Backscattering Contributions Research Articles

Enhanced POLYMER atmospheric correction algorithm for water-leaving radiance retrievals from hyperspectral/multispectral remote sensing data in inland and coastal waters.

Accurate retrieval of the water-leaving radiance from hyperspectral/multispectral remote sensing data in optically complex inland and coastal waters remains a challenge due to the excessive concentrations of phytoplankton and suspended sediments as well as the inaccurate estimation and extrapolation of aerosol radiance over the visible wavelengths. In recent years, reasonably accurate methods were established to estimate the enhanced contribution of suspended sediments in the near-infrared (NIR) and shortwave infrared (SWIR) bands to enable atmospheric correction in coastal waters, but solutions to derive the dominant phytoplankton contribution in the NIR and SWIR bands are less generalizable and subject to large uncertainties in the remotely-derived water color products. These issues are not only associated with the standard atmospheric correction algorithm in the SeaDAS processing system but with the non-traditional algorithms such as POLYMER (POLYnomial-based approach established for the atmospheric correction of MERIS data). This study attempts to enhance the POLYMER algorithm to enable atmospheric correction of hyperspectral and multispectral remote sensing data over a wide range of inland and ocean waters. The original POLYMER algorithm is less suitable owing to its complete reliance on a polynomial approach to model the atmospheric reflectance as a function of the wavelength and retrieve the water-leaving reflectance using two semi-analytical models (MM01 and PR05). The polynomial functions calculate the bulk atmospheric contribution instead of using an explicit method to estimate aerosol radiance separately, resulting the erroneous water color products in inland and coastal waters. The modified POLYMER algorithm (mPOLYMER) employs more realistic approaches to estimate aerosol contributions with a combination of UV and Visible-NIR bands and enables accurate retrievals of water-leaving radiance from both hyperspectral and multispectral remote sensing data. To assess the relative performance and wider applicability of mPOLYMER, the original and enhanced algorithms were tested on a variety of HICO, MSI and MODIS-Aqua data and the retrieved Lwn products were compared with AERONET-OC and OOIL-regional in-situ data. Expectedly, the mPOLYMER algorithm greatly improved the accuracy of Lwn (in terms of magnitude and spectral shape) when applied to MODIS-Aqua and HICO data in highly turbid productive waters (with higher concentrations of phytoplankton or with dense algal blooms) in Muttukadu Lagoon, Lake Erie, Yangtze River Estuary, Baltic Sea and Arabian Sea. In contrast, the original POLYMER algorithm overestimated Lwn in the visible and NIR bands and produced unphysical negative Lwn or distorted Lwn spectra in turbid productive waters. The mPOLYMER yielded a relative mean error reduction of more than 50% (i.e., from 79% to 34%) in Lwn for a large number of matchup data. The improved accuracy and data quality is because the mPOLYMER algorithm's funio and coefficients sufficiently accounted for the enhanced backscattering contribution of phytoplankton and suspended sediments in optically complex waters.

Mapping nanoscale carrier confinement in polycrystalline graphene by terahertz spectroscopy

Terahertz time-domain spectroscopy (THz-TDS) can be used to map spatial variations in electrical properties such as sheet conductivity, carrier density, and carrier mobility in graphene. Here, we consider wafer-scale graphene grown on germanium by chemical vapor deposition with non-uniformities and small domains due to reconstructions of the substrate during growth. The THz conductivity spectrum matches the predictions of the phenomenological Drude–Smith model for conductors with non-isotropic scattering caused by backscattering from boundaries and line defects. We compare the charge carrier mean free path determined by THz-TDS with the average defect distance assessed by Raman spectroscopy, and the grain boundary dimensions as determined by transmission electron microscopy. The results indicate that even small angle orientation variations below 5° within graphene grains influence the scattering behavior, consistent with significant backscattering contributions from grain boundaries.

