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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]

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]

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]

A low-cost (6 Euro per sensor), low-power (in the order of 200~\mu \textW per sensor), with high communication range (on the order of 250 m), scatter radio sensor network is presented, for soil moisture monitoring at multiple locations. The proposed network utilizes analog frequency modulation in a bistatic network architecture (i.e., the emitter and reader are not colocated), while the sensors operate simultaneously, using frequency-division multiple access. In contrast to prior art, this paper utilizes an ultralow-cost software-defined radio reader and offers custom microstrip capacitive sensing with simple calibration, as well as modulation pulses for each scatter radio sensor with 50% duty cycle; the latter is necessary for scalable network designs. The overall root mean squared error below 1% is observed, even for the range of 250 m. This is another small (but concrete) step for the adoption of scatter radio technology as a key enabling technology for scalable, large-scale, low-power, and cost environmental sensor networking. [ABSTRACT FROM PUBLISHER]

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]

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]

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]

Recently, we have proposed a retrieval technique based on an original polarimetric two-scale model (PTSM), which is able to estimate the volumetric water content of bare soils from polarimetric synthetic aperture radar (SAR) data. In this paper, to extend the field of application of our retrieval technique to moderately vegetated soils, we combine the PTSM with a randomly oriented dipole-cloud volumetric scattering model, thus obtaining a polarimetric two-scale two-component model (PTSTCM). By using this model we show that, in principle, suitable combinations of the polarimetric SAR channels, i.e., “modified copolarized ratio” and “modified copolarized correlation coefficient,” are related only to the surface parameters because the dependence on the unknown volumetric contribution intensity cancels out. This allows us to retrieve soil moisture from L-band SAR data not only for bare soils but also in moderately vegetated areas, interested by a nonnegligible volumetric scattering contribution, provided that the double-bounce scattering component is negligible. In addition, describing the surface component by using the PTSM allows us to mitigate the well-known problem of overestimating the volume component, which affects most model-based target decompositions and that may lead to the so-called “negative power problem.” Both the performance and validity limits of the estimation method are assessed by comparing the obtained soil-moisture retrieval results to “in situ” measurements. To this aim, data from SMEX'03 and AGRISAR'06 campaigns available in literature are considered. They refer to sites with a flat topography. In particular, we employ the AGRISAR database, which includes data from several fields covering a period that spans all the phases of vegetation growth, to explore the validity range of the method in terms of vegetation height. Results of PTSTCM are also compared with those of available three-component methods (3CMs) employing more simplified surface scattering models. It has turned out that the use of the PTSTCM provides more accurate results for low vegetation (average modulus of soil moisture relative error for vegetation height smaller than 50 cm: 18.5% for the PTSTCM and 34% for the 3CM). Conversely, for higher vegetation, 3CMs should be more conveniently employed (average modulus of relative error for vegetation height greater than 50 cm: 17.5% for the 3CM and about 100% for the PTSTCM). A simple method to adaptively and automatically (i.e., based on measured data) select between PTSTCM and 3CM on a pixel-by-pixel basis is finally suggested, leading to a less than 20% average modulus of relative error on the retrieved soil moisture for the considered fields over the entire vegetation growth cycle. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to investigate the estimation of the variation of ground resistance throughout the year by using artificial neural networks (ANNs). An ANN was trained, validated, and tested with different training algorithms by using experimental data of soil resistivity, ground resistance, and rainfall in order to select the optimum training algorithm and the respective parameters and predict the behavior of the ground resistance of a single rod. Moreover, a sensitivity analysis of the proposed ANN was carried out in order to determine the impact of certain factors on the efficiency of the ANN. The high value of the correlation index between estimated and experimental values demonstrates the high efficiency of the ANN. The proposed methodology based on ANN is a useful tool for the estimation of the grounding resistance during the year in case of difficulties in measuring its value. [ABSTRACT FROM AUTHOR]

