Environmental Science › Environmental Engineering

Soil Moisture and Remote Sensing

Works Count: 39053

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Description

This cluster of papers focuses on the remote sensing of soil moisture using satellite observations, data assimilation techniques, and hydrological modeling. It explores the spatial variability, temporal dynamics, and validation of soil moisture measurements at global scales. The research aims to improve our understanding of soil moisture patterns and their implications for water resource management.

Keywords

Remote Sensing; Soil Moisture; Satellite Observations; Data Assimilation; Hydrological Modeling; Global Monitoring; Microwave Retrieval; Spatial Variability; Temporal Dynamics; Validation

Description of a complete (interpolated) outgoing longwave radiation dataset

1996-01-01

Brant Liebmann

On the value of soil moisture measurements in vadose zone hydrology: A review

2008-04-01

Harry Vereecken , Johan Alexander Huisman , Heye Bogena +3 more

We explore and review the value of soil moisture measurements in vadose zone hydrology with a focus on the field and catchment scales. This review is motivated by the increasing … We explore and review the value of soil moisture measurements in vadose zone hydrology with a focus on the field and catchment scales. This review is motivated by the increasing ability to measure soil moisture with unprecedented spatial and temporal resolution across scales. We highlight and review the state of the art in using soil moisture measurements for (1) estimation of soil hydraulic properties, (2) quantification of water and energy fluxes, and (3) retrieval of spatial and temporal dynamics of soil moisture profiles. We argue for the urgent need to have access to field monitoring sites and databases that include detailed information about variability of hydrological fluxes and parameters, including their upscaled values. In addition, improved data assimilation methods are needed that fully exploit the information contained in soil moisture data. The development of novel upscaling methods for predicting effective moisture fluxes and disaggregation schemes toward integrating large‐scale soil moisture measurements in hydrological models will increase the value of soil moisture measurements. Finally, we recognize a need to develop strategies that combine hydrogeophysical measurement techniques with remote sensing methods.

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An Empirical Model for the Complex Dielectric Permittivity of Soils as a Function of Water Content

1980-10-01

James R. Wang , Thomas J. Schmugge

The recent measurements on the dielectric properties of soils have shown that the variation of dielectric constant with moisture content depends on soil types. The observed dielectric constant increases only … The recent measurements on the dielectric properties of soils have shown that the variation of dielectric constant with moisture content depends on soil types. The observed dielectric constant increases only slowly with moisture content up to a transition point. Beyond the transition it increases rapidly with moisture content. The moisture value at transition region was found to be higher for high clay content soils than for sandy soils. Many mixing formulas reported in the literature were compared with, and were found incompatible with, the measured dielectric variations of soil-water mixtures. A simple empirical model was proposed to describe the dielectric behavior of the soil-water mixtures. This model employs the mixing of either the dielectric constants or the refraction indices of ice, water, rock, and air, and treats the transition moisture value as an adjustable parameter. The calculated mixture dielectric constants from the model were found to be in reasonable agreement with the measured results over the entire moisture range of 0-0.5 cm3/cm3. The transition moistures derived from the model range from 0.16 to 0.33 and are strongly correlated with the wilting points of the soils estimated from their textures. This relationship between transition moisture and wilting point provides a means of estimating soil dielectric properties on the basis of texture information.

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Electromagnetic determination of soil water content: Measurements in coaxial transmission lines

1980-06-01

G. C. Topp , J. L. Davis , A. P. Annan

The dependence of the dielectric constant, at frequencies between 1 MHz and 1 GHz, on the volumetric water content is determined empirically in the laboratory. The effect of varying the … The dependence of the dielectric constant, at frequencies between 1 MHz and 1 GHz, on the volumetric water content is determined empirically in the laboratory. The effect of varying the texture, bulk density, temperature, and soluble salt content on this relationship was also determined. Time‐domain reflectometry (TDR) was used to measure the dielectric constant of a wide range of granular specimens placed in a coaxial transmission line. The water or salt solution was cycled continuously to or from the specimen, with minimal disturbance, through porous disks placed along the sides of the coaxial tube. Four mineral soils with a range of texture from sandy loam to clay were tested. An empirical relationship between the apparent dielectric constant K a and the volumetric water content θ v , which is independent of soil type, soil density, soil temperature, and soluble salt content, can be used to determine θ v , from air dry to water saturated, with an error of estimate of 0.013. Precision of θ v to within ±0.01 from K a can be obtained with a calibration for the particular granular material of interest. An organic soil, vermiculite, and two sizes of glass beads were also tested successfully. The empirical relationship determined here agrees very well with other experimenters' results, which use a wide range of electrical techniques over the frequency range of 20 MHz and 1 GHz and widely varying soil types. The results of applying the TDR technique on parallel transmission lines in the field to measure θ v versus depth are encouraging.

High dielectric constant microwave probes for sensing soil moisture

1974-01-01

J.R. Birchak , C. Gerald Gardner , Jackie E. Hipp +1 more

Implantable soil moisture sensors suitable for long-term monitoring of moisture in highway subgrades and for similar applications are needed. Two candidate designs of microwave sensors (operating range 4 to 6 … Implantable soil moisture sensors suitable for long-term monitoring of moisture in highway subgrades and for similar applications are needed. Two candidate designs of microwave sensors (operating range 4 to 6 GHz) have been investigated for such applications. One design uses the fringing field of a low-loss dielectric slab waveguide (relative dielectric constant of 25) to obtain good resolution for finely divided soil such as bentonite clay with moisture ranging from 10 to 50 percent by dry weight for effective sample volumes of 20 to 40 cm 2 . The response of the dielectric waveguide sensor has been calculated in terms of the effective dielectric constant of the soil-water mixture. A model based on index of refraction yields an effective dielectric constant in reasonable agreement with experiment when effects of ionic conduction are accounted for. Another sensor design, better adapted for coarse materials, such as crushed limestone aggregate, uses waves launched from a tapered dielectric slab. By using either frequency or spatial averaging methods, the launched wave sensor accommodated aggregate particles passed by a 0.63-cm mesh sieve, and was found to have satisfactory resolution for the range of 0- to 10-percent moisture by dry weight.

Trend-preserving blending of passive and active microwave soil moisture retrievals

2012-04-25

Y. Y. Liu , Wouter Dorigo , Robert Parinussa +5 more

A Review of Advances in Dielectric and Electrical Conductivity Measurement in Soils Using Time Domain Reflectometry

2003-11-01

David A. Robinson , Scott B. Jones , Jon M. Wraith +2 more

Substantial advances in the measurement of water content and bulk soil electrical conductivity (EC) using time domain reflectometry (TDR) have been made in the last two decades. The key to … Substantial advances in the measurement of water content and bulk soil electrical conductivity (EC) using time domain reflectometry (TDR) have been made in the last two decades. The key to TDR's success is its ability to accurately measure the permittivity of a material and the fact that there is a good relationship between the permittivity of a material and its water content. A further advantage is the ability to estimate water content and measure bulk soil EC simultaneously using TDR. The aim of this review is to summarize and examine advances that have been made in terms of measuring permittivity and bulk EC. The review examines issues such as the effective frequency of the TDR measurement and waveform analysis in dispersive dielectrics. The growing importance of both waveform simulation and inverse analysis of waveforms is highlighted. Such methods hold great potential for obtaining far more information from TDR waveform analysis. Probe design is considered in some detail and practical guidance is given for probe construction. The importance of TDR measurement sampling volume is considered and the relative energy storage density is modeled for a range of probe designs. Tables are provided that compare some of the different aspects of commercial TDR equipment, and the units are discussed in terms of their performance and their advantages and disadvantages. It is hoped that the review will provide an informative guide to the more technical aspects of permittivity and EC measurement using TDR for the novice and expert alike.

