from the soil was also studied and it was found that this effect can be compensated for by expressing soil moisture as a percent of field capacity for the soil. The results were compared with calculations based on a radiative transfer model for layered dielectrics and the agreement is very good at the longer wavelengths. At the shorter wavelengths, surface roughness effects are larger and the agreement becomes poorer.

  • Downscaling Coarse Scale Microwave Soil Moisture Product using Machine Learning

    NASA Astrophysics Data System (ADS)

    Abbaszadeh, P.; Moradkhani, H.; Yan, H.

    2016-12-01

    Soil moisture (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of soil moisture at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite soil moisture to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale soil moisture data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution soil moisture information that is currently used for land data assimilation applications.

  • Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation

    NASA Technical Reports Server (NTRS)

    Akbar, Ruzbeh; Cosh, Michael H.; O’Neill, Peggy E.; Entekhabi, Dara; Moghaddam, Mahta

    2017-01-01

    A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

  • Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation.

    PubMed

    Akbar, Ruzbeh; Cosh, Michael H; O’Neill, Peggy E; Entekhabi, Dara; Moghaddam, Mahta

    2017-07-01

    A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm’s performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

  • Enhancing SMAP Soil Moisture Retrievals via Superresolution Techniques

    NASA Astrophysics Data System (ADS)

    Beale, K. D.; Ebtehaj, A. M.; Romberg, J. K.; Bras, R. L.

    2017-12-01

    Soil moisture is a key state variable that modulates land-atmosphere interactions and its high-resolution global scale estimates are essential for improved weather forecasting, drought prediction, crop management, and the safety of troop mobility. Currently, NASA’s Soil Moisture Active/Passive (SMAP) satellite provides a global picture of soil moisture variability at a resolution of 36 km, which is prohibitive for some hydrologic applications. The goal of this research is to enhance the resolution of SMAP passive microwave retrievals by a factor of 2 to 4 using modern superresolution techniques that rely on the knowledge of high-resolution land surface models. In this work, we explore several super-resolution techniques including an empirical dictionary method, a learned dictionary method, and a three-layer convolutional neural network. Using a year of global high-resolution land surface model simulations as training set, we found that we are able to produce high-resolution soil moisture maps that outperform the original low-resolution observations both qualitatively and quantitatively. In particular, on a patch-by-patch basis we are able to produce estimates of high-resolution soil moisture maps that improve on the original low-resolution patches by on average 6% in terms of mean-squared error, and 14% in terms of the structural similarity index.

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