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In this theoretical paper, a method for temperature profile retrieval in frozen tundra soil is proposed based on the thermoevolutionary relationship between polarimetric multi-angular brightness temperature (TB) at 1.4 GHz, observed in sliding time window with a fixed duration, and the time series of soil temperature profiles, predicted by the heat equation. For TB modelling, the profiles of volumetric moisture, dry bulk density, and organic matter content in the active layer, measured at the Franklin Bluffs test site (North slope of Alaska, U.S.) from 12 December 2022 to 5 May 2023, were used. When solving the inverse problem, the retrieving temperature profiles were specified as a linear interpolation function between four known depths 0 cm, 25 cm, 50 cm, and 95 cm. The thermodynamic properties of the frozen active layer were considered independent of soil temperature, time, and vertical coordinates, which made it possible, based on weather station data, to determine the apparent value of the thermal diffusivity coefficient in the heat equation. The practically significant accuracy of retrieving soil temperature up to a depth of 70 cm with a root-mean-square error of 0.5–1.8°C and a coefficient of determination of 0.877–0.988 was demonstrated by the proposed method. The pursued research shows the advantage of the synergy of the heat equation and polarimetric multi-time compared to only single-moment TB observations for temperature profile retrieval in frozen tundra soil. [ABSTRACT FROM AUTHOR]

Tidal marshes play a globally critical role in carbon and hydrologic cycles by sequestering carbon dioxide from the atmosphere and exporting dissolved organic carbon to connected estuaries. These ecosystems provide critical habitat to a variety of fauna and also reduce coastal flood impacts. Accurate characterization of tidal marsh inundation dynamics is crucial for understanding these processes and ecosystem services. In this study, we developed remote sensing-based inundation classifications over a range of tidal stages for marshes of the Mid-Atlantic and Gulf of Mexico regions of the United States. Inundation products were derived from C-band and L-band synthetic aperture radar (SAR) imagery using backscatter thresholding and temporal change detection approaches. Inundation products were validated with in situ water level observations and radiometric modeling. The Michigan Microwave Canopy Scattering (MIMICS) radiometric model was used to simulate radar backscatter response for tidal marshes across a range of vegetation parameterizations and simulated hydrologic states. Our findings demonstrate that inundation classifications based on L-band SAR—developed using backscatter thresholding applied to single-date imagery—were comparable in accuracy to the best performing C-band SAR inundation classifications that required change detection approaches applied to time-series imagery (90.0% vs. 88.8% accuracy, respectively). L-band SAR backscatter threshold inundation products were also compared to polarimetric decompositions from quad-polarimetric Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2) and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) imagery. Polarimetric decomposition analysis showed a relative shift from volume and single-bounce scattering to double-bounce scattering in response to increasing tidal stage and associated increases in classified inundated area. MIMICS modeling similarly showed a relative shift to double-bounce scattering and a decrease in total backscatter in response to inundation. These findings have relevance to the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, as threshold-based classifications of wetland inundation dynamics will be employed to verify that NISAR datasets satisfy associated mission science requirements to map wetland inundation with classification accuracies better than 80% at 1 hectare spatial scales. [ABSTRACT FROM AUTHOR]

Monitoring the water status of forests is paramount for assessing vegetation health, particularly in the context of increasing duration and intensity of droughts. In this study, a methodology was developed for estimating forest water potential at the canopy scale from ground-based L-band radiometry. The study uses radiometer data from a tower-based experiment of the SMAPVEX 19-21 campaign from April to October 2019 at Harvard Forest, MA, USA. The gravimetric and the relative water content of the forest stand was retrieved from radiometer-based vegetation optical depth. A model-based methodology was adapted and assessed to transform the relative water content estimates into values of forest water potential. A comparison and validation of the retrieved forest water potential was conducted with in situ measurements of leaf and xylem water potential to understand the limitations and potentials of the proposed approach for diurnal, weekly and monthly time scales. The radiometer-based water potential estimates of the forest stand were found to be consistent in time with rPearson correlations up to 0.6 and similar in value, down to RMSE = 0.14 [MPa], compared to their in situ measurements from individual trees in the radiometer footprint, showing encouraging retrieval capabilities. However, a major challenge was the bias between the radiometer-based estimates and the in situ measurements over longer times (weeks & months). Here, an approach using either air temperature or soil moisture to update the minimum water potential of the forest stand ($\text{FW}{{\mathrm{P}}_{\text{min}}}$) was developed to adjust the mismatch. These results showcase the potential of microwave radiometry for continuous monitoring of plant water status at different spatial and temporal scales, which has long been awaited by forest ecologists and tree physiologists.