A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System

2008-10-28

Gianpaolo Balsamo , Anton Beljaars , Klaus Scipal +4 more

Abstract The Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) is used operationally in the Integrated Forecast System (IFS) for describing the evolution of soil, vegetation, and snow over … Abstract The Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) is used operationally in the Integrated Forecast System (IFS) for describing the evolution of soil, vegetation, and snow over the continents at diverse spatial resolutions. A revised land surface hydrology (H-TESSEL) is introduced in the ECMWF operational model to address shortcomings of the land surface scheme, specifically the lack of surface runoff and the choice of a global uniform soil texture. New infiltration and runoff schemes are introduced with a dependency on the soil texture and standard deviation of orography. A set of experiments in stand-alone mode is used to assess the improved prediction of soil moisture at the local scale against field site observations. Comparison with basin-scale water balance (BSWB) and Global Runoff Data Centre (GRDC) datasets indicates a consistently larger dynamical range of land water mass over large continental areas and an improved prediction of river runoff, while the effect on atmospheric fluxes is fairly small. Finally, the ECMWF data assimilation and prediction systems are used to verify the effect on surface and near-surface quantities in the atmospheric-coupled mode. A midlatitude error reduction is seen both in soil moisture and in 2-m temperature.

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Variability in surface moisture content along a hillslope transect: Rattlesnake Hill, Texas

1998-09-01

J. S. Famiglietti , J.W. Rudnicki , Matthew Rodell

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The Soil Moisture Active Passive (SMAP) Mission

2010-05-01

Dara Entekhabi , E. G. Njoku , Peggy O’Neill +7 more

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will … The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

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Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models

1985-01-01

Myron C. Dobson , F.T. Ulaby , M. Hallikainen +1 more

This paper is the second in a series evaluating the microwave dielectric behavior of soil-water mixtures as a function of water content and soil textural composition. Part II draws upon … This paper is the second in a series evaluating the microwave dielectric behavior of soil-water mixtures as a function of water content and soil textural composition. Part II draws upon the data presented in Part 1 [13] to develop appropriate empirical and theoretical dielectric mixing models for the 1.4-to 18-GHz region. A semiempirical mixing model based upon the index of refraction is presented, requiring only easily ascertained soil physical parameters such as volumetric moisture and soil textural composition as inputs. In addition, a theoretical model accounting explicitly for the presence of a hydration layer of bound water adjacent to hydrophilic soil particle surfaces is presented. A four-component dielectric mixing model treats the soil-water system as a host medium of dry soil solids containing randomly distributed and randomly oriented disc-shaped inclusions of bound water, bulk water, and air. The bulk water component is considered to be dependent upon frequency, temperature, and salinity. The soil solution is differentiated by means of a soil physical model into 1) a bound component and 2) a bulk soil solution. The performance of each model is evaluated as a function of soil moisture, soil texture, and frequency, using the dielectric measurements of five soils ranging from sandy loam to silty clay (as presented in Part I [13]) at frequencies between 1.4 and 18 GHz. The semiempirical mixing model yields an excellent fit to the measured data at frequencies above 4 GHz. At 1.

Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water

2011-05-01

Eric F. Wood , Joshua K. Roundy , Tara J. Troy +7 more

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs … Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface‐subsurface interactions due to fine‐scale topography and vegetation; improved representation of land‐atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 10 9 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

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Apparent soil electrical conductivity measurements in agriculture

2004-12-29

Dennis L. Corwin , Scott M. Lesch

Upscaling sparse ground‐based soil moisture observations for the validation of coarse‐resolution satellite soil moisture products

2012-01-31

Wade T. Crow , Aaron Berg , Michael H. Cosh +6 more

The contrast between the point‐scale nature of current ground‐based soil moisture instrumentation and the ground resolution (typically >10 2 km 2 ) of satellites used to retrieve soil moisture poses … The contrast between the point‐scale nature of current ground‐based soil moisture instrumentation and the ground resolution (typically >10 2 km 2 ) of satellites used to retrieve soil moisture poses a significant challenge for the validation of data products from current and upcoming soil moisture satellite missions. Given typical levels of observed spatial variability in soil moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint‐scale soil moisture estimates obtained from sparse ground‐based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite soil moisture products. This review paper describes the magnitude of the soil moisture upscaling problem and measurement density requirements for ground‐based soil moisture networks. Since many large‐scale networks do not meet these requirements, it also summarizes a number of existing soil moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite soil moisture validation using spatially sparse ground‐based observations.

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The Oklahoma Mesonet: A Technical Overview

1995-02-01

Fred V. Brock , Kenneth Crawford , R. L. Elliott +4 more

The Oklahoma mesonet is a joint project of Oklahoma State University and the University of Oklahoma. It is an automated network of 108 stations covering the state of Oklahoma. Each … The Oklahoma mesonet is a joint project of Oklahoma State University and the University of Oklahoma. It is an automated network of 108 stations covering the state of Oklahoma. Each station measures air temperature, humidity, barometric pressure, wind speed and direction, rainfall, solar radiation, and soil temperatures. Each station transmits a data message every 15 min via a radio link to the nearest terminal of the Oklahoma Law Enforcement Telecommunications System that relays it to a central site in Norman, Oklahoma. The data message comprises three 5-min averages of most data (and one 15-min average of soil temperatures). The central site ingests the data, runs some quality assurance tests, archives the data, and disseminates it in real time to a broad community of users, primarily through a computerized bulletin board system. This manuscript provides a technical description of the Oklahoma mesonet including a complete description of the instrumentation. Sensor inaccuracy, resolution, height with respect to ground level, and method of exposure are discussed.

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A model for microwave emission from vegetation‐covered fields

1982-12-20

T. Mo , Bhaskar J. Choudhury , Thomas J. Schmugge +2 more

A radiative transfer model for simulating the measured brightness temperatures over vegetation‐covered fields is studied. The model treats the vegetation as a uniform layer, or canopy, with a constant temperature … A radiative transfer model for simulating the measured brightness temperatures over vegetation‐covered fields is studied. The model treats the vegetation as a uniform layer, or canopy, with a constant temperature over a moist soil which emits polarized microwave radiation. The equation of radiative transfer is solved analytically subject to boundary conditions at the soil surface and canopy top. Scattering by the vegetation is primarily in the forward direction and given by a single scattering albedo ω * . The effect of soil surface roughness is introduced by modifying the smooth surface reflectivity with a roughness height parameter h and polarization mixing factor Q. The analytic formula for the microwave emission has four parameters, h , Q, τ 0 * (effective canopy optical thickness), and ω * . The model provides a good representation of the observed angular variations for both the horizontally and vertically polarized brightness temperatures at 1.4 GHz and 5 GHz frequencies over fields covered with grass, soybean, and corn. The effective canopy optical thickness is found to be directly proportional to the amount of water present in the vegetation canopy, while the effective canopy single scattering albedo depends on the type of vegetation.