The aim of this paper is to interpret and characterize the changes of the 3-D polarimetric scattering signatures of agricultural crops at C-band and to relate them to temporal changes of the soil and plant parameters. For this, a time series of multibaseline (MB) synthetic aperture radar (SAR) data acquired at C-band by the airborne F-SAR system of the German Aerospace Center over the Wallerfing test site in Germany was analyzed. The availability of MB SAR data enables the resolution of scattering contributions in height by means of SAR tomography. The tomographic profiles at different polarizations were analyzed regarding temporal changes for different crop types. First, it was investigated if the center of mass (CoM) of the vertical reflectivity profiles as a single parameter enables the tracking of changes in soil and vegetation. The results show that the vertical reflectivity profiles and their CoM do not allow resolving the ambiguity if a change originates from soil or vegetation dynamics as expected. Thus, the scattering contributions from ground and volume were separated in height, using a filtering approach, and used for the estimation of the ground and volume scattering powers by means of covariance matching. Comparing the outputs with coincident ground measurements showed that dielectric as well as geometric changes in the vegetation are traceable by the separated ground and volume powers. Finally, the estimated powers were analyzed with respect to orientation effects, i.e., to polarimetric anisotropic behavior. They were found to be not significant for the crops under study at C-band. [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]

In this paper, we address the problem of retrieving soil moisture over a grassland alpine area from Synthetic Aperture Radar (SAR) data using a statistical algorithm trained by simulations of a physical model. A time series of C-band VV-polarized Wide Swath images acquired by Envisat Advanced SAR (ASAR) in the snow-free periods of 2010 and 2011 was simulated using a discrete radiative transfer model (RTM). The test area was located in the Mazia valley, South Tyrol (Italy), where the main land types are meadows and pastures. Soil moisture was collected from five meteorological stations, two of which situated in meadows and the rest in pastures. The smallest and the highest RMSEs of the RTM simulations were 0.78 dB and 1.91 dB, respectively. After backscattering simulation, the top soil moisture was estimated using Gaussian Process Regression (GPR). GPR was trained with the backscatter model simulations (including terrain features) for 2010, and then used to predict moisture from radar observations acquired in 2011. The relative importance of different input features was also assessed. The RMSE of the predicted soil moisture for the largest training data set (including aspect as a terrain feature) was 5.6% Vol. and the corresponding correlation coefficient was 0.84. [ABSTRACT FROM PUBLISHER]

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]

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 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 investigate the magnitude of Sun-glint through modeling the Soil Moisture Active Passive (SMAP) brightness temperature (BT). Model results show that the specular reflection of Sun-glint in the L-band can spread over a wide range of view angles due to the roughness and undulation of the land surface and therefore affect SMAP radiometer observations. Due to SMAP’s low incidence angle (40°), Sun-glint in the specular direction is never observed, and only the noncoherent component of Sun-glint has influence on SMAP observations. Sun-glint is particularly an issue over wet soil surfaces at low solar zenith angles (SZAs), and caution has to be taken for the terrain effect even for high SZAs, because the local solar incident angle can be significantly changed by the terrain slope and then the specular reflection of Sun-glint can be viewed by SMAP. Model results also show that BT in V-pol is less contaminated by Sun-glint than that in H-pol. During an intense solar radio burst, it was found that the land surface BT in H-pol increased by 50 K in the forward scattering direction from the SMAP observation. This is roughly equivalent to 1 K increment by every 100 solar flux units on a dry soil and/or dense vegetation. When the solar activity is quiet in 2015, the Sun-glint from both wet land and ocean surfaces can reach up to 10 K in the SMAP L1B BT product. This paper suggests that BT observations around the solar specular direction should be masked for soil moisture retrieval at the L-band. [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]

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]

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]