Incorporation of first-order backscattered power in Water Cloud Model for improving the Leaf Area Index and Soil Moisture retrieval using dual-polarized Sentinel-1 SAR data

The novel modification in the WCM (mWCM) is proposed in this study to simulate total backscattering contribution and to improve Leaf Area Index (LAI) and Soil Moisture (SM) retrieval using Sentinel-1 Single Look Complex (SLC) datasets at VV and VH polarizations for the wheat crop. The intended modification was achieved through two steps; (1) Proposing the scaling constants of vegetation (fveg), soil (fsoil) and vegetation-soil interaction (finter) within the traditional WCM. The scaling constants are dimensionless quantity and were derived utilizing the degree of polarization (which were computed using the Hermitian covariance matrix); (2) Incorporation of the first order scattering component derived from the novel Vegetation-Soil Scattering Model (VSSM) to the total backscattering within the traditional WCM. The aim of including the vegetation-soil interaction in land surface models is to predict the combined effect of vegetation and soil on the total backscattering. The model parameters (i.e., A, B, C, and E) were calibrated using a non-linear least square regression algorithm. The accuracy of the retrieved and measured LAI and SM is evaluated using the different statistical indicators, e.g., coefficient of determination (R2), Root Mean Square Error (RMSE), and Nash Sutcliffe Efficiency (NSE). The retrieval from mWCM produced better accuracy with lower error than traditional WCM. The forward simulation results of mWCM revealed a notably higher accuracy for the total simulated radar backscattered coefficient at the VH polarization (σ0VH). The VH results showed a high R2 = 0.86, a high NSE = 0.85, and a low RMSE = 0.51 dB, outperforming the simulated σ0VV with R2 = 0.84, NSE = 0.84, RMSE = 0.66 dB. Consequently, the inversion of the mWCM yielded significantly improved accuracy in retrieving LAI at the VH polarization. The VH retrieval results exhibited a R2 = 0.80, NSE = 0.78, and RMSE = 0.44 m2m−2, while the VV polarization achieved an R2 = 0.78, NSE = 0.77, and RMSE = 0.53 m2m−2 for LAI estimation. In SM retrieval, higher accuracy was observed at VV polarization with R2 value of 0.77, NSE value = 0.79, and RMSE = 0.048 m3m−3 than the VH polarization with R2 = 0.75, NSE = 0.76, and RMSE = 0.050 m3m−3.

Comparison of Coherent to Incoherent Kirchhoff-Ray-Mode (KRM) Models in Predicting Backscatter by Swim-Bladder-Bearing Fish

The Kirchhoff-Ray-Mode (KRM) model has been used in fishery acoustics for more than two decades to predict backscatter by individuals or aggregations of swimbladdered fish. Backscatter contributions from a fluid-like fish body and gas-filled swimbladder are coherently summed to estimate backscatter from the whole fish. For field applications, where morphologies and orientations of individual fish and their swimbladders are unknown, the coherent KRM model based on a limited number of X-ray images containing precise morphological information on swimbladders, body sizes and orientations may not provide statistically stable and robust backscatter predictions. An incoherent KRM model is proposed, which is independent of the exact knowledge of body–swimbladder acoustic interactions. When backscatter contributions from fish bodies and swimbladders are comparable, differences in coherent and incoherent KRM model target strength (TS) predictions can be as high as a few decibels. Incoherent TS predictions as a function of frequency and orientation are statistically more stable and less sensitive to variations in anatomy among individual fish than those predicted using the traditional coherent KRM model.

High-Resolution Coastal Winds From the NOAA Near Real-Time ASCAT Processor

The NOAA near real-time operational ASCAT ocean surface wind vectors are produced at 12.5 and 25 km swath grid resolutions. To avoid land contamination due to the relatively large footprint size, the wind data in the inner most coastal regions (~15 - 25 km from the coast) are excluded from the final product. To obtain more retrievals in the coastal regions we utilize measurements containing up to 50% land contribution ratio and employ a combination of the enhanced resolution processing technique and a coastal normalized radar cross section correction method to achieve accurate wind retrievals to within ~1.0 - 2.5 km of the coast. The backscatter correction is implemented for each ASCAT coastal zone measurement. The mean and standard deviation of the land contribution for each measurement is determined, and then the land backscatter contribution is subtracted out from the actual measurement through an iteration method to estimate the ocean-only signal. An ocean calibration of the enhanced resolution backscatter was implemented before the wind retrieval step to improve the accuracy. Finally, a land contribution ratio ranking is implemented after the wind retrieval to further remove the remaining land contamination residuals. Additional quality control is also developed. The high-resolution coastal winds from ASCAT are validated against a variety of other independent wind measurements. The results are then presented and discussed.

Weak localization in disordered spin-1 chiral fermions

We theoretically investigate the quantum interference theory of magnetotransport of the three-component or spin-1 chiral fermions, which possess two linear Dirac bands and a flat band. For isotropic scalar impurities, the correction of conductivity from the coherent backscatter and non-coherent backscatter contributions cancel out in the intravalley scattering, leading to a weak localization correction to the Drude conductivity from the intervalley scattering. For the anisotropic impurities, the above cancelation is removed, we find the approximative quantum interference conductivity in the weak anisotropy case. The contributions from the chiral anomaly and classical Lorentz force are also discussed. Our work reveals some intriguing and detectable transport signatures of the novel spin-1 chiral fermions.

