Search

Soil Moisture and Ocean Salinity (SMOS) mission has successfully contributed to global soil moisture products since 2009. Validation and calibration activities were conducted worldwide, yet some of the validation results do not fulfill the targeted accuracy of ±0.04 $\text{m}^{3}\text{m}^{-3}$. This paper presented the site-specific calibration of the V620 retrieval algorithm with in situ data collected at selected agricultural sites in the humid tropical regions, Malaysia. This set of data has been validated where low accuracy of SMOS soil moisture products was found. To improve the SMOS soil moisture retrieval, calibration of SMOS soil moisture retrieval algorithm based on the L-band Microwave Emission and Biosphere model and SMOS Level 1C $\text{T}_{\mathrm {B}}$ products, considering the local parameters was conducted. The calibration proves that these site-specific parameters improve the product’s accuracy. Validation of SMOS Level 2 product with in situ data showed bias, root-mean-square error (RMSE), and unbiased RMSE (ubRMSE) ranging from 0.050 to 0.118 $\text{m}^{3}\text{m}^{-3}$ , 0.068 to 0.142 $\text{m}^{3}\text{m}^{-3}$ , and 0.069 to 0.103 $\text{m}^{3}\text{m}^{-3}$ , respectively. The soil moisture retrieval based on the calibrated model showed an improved bias of 0.020–0.056 $\text{m}^{3}\text {m}^{-3}$ and RMSE of 0.026–0.065 $\text{m}^{3}\text{m}^{-3}$. The ubRMSE ranges from 0.017 to 0.034 $\text{m}^{3}\text{m}^{-3}$. Recently released SMOS-IC V105 product was also validated, where small improvements were noticed when compared to the accuracy of SMOS Level 2. This paper shows the importance of local parameters in retrieving soil moisture with higher accuracy compared to the use of global generalized parameters that are used in the original SMOS soil moisture retrieval algorithm. [ABSTRACT FROM AUTHOR]

Polarization orientation angle (POA) is an important parameter of polarimetric radar scattering from slopes in mountainous region. It is known that surface tilted in azimuth direction and buildings not aligned in the along-track direction induce polarization orientation shifts. Earlier research has established orientation angle as a function of radar imaging geometry and surface slopes, and that POA estimation can be derived from polarimetric radar data using circular polarization. Besides these, polarimetric scattering from steep slopes and its relation to POA remain not well understood. In this paper, we address these issues by adopting a tilted surface model based on Bragg scattering. We have found that, as the azimuthal slope increases, $\vert $ VV $\vert $ decreases at a faster rate than $\vert $ HH $\vert $ , they become equal, when POA is ±45°, and $\vert $ HH $\vert >\vert $ VV $\vert $ afterward. In other words, the Pauli component, $\vert $ HH-VV $\vert $ reduced to zero at POA = ± 45°, and the typical Bragg scattering characteristics of $\vert $ VV $\vert >\vert $ HH $\vert $ does not apply when steep slope is present inducing $\vert $ POA $\vert > 45^{\circ }$. Furthermore, the cross-pol $\vert $ HV $\vert $ does not always increase with azimuth slope but also reaches a maximum then decreases to zero. In addition, we investigate the effect of soil moisture on polarimetric SAR (PolSAR) scattering characteristics of steep terrain and the effect of vegetation over surface on POA estimation. The latter is demonstrated with NASA/JPL TOPSAR L-band PolSAR data and C-band InSAR data. Another significance of this paper is that it provides a direct and rigorous derivation of POA equations. The earlier version was derived from a different concept. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) plays significant roles in various scientific fields, including agriculture, hydrology, meteorology, and ecology. However, the spatial resolutions of microwave SSM products are too coarse for regional applications. Most current optical/thermal infrared SSM retrieval models cannot directly estimate the quantitative volumetric soil water content without establishing empirical relationships between ground-based SSM measurements and satellite-derived proxies of SSM. Therefore, in this paper, SSM is estimated directly from 5-km-resolution Chinese Geostationary Meteorological Satellite FY-2E data based on an elliptical-new SSM retrieval model developed from the synergistic use of diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). The elliptical-original model was constructed for bare soil and did not consider the impacts of different fractional vegetation cover (FVC) conditions. To optimize the elliptical-original model for regional-scale SSM estimates, it is improved in this paper by considering the influence of FVC, which is based on a dimidiate pixel model and a Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index product. A preliminary validation of the model is conducted based on ground measurements from the counties of Maqu, Luqu, and Ruoergai in the source area of the Yellow River. A correlation coefficient (R) of 0.620, a root-mean-square error (RMSE) of 0.146 m3/m3, and a bias of 0.038 m3/m3 were obtained when comparing the in situ measurements with the FY-2E-derived SSM using the elliptical-original model. In contrast, the FY-2E-derived SSM using the elliptical-new model exhibited greater consistency with the ground measurements, as evidenced by an R of 0.845, an RMSE of 0.064 m3/m3, and a bias of 0.017 m3/m3. To provide accurate SSM estimates, high-accuracy FVC, LST, and NSSR data are required. To complement the point-scale validation conducted here, cross-comparisons with other existing SSM products will be conducted in the future studies. [ABSTRACT FROM AUTHOR]

A new method dedicated to the measurement of the moisture content and salinity of soil is proposed in this paper. The method takes the advantage of the most accurate permittivity sensor available at microwave frequencies based on a ${\mathrm {TE}}_{01\delta }$ mode dielectric resonator providing the effective complex permittivity of the measured mixture. The measurement has been undertaken mostly at 1 GHz due to the fact that conductive losses of liquids are substantially varying with salinity at low microwaves, thus, increasing measurement sensitivity. At first, intrinsic properties of the major components of the soil, such as sand, salt and saline water, are extracted from the effective parameters measured at microwave frequencies and compared with the Maxwell-Garnett mixing rule to extract their intrinsic properties. In case of the soil, the unknown moisture content and salinity are extracted from raw measurement results using an empirical permittivity model of a conductive mixture which, unlike to analytical dielectric mixing rules, accounts for a percolation threshold. [ABSTRACT FROM AUTHOR]