Soil moisture and wet biomass changes between two noninstantaneous SAR observations markedly affect the displacement estimates obtainable with Differential Interferometric Synthetic Aperture Radar (DInSAR). The separation, the modeling of these influences besides their uncoupling from the displacement signal, and the atmospheric disturbances are still unsolved issues for several repeat-pass interferometric applications. This paper focuses on the separation of vegetation changes from the other phase contributions affecting repeat-pass measurements over vegetated areas. These phase terms mainly relate to changes in soil moisture, atmospheric delays, and surface deformation. The separation is achieved with a first-order scattering solution decomposing the observed HH and VV DInSAR phases in the sum of several phase terms. The latter mainly consider the changes in soil surface scattering and in the two-way propagation through a vertically oriented vegetation canopy. No assumption is made on the spatiotemporal evolution of the displacement and atmosphere. The overall approach is tested on a L-band data set acquired over an agricultural area. Upon calibration, the model allows for estimating changes in wet biomass based on the nonzero HH–VV DInSAR phase difference observed over several birefringent agricultural fields. The obtained biomass estimates provide then a correction for the effect of vegetation changes on the observed HH and VV DInSAR phases. Deprived of the vegetation contribution, the remainder phase terms can be more easily explored for further analyses, e.g., the estimation of soil moisture changes and/or surface movements in vertically oriented vegetated areas. [ABSTRACT FROM AUTHOR]

An optimized two-zone model based on the critical degree of saturation is proposed in this paper for ampacity calculations of underground power cables. Moisture migration is fully considered based on geological soil models. Comprehensive finite-element method (FEM) simulations are performed to study the effect of the moisture content and its effects on soil thermal resistivity and critical temperature rise. The critical degree of saturation (Scr) is used in the improved two-zone model instead of the critical temperature rise (Δθx) used in the IEC Standard 60287. The comparative analysis presented between the new method and the IEC Standard shows that IEC is more susceptible to changes in the ambient conditions. The use of a fixed critical temperature rise may yield significant errors. Shortcomings of numerical solutions are discussed highlighting the advantages of the new (optimized) two-zone model. The optimized method is useful for the design of new installations as it accounts for changes in soil thermal resistivity and critical temperature rise. The new method has been validated with available test results. [ABSTRACT FROM AUTHOR]

Soil semiempirical dielectric models (SEMs) are powerful, and they are generally considered a useful hybrid of both empirical and physical models. In this paper, the Wang–Schmugge dielectric model is improved to more accurately estimate the relative complex dielectric constants (CDCs) of moist soils. Instead of the Debye relaxation spectrum of liquid water located outside of the soil (i.e., free out-of-soil water) adopted in the Wang–Schmugge model, the Debye relaxation formula related to the free-water component inside the soil [i.e., free soil water (FSW)], which is correlated with the soil texture, is employed in the improved SEM. In addition, the effective conductivity loss term related to both soil texture and soil moisture is introduced to explain the ionic conductivity losses of FSW. Since the soil moisture influence is reduced at high frequencies, the effective conductivity loss term related to only the soil texture is also analyzed for 14–18 GHz. As in the Wang–Schmugge model, the relative CDC of bound soil water varies with the soil volumetric moisture content when the soil moisture is lower than the maximum bound water fraction in the new model, which takes a different approach than the Mironov mineralogy-based SEM. The proposed model obtains better fitting results than the three most widely employed SEMs. The improved model exhibits a significantly improved accuracy with a higher correlation coefficient ($R^{2}$), a closer 1:1 relationship, and a lower root-mean-square error, including in the L-band, and especially in the imaginary part of the L-band. [ABSTRACT FROM AUTHOR]

In this paper, the capabilities of hybrid polarimetric synthetic aperture radar are investigated to estimate soil moisture on bare and vegetated agricultural soils. A new methodology based on a compact polarimetric decomposition, together with a surface component inversion, is developed to retrieve surface soil moisture. A model-based compact decomposition technique is applied to obtain the surface scattering component under the assumption of a randomly oriented vegetation volume. After vegetation removal, the surface scattering component is inverted for soil moisture (under vegetation) by comparison with a surface component modeled by two physics-based scattering models: The integral equation method (IEM) and the extended Bragg model (X-Bragg). The developed algorithm, based on a two-layer (random volume over ground) scattering model, is applied on a time series of hybrid polarimetric C-band RISAT-1 right circular transmit linear receive data acquired from April to October 2014 over the Wallerfing test site in Lower Bavaria, Germany. The retrieved soil moisture is validated against in situ frequency-domain reflectometry measurements. Including the entire growing season (all acquired dates) and all crop types, the estimated soil moisture values indicate an overall rmse of 7 vol.% using the X-Bragg model and 10 vol.% using the IEM model. The proposed hybrid polarimetric soil-moisture inversion algorithm works well for bare soils ( \mboxrmse=3.1–8.9 vol.%) with inversion rates of around 30–70%. The inversion rate for vegetation-covered soils ranges from 5% to 40%, including all phenological stages of the crops and different soil moisture conditions. [ABSTRACT FROM PUBLISHER]