Desertification Extraction Based on a Microwave Backscattering Contribution Decomposition Model at the Dry Bottom of the Aral Sea

The fine particles produced during the desertification process provide a rich material source for sand and dust activities. Accurately locating the desertified areas is a prerequisite for human intervention in sand and dust activities. In arid and semi-arid regions, due to very sparse vegetation coverage, the microwave surface scattering model is very suitable for describing the variation of topsoil property during the process of desertification. However, the microwave backscattering coefficient (MBC) trend of the soil during the desertification process is still unclear now. Moreover, the MBC of a resolution unit usually involves the contribution of soil and vegetation. These problems seriously limit the application of microwave remote sensing technology in desertification identification. In this paper, we studied the soil MBC change trend during the desertification process and proposed a microwave backscattering contribution decomposition (MBCD) model to estimate the soil MBC of a resolution unit. Furthermore, a simple microwave backscattering threshold (SMSBT) model was established to describe the severity of desertification. The MBCD and SMSBT models were verified qualitatively through landscape photos of sampling points from a field survey in November 2018. The results showed that the MBC would gradually decline with the deepening degree of desertification. The MBCD model and the corresponding least squares method can be used to estimate the soil MBC accurately, and the SMSBT model can accurately distinguish different degrees of desertification. The results of desertification classification showed that more than 68% of the dry bottom of the Aral Sea is suffering from different degrees of desertification.

Variability of scattering and backscattering of marine particles in relation to particle concentration, size distribution, and composition off the eastern hainan coast in the south China sea

The light scattering and backscattering coefficients of particles (bp(λ) and bbp(λ)) and their association with particle concentration, size distribution, and composition off the eastern Hainan coast in the South China Sea (SCS) were analyzed between August and September 2013. Variations in bp(532) and bbp(532) were correlated to changes in the LISST-based total cross-sectional area and volume concentration of particles (AC and VC). Two significantly different covariations between bbp(λ) and bp(λ) and chlorophyll a concentration (Chla) were observed, and all samples were clearly separated into two groups (Group A and Group B) using the threshold relationship between bbp and Chla. Samples in Group B, most of which were collected in the nepheloid layer, showed higher bp/AC and bbp/AC than those in Group A. This indicates the presence of inorganic particles with higher np in Group B than in Group A. The relationship between bbp and Chla for Group A could be well fitted by a published model involving a constant backscattering background independent of Chla. A significant relationship (R2 = 0.85) was observed between bbp and Chla in the Chla maximum layer, which was different from that in other layers. This finding might imply that both an increase in the micro-phytoplankton community and a decrease in the contribution of background backscattering to bbp at the Chla maximum layer. Understanding this variability of beam-scattering properties off the eastern Hainan coast can improve our understanding of marine biogeochemical processes at a regional scale, and can also promote the application of these regional bio-optical relationships for ocean color.

Retinal blood flow reversal quantitatively monitored in out-of-plane vessels with laser Doppler holography

Laser Doppler holography is a planar blood flow imaging technique recently introduced in ophthalmology to image human retinal and choroidal blood flow non-invasively. Here we present a digital method based on the Doppler spectrum asymmetry that reveals the local direction of blood flow with respect to the optical axis in out-of-plane vessels. This directional information is overlaid on standard grayscale blood flow images to depict flow moving towards the camera in red and flow moving away from the camera in blue, as in ultrasound color Doppler imaging. We show that thanks to the strong contribution of backscattering to the Doppler spectrum in out-of-plane vessels, the local axial direction of blood flow can be revealed with a high temporal resolution, which enables us to evidence pathological blood flow reversals. We also demonstrate the use of optical Doppler spectrograms to quantitatively monitor retinal blood flow reversals.