This paper investigated the sensitivity of passive microwave L-band soil moisture (SM) retrieval from multiangle airborne brightness temperature data obtained under morning and afternoon conditions from the National Airborne Field Experiment conducted in southeast Australia in 2006. Ground measurements at a dryland focus farm including soil texture, soil temperature, and vegetation water content were used as ancillary data to drive the retrieval model. The derived SM was then in turn evaluated with the ground-measured near-surface SM patterns. The results of this paper show that the Soil Moisture and Ocean Salinity target accuracy of 0.04 m3·m-3 for single-SM retrievals is achievable irrespective of the 6 A.M. and 6 P.M. overpass acquisition times for moisture conditions≥0.15 m3·m-3. Additional tests on the use of the air temperature as proxy for the vegetation temperature also showed no preference for the acquisition time. The performance of multiparameter retrievals of SM and an additional parameter proved to be satisfactory for SM modeling--independent of the acquisition time--with root-mean-square errors less than 0.06 m3·m-3 for the focus farm. [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) mission, launched in January 2015, was designed to provide a global soil moisture product at medium resolution (~9 km), by combining observations from its radar and radiometer. Several downscaling methods have been proposed by the SMAP team for this purpose. This paper evaluates another candidate downscaling method, namely, the Bayesian merging approach. While this has been tested using a synthetic data set across the USA, it is imperative that it can also be tested using the experimental data for a comprehensive range of land surface conditions (i.e., in different hydro-climatic regions) prior to a global application. Consequently, this paper applies this method using the data collected from SMAP experiments field campaigns in south-eastern Australia that closely simulated the SMAP data stream for a single SMAP radiometer pixel over a three-week interval. The method studied here differs from the linear downscaling methods of the SMAP mission, in that it uses a nonlinear method based on Bayes’ theorem. The medium-resolution soil moisture product is obtained using background soil moisture estimates that are updated according to the difference between the observed and predicted brightness temperatures and backscatter coefficients, relating the high- and low-resolution data. Results were assessed against a reference soil moisture map derived from high-resolution airborne radiometer observations. The root-mean-square-error and R^2 for the Bayesian merging method were found to be 0.02 cm3/cm3 and 0.55, respectively, at 9-km resolution, being similar to the SMAP’s “optional” downscaling method. [ABSTRACT FROM PUBLISHER]

This paper presents the methodology of EM-field localization in rectangular waveguide using dual layer metamaterial interface for sampling different soil samples. The prototype design consists of WR229 waveguide in which array of metamaterial unit-cell is loaded. A sample loading cell is created in the waveguide where two MTM array layers are kept at $\lambda _{g}/4$ spacing with respect to coaxial feed probe. The metamaterial unit-cell has two open-ring resonators connected with a short stub, and it helps to create a stronger localized resonance in the waveguide. This enables stronger EM field penetration into the soil sample that is under test. The fabricated device has dual resonance at 2.284 GHz and 3.091 GHz. The dielectric characterization of different dry-soil samples like black soil, silt soil, red soil and sand soil is presented. The applicability of the proposed device is also demonstrated for moisture analysis of soil samples with water content ranging from 2% to 14 %. The proposed device yields lower percentage error in extracting the dielectric constant values of the test soil samples. The on-field measurement of the soil sample which is extracted from different depths of soil surface is also presented and analyzed for dielectric estimation. The proposed device can be used in agriculture field testing application for testing properties of soil like dielectric constant and moisture level. [ABSTRACT FROM AUTHOR]

Recently, precision agriculture has gained substantial attention due to the ever-growing world population demands for food and water. Consequently, farmers will need water and arable land to meet this demand. Due to the limited availability of both resources, farmers need a solution that changes the way they operate. Precision irrigation is the solution to deliver bigger, better, and more profitable yields with fewer resources. Several machine learning-based irrigation models have been proposed to use water more efficiently. Due to the limited learning ability of these models, they are not well suited to unpredictable climates. In this context, this paper proposes a deep learning neural network-based Internet of Things (IoT)-enabled intelligent irrigation system for precision agriculture (DLiSA). This is a feedback integrated system that keeps its functionality better in the weather of any region for any period of time. DLiSA utilizes a long short-term memory network (LSTM) to predict the volumetric soil moisture content for one day ahead, irrigation period, and spatial distribution of water required to feed the arable land. It is evident from the simulation results that DLiSA uses water more wisely than state-of-the-art models in the experimental farming area. [ABSTRACT FROM AUTHOR]

The scarcity of water resources throughout the world demands its optimum utilization in various sectors. Smart Sensing-enabled irrigation management systems are the ideal solutions to ensure the optimum utilization of water resources in the agriculture sector. This paper presents a wireless sensor network-enabled Decision Support System (DSS) for developing a need-based irrigation schedule for the orange orchard. For efficient monitoring of various in-field parameters, our proposed approach uses the latest smart sensing technology such as soil moisture, leaf-wetness, temperature and humidity. The proposed smart sensing-enabled test-bed was deployed in the orange orchard of our institute for approximately one year and successfully adjusted its irrigation schedule according to the needs and demands of the plants. Moreover, a modified Longest Common SubSequence (LCSS) mechanism is integrated with the proposed DSS for distinguishing multi-valued noise from the abrupt changing scenarios. To resolve the concurrent communication problem of two or more wasp-mote sensor boards with a common receiver, an enhanced RTS/CTS handshake mechanism is presented. Our proposed DSS compares the most recently refined data with pre-defined threshold values for efficient water management in the orchard. Irrigation activity is scheduled if water deficit criterion is met and the farmer is informed accordingly. Both the experimental and simulation results show that the proposed scheme performs better in comparison to the existing schemes. [ABSTRACT FROM AUTHOR]