Soil moisture maps provide quantitative information that, along with climate and energy balance, is critical to integrate with hydrologic processes for characterizing landscape conditions. However, soil moisture maps are difficult to produce for natural landscapes because of vegetation cover and complex topography. Satellite-based L-band microwave sensors are commonly used to develop spatial soil moisture data products, but most existing L-band satellites provide only coarse scale (one to tens of kilometers grid size), information that is unsuitable for measuring soil moisture variation at hillslope or watershed-scales. L-band sensors are typically deployed on satellite platforms and aircraft but have been too large to deploy on small uncrewed aircraft systems (UAS). There is a need for greater spatial resolution and development of effective measures of soil moisture across a variety of natural vegetation types. To address these challenges, a novel UAS-based L-band radiometer system was evaluated that has recently been tested in agricultural settings. In this study, L-band UAS was used to map soil moisture at 3–50-m (m) resolution in a 13 square kilometer (km2) mixed grassland-forested landscape in Sonoma County, California. The results represent the first application of this technology in a natural landscape with complex topography and vegetation. The L-band inversion of the radiative transfer model produced soil moisture maps with an average unbiased root mean squared error (ubRMSE) of 0.07 m3/m3 and a bias of 0.02 m3/m3. Improved fine-scale soil moisture maps developed using UAS-based systems may be used to help inform wildfire risk, improve hydrologic models, streamflow forecasting, and early detection of landslides. [ABSTRACT FROM AUTHOR]

Accurate surface soil moisture (SM) data are crucial for agricultural management in Jiangsu Province, one of the major agricultural regions in China. However, the seasonal performance of different SM products in Jiangsu is still unknown. To address this, this study aims to evaluate the applicability of four L-band microwave remotely sensed SM products, namely, the Soil Moisture Active Passive Single-Channel Algorithm at Vertical Polarization Level 3 (SMAP SCA-V L3, hereafter SMAP-L3), SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB), Soil Moisture and Ocean Salinity in version IC (SMOS-IC), and SMAP-INRAE-BORDEAUX (SMAP-IB) in Jiangsu at the seasonal scale. In addition, the effects of dynamic environmental variables such as the leaf vegetation index (LAI), mean surface soil temperature (MSST), and mean surface soil wetness (MSSM) on the performance of the above products are investigated. The results indicate that all four SM products exhibit significant seasonal differences when evaluated against in situ observations between 2016 and 2022, with most products achieving their highest correlation (R) and unbiased root-mean-square difference (ubRMSD) scores during the autumn. Conversely, their performance significantly deteriorates in the summer, with ubRMSD values exceeding 0.06 m3/m3. SMOS-IC generally achieves better R values across all seasons but has limited temporal availability, while SMAP-IB typically has the lowest ubRMSD values, even reaching 0.03 m3/m3 during morning observation in the winter. Additionally, the sensitivity of different products' skill metrics to environmental factors varies across seasons. For ubRMSD, SMAP-L3 shows a general increase with LAI across all four seasons, while SMAP-IB exhibits a notable increase as the soil becomes wetter in the summer. Conversely, wet conditions notably reduce the R values during autumn for most products. These findings are expected to offer valuable insights for the appropriate selection of products and the enhancement of SM retrieval algorithms. [ABSTRACT FROM AUTHOR]

In view of the problem of heavy weight of the low-frequency band metamaterial absorber, the weight of the metamaterial absorber is mainly concentrated in the medium dielectric substrate. Different from the previous metamaterial absorbers based on continuous dielectric substrate, a new lightweight metamaterial absorber based on discontinuous dielectric medium is proposed and verified in the L-band (1–2 GHz). Compared with metamaterial absorber based on the continuous dielectric substrate, the weight of the metamaterial absorber designed based on discontinuous media is reduced by 42.58%. The dual-peak reflection losses of −17.29 dB and −15.99 dB are obtained at 1.23 GHz and 1.72 GHz, respectively. Our work provides a new method for constructing light dual-peak metamaterial absorbers in the L-band. [ABSTRACT FROM AUTHOR]

In this demonstration, we present two erbium-doped fiber (EDF) lasers, with series and parallel three sub-ring configurations, respectively, to achieve tunable channel output and stable single longitudinal mode (SLM) operation in the L-band range. Here, the fiber ring cavity contains the L-band EDF as a gain medium. Based on the measured results of the two quad-ring structures of the EDF lasers, tunable output bandwidth for the two lasers can be obtained from 1558.0 to 1618.0 nm simultaneously. All the 3 dB linewidths measured for both fiber lasers are 312.5 Hz over the effective wavelength output range. Furthermore, the related optical signal-to-noise ratio (OSNR), output power, output stabilities of the central wavelength and power, and equal output power range of the two proposed EDF lasers are also examined and discussed. [ABSTRACT FROM AUTHOR]

Soil Moisture (SM) is a key parameter in northern Arctic and sub-Arctic (A-SA) environments that are highly vulnerable to climate change. We evaluated six SM satellite passive microwave datasets using thirteen ground-based SM stations across Northwestern America. The best agreement was obtained with SMAP (Soil Moisture Active Passive) products with the lowest RMSD (Root Mean Square Difference) (0.07 m[Formula: see text] m[Formula: see text]) and the highest R (0.55). ESA CCI (European Space Agency Climate Change Initiative) also performed well in terms of correlation with a similar R (0.55) but showed a strong variation among sites. Weak results were obtained over sites with high water body fractions. This study also details and evaluates a dedicated retrieval of SM from SMOS (Soil Moisture and Ocean Salinity) brightness temperatures based on the [Formula: see text] model. Two soil dielectric models (Mironov and Bircher) and a dedicated soil roughness and single scattering albedo parameterization were tested. Water body correction in the retrieval shows limited improvement. The metrics of our retrievals (RMSD = 0.08 m[Formula: see text] m[Formula: see text] and R = 0.41) are better than SMOS but outperformed by SMAP. Passive microwave satellite remote sensing is suitable for SM retrieval in the A-SA region, but a dedicated approach should be considered.