L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields

2006-12-15

Jean‐Pierre Wigneron , Yann H. Kerr , Philippe Waldteufel +7 more

The Global Soil Moisture Data Bank

2000-06-01

Alan Robock , Konstantin Y. Vinnikov , Govindarajalu Srinivasan +5 more

Soil moisture is an important variable in the climate system. Understanding and predicting variations of surface temperature, drought, and flood depend critically on knowledge of soil moisture variations, as do … Soil moisture is an important variable in the climate system. Understanding and predicting variations of surface temperature, drought, and flood depend critically on knowledge of soil moisture variations, as do impacts of climate change and weather forecasting. An observational dataset of actual in situ measurements is crucial for climatological analysis, for model development and evaluation, and as ground truth for remote sensing. To that end, the Global Soil Moisture Data Bank, a Web site (http://climate.envsci.rutgers.edu/soil_moisture) dedicated to collection, dissemination, and analysis of soil moisture data from around the globe, is described. The data bank currently has soil moisture observations for over 600 stations from a large variety of global climates, including the former Soviet Union, China, Mongolia, India, and the United States. Most of the data are in situ gravimetric observations of soil moisture; all extend for at least 6 years and most for more than 15 years. Most of the stations have grass vegetation, and some are agricultural. The observations have been used to examine the temporal and spatial scales of soil moisture variations, to evaluate Atmospheric Model Intercomparison Project, Project for Intercomparison of Land-Surface Parameterization Schemes, and Global Soil Wetness Project simulations of soil moisture, for remote sensing of soil moisture, for designing new soil moisture observational networks, and to examine soil moisture trends. For the top 1-m soil layers, the temporal scale of soil moisture variation at all midlatitude sites is 1.5 to 2 months and the spatial scale is about 500 km. Land surface models, in general, do not capture the observed soil moisture variations when forced with either model-generated or observed meteorology. In contrast to predictions of summer desiccation with increasing temperatures, for the stations with the longest records summer soil moisture in the top 1 m has increased while temperatures have risen. The increasing trend in precipitation more than compensated for the enhanced evaporation.

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Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations

1985-01-01

M. Hallikainen , F.T. Ulaby , Myron C. Dobson +2 more

This is the first paper in a two-part sequence that evaluates the microwave dielectric behavior of soil-water mixtures as a function of water content, temperature, and soil textural composition. Part … This is the first paper in a two-part sequence that evaluates the microwave dielectric behavior of soil-water mixtures as a function of water content, temperature, and soil textural composition. Part I presents the results of dielectric constant measurements conducted for five different soil types at frequencies between 1.4 and 18 GHz. Soil texture is shown to have an effect on dielectric behavior over the entire frequency range and is most pronounced at frequencies below 5 GHz. In addition, the dielectric properties of frozen soils suggest that a fraction of the soil water component remains liquid even at temperatures of -24° C. The dielectric data as measured at room temperature are summarized at each frequency by polynomial expressions dependent upon both the volumetric moisture content m and the percentage of sand and clay contained in the soil; separate polynomial expressions are given for the real and imaginary parts of the dielectric constant. In Part II, two dielectric mixing models will be presented to account for the observed behavior: 1) a semiempirical refractive mixing model that accurately describes the data and requires only volumetric moisture and soil texture as inputs, and 2) a theoretical four-component mixing model that explicitly accounts for the presence of bound water.

Vegetation effects on the microwave emission of soils

1991-06-01

T. Jackson , Thomas J. Schmugge

Vegetation modeled as a water cloud

1978-03-01

E. Attema , F.T. Ulaby

Because the microwave dielectric constant of dry vegetative matter is much smaller (by an order of magnitude or more) than the dielectric constant of water, and because a vegetation canopy … Because the microwave dielectric constant of dry vegetative matter is much smaller (by an order of magnitude or more) than the dielectric constant of water, and because a vegetation canopy is usually composed of more than 99% air by volume, it is proposed that the canopy can be modeled as a water cloud whose droplets are held in place by the vegetative matter. Such a model was developed assuming that the canopy “cloud” contains identical water droplets randomly distributed within the canopy. By integrating the scattering and attenuation cross‐section contributions of N droplets per unit volume over the signal pathlength through the canopy, an expression was derived for the backscattering coefficient as a function of three target parameters: volumetric moisture content of the soil, volumetric water content of the vegetation, and plant height. Regression analysis of the model predictions against scattering data acquired over a period of four months at several angles of incidence (0°–70°) and frequencies (8–18 GHz) for HH and VV polarizations yields correlation coefficients that range from .7 to .99 depending on frequency, polarization, and crop type. The corresponding standard errors of estimate range from 1.1 to 2.6 dB.

Calibration of time domain reflectometry for water content measurement using a composite dielectric approach

1990-10-01

Kurt Roth , Rainer Schulin , H. Flühler +1 more

Time domain reflectometry (TDR) has been developed to an operational level for the measurement of soil water content during the past decade. Because it is able to provide fast, precise … Time domain reflectometry (TDR) has been developed to an operational level for the measurement of soil water content during the past decade. Because it is able to provide fast, precise and nondestructive in situ measurements, it has become an alternative to the neutron scattering method, in particular for monitoring water content under field conditions. One of the major disadvantages of the neutron scattering technique is that, due to the relatively high sensitivity of the signal to factors other than water content, site‐specific calibration is usually required. In this paper a calibration curve for the TDR method is presented which is not restricted to specific soil conditions. The calibration is based on the dielectric mixing model of Dobson et al. (1985). Measurements of volumetric water content and dielectric number at eleven different field sites representing a wide range of soil types were used to determine the parameter of the model by weighted nonlinear regression. The uncertainty (root mean square error) of water content values calculated with the optimized calibration curve was estimated not to exceed 0.013 cm 3 /cm 3 . This value is comparable to the precision of the thermogravimetric method. From a sensitivity analysis it was determined that the temperature dependence of the TDR signal may have to be corrected to obtain optimum accuracy.

Backscattering from a randomly rough dielectric surface

1992-03-01

A.K. Fung , Z. Li , K.S. Chen

A backscattering model for scattering from a randomly rough dielectric surface is developed. Both like- and cross-polarized scattering coefficients are obtained. The like-polarized scattering coefficients contain single scattering terms and … A backscattering model for scattering from a randomly rough dielectric surface is developed. Both like- and cross-polarized scattering coefficients are obtained. The like-polarized scattering coefficients contain single scattering terms and multiple scattering terms. The single scattering terms are shown to reduce to the first-order solutions derived from the small perturbation method when the roughness parameters satisfy the slightly rough conditions. When surface roughnesses are large but the surface slope is small, only a single scattering term corresponding to the standard Kirchhoff model is significant. If the surface slope is large, the multiple scattering term will also be significant. The cross-polarized backscattering coefficients satisfy reciprocity and contain only multiple scattering terms. The difference between vertical and horizontal scattering coefficients increases with the dielectric constant and is generally smaller than that predicted by the first-order small perturbation model. Good agreements are obtained between this model and measurements from statistically known surfaces.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Soil moisture retrieval from AMSR-E

2003-02-01

E. G. Njoku , Thomas J. Jackson , V. Lakshmi +2 more

The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing … The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing capability over previous spaceborne radiometers such as the Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager due to its combination of low frequency and higher spatial resolution (approximately 60 km at 6.9 GHz). The AMSR-E soil moisture retrieval approach and its implementation are described in this paper. A postlaunch validation program is in progress that will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data. Key aspects of the validation program include assessments of the effects on retrieved soil moisture of variability in vegetation water content, surface temperature, and spatial heterogeneity. Examples of AMSR-E brightness temperature observations over land are shown from the first few months of instrument operation, indicating general features of global vegetation and soil moisture variability. The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals.

Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network

2008-05-29

Yunseop Kim , Robert G. Evans , William M. Iversen

Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that … Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller.

A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index

2001-01-01

Manfred Owe , Richard de Jeu , Jeffrey P. Walker

A methodology for retrieving surface soil moisture and vegetation optical depth from satellite microwave radiometer data is presented. The procedure is tested with historical 6.6 GHz H and V polarized … A methodology for retrieving surface soil moisture and vegetation optical depth from satellite microwave radiometer data is presented. The procedure is tested with historical 6.6 GHz H and V polarized brightness temperature observations from the scanning multichannel microwave radiometer (SMMR) over several test sites in Illinois. Results using only nighttime data are presented at this time due to the greater stability of nighttime surface temperature estimation. The methodology uses a radiative transfer model to solve for surface soil moisture and vegetation optical depth simultaneously using a nonlinear iterative optimization procedure. It assumes known constant values for the scattering albedo and roughness, and that vegetation optical depth for H-polarization is the same as for V-polarization. Surface temperature is derived by a procedure using high frequency V-polarized brightness temperatures. The methodology does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes and may be applied to other wavelengths. Results compare well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors.