Agriculture Cyber-Physical System (A-CPS) is becoming increasingly important in enhancing crop quality and productivity by utilizing minimum cropland. This paper introduces the innovative idea of the Internet-of-Agro-Things (IoAT) with an explanation of the automatic detection of plant disease for the development of ACPS. Majority of the crops were infected by microbial diseases in conventional agriculture. Also, the constantly mutating pathogens cannot be known to the knowledge of the farmer, due to which, there arises a demand to develop a disease prediction system. To prevent this, we use a trained Convolutional Neural Network (CNN) model to perform an analysis of the crop image captured by a health maintenance system. The image capturing along with continuous sensing and intelligent automation is performed by the solar sensor node. The sensor node houses a developed soil moisture sensor which has a high longevity compared to its peers. A real time implementation of the proposed system is demonstrated using a solar sensor node with a camera module, a microcontroller and a smartphone application using which a farmer can monitor the field. The prototype was deployed for three months and has achieved a robust performance by remaining rust-free and sustaining the varied weather conditions. An accuracy of 99.24% is achieved by the proposed plant disease prediction framework. [ABSTRACT FROM AUTHOR]

The estimation of soil moisture and crop biomass based on differential interferometry is questioned by the influence of intercepted rain (i.e., plant surface moisture) on repeat-pass observables. The magnitude, the origin of this effect, as well as its dependence on system and crop biophysical parameters have been only marginally addressed so far. This paper intends to investigate these aspects within the frame of a laboratory experiment carried out in a highly controlled electromagnetic environment. The collection of multifrequency and fully polarimetric scatterometer profiles offers a distinctive data set, which helps to understand the variations of the interferometric observables in response to a varying plant surface moisture. These changes are assessed by comparing the predictions of a first-order scattering solution with the impact found in the experimental data. Furthermore, the connection between plant surface moisture and differential interferometric observables (i.e., the magnitude and phase of the interferometric coherence) is empirically tested with the aid of regression techniques. Irrespective of frequency and polarization, intercepted water is found to impact the interferometric coherence in a similar way as changes in soil and/or plant water status, i.e., the increase of the sensor to target optical path. Changes of plant surface moisture might be erroneously mistaken either for soil water content or fresh biomass variations. Therefore, this paper raises the possibility that, in certain circumstances, intercepted water might represent a potential source of bias for the estimation of these two land surface parameters. [ABSTRACT FROM PUBLISHER]

We develop a methodology for modeling coherent electromagnetic scattering from rough surfaces with buried inhomogeneities in three dimensions. The inhomogeneities considered in this paper include random spherical media, random cylindrical media, and root-like cylindrical clusters. They are used to simulate rocks, ice particles, and vegetation roots buried beneath the ground surface that can be seen by the low-frequency radars in Earth remote sensing applications. The approach we develop first calculates volumetric scattering from the media using coherent approaches, including both the conventional recursive transition matrix (T-matrix) method as well as a new generalized iterative extended boundary condition method we developed for tilted finite cylinders, and then transforms the T-matrix to the scattering matrix, which is then used to form the full scattered field of layered structures with rough surface and subsurfaces. We validate the methodology by comparing with other numerical solutions for special cases, and show sensitivity results for scattering from rough surface with buried random spherical media and random cylindrical media of different densities. We also construct a basic root model and calculate the scattering cross sections from single and multiple root clusters with or without a subsurface interface underneath. With the approach developed in this paper, we are able to study the sensitivity of radar signals to subsurface scatterers. For example, our simulations show that, depending on their density and water content, buried roots could enhance the backscatter from a single rough surface by as much as 5 dB in co-pol components, and substantially more in cross-pol components. The results of this model are expected to enable more accurate geophysical retrievals of soil moisture as well as soil organic content. [ABSTRACT FROM PUBLISHER]

In this paper, an attempt is made to investigate the angular signatures of bistatic scattering, in the azimuthal direction, from rough surfaces, with the aim of deepening our understanding of the bistatic scattering behaviors and exploring its potential applications. Three distinct angular features, dip angle, scattering strength, and angular width, as a function of the surface roughness and dielectric constant, are identified. Brewster’s scattering, and its role in angular behavior, is examined at limited extent. Results reveal that the angular features strongly correlate with the surface parameters and scattering geometry. For small scattering angle, dip angle and width are independent of surface roughness. Comparatively, for larger incident and scattering angles, beyond 50°, the dip angle and scattering strength are sensitive, simultaneously, to rms height and dielectric constant, while the dip width only responses to rms height. Dips, induced by Brewster’s scattering effect, not only shift in the polar direction, but also in the azimuthal direction, and are strongly dependent on surface parameters and bistatic geometry. Increasing the surface roughness or, equivalently, the incident angle tends to promote the disappearance of dips. The main contributions of this paper can be summarized as follows: 1) quantitative description of dip features, including angle, scattering strength, and angular width; 2) comprehensive characterization of the dip features and their dependence on surface parameters and bistatic geometries; and 3) limited investigation of the behavior of Brewster’s scattering-induced dip. [ABSTRACT FROM PUBLISHER]

The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution soil moisture and freeze–thaw state every 2–3 days using an L-band (active) radar and an L-band (passive) radiometer. The Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP. [ABSTRACT FROM PUBLISHER]

Soil salinization is one of the most common land desertification processes that can be found worldwide. It is a certainly severe environment hazard and threatens the stability of ecosystems. As a rapid and inexpensive tool, remote sensing technology combining with the measurements of soil spectra has been widely concerned on identifying and mapping salt effect on lands. However, as effects of the soil moisture often immerge the effects of salt to soil reflectance spectra, soil moisture became a major factor to restrict soil salinity monitoring from soil reflectance. High soil moisture content will lead to failure on soil salinity estimation from soil reflectance data. In this paper, a semianalytical model using an exponent function was developed to estimate soil salt content (SSC) under different moisture levels based on a control laboratory experiment. And the root-mean-square error and mean relative error were 0.799 g/kg and 31.294%, respectively, when the model was applied to estimate SSCs by wet soil reflectance. To sum up, considering both effects of soil moisture and soil salt on soil reflectance, the semianalytical model reduced SSC estimated error. The approach presented in this paper provides a new way of estimating soil salinity from soil spectra under various soil moisture conditions, and it will be a potential application for large-scale SSC mapping. [ABSTRACT FROM PUBLISHER]

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3 ubRMSE, −0.015 m3/m3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m3/m3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation. [ABSTRACT FROM PUBLISHER]