The soil moisture products derived from the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions have garnered widespread adoption in drought surveillance, meteorological/climatic forecasting, and hydrological investigations. Nevertheless, satellite platforms inherently suffer from coarse spatial resolutions (approximately 43 km), stemming from constraints on antenna dimensions, thereby posing challenges in leveraging their data for agricultural and ecological endeavors that necessitate meter-scale soil moisture maps. This research endeavor innovatively employed a Portable L-band Radiometer (PoLRa), leveraging microstrip patch array antenna technology, to facilitate portable and cost-effective soil moisture monitoring within the context of the Hulunbeier Prairie Experiments in China. Three distinct soil moisture datasets were procured utilizing Scheme I, a PoLRa-based default iteration of the tau-omega semi-empirical model; Scheme II, a brightness temperature forward simulation akin to the CMEM (Community Microwave Emission Model) framework; and Scheme III, a regression-based approach. The findings are: 1. Employing the brightness temperature data as input, the CMEM-inspired schemes achieve soil moisture retrieval with an RMSE (Root Mean Square Error) of approximately 0.06 cm3/cm3, whereas the regression model between retrieved and observed data manifests a linear bias. 2. The CMEM forward simulation scheme outperforms the tau-omega model but necessitates more intricate modules for the retrieval process. 3. Both in terms of linear bias and RMSE, the regression-based scheme exhibits the poorest performance. This study anticipates enhancing the applicability of soil moisture remote sensing devices and methodologies across diverse disciplines, including agriculture, meteorology, and soil science, by advancing the precision and accessibility of soil moisture monitoring at finer scales.

Soil moisture maps provide quantitative information that, along with climate and energy balance, is critical to integrate with hydrologic processes for characterizing landscape conditions. However, soil moisture maps are difficult to produce for natural landscapes because of vegetation cover and complex topography. Satellite-based L-band microwave sensors are commonly used to develop spatial soil moisture data products, but most existing L-band satellites provide only coarse scale (one to tens of kilometers grid size), information that is unsuitable for measuring soil moisture variation at hillslope or watershed-scales. L-band sensors are typically deployed on satellite platforms and aircraft but have been too large to deploy on small uncrewed aircraft systems (UAS). There is a need for greater spatial resolution and development of effective measures of soil moisture across a variety of natural vegetation types. To address these challenges, a novel UAS-based L-band radiometer system was evaluated that has recently been tested in agricultural settings. In this study, L-band UAS was used to map soil moisture at 3–50-m (m) resolution in a 13 square kilometer (km2) mixed grassland-forested landscape in Sonoma County, California. The results represent the first application of this technology in a natural landscape with complex topography and vegetation. The L-band inversion of the radiative transfer model produced soil moisture maps with an average unbiased root mean squared error (ubRMSE) of 0.07 m3/m3 and a bias of 0.02 m3/m3. Improved fine-scale soil moisture maps developed using UAS-based systems may be used to help inform wildfire risk, improve hydrologic models, streamflow forecasting, and early detection of landslides.

An algorithm for classifying crop areas using multi-frequency Synthetic Aperture Radar (SAR) and optical data is evaluated for the upcoming NASA ISRO SAR (NISAR) mission and its active crop area products. Two time-series of L-band ALOS-2 and C-band Sentinel-1A images over an agricultural region in the Southern United States are used as the input, as well as high-resolution Planet optical data. To overcome the delay by at least one year of existing landcover maps, training and validation sets of crop/non-crop polygons are derived with the contemporary Planet images. The classification results show that the 80% requirement on the NISAR science accuracy is achievable only with L-band HV input and with a resolution of 100 m. In comparison, HH polarized images do not meet this target. The spatial resolution is a key factor: 100 m is necessary to accomplish the 80% goal, while 10 m do not produce the desired accuracy. Unlike the previous study reporting that C-band performs better than L-band, we found otherwise in this study. This suggests that the performance likely depends on the site of interest and crop types. Alternative to the SAR images, the Normalized Difference Vegetation Index (NDVI) from the Planet data is not effective either as an input to the classification algorithm or as ground truth for training the algorithm. The reason is that NDVI becomes saturated and temporally static, thus rendering crop pixels to be misclassified as non-crop. [ABSTRACT FROM AUTHOR]

This paper proposes a co-planar waveguide-fed circularly polarized L-band antenna for GNSS Applications. The Fibonacci series inspired us to decide the physical dimensions of the radiating structure. The proposed antenna with the dimensions of 60 × 40 × 0.8 mm3 is fabricated by etching out several slots from the patch and ground plane as well. The circular polarization (CP) behavior is observed from different shaped slots in the ground plane as well as in radiating patches. The experimental and simulation results revealed good agreement over the entire operating frequency range. [ABSTRACT FROM AUTHOR]

The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of this uncertainty. However, there is a scarcity of research in this field. Therefore, in this study, we computed surface melt in the vicinity of Automatic Weather Stations (AWSs) by employing Defense Meteorological Satellite Program (DMSP) Ka-band data and Soil Moisture and Ocean Salinity (SMOS) satellite L-band data and extracted SGL pixels by utilizing Sentinel-2 data. A comparison between surface melt results derived from AWS air temperature estimates and those obtained with remote sensing inversion in the two different bands was conducted for sites below the mean snowline elevation during the summers of 2016 to 2020. Compared with sites with no SGLs, the commission error (CO) of DMSP morning and evening data at sites where these water bodies were present increased by 36% and 30%, respectively, and the number of days with CO increased by 12 and 3 days, respectively. The omission error (OM) of SMOS morning and evening data increased by 33% and 32%, respectively, and the number of days with OM increased by 17 and 21 days, respectively. Identifying the source of error is a prerequisite for the improvement of surface melt algorithms, for which this study provides a basis. [ABSTRACT FROM AUTHOR]