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]

The Soil Moisture and Ocean Salinity (SMOS) mission launched by the European Space Agency in 2009 is devoted to the monitoring of soil moisture and ocean salinity at global scale from L-band spaceborne radiometric observations obtained with a 2-D interferometer. This paper is concerned with the polarization leakage or coupling between SMOS antennas. More precisely, we analyze the impact of the cross-polar antenna patterns on both the image reconstruction procedure and the scene-dependent bias correction. Depending on the level of this coupling, several solutions will be proposed for the retrieval of brightness temperature maps. We will show that the effect of the polarization leakage is relatively small if the interferometric data or correlations are obtained from antennas operating in the same polarization. On the other hand, we will show that the correlations associated to antennas operating in opposite polarizations are highly coupled, and therefore, the polarization leakage should always be considered in the reconstruction. The proposed solutions are compared, over the ocean, to a simulated brightness temperature model and, over the land, to in situ soil moisture data. [ABSTRACT FROM PUBLISHER]

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]

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]

There is growing interest in multifunctional sensors that expand sensing capabilities, especially for environmental measurement needs. This paper aims to develop a multifunctional penta-needle thermo-dielectric sensor (MPTDS) by coupling an electromagnetic (EM) sensor for determining water content with a penta-needle heat pulse probe (PHPP) for determining thermal properties. The thermal properties were inverse fit to the measured temperature rise at four surrounding thermistor needles from an 8-s heat pulse on a central heater needle. The dielectric permittivity was obtained from an impedance measurement made on the heater needle and two adjacent thermistor needles, leading to water content estimates. To determine water content and thermal properties for porous media, a two-step permittivity calibration and an in-situ effective needle spacing calibration were also made. The prototype MPTDS was evaluated in fine quartz sand across a range of water contents from dry to saturated to determine dielectric permittivity, thermal conductivity, and thermal diffusivity, followed by the accurate calculation of water content and soil heat capacity. The limited comparison here demonstrates substantially improved substrate water content determination accuracy from the EM sensor (RSME = 0.012 cm3/cm ^3) when compared with heat-pulse estimates (RSME = 0.042 cm3/cm3). [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]

Soil moisture content (SMC) and above-ground biomass (AGB) are key parameters for the understanding of both the hydrological and carbon cycles. From an economical perspective, both SMC and AGB play a significant role in the agricultural sector, one of the most relevant markets worldwide. This paper assesses the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to soil moisture and vegetation biomass from an experimental point of view. For that, three scientific flights were performed in order to acquire GNSS reflectometry (GNSS-R) polarimetric observations over a wide range of terrain conditions. The GNSS-R data were used to obtain the right–left and right–right reflectivity components, which were then georeferenced according to the transmitting GNSS satellite and receiver positions. It was determined that for low-altitude GNSS-R airborne platforms, the reflectivity polarization ratio provides a highly reliable observable for SMC due to its high stability with respect to surface roughness. A correlation coefficient \bf r^\bf 2 of 0.93 and a sensitivity of 0.2 dB/SMC (%) were obtained for moderately vegetated fields with a surface roughness standard deviation below 3 cm. Similarly, the copolarized reflection coefficient shows a stable sensitivity to forest AGB with \bf r^\bf 2 equal to 0.9 with a stable sensitivity of 1.5 dB/(100 t/ha) up to AGB values not detectable by other remote sensing systems. [ABSTRACT FROM PUBLISHER]