One-dimensional scattering of two-dimensional fermions near quantum criticality

Forward scattering and backscattering play an exceptional role in the physics of two-dimensional interacting fermions. In a Fermi liquid, both give rise to a nonanalytic ${\ensuremath{\omega}}^{2}ln(\ensuremath{\omega})$ form of the fermionic scattering rate at second order in the interaction. Here we argue that higher powers of $ln(\ensuremath{\omega})$ appear in the backscattering contribution at higher orders. We show that these terms come from ``planar'' processes, which are effectively one-dimensional. This is explicitly demonstrated by extending a Fermi liquid to the limit of $N\ensuremath{\gg}1$ fermionic flavors, when only planar processes survive. We sum the leading logarithms for the case of a two-dimensional Fermi liquid near a nematic transition, and we obtain an expression for the scattering rate at $T=0$ to all orders in the interaction. For a repulsive interaction, the resulting rate is logarithmically suppressed, and the result is valid down to $\ensuremath{\omega}=0$. For an attractive interaction, the ground state is an $s$-wave superconductor with a gap ${\mathrm{\ensuremath{\Delta}}}_{0}$. We show that in this case the scattering rate increases as $\ensuremath{\omega}$ is reduced toward ${\mathrm{\ensuremath{\Delta}}}_{0}$. At $\ensuremath{\omega}\ensuremath{\ge}{\mathrm{\ensuremath{\Delta}}}_{0}$, the behavior of the scattering rate is rather unconventional as many pairing channels compete near a nematic critical point, and the $s$-wave wins only by a narrow margin. We take superconductivity into consideration and obtain the scattering rate also at smaller $\ensuremath{\omega}\ensuremath{\simeq}{\mathrm{\ensuremath{\Delta}}}_{0}$.

Interpretation of Spectral LiDAR Backscattering off the Florida Coast

A multispectral backscattering LiDAR (Light detection and range) system (hereafter Oculus) was integrated into a wave glider and used to estimate the scattering order (i.e., single vs multiple collisions) of LIDAR backscattering, the water inherent optical properties (IOPs), the biogeo-chemical characteristics of particulate scatterers (i.e., relative size, composition) and their motion) on shelf waters of South East Florida. Oculus has a dual-wavelength configuration (473 and 532 nm) and two detection geometries (off- and on-axis). Characteristics of scatterers were investigated based on two complementary LiDAR-derived proxies (the Structural Dissimilarity Index and the spectral slope of LiDAR backscattering). In March 2017, field measurements showed a covariation between direct and diffuse backscattering contributions during morning hours and away from shore. LiDAR attenuation coefficients explained up to 57% of IOPs variability. The analysis of LiDAR-derived proxies suggested higher turbidity and larger particulates near the coast

Ka-Band Radar Cross-Section of Breaking Wind Waves

The effective normalized radar cross section (NRCS) of breaking waves, σwb, is empirically derived based on joint synchronized Ka-band radar and video records of the sea surface from a research tower. The σwb is a key parameter that, along with the breaker footprint fraction, Q, defines the contribution of non-polarized backscattering, NP =σwbQ, to the total sea surface NRCS. Combined with the right representation of the regular Bragg and specular backscattering components, the NP component is fundamental to model and interpret sea surface radar measurements. As the first step, the difference between NRCS values for breaking and non-breaking conditions is scaled with the optically-observed Q and compared with the geometric optics model of breaker backscattering. Optically-derived Q might not be optimal to represent the effect of breaking waves on the radar measurements. Alternatively, we rely on the breaking crest length that is firmly detected by the video technique and the empirically estimated breaker decay (inverse wavelength) scale in the direction of breaking wave propagation. A simplified model of breaker NRCS is then proposed using the geometric optics approach. This semi-analytical model parameterizes the along-wave breaker decay from reported breaker roughness spectra, obtained in laboratory experiments with mechanically-generated breakers. These proposed empirical breaker NRCS estimates agree satisfactorily with observations.

An approach for soil moisture estimation using urban and vegetation fraction cover from coarse resolution Scatsat-1 data

Soil moisture (SM) estimation using coarse resolution satellite data, like Scatsat-1 scatterometer data (2 km spatial resolution), is still challenging due to various land cover classes present within a single pixel. To estimate SM from coarse resolution data, there is a necessity to study the contribution of different land covers to the total backscatter signal of the single pixel. The estimation of SM is very much dependent on the accuracy of the backscatter signal from the soil component. Therefore, this paper proposes a modified water cloud model to compute the soil-backscattering signal by correcting the backscatter contribution due to urban and vegetation fraction cover. The backscattering signal from the soil surface is then used to retrieve SM. The retrieved SM is compared with the ground truth SM using the error metrics such as root mean square error (RMSE), mean bias (b), correlation coefficient (r), and average relative error (ARE). The proposed model is tested in three conditions: first, when only vegetation correction is applied, RMSE, mean bias, r, and ARE are found to be 0.086 m3/m3, 0.70 m3/m3, 0.534, and 0.24, respectively, and results show an overestimation in SM. Second, when both vegetation and urban correction are applied using the actual fraction, the accuracy is improved with an RMSE, mean bias, and ARE of 0.071 m3/m3 , 0.056 m3/m3, and 0.20, respectively. However, in the third condition, when the urban fraction cover is considered twice the actual value, the RMSE and mean bias are found to be 0.22 m3/m3 and −0.10 m3/m3, respectively, and results show an underestimation in SM.