Recently, two-dimensional (2D) materials have attracted increasing attention as promising saturable absorbers (SAs) due to their excellent optoelectronic properties, which enable ultrashort duration, low time jitter, and good stability. Here, we have successfully proposed and demonstrated an L-band (@1568 nm) mode-locked fiber laser assisted by GeS2–SA. A high signal-to-noise ratio (SNR) ultrafast pulse generation with weak correlation van der Waals GeS2 nanosheets has been prepared for the development of ultrashort pulse output. The modulation depth and saturation intensity of GeS2–SA are 6.1% and 0.012 MW/cm2, respectively. Nanoscale GeS2–SA-based erbium-doped fiber pulse laser is demonstrated with a high SNR of 71 dB and a pulse duration of 907 fs. This result extends the family of 2D material-based L-band pulsed lasers and proves that the GeS2 nanosheets hold promise for application in ultrafast optics. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a crucial environmental variable, and it plays an important role in energy and water cycles. SM data retrieval based on microwave satellite remote sensing has garnered significant attention due to its spatial continuity, wide observational coverage, and relatively low cost. Validating the accuracy of satellite remote sensing SM products is a critical step in enhancing data credibility, which plays a vital role in ensuring the effective application of satellite remote sensing data across various fields. Firstly, this study focused on Henan Province and evaluated the accuracy of the SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture (SPL3SMP_E) product along with its application in agriculture. The evaluation was based on in situ SM data from 55 stations in Henan Province. The assessment metrics used in this study include mean difference (MD), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the Pearson correlation coefficient (R). The time span of this study is from 2017 to 2020. The evaluation results indicated that the SPL3SMP_E soil moisture product performs well, as reflected by an ubRMSE value of 0.045 (m3/m3), which was relatively close to the product's design accuracy of 0.04 (m3/m3). Moreover, the accuracy of the product was unaffected by temporal factors, but the product exhibited strong spatial aggregation, which was closely related to land use types. Then, this study explored the response of the SPL3SMP_E product to irrigation signals. The precipitation and irrigation data from Henan Province were employed to investigate the response of the SPL3SMP_E soil moisture product to irrigation. Our findings revealed that the SPL3SMP_E soil moisture product was capable of capturing over 70% of irrigation events in the study area, indicating its high sensitivity to irrigation signals in this region. In this study, the SPL3SMP_E product was also employed for monitoring agricultural drought in Henan Province. The findings revealed that the collaborative use of the SPL3SMP_E soil moisture product and machine learning algorithms proves highly effective in monitoring significant drought events. Furthermore, the integration of multiple indices demonstrated a notable enhancement in the accuracy of drought monitoring. Such an evaluation holds significant implications for the effective application of satellite remote sensing SM data in agriculture and other domains. [ABSTRACT FROM AUTHOR]

P-band radiometry has been demonstrated to have a deeper sensing depth than L-band, making the consideration of multilayer microwave interactions necessary. In addition, the scattering and phase interference effects are different at the P-band, requiring a reconsideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L- and P-bands is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multilayer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L- and P-bands radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band and 2 K at L-band), correlating strongly with soil moisture variations at 0–10 cm. Findings suggest that coherent and incoherent models performed similarly; thus, incoherent models may be preferable for estimating TB at L- and P-bands due to reduced computational complexity.

The goal of this work is to assess the impact of polarimetric SAR decompositions for soil moisture retrieval, and identify the decomposition that performs best for varying vegetation covers and soil conditions. Seven polarimetric decompositions are applied to three L-band radar time-series to evaluate their relative performances for future inclusion within a soil moisture retrieval scheme. Three agricultural sites with different soil and vegetation characteristics are selected across a latitudinal gradient in America. Two time-series of quad-polarimetric data collected by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne instrument are considered for the first two sites, while quad-polarimetric images acquired by the SAOCOM-1A mission are examined for the third site. We extract a set of radar polarimetric descriptors, including the backscattering coefficients, to analyze their sensitivity to soil moisture and vegetation through correlation analysis. We also apply a simple linear regression model to each crop type and site for estimating soil moisture (or Soil Water Index) by alternatively considering a combination of the decomposition powers and of the total backscattering coefficients (${{\bm{\gamma }}}^0,\ {{\bm{\sigma }}}^0$). The linear regression analysis shows that the estimates are generally comparable in terms of linear correlation and root mean square error. Results also reveal that the sensitivity of polarimetric decomposition descriptors to soil moisture and vegetation parameters depend both on crop type and area of interest, without significant differences among the various decompositions tested in this study.