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

Applications, such as the long term in-situ monitoring of geotechnical structures and of agricultural soils, motivate the use of wireless sensors that are of a passive design. However, the effectiveness of such passive devices will be dependent on the power received via a forward interrogation link and the power returned to a data collector via a reverse link. For these applications, link losses will be determined by the transmission medium, i.e., soil. This paper presents a pathloss study of microwave attenuation versus soil type and moisture. Then, we present results for a passive frequency doubling reflectenna (FDR), operating at 1.3 GHz for the forward link and 2.6 GHz for the reverse, buried at various depths in agricultural soil of different moistures. Results indicate that a FDR-based wireless sensors operating in this frequency range could be effectively embedded to a depth of 0.5 m or more, significantly deeper than reported for other passive technologies. [ABSTRACT FROM PUBLISHER]

As a cost-effective remote sensing technique, global navigation satellite system reflectometry (GNSS-R) has recently drawn significant attention from both academia and industry. However, research on GNSS-R has mainly been focused on the global positioning system which consists of only medium earth orbit satellites, while the use of geostationary earth orbit (GEO) satellites, such as those in BeiDou navigation satellite system, has received little attention. This paper investigates the GEO-satellitebased GNSS-R with a focus on the application of soil moisture retrieval. Because GEO satellites remain static with the earth, the models of the reflected GNSS signals can be considerably simplified and their signals can be used to estimate soil moisture with a high update rate such as once per hour. Two new soil moisture estimation approaches using GEO signals are proposed, which are termed GEO interferometric reflectometry (GEO-IR) and GEO reflectometry (GEO-R). Two theoretical models (linear and second order) are developed for signal-to-noise ratio (SNR)-based GEO-IR as well as for phase-based GEO-IR. Meanwhile, two empirical models (linear and second order) are developed for signal amplitude-based GEO-R as well as for SNR ratiobased GEO-R. Experimental data sets collected from three different geographical regions were used to evaluate the proposed methods. The results demonstrate that the proposed GEO-IR and GEO-R are able to monitor soil moisture reliably under bare soil condition, augmenting GNSS-R through significantly reduced processing complexity and increased temporal coverage. [ABSTRACT FROM AUTHOR]

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

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]

Uncertainty in surface soil roughness strongly degrades the performance of surface soil moisture (SSM) and vegetation water content (VWC) retrieval from passive microwave observations. This paper proposes an algorithm to objectively determine the surface soil roughness parameter of the radiative transfer model by fusing microwave and optical satellite observations. It is then demonstrated in a semiarid in situ observation site. The roughness correction of this new algorithm positively impacted the performance of SSM (root-mean-square error reduced from 0.088 to 0.070) and VWC retrieval from the Advanced Microwave Scanning Radiometer 2 and Moderate Resolution Imaging Spectroradiometer. Since this surface soil roughness correction may be transferrable to other microwave satellite retrieval algorithms such as those for the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive satellites, this new algorithm can contribute to many microwave earth surface observation satellite missions. [ABSTRACT FROM PUBLISHER]

There is a need for scattering models that link quantitatively synthetic aperture radar (SAR) interferometric observables to soil moisture. In this paper, we propose a model based on plane waves and the Born approximation, deriving first the vertical complex wavenumbers in the soil as a function of geometrical and dielectric properties and successively the complex interferometric coherences. It is observed that soil moisture behaves on the phase in a similar way as tomography does, breaking the phase consistency in triplets of interferograms. The proposed model is validated with L-band airborne SAR data; preliminary inversion results based on interferogram triplets and coherence magnitudes are presented. [ABSTRACT FROM AUTHOR]