This paper attempts to probabilistically estimate the surface soil moisture content (SMC) by using the synthetic aperture radar data provided by radar imaging satellite1. The novelty of this paper lies in: 1) developing a combined index to understand the role of all the backscattering coefficients with different polarization and soil texture information in influencing the SMC; 2) using normalized incidence angles, which enables the model to be applicable for different incidence angles; and 3) determination of uncertainty range of the estimated SMC. The dimensionality problem, which is frequently observed in the multivariate analysis, is reduced in the development of the combined index by the use of supervised principal component analysis (SPCA). The SPCA also ensures the maximum attainable association between the developed combined index and surface SMC above wilting point (WP). The association between the combined index and the surface SMC above WP is modeled through joint probability distribution by using the Frank copula. The model is developed and validated with the field soil moisture values over 334 monitoring points within the study area. The outcomes obtained by applying the proposed model indicate an encouraging potential of the model to be applied for bareland and vegetated land (<30 cm height). [ABSTRACT FROM PUBLISHER]

Previous papers proposed a method to find the maximum possible dried area of soil expected due to heating by underground cables. A method was also proposed to include this dried soil area in cable ampacity calculations. This assumed that cables would be loaded 100% of the time. To dry soil to the maximum possible extent may take very long time periods, and in many installations, the cables actually carry current for a limited time. In these cases, the soil may never dry to its maximum extent. This paper proposes a method to approximately determine the radius of dried soil that will occur due to cable heating for limited periods of time. It similarly proposes a method to approximate the time it will take for the dried soil to be replenished to its original moisture content after heating by the cables has ceased. [ABSTRACT FROM AUTHOR]

Since its launch in 2009, the European Space Agency mission Soil Moisture and Ocean Salinity (SMOS) has provided valuable information on soil moisture, sea surface salinity data, and other geophysical variables. Due to its innovative instrument, an L-band 2-D synthetic aperture interferometric radiometer, SMOS is able to provide high-resolution L2 data (as compared with other L-band missions). However, SMOS processing is complex, giving rise to the emergence of some unexpected biases. In this paper, we have analyzed the spatial structure of two-point correlations owing to the SMOS synthetic antenna, finding that they are not negligible. Those correlations can be characterized by means of effective point spread functions (PSFs). This paper indicates that the SMOS PSF matrix can be computed in a fast way from measured data without the need for any model or auxiliary data. Furthermore, this matrix can be described in terms of a convolution kernel. The knowledge of that convolution kernel can be used to improve the quality of the SMOS image and to assess the effect of changes of processing procedures, including calibration methods. [ABSTRACT FROM PUBLISHER]

Inefficient soil management is a hindrance to food productivity and a contributing factor to the degradation of soil quality. Despite decades of research confirming the benefits of using sensors in effective soil management and crop yield, they are still not widely used in farms for several reasons including high cost, difficulty in large area deployment, and requirement of labor-intensive wiring. In this paper, we report a low-cost battery-less chipless wireless sensor tag that can be batch-manufactured for large-scale agricultural surveillance. The sensor tag works based on backscattering techniques for the wireless measurements of the volumetric water content (VWC) of the soil. The sensor tag consists of shorted dipole resonators whose resonance frequencies are dependent on the effective permittivity of the VWC of its surrounding soil. The resonant frequency of the sensor tag is measured by an orthogonally polarized reader antenna that observes the peak in the backscattered spectrum. As a proof-of-concept, a sensor tag with two resonators of length 10 cm and 9 cm, is buried at a depth of 10 cm in the soil and is wirelessly read from a free-space distance of 50 cm, to demonstrate its capability to measure the changes in VWC. The resonators of length 10 cm and 9 cm have demonstrated a linear change of −3.92 MHz/VWC and −4.85 MHz/VWC, respectively, within the VWC range 4-27%. By changing the functional passivation layers on the demonstrated sensor tag, this concept can be applied to a wide range of low-cost subsoil sensors for precision agriculture applications. [ABSTRACT FROM AUTHOR]

NASA's Soil Moisture Active Passive (SMAP) mission objective is global mapping of surface volumetric soil moisture at 10-km resolution every two to three days and with accuracy of 0.04 cm3 cm−3 (one sigma). In order to achieve this resolution and accuracy, the SMAP utilizes L-band radar and L-band radiometer measurements. The instruments share a rotating 6-m mesh reflector antenna that scans across a 1000-km swath in order to meet the required data refresh rate. The Level-2 Active–Passive soil moisture product (L2_SM_AP) at 9 km is retrieved from the disaggregated/downscaled brightness temperature obtained by merging of active and passive L-band observations. The baseline L2_SM_AP algorithm disaggregates the coarse-resolution (∼36 km) brightness temperatures of the SMAP L-band radiometer using the high-resolution (∼3 km) backscatter data from the SMAP L-band radar with unfocused synthetic aperture processing. The inversion of brightness temperature to estimate surface soil moisture is more mature when compared with inversions of radar backscatter. This is the primary driver of the brightness temperature disaggregation approach to the combined active–passive surface soil moisture product. Furthermore, this approach allows some consistency with the coarse-resolution radiometer-only surface soil moisture product since the disaggregated brightness temperatures sums to the radiometer measurement. The disaggregated brightness temperature contains instrument errors (∼0.7 dB for co-pol backscatter and ∼1.0 dB for cross-pol backscatter, and ∼1.3 K in brightness temperature) inherent in the radar and radiometer. Furthermore, the algorithm has two critical parameters that add uncertainty. Finally, correction of the land brightness temperature (used in the inversion) for water body contributions is a source of uncertainty. In this paper, we introduce analytical expressions for the SMAP downscaled brightness temperature due to all these sources of uncertainty. The expressions allow estimation of uncertainty (in kelvin) for each data granule of the SMAP L2_SM_AP product. Since the uncertainties depend on the given ground conditions, e.g., existing water body fraction and local algorithm parameters that depend on vegetation cover and landscape heterogeneity, it is necessary to evaluate the uncertainty for each data granule. In this paper, we show that the uncertainty expressions closely match Monte Carlo simulations with an overall difference of only ∼0.1 K. Whereas Monte Carlo estimates of uncertainty can only be afforded for a nominal case (such as those typically reported in Algorithm Theoretical Basis Documents as uncertainty tables), the analytical expressions allow uncertainty estimates for every data granule. The expressions are now used to provide uncertainty standard deviation of downscaled brightness temperature at 9 km in the SMAP L2_SM_AP product. These standard deviations are useful for the following: 1) guidance on the expected level of error in the estimate brightness temperature due to the downscaling process and 2) observation error in direct radiance data assimilation. [ABSTRACT FROM PUBLISHER]