Passive microwave remote sensing is a vital tool for acquiring valuable information regarding the Earth's surface, with significant applications in agriculture, water management, forestry, and various environmental disciplines. Precision agricultural (PA) practices necessitate the availability of field-scale, high-resolution remote sensing data products. This study focuses on the design and development of a cost-effective, portable L-band microwave radiometer capable of operating from an unmanned aircraft system platform to measure high-resolution surface brightness temperature ($T_{B}$). This radiometer consists of a dual-polarized (Horizontal polarized, H-pol and Vertical polarized, V-pol) antenna and a software-defined radio-based receiver system with a 30 MHz sampling rate. The post-processing methodology encompasses the conversion of raw in-phase and quadratic (I&Q) surface emissions to radiation $T_{B}$ through internal and external calibrations. Radiometric measurements were conducted over an experimental site covering both bare soil within an agricultural field and a large water body. The results yielded a high-resolution $T_{B}$ map that effectively delineated the boundaries between land and water, and identified land surface features. The radiometric temperature measurements of the sky and blackbody demonstrated a standard deviation of 0.95 K for H-pol and 0.57 K for V-pol in the case of the sky and 0.39 K for both H-pol and V-pol in the case of the blackbody observations. The utilization of I&Q samples acquired via the radiometer digital back-end facilitates the generation of different time–frequency (TF) analyses through short-time Fourier transform and power spectral density (PSD). The transformation of radiometer samples into TF representations aids in the identification and mitigation of radio frequency interference originating from the instrument itself and external sources.

A new forward model (FM) was developed to characterize the influence of precipitation on L-band passive ocean surface measurements. The FM, which relates rain-induced brightness temperature (TB) variations to the rain rate and wind speed (WS), was established through a machine learning approach (referred to as the ML-FM). The soil moisture active passive (SMAP) data matched with integrated multisatellite retrievals for global precipitation measurement (IMERG) rain rate data and cross-calibrated multiplatform (CCMP) wind data were binned as a function of the rain rate, WS, and wind direction. The ML-FM was validated by comparing the simulated top-of-atmosphere (TOA) TB values with SMAP measurements. The results showed favorable agreement between the ML-FM outputs and SMAP data, with a root mean square error (RMSE) smaller than 0.55 K for both the horizontal and vertical polarizations. The validation results for ensuring more reasonable rainfall intensity distributions showed that the ML-FM returned stable results with a slightly reduced RMSE of ∼0.75 K for both the horizontal and vertical polarizations. Based on the ML-FM, we found that sea surface emission exhibited significant dependence on the rain rate for both polarizations. In addition, the ML-FM demonstrated signal saturation when the rain rate exceeded 45 mm/h, while precipitation slightly affected the directional characteristics of sea surface emission. These effects accounted for ∼0.3 K at a rain rate of 50 mm/h. Overall, our analyses demonstrated that the proposed ML-FM achieved superior performance in retrieving the TOA TB for both the vertical and horizontal polarizations with a higher accuracy than existing models.

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (SM), soil temperature, soil salinity, and vegetation, using L-band microwave brightness temperature (TB) observations and retrievals from the soil moisture ocean salinity (SMOS) and soil moisture active passive satellite missions, Catchment land surface model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based SM outperformed CLSM SM when evaluated against field data, and the forward L-band TB simulations derived from in situ SM and soil temperature performed better than those derived from CLSM estimates when evaluated against SMOS TB observations. The surface salinity for the sampled pixels was on average only 4 mg/g and only locally influenced the TB simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize TB simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multiangular SMOS TB data and constraints of CLSM SM and soil temperature. However, the latter modeled SM was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo, and surface roughness resulted in realistic values.

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data. [ABSTRACT FROM AUTHOR]

This paper designs an L-band wide stopband bandpass filter by applying low-temperature cofired ceramic (LTCC) technology to the global positioning system (GPS) frequency band. Taking the Chebyshev filter as a prototype, an equivalent collector element (capacitive and inductor) structure is adopted to fully use the three-dimensional package structure of LTCC to reduce the filter size. The filter is integrated into an eight-layer LTCC dielectric, and the series–parallel connection of the collector elements in the resonance unit is utilized to produce out-of-band transmission zeros, while the input and output ports' capacitance is adjusted to control the bandwidth. Harmonic suppression is achieved by cascading two new compact stopband filters, while the size increase is insignificant due to LTCC technology. The simulation results are as follows: the center frequency is 1.575 GHz, 1 dB relative bandwidth is 6.3%, insertion loss in the passband is as slight as 1.6 dB, return loss is better than 30 dB, rejection bandwidth up to 16 GHz is more than 44 dB, and the volume of the whole filter is 6.2 × 3.7 × 0.78 m m 3 . [ABSTRACT FROM AUTHOR]

P-band radiometry has been demonstrated to have a deeper sensing depth than L-band, making the consideration of multilayer microwave interactions necessary. In addition, the scattering and phase interference effects are different at the P-band, requiring a reconsideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L- and P-bands is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multilayer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L- and P-bands radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band and 2 K at L-band), correlating strongly with soil moisture variations at 0–10 cm. Findings suggest that coherent and incoherent models performed similarly; thus, incoherent models may be preferable for estimating TB at L- and P-bands due to reduced computational complexity. [ABSTRACT FROM AUTHOR]

In this demonstration, we present two erbium-doped fiber (EDF) lasers, with series and parallel three sub-ring configurations, respectively, to achieve tunable channel output and stable single longitudinal mode (SLM) operation in the L-band range. Here, the fiber ring cavity contains the L-band EDF as a gain medium. Based on the measured results of the two quad-ring structures of the EDF lasers, tunable output bandwidth for the two lasers can be obtained from 1558.0 to 1618.0 nm simultaneously. All the 3 dB linewidths measured for both fiber lasers are 312.5 Hz over the effective wavelength output range. Furthermore, the related optical signal-to-noise ratio (OSNR), output power, output stabilities of the central wavelength and power, and equal output power range of the two proposed EDF lasers are also examined and discussed.