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Soil Moisture Measurement for Ecological and Hydrological Watershed‐Scale Observatories: A Review

2008-02-01

David A. Robinson , Colin S. Campbell , J. W. Hopmans +6 more

At the watershed scale, soil moisture is the major control for rainfall–runoff response, especially where saturation excess runoff processes dominate. From the ecological point of view, the pools of soil … At the watershed scale, soil moisture is the major control for rainfall–runoff response, especially where saturation excess runoff processes dominate. From the ecological point of view, the pools of soil moisture are fundamental ecosystem resources providing the transpirable water for plants. In drylands particularly, soil moisture is one of the major controls on the structure, function, and diversity in ecosystems. In terms of the global hydrological cycle, the overall quantity of soil moisture is small, ∼0.05%; however, its importance to the global energy balance and the distribution of precipitation far outweighs its physical amount. In soils it governs microbial activity that affects important biogeochemical processes such as nitrification and CO2 production via respiration. During the past 20 years, technology has advanced considerably, with the development of different electrical sensors for determining soil moisture at a point. However, modeling of watersheds requires areal averages. As a result, point measurements and modeling grid cell data requirements are generally incommensurate. We review advances in sensor technology, particularly emerging geophysical methods and distributed sensors, aimed at bridging this gap. We consider some of the data analysis methods for upscaling from a point to give an areal average. Finally, we conclude by offering a vision for future research, listing many of the current scientific and technical challenges.

Measuring soil moisture with imaging radars

1995-07-01

P. Dubois , J. van Zyl , Ted Engman

An empirical algorithm for the retrieval of soil moisture content and surface root mean square (RMS) height from remotely sensed radar data was developed using scatterometer data. The algorithm is … An empirical algorithm for the retrieval of soil moisture content and surface root mean square (RMS) height from remotely sensed radar data was developed using scatterometer data. The algorithm is optimized for bare surfaces and requires two copolarized channels at a frequency between 1.5 and 11 GHz. It gives best results for kh/spl les/2.5, /spl mu//sub /spl upsi///spl les/35%, and /spl theta//spl ges/30/spl deg/. Omitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplifies the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI>0.4) cause the algorithm to underestimate soil moisture and overestimate RMS height. A simple criteria based on the /spl sigma//sub hv//sup 0///spl sigma//sub vv//sup 0/ ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometer data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1991 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 4.2% soil moisture.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

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The SMOS Soil Moisture Retrieval Algorithm

2012-03-02

Yann H. Kerr , Philippe Waldteufel , Philippe Richaume +7 more

The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre … The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.

The Global Land Data Assimilation System

2004-03-01

Matthew Rodell , Paul R. Houser , U. Jambor +7 more

A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, … A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation-based data, runs globally at high resolution (0.25°), and produces results in near–real time (typically within 48 h of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based “tiling” approach is used to simulate subgrid-scale variability, with a 1-km global vegetation dataset as its basis. Soil and elevation parameters are based on high-resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employed as forcing data. The high-quality, global land surface fields provided by GLDAS will be used to initialize weather and climate prediction models and will promote various hydrometeorological studies and applications. The ongoing GLDAS archive (started in 2001) of modeled and observed, global, surface meteorological data, parameter maps, and output is publicly available.

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A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data

1999-11-01

Wolfgang Wagner , Guido Lemoine , Helmut Rott

Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans

2003-12-22

Thomas J. Jackson , Daoyi Chen , Michael H. Cosh +5 more

Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission

2001-01-01

Yann H. Kerr , Philippe Waldteufel , Jean‐Pierre Wigneron +3 more

Microwave radiometry at low frequencies (L-band: 1.4 GHz, 21 cm) is an established technique for estimating surface soil moisture and sea surface salinity with a suitable sensitivity. However, from space, … Microwave radiometry at low frequencies (L-band: 1.4 GHz, 21 cm) is an established technique for estimating surface soil moisture and sea surface salinity with a suitable sensitivity. However, from space, large antennas (several meters) are required to achieve an adequate spatial resolution at L-band. So as to reduce the problem of putting into orbit a large filled antenna, the possibility of using antenna synthesis methods has been investigated. Such a system, relying on a deployable structure, has now proved to be feasible and has led to the Soil Moisture and Ocean Salinity (SMOS) mission, which is described. The main objective of the SMOS mission is to deliver key variables of the land surfaces (soil moisture fields), and of ocean surfaces (sea surface salinity fields). The SMOS mission is based on a dual polarized L-band radiometer using aperture synthesis (two-dimensional [2D] interferometer) so as to achieve a ground resolution of 50 km at the swath edges coupled with multiangular acquisitions. The radiometer will enable frequent and global coverage of the globe and deliver surface soil moisture fields over land and sea surface salinity over the oceans. The SMOS mission was proposed to the European Space Agency (ESA) in the framework of the Earth Explorer Opportunity Missions. It was selected for a tentative launch in 2005. The goal of this paper is to present the main aspects of the baseline mission and describe how soil moisture will be retrieved from SMOS data.

An empirical model and an inversion technique for radar scattering from bare soil surfaces

1992-03-01

Yisok Oh , Kamal Sarabandi , F.T. Ulaby

Polarimetric radar measurements were conducted for bare soil surfaces under a variety of roughness and moisture conditions at L-, C-, and X-band frequencies at incidence angles ranging from 10 degrees … Polarimetric radar measurements were conducted for bare soil surfaces under a variety of roughness and moisture conditions at L-, C-, and X-band frequencies at incidence angles ranging from 10 degrees to 70 degrees . Using a laser profiler and dielectric probes, a complete and accurate set of ground truth data was collected for each surface condition, from which accurate measurements were made of the rms height, correlation length, and dielectric constant. Based on knowledge of the scattering behavior in limiting cases and the experimental observations, an empirical model was developed for sigma degrees /sub hh/, sigma degrees /sub vv/, and sigma degrees /sub hv/ in terms of ks (where k=2 pi / lambda is the wave number and s is the rms height) and the relative dielectric constant of the soil surface. The model, which was found to yield very good agreement with the backscattering measurements of the present study as well as with measurements reported in other investigations, was used to develop an inversion technique for predicting the rms height of the surface and its moisture content from multipolarized radar observations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle

2010-04-14

Yann H. Kerr , Philippe Waldteufel , Jean‐Pierre Wigneron +7 more

It is now well understood that data on soil moisture and sea surface salinity (SSS) are required to improve meteorological and climate predictions. These two quantities are not yet available … It is now well understood that data on soil moisture and sea surface salinity (SSS) are required to improve meteorological and climate predictions. These two quantities are not yet available globally or with adequate temporal or spatial sampling. It is recognized that a spaceborne L-band radiometer with a suitable antenna is the most promising way of fulfilling this gap. With these scientific objectives and technical solution at the heart of a proposed mission concept the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) mission as its second Earth Explorer Opportunity Mission. The development of the SMOS mission was led by ESA in collaboration with the Centre National d'Etudes Spatiales (CNES) in France and the Centro para el Desarrollo Tecnologico Industrial (CDTI) in Spain. SMOS carries a single payload, an L-Band 2-D interferometric radiometer operating in the 1400-1427-MHz protected band . The instrument receives the radiation emitted from Earth's surface, which can then be related to the moisture content in the first few centimeters of soil over land, and to salinity in the surface waters of the oceans. SMOS will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe. SMOS has a revisit time of less than 3 days so as to retrieve soil moisture and ocean salinity data, meeting the mission's science objectives. The caveat in relation to its sampling requirements is that SMOS will have a somewhat reduced sensitivity when compared to conventional radiometers. The SMOS satellite was launched successfully on November 2, 2009.