A study to determine radio-frequency interference (RFI) in low-frequency passive microwave observations of the Advanced Microwave Scanning Radiometer-2 (AMSR2) is performed. RFI detection methods, such as the spectral difference method, have already been applied on microwave satellite sensors. However, these methods may result in false RFI detection, particularly in zones with extreme environmental conditions. To overcome this problem, this paper proposes an approach that uses the additional 7.3-GHz channel of the AMSR2 sensor in a new RFI detection method. This method uses calculated standard errors of estimate to detect RFI contamination in 6.9- and 7.3-GHz observations. It was found that 6.9-GHz observations are mainly contaminated in the USA, India, Japan, and parts of Europe. The 7.3-GHz observations are contaminated in South America, Ukraine, the Middle East, Southeast Asia, and Russia. The fact that these channels are not affected by RFI in exactly the same regions is useful for studies that prefer C-band brightness temperature observations (e.g., soil moisture retrieval algorithms). Therefore, a decision tree approach was set up to determine RFI and to select reliable brightness temperature observations in the lowest frequency free of any man-made contamination. The result is a reduction of the total contaminated pixels in the 6.9-GHz observations of 66% for horizontal observations and even 85% for vertical observations when 7.3 and 10.7 GHz are used. By linking RFI maps with civilization maps, this paper further shows that RFI sources at the C-band frequency are mainly located in urbanized areas. [ABSTRACT FROM PUBLISHER]

Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Données SMOS) Level-3 product of the Soil Moisture and Ocean Salinity (SMOS) mission, form an important information source, particularly for updating land surface models. However, the coarse resolution of the SMOS product requires additional treatment if it is to be used in applications at higher resolutions. Furthermore, the remotely sensed soilmoisture often does not reflect the climatology of the soil moisture predictions, and the bias between model predictions and observations needs to be removed. In this paper, a statistical framework is presented that allows for the downscaling of the coarse-scale SMOS soil moisture product to a finer resolution. This framework describes the interscale relationship between SMOS observations and model-predicted soil moisture values, in this case, using the variable infiltration capacity (VIC) model, using a copula. Through conditioning, the copula to a SMOS observation, a probability distribution function is obtained that reflects the expected distribution function of VIC soilmoisture for the given SMOS observation. This distribution function is then used in a cumulative distribution function matching procedure to obtain an unbiased fine-scale soil moisture map that can be assimilated into VIC. The methodology is applied to SMOS observations over the Upper Mississippi River basin. Although the focus in this paper is on data assimilation apcations, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required. [ABSTRACT FROM AUTHOR]

This paper develops a new model for surface soil moisture (SSM) retrieval from CBERS-02B images. The paper first analyzes the existing SSM retrieval model from Landsat TM imagery and establishes the spectral radiance relationship of each band between Landsat TM and CBERS-02B. The model associated parameters including mean reflectance, mean atmospheric transmittance, and mean sun radial brightness of each band between Landsat TM and CBERS-02B is established. The model is finally adjusted by considering the differences of response frequency and sensitivity in the two satellite sensors. Two test areas, Jili Village of Laibin county, Guangxi Province, China and Yuanjiaduan Village of Jiujiang County, JiangXi Province, China are chosen to verify the correctness of the developed model. The SSMs retrieved from Landsat TM imagery are chosen as references. The accuracy of the proposed model is evaluated through correlation coefficient and root-mean-square error (RMSE) relative to the SSMs retrieved from Landsat TM images. The verified results discover that the relative accuracy of the average SSMs retrieved by the proposed model from CBERS-02B can reach over 91.0% when compared to the SSMs retrieved from Lansat TM. In addition, six types of lands are used to further evaluate the accuracy of the proposed model. The experimental results in two areas show that the correlation coefficient and the RMSE between two SSMs from CBERS-02B and Landsat TM achieves over 0.9 and 0.011 (\bfm^3/\bfm^3), respectively, in both rocky desertification land and dry land; achieve over 0.81 and 0.09 (\bfm^3/\bfm^3), respectively, in rice field, shrub land, and woodland. These results demonstrate that the model developed in this paper can effectively calculate the SSMs for CBERS-02B satellite imagery. [ABSTRACT FROM PUBLISHER]

This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a focus on mountain areas. The novelties of the paper are: the extension of an already developed method to coarse resolution data (150 m) in mountain environment with high land heterogeneity, with only VV polarization and the proper selection of input features. During the result analysis, several algorithm characteristics were clearly identified: 1) the performances showed to be strongly related to input features such as topography and vegetation indices; 2) the algorithm needs a training phase; 3) the averaging window needs to be proper selected to take into account both the speckle noise and the characteristics of the area under investigation; and 4) the algorithm, being data driven, can be considered as site dependent. The experimental analysis is carried out on images acquired over the Südtirol/Alto Adige Province in Italy during 2010–2011 from the RADARSAT2 and Envisat ASAR in Wide Swath mode. SMC maps were compared with spatially distributed ground measurements, resulting in a root mean squared error (RMSE) value ranging from 0.045 to $0.07\;{{\rm m}^3}/{{\rm m}^3}$. Concerning the multiscale analysis, the results indicated that RADARSAT2 maps are able to detect the spatial heterogeneity and soil moisture dynamics at local scale, while ASAR WS SMC maps are able to identify mainly the two main classes of pasture and meadows. When these estimates are compared with SMC values from meteorological stations a RMSE value of $0.10\;{{\rm m}^3}/{{\rm m}^3}$ for both satellites indicated a reduced capability to follow the temporal dynamics. [ABSTRACT FROM PUBLISHER]