A rigorous TomoSAR imaging procedure is proposed to acquire high-resolution L-band images of a forest in a local area of interest. A focusing function is derived to relate the backscattered signals to the reflectivity function of the forest canopies without resorting to calibration. A forest voxel model is compiled to simulate different tree species, with the dielectric constant modeled with the Maxwell-Garnett mixing formula. Five different inverse methods are applied on two forest scenarios under three signal-to-noise ratios in the simulations to validate the efficacy of the proposed procedure. The dielectric-constant profile of trees can be used to monitor the moisture content of the forest. The use of a swarm of unmanned aerial vehicles (UAVs) is feasible to carry out TomoSAR imaging over a specific area to pinpoint potential spots of wildfire hazards. [ABSTRACT FROM AUTHOR]

Based on laterally diffused metal-oxide-semiconductor (LDMOS) devices, a broadband RF power amplifier for L-band was developed. The amplifier is composed of two stages of amplification and cascade connection. In order to achieve broadband and good output standing wave, the final stage power amplifier adopts a balanced topology circuit structure; the inter-stage matching network uses the microstrip line and capacitor hybrid matching method to achieve broadband matching. The final measured data are as follows: the frequency covers 0.96 GHz~1.4 GHz, the overall output power of the power amplifier reaches 50 dBm (100 W), the power gain is greater than 30 dB, and the efficiency is greater than 45%. The power backoff is 8 dB, and when the output power is 42 dBm, the adjacent channel power ratio (ACPR) is -40 dBC. The indicators show that the power amplifier module can be well used in radar and wireless communication transmitters.

Salinity is a key parameter that affects the surface, deep circulations, and heat transport of oceans. Sea surface salinity (SSS) represents the salinity at the ocean surface and impacts atmosphere – ocean interactions and vertical ocean circulation. To monitor SSS, three passive microwave radiometers with an L-band (1.4 GHz) have been launched since 2009. The scientific need for SSS retrieval and estimation has grown in recent years; however, the operational retrieval of SSS via satellite remote sensing still faces significant challenges. This study provides a review of satellite-based SSS retrieval methods and guidelines to encourage future research. This paper introduces satellite-derived SSS research trends and summarizes the representative SSS satellite sensors and their retrieval methods. The limitations and challenges of satellite-derived SSS are then discussed. The errors from the retrieval algorithms, discrepancies in the spatio-temporal scales of in situ and remote sensing, and limitations of the satellite-derived SSS are then detailed. Finally, our paper provides suggestions for the future directions of SSS remote sensing in five ways: mitigation of measurement errors, improvement of currently available SSS products, enhancement of the usage of in situ data, reconstruction of three-dimensional salinity information, and synergetic uses of multi-satellite missions.

Radio frequency interference (RFI) represents all unwanted signals detected by radio receivers of a telescope. Unfortunately, the presence of RFI is significantly increasing with the technological development of wireless systems around the world. For this reason, RFI measurement campaigns are periodically necessary to map the RFI scenario around a telescope. The Sardinia Radio Telescope (SRT) is an Italian instrument that was designed to operate in a wide frequency band between 300 MHz and 116 GHz. One of the receivers of the telescope is a coaxial cryogenic receiver that covered a portion of the P and L bands (i.e., 305–410 MHz and 1300–1800 MHz) in its original version. Although the receiver was used for years to observe bright sources with sufficient results, its sub-bands can be redesigned considering the most recently evolved RFI scenario. In this paper, the results of a RFI measurement campaign are reported and discussed. On the basis of these results, the new sub-bands of the L-P receiver, together with the design of the new microwave filter selector block of the SRT receiver, are presented. In this way, SRT will cover up to 120 MHz and 460 MHz of −3 dB bandwidth at the P-band (290–410 MHz) and L-band (1320–1780 MHz), respectively. The bands of these filters are selected to reject the main RFI with high levels of amplitude and optimize the estimated antenna temperature and sensitivity of the receiver during the research activities, such as pulsar observations, very long baseline interferometer applications and spectroscopy science. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive study of the potential utilization of the L-band and S-band frequency ranges for satellite non-terrestrial network (NTN) technologies. This study encompasses an interference analysis in the S-band, investigating the coexistence of NTN satellite systems with mobile satellite networks such as Omnispace and Lyra, and an interference analysis in the L-band between NTN satellites and the mobile satellite network Inmarsat. This study simulates an NTN satellite network with typical characteristics defined by 3GPP and ITU-R for the n255 and n256 bands. Furthermore, it provides calculations illustrating the signal-to-noise ratio degradation of low-Earth-orbit (LEO), medium-Earth-orbit (MEO), and geostationary-Earth-orbit (GEO) satellite networks operating in the L-band and S-band when exposed to interference from NTN satellites. [ABSTRACT FROM AUTHOR]