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Michigan microwave canopy scattering model

1990-07-01

F.T. Ulaby , Kamal Sarabandi , Kyle C. McDonald +2 more

Abstract Abstract The Michigan Microwave Canopy Scattering model (MIMICS) is based on a first-order solution of the radiative-transfer equation for a tree canopy comprising a crown layer, a trunk layer … Abstract Abstract The Michigan Microwave Canopy Scattering model (MIMICS) is based on a first-order solution of the radiative-transfer equation for a tree canopy comprising a crown layer, a trunk layer and a rough-surface ground boundary. The crown layer is modelled in terms of distributions of dielectric cylinders (representing needles and/or branches) and discs (representing leaves), and the trunks are treated as dielectric cylinders of uniform diameter. This report describes MIMICS I, which pertains to tree canopies with horizontally continuous (closed) crowns. The model, which is intended for use in the 0·5-10GHz region at angles greater than 10° from normal incidence, is formulated in terms of a 4 × 4 Stokes-like transformation matrix from which the backscattering coefficient can be computed for any transmit/receive polarization configuration.

Passive microwave remote sensing of soil moisture

1996-10-01

E. G. Njoku , Dara Entekhabi

The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

2011-05-30

Wouter Dorigo , Wolfgang Wagner , Roland Hohensinn +7 more

Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil … Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status May 2011), the ISMN contains data of 19 networks and more than 500 stations located in North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data.

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Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

2011-02-01

Y. Y. Liu , Robert Parinussa , Wouter Dorigo +5 more

Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a … Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m3 m−3) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 ("transitional regions"), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

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Dielectric properties of soils in the 0.3-1.3-GHz range

1995-05-01

N.R. Peplinski , F.T. Ulaby , M.C. Dobson

In 1985, the authors reported the development of a semiempirical dielectric model for soils, covering the frequency range between 1.4 and 18 GHz. The model provides expressions for the real … In 1985, the authors reported the development of a semiempirical dielectric model for soils, covering the frequency range between 1.4 and 18 GHz. The model provides expressions for the real and imaginary parts of the relative dielectric constant of a soil medium in terms of the soil's textural composition (sand, silt, and clay fractions), the bulk density and volumetric moisture content of the soil, and the dielectric constant of water at the specified microwave frequency and physical temperature. This communication provides similar expressions for the 0.3-1.3-GHz range. Upon comparing experimental results measured in this study with predictions based on the semiempirical model, it was found that the model underpredicts the real part of the dielectric constant for high-moisture cases and underestimates the imaginary part for all soils and moisture conditions. A small linear adjustment has been introduced to correct the expression for the real part and a new equation was generated for the effective conductivity to correct the expression for the imaginary part. In addition, dielectric measurements were made to evaluate the dependence of the dielectric constant on clay type. The results show significant variations for the real part and large variations for the imaginary part among soils with the same clay fractions but with clays of different specific surface areas.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

The complex dielectric constant of pure and sea water from microwave satellite observations

2004-09-01

Thomas Meißner , F. J. Wentz

We provide a new fit for the microwave complex dielectric constant of water in the salinity range between 0-40 ppt using two Debye relaxation wavelengths. For pure water, the fit … We provide a new fit for the microwave complex dielectric constant of water in the salinity range between 0-40 ppt using two Debye relaxation wavelengths. For pure water, the fit is based on laboratory measurements in the temperature range between -20/spl deg/C and +40/spl deg/C including supercooled water and for frequencies up to 500 GHz. For sea water, our fit is valid for temperatures between -2/spl deg/C and +29/spl deg/C and for frequencies up to at least 90 GHz. At low frequencies, our new model is a modified version of the Klein-Swift model. We compare the results of the new fit with various other models and provide a validation using an extensive analysis of brightness temperatures from the Special Sensor Microwave Imager.

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Multisensor historical climatology of satellite‐derived global land surface moisture

2008-01-18

Manfred Owe , Richard de Jeu , Thomas Holmes

A historical climatology of continuous satellite‐derived global land surface soil moisture is being developed. The data consist of surface soil moisture retrievals derived from all available historical and active satellite … A historical climatology of continuous satellite‐derived global land surface soil moisture is being developed. The data consist of surface soil moisture retrievals derived from all available historical and active satellite microwave sensors, including Nimbus‐7 Scanning Multichannel Microwave Radiometer, Defense Meteorological Satellites Program Special Sensor Microwave Imager, Tropical Rainfall Measuring Mission Microwave Imager, and Aqua Advanced Microwave Scanning Radiometer for EOS, and span the period from November 1978 through the end of 2007. This new data set is a global product and is consistent in its retrieval approach for the entire period of data record. The moisture retrievals are made with a radiative transfer‐based land parameter retrieval model. The various sensors have different technical specifications, including primary wavelength, spatial resolution, and temporal frequency of coverage. These sensor specifications and their effect on the data retrievals are discussed. The model is described in detail, and the quality of the data with respect to the different sensors is discussed as well. Examples of the different sensor retrievals illustrating global patterns are presented. Additional validation studies were performed with large‐scale observational soil moisture data sets and are also presented. The data will be made available for use by the general science community.

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Hydrologic Data Assimilation with the Ensemble Kalman Filter

2002-01-01

Rolf H. Reichle , Dennis McLaughlin , Dara Entekhabi

Soil moisture controls the partitioning of moisture and energy fluxes at the land surface and is a key variable in weather and climate prediction. The performance of the ensemble Kalman … Soil moisture controls the partitioning of moisture and energy fluxes at the land surface and is a key variable in weather and climate prediction. The performance of the ensemble Kalman filter (EnKF) for soil moisture estimation is assessed by assimilating L-band (1.4 GHz) microwave radiobrightness observations into a land surface model. An optimal smoother (a dynamic variational method) is used as a benchmark for evaluating the filter's performance. In a series of synthetic experiments the effect of ensemble size and non-Gaussian forecast errors on the estimation accuracy of the EnKF is investigated. With a state vector dimension of 4608 and a relatively small ensemble size of 30 (or 100; or 500), the actual errors in surface soil moisture at the final update time are reduced by 55% (or 70%; or 80%) from the value obtained without assimilation (as compared to 84% for the optimal smoother). For robust error variance estimates, an ensemble of at least 500 members is needed. The dynamic evolution of the estimation error variances is dominated by wetting and drying events with high variances during drydown and low variances when the soil is either very wet or very dry. Furthermore, the ensemble distribution of soil moisture is typically symmetric except under very dry or wet conditions when the effects of the nonlinearities in the model become significant. As a result, the actual errors are consistently larger than ensemble-derived forecast and analysis error variances. This suggests that the update is suboptimal. However, the degree of suboptimality is relatively small and results presented here indicate that the EnKF is a flexible and robust data assimilation option that gives satisfactory estimates even for moderate ensemble sizes.

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GLEAM v3: satellite-based land evaporation and root-zone soil moisture

2017-05-17

Brecht Martens , Diego G. Miralles , Hans Lievens +6 more

Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since … Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 36-year data set spanning 1980–2015, referred to as v3a (based on satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multi-source precipitation product), and two satellite-based data sets. The latter share most of their forcing, except for the vegetation optical depth and soil moisture, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative, ESA CCI) for the v3b data set (spanning 2003–2015) and observations from the Soil Moisture and Ocean Salinity (SMOS) satellite in the v3c data set (spanning 2011–2015). Here, these three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in the case of the v3a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the v3b and c data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar to the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements ranging between 0.78 and 0.81 for the different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at <a href="www.GLEAM.eu" target="_blank">www.GLEAM.eu</a> and may be used for large-scale hydrological applications, climate studies, or research on land–atmosphere feedbacks.