This paper presents a new technique for automated detection of multi-year (MY) and first-year (FY) sea ice from RADARSAT-2 dual-polarization HH–HV ScanSAR Wide images under cold environmental conditions. The approach is applied to 2.05 km $\times2.05$ km ($41 \times 41$ pixels) spatial window in the situation where the area is labeled as ice by our recently introduced ice and open water detection approach. The probability of the presence of MY ice is modeled as a function of the two selected predictor parameters computed over each spatial window: the HV/HH polarization ratio and the standard deviation of the HV signal. The proposed MY ice probability model was built based on thousands of synthetic aperture radar (SAR) images and corresponding Canadian Ice Service (CIS) Image Analysis products covering the 2010–2016 time period, not including 2013. Our verification results for the independent testing subset for the year 2013 against the CIS Image Analysis products suggest that approximately 50% of pure MY and FY ice samples were classified with an accuracy of 98.2%. The incidence angle correction of HH and HV backscatter does not improve MY and FY ice detection results in the space of the selected predictor parameters. The proposed technique will be used as part of the Environment and Climate Change Canada Regional Ice-Ocean Prediction System in support of assimilation of ice thickness retrievals from Cryosat-2 and Soil Moisture and Ocean Salinity mission data. [ABSTRACT FROM AUTHOR]

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

An appropriate proportion of water content in the soil is critical for plant growth [1]. The use of capacitive soil moisture sensor exploits the very property of soil that the dielectric value of soil varies with the amount of water content [2], [3]. Use of a capacitive sensor for soil moisture measurement provides some advantages such as better accuracy after soil-specific calibration, high sensitivity, flexibility in probe design, easy installation and cost-effectiveness. A copper cladded epoxy material, coated with a thin film of polymethyl methacrylate (PMMA) polymer, is an established procedure to fabricate the sensor [3], [4], which has been used here for soil moisture sensing. This paper describes the development of a rechargeable battery-operated hand-held meter for fieldwork by using this PMMA coated sensor along with a suitable signal conditioning circuit. The thermo-gravimetric method has been utilized to obtain the actual moisture content of soil [5] and calibrate it with respect to the voltage output of the meter. Moreover, with the moisture content, surrounding temperature and humidity are also useful atmospheric parameters used by weather stations [6]. For this, a DHT12 sensor has been integrated with this hand-held meter. IoT extension of measured data is implemented by a SIM900A GPRS/GSM modem to make the meter smart. [ABSTRACT FROM AUTHOR]

This paper presents a comparison between the Sentinel-1/Sentinel-2-derived soil moisture product at plot scale (S2MP) and the new Copernicus surface soil moisture (C-SSM) product at 1-km scale over a wide region in southern France. In this study, both products were first evaluated using in situ measurements obtained by the calibrated time delay reflectometer in field campaigns. The accuracy against the in situ measurements was defined by the correlation coefficient R, the root mean square difference (RMSD), and the bias and the unbiased root mean square difference (ubRMSD). Then, the soil moisture estimations from both SSM products were intercompared over one year (October 2016–October 2017). Both products show generally good agreement with in situ measurements. The results show that using in situ measurements collected over agricultural areas and grasslands, the accuracy of the C-SSM is good (RMSD = 6.0 vol%, ubRMSD = 6.0 vol%, and R = 0.48) but less accurate than the S2MP (RMSD = 4.0 vol%, ubRMSD = 3.9 vol%, and R = 0.77). The intercomparison between the two SSM products over one year shows that both products are highly correlated over agricultural areas that are mainly used for cereals (R value between 0.5 and 0.9 and RMSE between 4 and 6 vol%). Over areas containing forests and vineyards, the C-SSM values tend to overestimate the S2MP values (bias > 5 vol%). In the case of well-developed vegetation cover, the S2MP does not provide SSM estimations while C-SSM sometimes provides underestimated SSM values. [ABSTRACT FROM AUTHOR]

Both the European Space Agency's soil moisture and ocean salinity (SMOS) mission and the National Aeronautics and Space Administration's soil moisture active passive (SMAP) mission employ L-band (1.413 GHz) radiometers to observe brightness temperatures at ∼40-km spatial resolution to subsequently derive global soil moisture every two to three days with a target accuracy of 0.04 m3/m3. However, the man-made structures that dominate urban areas in many of the SMOS and SMAP radiometers pixels may confound the interpretation of their radiometric observations if not taken into account, and thus, degrade the soil moisture retrieval accuracy. This paper investigates the effect that urban areas are expected to have on the SMOS and SMAP soil moisture retrieval accuracy using experimental data from the Australian airborne field campaigns performed over the past six years. Taking the total radiometric error budgets for the SMOS (3.95 K) and the SMAP (1.3 K) missions as conservative benchmarks for radiometric “error” that can be tolerated to achieve the 0.04 m3/m3 target accuracy, urban fraction thresholds of 6.6% and 2.2% were obtained for the SMOS and SMAP pixels, respectively, under warm dry (soil moisture < 0.15 m3/m3) conditions, increasing to 16.8% and 5.2% under cold and/or wet conditions. These results have been extrapolated globally, assuming that the microwave behavior of the cities analyzed here is representative of those elsewhere, to identify the SMOS and SMAP pixels that are expected to be adversely affected by urban areas if not explicitly taken into account in retrieval algorithms. [ABSTRACT FROM AUTHOR]

The polarimetric two-scale model (PTSM) was introduced a few years ago as an electromagnetic scattering model to be used within algorithms for soil moisture retrieval from polarimetric synthetic aperture radar (SAR) data. PTSM inherits the ability to account for depolarization effects from the original two-scale model (TSM), and, with respect to the latter, it has the advantage to provide closed-form expressions of the elements of the covariance matrix that hold for moderate large-scale surface slopes. This allows a very fast evaluation of scattering, since numerical integration needed by the original TSM is avoided. The TSM, also called composite model (CM), has been extensively used to study scattering from the sea surface, so that it is natural to explore the use of PTSM for the same purpose. However, in its current formulation, PTSM assumes that large-scale surface slope distribution and small-scale roughness spectrum are isotropic. This is not realistic for the sea surface, for which anisotropy is dictated by the wind direction. Accordingly, we here extend PTSM to account for surface roughness anisotropy, so obtaining the anisotropic PTSM (A-PTSM). In addition, as a second contribution, we provide A-PTSM expressions also in the circular polarization basis, which may be useful for some SAR sensor polarimetric configurations. Finally, we compare A-PTSM results with sea surface scattering measurements available in the literature and with results of the second-order small-slope approximation (SSA2). In particular, as a third original contribution of this paper, an analytical closed-form expression of the ratio of crosspolarized normalized radar cross sections (NRCSs) obtained by SSA2 and A-PTSM is provided. [ABSTRACT FROM AUTHOR]