The FMPL-2 payload on board the 3 Cat-5/A 6 Unit CubeSat, part of the FSSCat CubeSat mission, includes a dual L-Band Microwave Radiometer and a Global Navigation Satellite System Reflectometer, in one instrument, implemented in a Software Defined Radio. One of the design challenges of this payload was its Nadir looking Antenna, which had to be directive (> 12 dB), dual-band at 1400–1427 MHz and 1575.42 MHz, left-hand circularly polarized, and with important envelope restrictions, notably with a low profile. After a trade-off analysis, the best design solution appeared to be an array of six elements each of them being a stacked dual-band patch antenna, with diagonal feed to create the circular polarization, and a six to one stripline combiner. The design process of the elementary antennas first includes a theoretical analysis, to obtain the approximate dimensions. Then, by means of numerical simulations, prototyping, and adjusting the results in the simulations, the manufacturing errors and dielectric constant tolerances, to which patch antennas are very sensitive, can be characterized. A similar approach is taken with the combiner. This article includes the theoretical analysis, simulations, and prototype results, including the Flight Model assembly and characterization. • Dual-band circular polarization patch array with combiner for the FSSCat mission. • Designed for very constrained envelope restrictions, and to withstand adverse space conditions. • Presented: theoretical analysis, simulations, prototyping, and flight model. [ABSTRACT FROM AUTHOR]

As an emerging technique, P-band (0.3–1 GHz) may improve soil moisture remote sensing compared to L-band (1.4 GHz) Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, because of its greater moisture retrieval depth resulting from its longer wavelength. Consequently, a number of tower-based experiments were undertaken in VIC, Australia, to understand and quantify potential improvements. The study reported here has extended the evaluation of the tau-omega model to a scenario with a dense corn canopy whose vegetation water content (VWC) reached ~20 kg/m2, and compared the soil moisture retrieval performance at P- and L-band. Based on the locally calibrated parameters, the results from both the single-channel algorithm (SCA) and dual-channel algorithm (DCA) approaches presented a clear reduction in vegetation impact at the P-band compared to L-band. While the root-mean-square error (RMSE) for the P-band did not achieve the 0.04- $\text{m}^{3}/\text{m}^{3}$ target accuracy of SMOS and SMAP, i.e., 0.054 $\text{m}^{3}/\text{m}^{3}$ for the SCA and 0.074 $\text{m}^{3}/\text{m}^{3}$ for the DCA, this performance can be regarded as acceptable considering the extremely high VWC. In comparison, the RMSEs at L-band were larger than 0.1 $\text{m}^{3}/\text{m}^{3}$ for both the SCA and the DCA approaches. Additionally, DCA performed better in correlation coefficient and unbiased RMSE, while SCA performed better in RMSE at the P-band due to the larger bias when using DCA. Moreover, the calibrated vegetation parameters at the P-band were found to apply to broader conditions than those at the L-band, likely due to the reduced vegetation impact. [ABSTRACT FROM AUTHOR]

Spaceborne L-band data have the potential to monitor flooded and irrigated areas. However, further studies are needed to assess in real cases the impact of flood-irrigated crops on soil moisture and ocean salinity (SMOS) and soil moisture active passive (SMAP) surface soil moisture (SSM) data. This letter demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated areas at the seasonal scale and at a 25-km resolution in the Telangana State in southern India. Over irrigated areas, both SMOS level 3 (L3) SSM and SMAP L3 enhanced SSM products present a bimodal annual cycle, with a peak of SSM during the monsoon (wet) season corresponding to rainfall and irrigation, and a peak during the dry season due to irrigation activities solely. The second peak is absent or has a very small amplitude in areas where rice represents a small fraction (typically below 5%–10%). More importantly, the amplitude of the second SSM peak is significantly correlated with the rice cover fraction within $25\times25$ km2 pixels ($R = 0.81$ for SMOS and 0.77 for SMAP), showing its potential to assess crop fraction and hence the water used for irrigation. The SMOS/SMAP L3 SSM peak during the dry period occurs several months before the harvest, constituting an indicator for rice stocks at the end of the season. However, the irrigation signature is absent from the SMAP level 4 SSM product derived from the assimilation of SMAP brightness temperatures (Tbs) in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) systems are one of the best resources to gather information in polar environments, but the detection and monitoring of sea ice types and icebergs using them is still a challenge. Limitations using single-frequency images in sea ice characterization are well known, and using different SAR bands has been revealed to be useful. In this paper, we present the quantitative results of an intercomparison experiment conducted by the Argentine Naval Hydrographic Service (SHN) using X-, C-, and L-bands from COSMO-SkyMed, Sentinel-1, and SAOCOM satellites, respectively. The aim of the experiment was to evaluate SAOCOM for its use on SHN products. There were 25 images with different SAR parameters that were analyzed, incorporating the diversity in the information that everyday Ice Services attend to. Particularly, iceberg detections, fast first-year ice, and belts and strips were studied in the Antarctic Sound, the surroundings of Marambio Island, and Erebus and Terror Gulf. The results show that the HV polarization channel of the L-band provides useful information for iceberg detection and fast first-year ice surface feature recognition and is a promising frequency for the study of strip identification under windy sea conditions and snow accumulation on first-year ice.