Using Microwave Profile Radar to Estimate Forest Canopy Leaf Area Index: Linking 3D Radiative Transfer Model and Forest Gap Model

Profile radar allows direct characterization of the vertical forest structure. Short-wavelength, such as Ku or X band, microwave data provide opportunities to detect the foliage. In order to exploit the potential of radar technology in forestry applications, a helicopter-borne Ku-band profile radar system, named Tomoradar, has been developed by the Finnish Geospatial Research Institute. However, how to use the profile radar waveforms to assess forest canopy parameters remains a challenge. In this study, we proposed a method by matching Tomoradar waveforms with simulated ones to estimate forest canopy leaf area index (LAI). Simulations were conducted by linking an individual tree-based forest gap model ZELIG and a three-dimension (3D) profile radar simulation model RAPID2. The ZELIG model simulated the parameters of potential local forest succession scene, and the RAPID2 model utilized the parameters to generate 3D virtual scenes and simulate waveforms based on Tomoradar configuration. The direct comparison of simulated and collected waveforms from Tomoradar could be carried out, which enabled the derivation of possible canopy LAI distribution corresponding to the Tomoradar waveform. A 600-m stripe of Tomoradar data (HH polarization) collected in the boreal forest at Evo in Finland was used as a test, which was divided into 60 plots with an interval of 10 m along the trajectory. The average waveform of each plot was employed to estimate the canopy LAI. Good results have been found in the waveform matching and the uncertainty of canopy LAI estimation. There were 95% of the plots with the mean relative overlapping rate (RO) above 0.7. The coefficients of variation of canopy LAI estimates were less than 0.20 in 80% of the plots. Compared to lidar-derived canopy effective LAI estimation, the coefficient of determination was 0.46, and the root mean square error (RMSE) was 1.81. This study established a bridge between the Ku band profile radar waveform and the forest canopy LAI by linking the RAPID2 and ZELIG model, presenting the uncertainty of forest canopy LAI estimation using Tomoradar. It is worth noting that since the difference of backscattering contribution is caused by both canopy structure and tree species, similar waveforms may correspond to different canopy LAI, inducing the uncertainty of canopy LAI estimation, which should be noticed in forest parameters estimation with empirical methods.

Sentinel-1 based soil freeze/thaw estimation in boreal forest environments

A method for the retrieval of soil freeze/thaw (F/T) state in the boreal forest region using SAR is presented in this paper. The method utilizes Sentinel-1 data and is thus suitable for continuous near real-time monitoring. The main challenge with the C-band VV-polarization signal is the sensitivity to vegetation and especially to forest canopies. A relatively simple zeroth-order model is used for the retrieval of the ground and the canopy backscatter contributions in 1 km cell size. These backscatter components are then used to identify the F/T state of the soil by comparing them to corresponding reference values representing frozen and thawed conditions. The classification algorithm is based on threshold values applied on the Euclidian distances between the retrieved backscatter and the reference values. The method is tested for three test areas across Finland, having different forest properties: Sodankylä, Nurmes and Tampere, located in northern, central and southern Finland, respectively. We first evaluated whether the use of canopy cover (CC) or stem volume (SV) as the parameter describing the forest conditions provide better model accuracy. We then assessed the Sentinel-1 based soil F/T estimates by comparing them to automatic in situ observations and the SMOS (Soil Moisture and Ocean Salinity) based soil F/T product. The model performance was generally better when SV was used as the forest parameter. Nevertheless, for both CC and SV, the RMSE between the modeled and the observed backscatter was considerably lower than the seasonal variation of the backscatter. In Sodankylä and Nurmes, the Sentinel-1 based F/T estimates were well in line with the in situ observations and the SMOS F/T product. The Sentinel-1 retrievals measuring the top soil layer were fast to react to air temperature changes between negative and positive Celsius degrees, showing similarity of 94–99% with the air temperature measurements. In Tampere the method showed weaker results; the similarity with the air temperature observations was 64%. Overall, a correct vertical freezing pattern of the soil was demonstrated in this study, with Sentinel-1 sensitive to the top soil layer, in situ sensors measuring at 5 cm depth, and SMOS reaching to 5–15 cm soil depth. Additional assessment should be conducted in southern Finland.