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ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions

2017-07-26

Wouter Dorigo , Wolfgang Wagner , Clément Albergel +7 more

Radar Interferometry

2001-01-01

Ramon F. Hanssen

GNSS interferometric reflectometry as a passive remote sensing method for studying environmental phenomena

2025-06-25

Mahmoud Abdelhamid | Journal of Applied Geodesy

Abstract Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) is a versatile remote sensing technique that utilizes reflected GNSS signals for environmental monitoring. By analyzing interference patterns between direct and reflected … Abstract Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) is a versatile remote sensing technique that utilizes reflected GNSS signals for environmental monitoring. By analyzing interference patterns between direct and reflected GNSS signals, GNSS-IR enables the estimation of surface characteristics such as soil moisture, water level, and snow depth. This study investigates snow accumulation, soil moisture, and water level measurements using GNSS-IR over extended periods, providing insights into climate-related trends. Data from GNSS stations in both the northern and southern hemispheres were analyzed. Snow depth was monitored at two stations over a decade, revealing fluctuations in accumulation and highlighting potential impacts of climate change. Soil moisture was analyzed at a grass-covered site, with comparisons before and after vegetation correction. Water levels were measured using GNSS-IR data near a coastal stream, capturing clear tidal signatures. Results show consistent reflector height variations corresponding to snow depth changes, soil moisture dynamics, and water level oscillations (including tides), demonstrating the robustness of GNSS-IR for environmental monitoring of snow, soil moisture, and sea level.

Effects of Soil Moisture in Northern Tibetan Plateau on Summer Precipitation in Northwest China

2025-06-23

Fuquan Lu , Haipeng Yu , Zeyong Hu +6 more | Journal of Geophysical Research Atmospheres

Abstract Soil moisture (SM) on the Tibetan Plateau (TP), a crucial climate variable with “memory,” influences the East Asia climate by modulating surface energy and water vapor exchanges. Understanding the … Abstract Soil moisture (SM) on the Tibetan Plateau (TP), a crucial climate variable with “memory,” influences the East Asia climate by modulating surface energy and water vapor exchanges. Understanding the relationship between TP soil moisture (TPSM) and summer precipitation in Northwest China (NWC) is essential for improving climate predictions for East Asia. However, most existing studies have focused on the connection between TPSM and the climate of East Asian monsoon region, whereas the mechanisms by which TPSM influence precipitation in NWC, a nonmonsoonal area, remain underexplored. This study investigates how spring anomalies of TPSM persist into summer and influence summer precipitation in NWC. The results indicate that anomalies in spring TPSM can initiate a positive feedback with precipitation, which affects the intensity of plateau monsoon and contributes to the persistence of SM anomaly from spring to summer. During summer, positive SM anomalies in northern TP facilitate maintaining cyclonic circulation anomalies over western TP and trigger eastward‐propagating Rossby waves that induce anticyclonic circulation anomalies over eastern NWC. The anomalous circulation results in upward motion in the western NWC and subsidence in the eastern NWC enhancing precipitation in the western NWC while reducing the precipitation in the north. Finally, the findings from the circulation analysis are validated through numerical model simulations. This study provides valuable insights into the climatic effects of TPSM and offers important implications for precipitation prediction in NWC.

Current Status of Application of Spaceborne GNSS-R Raw Intermediate-Frequency Signal Measurements: Comprehensive Review

2025-06-22

Qiulan Wang , Jinwei Bu , Yutong Wang +3 more | Remote Sensing

In recent years, spaceborne Global Navigation Satellite System reflectometry (GNSS-R) technology has made significant progress in the fields of Earth observation and remote sensing, with a wide range of applications, … In recent years, spaceborne Global Navigation Satellite System reflectometry (GNSS-R) technology has made significant progress in the fields of Earth observation and remote sensing, with a wide range of applications, important research value, and broad development prospects. However, despite existing research focusing on the application of spaceborne GNSS-R L1-level data, the potential value of raw intermediate-frequency (IF) signals has not been fully explored for special applications that require a high accuracy and spatiotemporal resolution. This article provides a comprehensive overview of the current status of the measurement of raw IF signals from spaceborne GNSS-R in multiple application fields. Firstly, the development of spaceborne GNSS-R microsatellites launch technology is introduced, including the ability of microsatellites to receive GNSS signals and receiver technique, as well as related frequency bands and technological advancements. Secondly, the key role of coherence detection in spaceborne GNSS-R is discussed. By analyzing the phase and amplitude information of the reflected signals, parameters such as scattering characteristics, roughness, and the shape of surface features are extracted. Then, the application of spaceborne GNSS-R in inland water monitoring is explored, including inland water detection and the measurement of the surface height of inland (or lake) water bodies. In addition, the widespread application of group delay sea surface height measurement and carrier-phase sea surface height measurement technology in the marine field are also discussed. Further research is conducted on the progress of spaceborne GNSS-R in the retrieval of ice height or ice sheet height, as well as tropospheric parameter monitoring and the study of atmospheric parameters. Finally, the existing research results are summarized, and suggestions for future prospects are put forward, including improving the accuracy of signal processing and reflection signal analysis, developing more advanced algorithms and technologies, and so on, to achieve more accurate and reliable Earth observation and remote sensing applications. These research results have important application potential in fields such as environmental monitoring, climate change research, and weather prediction, and are expected to provide new technological means for global geophysical parameter retrieval.

Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China

2025-06-21

Zhonghe Zhao , Yuyang Li , Kun Liu +4 more | Remote Sensing

Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural … Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies—such as high spatiotemporal resolution optical, radar, and thermal infrared sensors—has opened new avenues for efficient soil moisture retrieval. However, the accuracy of soil moisture retrieval decreases significantly when the soil is covered by vegetation. This study proposes a multi-modal remote sensing collaborative retrieval framework that integrates UAV-based multispectral imagery, Sentinel-1 radar data, and in situ ground sampling. By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. The results demonstrate that the retrieval performance of the model was significantly improved across different soil depths (0–5 cm, 5–10 cm, 10–15 cm, 15–20 cm). After vegetation suppression, the coefficient of determination (R2) exceeded 0.8 for all soil layers, while the mean absolute error (MAE) decreased by 35.1% to 49.8%. This research innovatively integrates optical–radar–thermal multi-source data and a physically driven vegetation suppression strategy to achieve high-accuracy, meter-scale dynamic mapping of soil moisture in vegetated areas. The proposed method provides a reliable technical foundation for precision irrigation and drought early warning.

Multimodal GNSS-R self-supervised learning as a generalist Earth surface monitor

2025-06-21

Daixin Zhao , Konrad Heidler , Milad Asgarimehr +4 more | International Journal of Applied Earth Observation and Geoinformation

Soil moisture retrieval and spatiotemporal variation analysis based on deep learning

2025-06-21

Zihan Zhang , Jinjie Wang , Jianli Ding +3 more | Agricultural Water Management

High Spatial Resolution Soil Moisture Mapping over Agricultural Field Integrating SMAP, IMERG, and Sentinel-1 Data in Machine Learning Models

2025-06-21

Diego Tola , Lautaro Bustillos , F. Arragan +7 more | Remote Sensing

Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, … Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R2 increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R2 = 0.86, RMSE = 2.55%).