In this paper, a numerical modeling approach was used to evaluate the semiempirical H–Q model used in the Soil Moisture and Ocean Salinity (SMOS) retrieval algorithm to account for roughness effects over bare soil. The H–Q model uses four parameters, HR, QR, NRH, and NRV, which are usually calibrated at the ground scale for different surface types. The aim of this paper is to investigate whether these empirical parameters could be linked to the physical roughness parameters of standard deviation of surface heights \sigma and autocorrelation length Lc. First, a numerical modeling approach was used to calculate rough soil emissivities for different roughness and soil moisture conditions. Second, H –Q model parameters were retrieved by minimizing a cost function between these emissivities and those calculated by the H–Q model. It was found that the retrieved HR could be related directly to Zs = \sigma^2/Lc and that QR, NRV, and NRH were dependent on HR. HR was found to have a negligible dependence on soil moisture. Based on these results, a new model was proposed where the four H–Q model parameters were calibrated to Zs. This model was tested on the PORTOS 1993 data set and found to yield a root-mean-square difference between the retrieved and measured soil moisture values of \sim\! 0.03\ \m^3/\m^3, which was within the desired 0.04-\m^3/\m^3 error margin for the SMOS mission. [ABSTRACT FROM AUTHOR]

Regional soil moisture can be estimated by assimilating satellite microwave brightness temperature into a land surface model (LSM). This paper explores how to improve soil moisture estimation based on sensitivity analysis when assimilating Advanced Microwave Scanning Radiometer for the Earth Observing System brightness temperatures. By assimilating a lower and a higher frequency combination, the land data assimilation system (LDAS) used in this paper estimates first model parameters in a calibration pass and then estimates soil moisture in an assimilation pass. The ground truth of soil moisture was collected at a soil moisture network deployed in a semiarid area of Mongolia. Analyzed are the effects of assimilating different polarizations, frequencies, and satellite overpass times on the accuracy of the estimated soil moisture. The results indicate that assimilating the horizontal polarization signal underestimates soil moisture and assimilating the daytime signal overestimates soil moisture. The former is due to improper parameter estimation perhaps caused by high sensitivity of the horizontal polarization to land surface heterogeneity, and the latter is due to the effective soil temperature for microwave emission in the daytime being close to the one at a soil depth of several centimeters but not to the surface skin temperature simulated in the LSM. Therefore, assimilating the nighttime vertical polarizations in the LDAS is recommended. A further analysis shows that assimilating different frequency combinations produces different soil moisture estimates, and none is always superior to the others, because different frequency signals may be contaminated by varying clouds and/or water vapor with different degrees. Thus, an ensemble estimation based on frequency combinations was proposed to filter off, to some extent, the stochastic frequency-dependent biases. The ensemble estimation performs more robust when driven by different forcing data. [ABSTRACT FROM PUBLISHER]

The forthcoming two-satellite GMES Sentinel-1 constellation is expected to render systematic surface soil moisture retrieval at 1 km resolution using C-band SAR data possible for the first time from space. Owing to the constellation's foreseen coverage over the Sentinel-1 Land Masses acquisition region—global approximately every six days, nearly daily over Europe and Canada depending on latitude—in the high spatial and radiometric resolution Interferometric Wide Swath (IW) mode, the Sentinel-1 mission shows high potential for global monitoring of surface soil moisture by means of fully automatic retrieval techniques. This paper presents the potential for providing such a service systematically over Land Masses and in near real time using a change detection approach, concluding that such a service is—subject to the mission operating as foreseen—expected to be technically feasible. The work presented in this paper was carried out as a feasibility study within the framework of the ESA-funded GMES Sentinel-1 Soil Moisture Algorithm Development (S1-SMAD) project. [ABSTRACT FROM PUBLISHER]

In this paper, we study the radar retrieval of soil moisture as well as canopy parameters in a range of boreal forests. The retrieval is formulated as an optimization problem where the difference between data and prediction of a forward scattering model is minimized. The forward model is a discrete scatterer radar model, and the optimization algorithm is a global optimization scheme known as simulated annealing. The inversion method is first applied to synthetic data assuming hypothetical allometric relationships to make the retrieval possible by reducing the number of unknown vegetation parameters. The inversion algorithm is then validated using the data acquired with the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in June 2010 in central Canada boreal forests in support of the prelaunch calibration and validation activities of NASA's Soil Moisture Active and Passive (SMAP) mission. The inversion results for synthetic data show that the absolute retrieval error in soil moisture and relative retrieval error in canopy height are small, while the relative output error in trunk density could be large. The inversion results for actual field data show a great accuracy in soil moisture retrieval for Old Jack Pine and Young Jack Pine forests but show large retrieval errors for many of the radar pixels in the Old Black Spruce site. This paper shows that L-band radar is capable of retrieving surface soil moisture in forests with a high biomass where the forest structure allows soil moisture information to be carried by scattering mechanisms. [ABSTRACT FROM AUTHOR]