PolInSAR decorrelation-based decomposition modelling of spaceborne multifrequency SAR data

ABSTRACT The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features.

The backscattering contribution of soybean pods at L-band

L-band (1.25 GHz) radar measurements of a soybean canopy indicate that the emergence of seed pods is a significant contributor to the backscatter during the late stages of the growing season. In order to validate the measured data, a realistic scattering model of the soybean canopy is developed. The parameters of the soybean canopy and underlying soil used in the model vary over the growing season based on in situ measurements. Scattering amplitudes for soybean leaves are modeled analytically by using a thin disk approximation; stem and pods are jointly modeled using a numerical electromagnetic field solver. These scattering amplitudes are together incorporated into a coherent scattering model to obtain the backscattering coefficient for VV- and HH-polarizations. The modeling results show good agreement with the radar field measurements, having RMSEs of 0.51 dB for VV-pol and 1.1 dB for HH-pol. Both measured data and modeled results show that the change of soil moisture can be accurately monitored by L-band backscatter. It is also found that the difference between HH- and VV-polarized backscatter increases as the size of the soybean pods becomes larger. A method is developed here to estimate the number of pods in a soybean canopy based on polarimetric radar backscatter at L-band.

Grain Size Dependence of Brightness Phase Curves of the Lunar Surface

AbstractThe lunar opposition effect is the increase of brightness on the lunar surface when the phase angle approaches 0°, which is caused mainly by shadow‐hiding and coherent backscattering. However, the contribution of coherent backscattering is not yet well established. This study analyzes the contribution of coherent backscattering to the opposition effect using the correlation between the contribution of increased brightness of the theoretical coherent backscattering and the grain size of the reflecting surface. We analyze the correlation between the two using data from the Lunar Reconnaissance Orbiter Wide Angle Camera and median grain size d calculated by linear polarization observations. The size of effective scatterers of coherent backscattering is similar to the observation wavelength. In this case, the effective scatterer size for coherent backscattering is 0.6 μm. The results of our analyses show that there is no correlation between d and reflectance phase curve in the phase angle range from 0° to 6°. In addition, we analyze the distribution of grain size of lunar samples collected during the Apollo and Luna missions. We find an inversely proportional correlation between mean grain sizes and submicron grain content in the lunar samples. Therefore, d seems to indicate the content of effective scatterer. The strength of the coherent backscattering opposition effect seems to be affected by the content of the submicron grains. Thus, the lack of correlation between d and the opposition effect seems to imply minimal contribution of coherent backscattering to the lunar opposition effect.

Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia.

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: (i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only; (ii) using SAR VV and the standard deviation of surface heights (), and (iii) SAR VV, , and optimal surface correlation length (). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters ( and ) were incorporated into the prediction model. Consequently, the ANN’s prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE) = 0.026 cm3/cm3, and r = 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas.

The Capability of Integrating Optical and Microwave Data for Detecting Soil Moisture in an Oasis Region

In the earth ecosystem, surface soil moisture is an important factor in the process of energy exchange between land and atmosphere, which has a strong control effect on land surface evapotranspiration, water migration, and carbon cycle. Soil moisture is particularly important in an oasis region because of its fragile ecological environment. Accordingly, a soil moisture retrieval model was conducted based on Dubois model and ratio model. Based on the Dubois model, the in situ soil roughness was used to simulate the backscattering coefficient of bare soil, and the empirical relationship was established with the measured soil moisture. The ratio model was used to eliminate the backscattering contribution of vegetation, in which three vegetation indices were used to characterize vegetation growth. The results were as follows: (1) the Dubois model was used to calibrate the unknown parameters of the ratio model and verified the feasibility of the ratio model to simulate the backscattering coefficient. (2) All three vegetation indices (Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Enhanced Vegetation Index (EVI)) can represent the scattering characteristics of vegetation in an oasis region, but the VWC vegetation index is more suitable than the others. (3) Based on the Dubois model and ratio model, the soil moisture retrieval model was conducted, and the in situ soil moisture was used to analyze the accuracy of the simulated soil moisture, which found that the soil moisture retrieval accuracy is the highest under VWC vegetation index, and the coefficient of determination is 0.76. The results show that the soil moisture retrieval model conducted on the Dubois model and ratio model is feasible.