Measuring soil moisture change and surface erosion from sparse rainstorms in hyper-arid terrains

2025-06-20

Jonathan C.L. Normand , Essam Heggy | International Journal of Applied Earth Observation and Geoinformation

Generation, Calibration, Retrieval and Validation of EOS-06 Scatterometer Data Products

2025-06-19

Suchandra Aich Bhowmick , Abhisek Chakraborty , Rajesh Sikhakolli +7 more | Journal of the Indian Society of Remote Sensing

Improving Soil Moisture Estimation by Integrating Remote Sensing Data into HYDRUS-1D Using an Ensemble Kalman Filter Approach

2025-06-19

Yinghao Sun , Quanming Liu , Chunjuan Wang +2 more | Agriculture

Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when … Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework that combines satellite-derived soil moisture estimates, ground-based observations, the HYDRUS-1D vadose zone model, and the ensemble Kalman filter (EnKF) data assimilation method to improve soil moisture simulations over saline-affected farmland in the Hetao irrigation district. Vegetation effects were first removed using the water cloud model; after correction, a cubic regression using the vertical transmit/vertical receive (VV) signal retrieved surface moisture with an R2 value of 0.7964 and a root mean square error (RMSE) of 0.021 cm3·cm−3. HYDRUS-1D, calibrated against multi-depth field data (0–80 cm), reproduced soil moisture profiles at 17 sites with RMSEs of 0.017–0.056 cm3·cm−3. The EnKF assimilation of satellite and ground observations further reduced the errors to 0.008–0.017 cm3·cm−3, with the greatest improvement in the 0–20 cm layer; the accuracy declined slightly with depth but remained superior to either data source alone. Our study improves soil moisture simulation accuracy and closes the knowledge gaps in multi-source data integration. This framework supports sustainable land management and irrigation policy in vulnerable farming regions.

A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data

2025-06-19

Yidi Wei , Qing Xu , Xiaobin Yin +2 more | Frontiers in Marine Science

Introduction Sea surface salinity (SSS) is a critical parameter for understanding ocean circulation, marine ecosystem processes, and climate change. Despite advancements in satellite-based radiometry such as NASA’s Soil Moisture Active … Introduction Sea surface salinity (SSS) is a critical parameter for understanding ocean circulation, marine ecosystem processes, and climate change. Despite advancements in satellite-based radiometry such as NASA’s Soil Moisture Active Passive (SMAP), significant challenges persist in coastal SSS retrieval due to radio frequency interference (RFI), land-sea contamination, and complex interactions of nearshore dynamic processes. Method This study proposes a deep neural network (DNN) framework that integrates SMAP L-band brightness temperature data with ancillary oceanographic and geographic parameters such as sea surface temperature, the shortest distance to the coastline (dis) to enhance SSS estimation accuracy in the Yellow and East China Seas. The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning. Results and discussion Systematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). Compared with SMAP SSS products, the baseline DNN demonstrates a reduction of 33.8% and 7.3% in RMSE in nearshore (dis ≤ 50 km) and offshore regions (50 km&lt;dis ≤ 200 km), respectively. The specific models constructed for nearshore and offshore areas, as well as for the four seasons, further improves salinity retrieval accuracy, especially in nearshore regions, highlighting the effectiveness of regional and seasonal optimization strategies in complex coastal environments. The DNN framework significantly mitigates coastal salinity biases caused by RFI and land contamination, providing a robust tool for applications such as coastal hydrological monitoring and marine resource management.

Caracterização dos atributos morfológicos de solos para automatização das informações hídricas e térmicas

2025-06-18

David Cardoso , José Alberto Ferreira Cardoso , Ezequiel Lopes do Carmo +3 more | Brazilian Journal of Development

O solo e a água são fundamentais para produção de alimentos e matérias-primas. No entanto, a carência de dados físico-hídricos e o manejo inadequado podem causar impactos negativos. A automatização … O solo e a água são fundamentais para produção de alimentos e matérias-primas. No entanto, a carência de dados físico-hídricos e o manejo inadequado podem causar impactos negativos. A automatização na avaliação do solo pode aprimorar as decisões no campo. Portanto, objetivou-se caracterizar os atributos morfológicos de três tipos de solos para subsidiar no sistema automatizado de informações hídricas e térmicas. Após a abertura das trincheiras e preparo dos perfis de solos, foi realizado o procedimento de descrição morfológica que abrangeu profundidade e espessura, coloração, contraste e forma de transição de horizontes. No perfil 1 exibiu cores mais neutras e amareladas (2.5 Y), com horizonte B mais fino comparado ao perfil 2. No perfil 2 teve menor profundidade do horizonte A e coloração avermelhada (2.5 YR), indicando maior presença de óxidos de ferro, enquanto no perfil 3 apresentou tonalidade amarelo-avermelhada menos intensa (7.5-10 YR). Concluiu-se que houve variações nos atributos dos horizontes de solos, os quais representam potenciais influenciadores sobre a condutividade elétrica e no comportamento térmico, evidenciando a necessidade de realização da calibração específica dos sensores para cada condição edáfica; a presença de stone-line não comprometerá a instalação de sensores até 30 cm de profundidade na trincheira 1, 60 cm na trincheira 2 e 50 cm na trincheira 3; e que as trincheiras apresentam características visuais compatíveis com as classes Neossolo (trincheira 1), Latossolo (trincheira 2) e Planossolo (trincheira 3).

Response of Soil Moisture to Precipitation in the Source Region of the Yellow River

2025-06-18

Xinyi Gu , Xianhong Meng , Xianyu Yang +5 more | Advances in Atmospheric Sciences

Implementation of global soil databases in the Noah-MP model and the effects on simulated mean and extreme soil hydrothermal changes

2025-06-18

Kazeem A. Ishola , Gerald Mills , Ankur Prabhat Sati +7 more | Hydrology and earth system sciences

Abstract. Soil properties and their associated hydrophysical parameters represent a significant source of uncertainty in land surface models (LSMs), with consequent effects on simulated sub-surface thermal and moisture characteristics, surface … Abstract. Soil properties and their associated hydrophysical parameters represent a significant source of uncertainty in land surface models (LSMs), with consequent effects on simulated sub-surface thermal and moisture characteristics, surface energy exchanges, and turbulent fluxes. These effects can result in large model differences, particularly during extreme events. As is typical of many model-based approaches, spatial soil information such as the location, extent, and depth of soil textural classes is derived from coarse-scale soil information and employed largely due to its being readily availability rather than its suitability. However, the use of a particular spatial soil dataset can have important consequences for many of the processes simulated within an LSM. This study investigates model uncertainty in the Noah-MP model in simulating soil moisture (expressed as a ratio of water to soil volume, m3 m−3) and soil temperature changes, associated with two widely used global soil databases (STATSGO and SoilGrids). Both soil datasets produced significant dry biases in loam soils of 0.15 and 0.10 m3 m−3 during a wet and dry period, respectively. The spatial disparities between STATSGO and SoilGrids also influenced the simulated regional soil hydrothermal changes and extremes. SoilGrids was found to intensify drought characteristics – shifting low and moderate drought areas into the extreme and exceptional classifications – relative to STATSGO. Our results demonstrate that the coarse STATSGO performs as well as the fine-scale SoilGrids soil database, though the latter represents the soil moisture dynamics better. However, the results underscore the need for greater collaborative efforts to develop more detailed regionally derived soil texture characteristics and to improve pedotransfer function (PTF) parameterizations for better representations of soil properties in LSMs. Enhancing these soil property representations in LSMs is essential for improving operational modeling and forecasting of hydrological processes and extremes.