The present paper analyzes the effects of roughness on the surface emission at L-band based on observations acquired during a long-term experiment. At the Surface Monitoring of the Soil Reservoir Experiment site near Toulouse, France, a bare soil was plowed and monitored over more than a year by means of an L-band radiometer, profile soil moisture and temperature sensors, and a local weather station, accompanied by 12 roughness campaigns. The aims of this paper are the following: 1) to present this unique database and 2) to use this data set to investigate the semiempirical parameters for the roughness in L-band Microwave Emission of the Biosphere, which is the forward model used in the Soil Moisture and Ocean Salinity soil moisture retrieval algorithm. In particular, we studied the link between these semiempirical parameters and the soil roughness characteristics expressed in terms of standard deviation of surface height (σ) and the correlation length (LC). The data set verifies that roughness effects decrease the sensitivity of surface emission to soil moisture, an effect which is most pronounced at high incidence angles and soil moisture and at horizontal polarization. Contradictory to previous studies, the semiempirical parameter Qr was not found to be equal to 0 for rough conditions. A linear relationship between the semiempirical parameters N and σ was established, while NH and NV appeared to be lower for a rough (NH ~ 0.59 and NV ~ -0.3) than for a quasi-smooth surface. This paper reveals the complexity of roughness effects and demonstrates the great value of a sound long-term data set of rough L-band surface emissions to improve our understanding on the matter. [ABSTRACT FROM AUTHOR]

The launch of the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) satellite mission in November 2009 opened a new era of global passive monitoring at L-band (1.4-GHz band reserved for radio astronomy). The main objective of the mission is to measure soil moisture and sea surface salinity; the sole payload is the Microwave Imaging Radiometer using Aperture Synthesis. As part of comprehensive calibration and validation activities, several ground-based L-band radiometers, so-called ETH L-Band radiometers for soil moisture research (ELBARA-II), have been deployed. In this paper, we analyze a comprehensive set of measurements from one ELBARA-II deployment site in the northern boreal forest zone. The focus of this paper is in the detection of the evolution of soil frost (a relevant topic, e.g., for the study of carbon and methane cycles at high latitudes). We investigate the effects that soil freeze/thaw processes have on the L-band signature and present a simple modeling approach to analyze the relation between frost depth and the observed brightness temperature. Airborne observations are used to expand the analysis for different land cover types. Finally, the first SMOS observations from the same period are analyzed. Results show that soil freezing and thawing processes have an observable effect on the L-band signature of soil. Furthermore, the presented emission model is able to relate the observed dynamics in brightness temperature to the increase of soil frost. [ABSTRACT FROM AUTHOR]

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

This paper addresses the optimization and implementation of a fringing field capacitive soil moisture sensor using the printed circuit board technology. It includes the analysis of a novel configuration of an interdigital sensor for measuring soil moisture with two existing configurations. The optimized designs were simulated by using a 3-D finite-element method and fabricated by using a copper clad board. The performance of the fabricated sensors was evaluated using four soil samples collected from different locations. The observations were compared with the standard gravimetric method to evaluate the soil water content of the samples. The characterization method and the results of the whole sensing system are discussed in terms of calibration, dynamic test, and repeatability. [ABSTRACT FROM AUTHOR]

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

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

Remote sensing inversion of vegetation-covered soil moisture is often affected by crop canopy, surface roughness, and other factors. In order to eliminate the scattering influence of vegetation effectively, this paper developed a modified vegetation backscattering model to retrieve vegetation-covered soil moisture based on multi-temporal RADARSAT-2 data and field measurements. This model combined the advantages of optical and radar methods by considering scattering contributions of underlying bare soil and vegetation canopy. Vegetation coverage was used to separate the scattering mechanism of the vegetation from bare soil component in a pixel. In addition, advanced integral equation method was presented to define the scattering of underlying bare soil. PROSAIL optical model was applied to calculate crown water content, which is an important variable associated with the scattering of vegetation canopy. Results demonstrated that the modified model on March 29, 2014 performed better in soil moisture retrieval than that at other growth stages with R2 of 0.806 and root-mean-square error of 0.043 m3·m−3, respectively. Soil moisture can be effectively retrieved by using the modified model in an agricultural region where the surface type is ranging from relatively sparse to full cover. Overall, the modified model provides an insight into extensive application of vegetation-covered soil moisture retrieval in agricultural regions. [ABSTRACT FROM AUTHOR]

This paper describes the fringing electric field modeling and performance of four different shaped poly methyl methacrylate-coated capacitive sensors for soil moisture measurement. Experiments are carried out in the laboratory by placing the sensor inside the soil. The analytical solution for such fringing field system is difficult to obtain, so COMSOL Multiphysics software is used for modeling the capacitive sensor surrounded by the soil. Experimental data are compared with the software simulated value. Statistical root-mean-square error (RMSE) and percentage of normalized RMSE for polynomial fit are studied for the sensitivity and performance analysis. Effect of external temperature on the performance of the sensor is also discussed. The proposed signal conditioning circuit is implemented in printed circuit board to measure the output voltage with respect to capacitance change. Obtained results are transmitted through a global system for mobile communication module for remote observation of soil moisture. The thermogravimetric method is used for voltage to soil moisture calibration, and based on the experimental data, results are analyzed further. A review of some relevant fringing field capacitive sensors and their instrumentation system reported in past 7–8 years are studied for comparison with the proposed sensor system. [ABSTRACT FROM AUTHOR]

This paper focuses on assessing the effectiveness of applying orientation angle calibration to polarimetric synthetic aperture radar (PolSAR) data for soil moisture estimation. We employ Cloude-decomposition-based method to estimate the orientation angle because it can relate a scatter-distributed pixel to its major component of an equivalent "pure target," use the Jet Propulsion Laboratory/Airborne Synthetic Aperture Radar L-band fully polarimetric data to validate the proposed method, and observe results in good agreement after orientation angle compensation is employed. Specifically, root mean square errors of measured radar backscattering coefficients σhh0 and σvv0 and copolarization ratio versus advanced integral equation model predictions are reduced significantly from 1.95, 1.33, and 2.03 dB to 1.30, 1.15, and 1.43 dB, respectively. The compensated copolarized backscattering coefficients are also used as inputs to a novel inversion model to estimate the dielectric factor Rhh and volumetric soil moisture mv. The results show that the estimation errors are reduced significantly from 0.075 to 0.054 and 0.056 to 0.041 for Rhh and mv, respectively. This paper demonstrates the advantage of orientation angle calibration as a preprocessing for estimating bare soil moisture, particularly in agricultural areas, and the preponderance of fully PolSAR data on soil moisture estimation over dual and single polarizations. [ABSTRACT FROM AUTHOR]