Surface upward longwave radiation (SULR) is a significant component of the surface radiation budget and is closely linked with evapotranspiration, soil moisture, and surface cooling on clear nights. Therefore, accurately estimating SULR is essential to better understand its spatiotemporal dynamics or to characterize the thermal environment of a given land surface. Currently, most methods for estimating SULR (including the physical and hybrid methods) fail to account for the thermal anisotropy, which can introduce significant errors into the calculation. We previously proposed the combined algorithm that considers the thermal anisotropy to more accurately estimate the SULR. However, this proposed method has several shortcomings. For example, it considers the directionality of the emissivity and the effective temperature separately under the support of a parametric directional emissivity model. However, the directional emissivity model is not maturely developed for different land surface types, especially on nonvegetated surfaces. And the separation of land surface temperature and emissivity may undermine the estimation accuracy. Furthermore, this proposed method requires a series of input parameters that is not always available, limiting its applicability. In this paper, we present a refined algorithm that uses a kernel-driven model and the technique of band conversion to calculate the SULR directly based on surface-leaving radiances. This direct physical algorithm is then applied to the Wide-angle infrared Dual-mode line/area Array Scanner data set and validated using longwave radiation data collected by automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results of these tests suggest that the direct algorithm works effectively. The root-mean-square error (RMSE) and mean bias error (MBE) of the direct algorithm on maize surfaces are 4.417 and 0.474 \text W\cdot ~\text m^-2 , respectively. When the thermal anisotropy is incorporated, the RMSE and absolute MBE decrease by a maximum of 4.734 and 7.414 \mathrm W\cdot \mathrm m^\mathrm -2 , respectively. Different land types yield different results: for vegetable surfaces, the estimation biases of the direct model are approximately -2\mathrm W\cdot \mathrm m^\mathrm -2 , whereas orchard surfaces yield biases are between −2 and -3.5\mathrm W\cdot \mathrm m^-2 , and village surfaces yield biases exceeding - 10~\mathrm W\cdot \mathrm m^-2 . These differences can be attributed to the varying effects of the kernel-driven model across different types of land surfaces. The RMSE and absolute MBE obtained using the direct algorithm are slightly smaller (0.587 and 1.685 \mathrm W\cdot \mathrm m^\mathrm -2 , respectively) than those obtained using the combined algorithm; they are also smaller than the results of the traditional temperature–emissivity algorithm (by 8.7 and 11.7 \mathrm W\cdot \mathrm m^-2 , respectively). [ABSTRACT FROM PUBLISHER]

Coarse ground resolution limits the accuracy of current passive L-band satellite missions for soil moisture retrieval such as soil moisture and ocean salinity and soil moisture active and passive, as many of their pixels contain different land cover types. One of the significant uncertainty factors is introduced by dense vegetation, especially in areas where land cover varies between forest and open areas, which is often the case in inhabited areas in the Boreal zone. In this paper, we study the soil moisture retrieval with the help of fine resolution L-band airborne radiometer in case of heterogeneous pixels in the boreal forest region, and attempt to quantify and isolate the influence of the different terrain types. The work is based on a series of airborne radiometer measurements with the HUT-2D interferometric radiometer in Finland. Radiometer-derived soil moisture values are compared with soil moisture estimates given by watershed simulation and forecasting system model, which is validated and refined by using in-situ measurements so that its accuracy is augmented. Our results suggest that it is possible to retrieve soil moisture in agricultural areas in a heterogeneous pixel with substantial (30 $\%$) forest cover achieving accuracy of 0.032 m $^3$/m $^3$ and that L-band radiometry may be useful in research of hydrological processes in wetlands. [ABSTRACT FROM PUBLISHER]

The Surface Moisture and Ocean Salinity (SMOS) mission senses ocean salinity and soil moisture by measuring Earth’s brightness temperature using interferometry in the L-band. These interferometry measurements known as visibilities constitute the SMOS L1A data product. Despite the L-band being reserved for Earth observation, the presence of illegal emitters causes radio frequency interference (RFI) that masks the energy radiated from the Earth and strongly corrupts the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this paper, we propose a variational model to recover superresolved, denoised brightness temperature maps by decomposing the images into two components: an image $T$ that models the Earth’s brightness temperature and an image $O$ modeling the RFIs. [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]