Data-driven scaling methods for soil moisture cosmic ray neutron sensors

2025-06-18

Roland Baatz , Patrick Davies , Paolo Nasta +1 more | Hydrology and earth system sciences

Abstract. Cosmic ray neutron sensors (CRNSs) are state-of-the-art tools for field-scale soil moisture measurements, yet uncertainties persist due to traditional methods for estimating scaling parameters that lack the capacity to … Abstract. Cosmic ray neutron sensors (CRNSs) are state-of-the-art tools for field-scale soil moisture measurements, yet uncertainties persist due to traditional methods for estimating scaling parameters that lack the capacity to account for site-specific and sensor-specific characteristics. This study introduces a novel, data-driven approach to estimate key scaling parameters (beta, psi, and omega) by directly calculating scaling parameters from measurement data, emphasizing local environmental factors and sensor attributes. The method demonstrates reliability and robustness, with strong correlations between estimated scaling parameters and environmental factors such as cutoff rigidity, latitude, and elevation, as well as consistency with semi-analytical traditional methods, e.g. for beta an R2 of 0.46. The study also reveals systematically higher variability in calibration parameters than previously assumed, underscoring the importance of this new method, of data quality, and of the careful selection of Neutron Monitor Database (NMDB) reference sites. The new method reduces RMSE by up to 25 %, with differences in soil moisture estimates between traditional and data-driven methods reaching 0.04 m3 m−3 and up to 0.12 m3 m−3 under certain conditions. Sensitivity analysis shows that soil moisture estimation is most influenced by scaling parameters in the wet end of the soil moisture spectrum. By improving the accuracy of CRNS data, this approach enhances soil moisture estimation and supports better decisions in agriculture, hydrology, and climate monitoring. Future research should focus on refining these scaling methods and enhancing data quality to further improve CRNS measurement accuracy.

Validation of the NISAR Multi-Scale Soil Moisture Retrieval Algorithm across various Spatial Resolutions and Landcovers using the ALOS-2 PALSAR data

2025-06-17

Preet Lal , Gurjeet Singh , Narendra Das +1 more | Journal of Remote Sensing

Advancing soil moisture retrieval from SAR data using meta-heuristic optimization XGBoost and SHAP analysis

2025-06-17

C. Hou , Mou Leong Tan , Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬ +1 more | International Journal of Remote Sensing

Soil moisture mapping in Indian tropical islands with C-band SAR and artificial neural network models

2025-06-17

Sirisha Adamala , A. Velmurugan , T.P. Swarnam +6 more | Environmental Monitoring and Assessment

Leveraging a Novel Straightforward Integration Approach for Independent CYGNSS Soil Moisture Retrieval across Vegetated Regions

2025-06-16

Ziyue Zhu , Hoang Hai Nguyen , V. Lakshmi +1 more | Journal of Remote Sensing

Estimation of agricultural flood irrigation water consumption in the Heihe River Basin, China, using satellite based daily land surface evapotranspiration and soil moisture

2025-06-16

Lirong Huo , Qiaoyun Xie , Liang Sun +5 more | Journal of Hydrology Regional Studies

Pooled error variance and covariance estimation of sparse in situ soil moisture sensor measurements in agricultural fields in Flanders

2025-06-13

Marit Hendrickx , Jan Vanderborght , Pieter Janssens +4 more | SOIL

Abstract. Accurately quantifying errors in soil moisture measurements from in situ sensors at fixed locations is essential for reliable state and parameter estimation in probabilistic soil hydrological modeling. This quantification … Abstract. Accurately quantifying errors in soil moisture measurements from in situ sensors at fixed locations is essential for reliable state and parameter estimation in probabilistic soil hydrological modeling. This quantification becomes particularly challenging when the number of sensors per field or measurement zone (MZ) is limited. When direct calculation of errors from sensor data in a certain MZ is not feasible, we propose to pool systematic and random errors of soil moisture measurements for a specific measurement setup and derive a pooled error covariance matrix that applies to this setup across different fields and soil types. In this study, a pooled error covariance matrix was derived using soil moisture sensor measurements from three TEROS 10 (Meter Group, Inc., USA) sensors per MZ and soil moisture sampling campaigns conducted over three growing seasons, covering 93 cropping cycles in agricultural fields with diverse soil textures in Belgium. The MZ soil moisture estimated from a composite of nine soil samples with a small standard error (0.0038 m3 m−3) was considered the “true” MZ soil moisture. Based on these measurement data, we established a pooled linear recalibration of the TEROS 10 manufacturer's sensor calibration function. Then, for each individual sensor as well as for each MZ, we identified systematic offsets and temporally varying residual deviations between the calibrated sensor data and sampling data. Sensor deviations from the “true” MZ soil moisture were defined as observational errors and lump both measurement errors and representational errors. Since a systematic offset persists over time, it contributes to the temporal covariance of sensor observational errors. Therefore, we estimated the temporal covariance of observational errors of the individual and the MZ-averaged sensor measurements from the variance of the systematic offsets across all sensors and MZ averages, while the random error variance was derived from the variance of the pooled residual deviations. The total error variance was then obtained as the sum of these two components. Due to spatial soil moisture correlation, the variance and temporal covariance of MZ-averaged sensor observational errors could not be derived accurately from the individual sensor error variances and temporal covariances, assuming that the individual observational errors of the three sensors in a MZ were not correlated with each other. The pooled error covariance matrix of the MZ-averaged soil moisture measurements indicated a significant autocorrelation of sensor observational errors of 0.518, as the systematic error standard deviation (σα‾= 0.033 m3 m−3) was similar to the random error standard deviation (σϵ‾= 0.032 m3 m−3). To illustrate the impact of error covariance in probabilistic soil hydrological modeling, a case study was presented incorporating the pooled error covariance matrix in a Bayesian inverse modeling framework. These results demonstrate that the common assumption of uncorrelated random errors to determine parameter and model prediction uncertainty is not valid when measurements from sparse in situ soil moisture sensors are used to parameterize soil hydrological models. Further research is required to assess to what extent the error covariances found in this study can be transferred to other areas and how they impact parameter estimation in soil hydrological modeling.

Effect of daily rainfall characteristics on surface soil moisture variability

2025-06-12

Mir Farhan Ul Haq , Tushar Apurv | Hydrological Sciences Journal

A critical review on the applications of Sentinel satellite datasets for soil moisture assessment in crop production

2025-06-10

Anela Mkhwenkwana , Trylee Nyasha Matongera , Ciara Blaauw +1 more | International Journal of Applied Earth Observation and Geoinformation

Study on 2007–2021 Drought Trends in Basilicata Region Based on the AMSU-Based Soil Wetness Index

2025-06-09

Raffaele Albano , Meriam Lahsaini , Arianna Mazzariello +2 more | Land

Soil moisture (SM) plays a fundamental role in the water cycle and is an important variable for all processes occurring at the lithosphere–atmosphere interface, which are strongly affected by climate … Soil moisture (SM) plays a fundamental role in the water cycle and is an important variable for all processes occurring at the lithosphere–atmosphere interface, which are strongly affected by climate change. Among the different fields of application, accurate SM measurements are becoming more relevant for all studies related to extreme event (e.g., floods, droughts, and landslides) mitigation and assessment. In this study, data acquired by the advanced microwave sounding unit (AMSU) onboard the European Meteorological Operational Satellite Program (MetOP) satellites were used for the first time to extract information on the variability of SM by implementing the original soil wetness index (SWI). Long-term monthly SWI time series collected for the Basilicata region (southern Italy) were analyzed for drought assessment during the period 2007–2021. The accuracy of the SWI product was tested through a comparison with SM products derived by the Advanced SCATterometer (ASCAT) over the 2013–2016 period, while the Standardized Precipitation-Evapotranspiration Index (SPEI) was used to assess the relevance of the long-term achievements in terms of drought analysis. The results indicate a satisfactory accuracy of the SWI, with the mean correlation coefficient values with ASCAT higher than 0.7 and a mean normalized root mean square error less than 0.155. A negative trend in SWI during the 15-year period was found using both the original and deseasonalized series (linear and Sen’s slope ~−0.00525), confirmed by SPEI (linear and Sen’s slope ~−0.00293), suggesting the occurrence of a marginal long-term dry phase in the region. Although further investigations are needed to better assess the intensity and main causes of the phenomena, this result indicates the contribution that satellite data/products can offer in supporting drought assessment.