This paper presents the design of a high-voltage pulse-based radar and a supervised data processing method for soil moisture estimation. The goal of this research was to design a pulse-based radar to detect changes in soil moisture using a cross-hole approach. The pulse-based radar with three transmitting antennas was placed into a 12 m deep hole, and a receiver with three receive antennas was placed into a different hole separated by 100 m from the transmitter. The pulse generator was based on a Marx generator with an LC filter, and for the receiver, the high-frequency data acquisition card was used, which can acquire signals using 3 Gigabytes per second. Used borehole antennas were designed to operate in the wide frequency band to ensure signal propagation through the soil. A deep regression convolutional network is proposed in this paper to estimate volumetric soil moisture using time-sampled signals. A regression convolutional network is extended to three dimensions to model changes in wave propagation between the transmitted and received signals. The training dataset was acquired during the period of 73 days of acquisition between two boreholes separated by 100 m. The soil moisture measurements were acquired at three points 25 m apart to provide ground truth data. Additionally, water was poured into several specially prepared boreholes between transmitter and receiver antennas to acquire additional dataset for training, validation, and testing of convolutional neural networks. Experimental results showed that the proposed system is able to detect changes in the volumetric soil moisture using Tx and Rx antennas. [ABSTRACT FROM AUTHOR]

Forests play a crucial part in regulating global climate change since their aboveground biomass (AGB) relates to the carbon cycle, and its changes affect the main carbon pools. At present, the most suitable available SAR data for wall-to-wall forest AGB estimation are exploiting an L-band polarimetric SAR. However, the saturation issues were reported for AGB estimation using L-band backscatter coefficients. Saturation varies depending on forest structure. Polarimetric information has the capability to identify different aspects of forest structure and therefore shows great potential for reducing saturation issues and improving estimation accuracy. In this study, 121 polarimetric decomposition observations, 10 polarimetric backscatter coefficients and their derived observations, and six texture features were extracted and applied for forest AGB estimation in a tropical forest and a boreal forest. A parametric feature optimization inversion model (Multiple linear stepwise regression, MSLR) and a nonparametric feature optimization inversion model (fast iterative procedure integrated into a K-nearest neighbor nonparameter algorithm, KNNFIFS) were used for polarimetric features optimization and forest AGB inversion. The results demonstrated the great potential of L-band polarimetric features for forest AGB estimation. KNNFIFS performed better both in tropical (R2 = 0.80, RMSE = 22.55 Mg/ha, rRMSE = 14.59%, MA%E = 12.21%) and boreal (R2 = 0.74, RMSE = 19.82 Mg/ha, rRMSE = 20.86%, MA%E = 20.19%) forests. Non-model-based polarimetric features performed better compared to features extracted by backscatter coefficients, model-based decompositions, and texture. Polarimetric observations also revealed site-dependent performances. [ABSTRACT FROM AUTHOR]

Precision irrigation management and crop water stress assessment rely on accurate estimation of root zone soil moisture. However, only the top 5 cm soil moisture can be estimated using the two current passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), which operate at L-band (wavelength of ∼21 cm). Since the contributing depth of the soil to brightness temperature increases with observation wavelength, it is expected that a P-band (wavelength of ∼40 cm) radiometer could potentially provide soil moisture information from deeper layers of the soil profile. Moreover, by combining both L- and P- bands, it is hypothesized that the soil moisture profile can be estimated even beyond their individual observation depths. The aim of this study was to demonstrate the potential of combined L-band and P-band radiometer observations to estimate the soil moisture profile under flat bare soil using a stratified coherent forward model. Brightness temperature observations at L-band and P-band from a tower based experimental site across a dry (April 2019) and a wet (March 2020) period, covering different soil moisture profile shapes, were used in this study. Results from an initial synthetic study showed that the performance of a combined L-band and P-band approach was better than the performance of using either band individually, with an average depth over which reliable soil moisture profile information could be estimated (i.e. with a target root mean square error (RMSE) of less than 0.04 m3/m3) being 20 cm for linear and 15 cm for second-order polynomial functions. Other functions were also tested but found to have a poorer performance. Applying the method to the tower-based brightness temperature achieved an average estimation depth of 28 cm (20 cm) and 5 cm (5 cm) during the dry and wet periods respectively when using a second-order polynomial (linear) function. These findings highlight the opportunity of a satellite mission with L-band and P-band observations to accurately estimate the soil moisture profile to as deep as 30 cm globally.

The Chinese BeiDou navigation satellite system (BDS) has already completed its three phases and developed into a global navigation satellite system for open positioning, navigation, and timing services. The BDS-3 satellites feature the inter-satellite link (ISL). The ISL observation-related error model and ambiguity resolution for L-band observation are the crucial factors in precise data processing. In this study, we present for the first time the impact of ambiguity resolution on phase center offsets (PCOs) and hardware delay estimation of BDS-3 inter-satellite links. Two weeks’ L-band observations from 99 globally distributed ground stations and Ka-band ISL observations are collected for experimental validation and analysis. First, network solutions with and without ISL observations are conducted to investigate the role of ISL observation in ambiguity resolution. Afterward, ISL observation-related errors, mainly PCOs and hardware delays, are estimated by processing L-band with ISL observations with and without ambiguity resolution to analyze the impact of ambiguity resolution on these two factors. Finally, orbit accuracy in the network solution is assessed to further validate the effectiveness of the estimated PCOs and hardware delays in our experiment. The result indicates that introducing the ISL can slightly improve the fixing rate compared to only L-band observations from 83.7% to 84.3%. Furthermore, ambiguity resolution has a positive influence on the stability of estimated PCOs and hardware delays in turn, although the root mean square (RMS) values basically remain unchanged. The standard deviation (STD) of the x-offset is reduced from 0.021 m to 0.012 m, a significant improvement of about 43%, and 0.022 m–0.016 m, with an improvement of about 27%, for the y-offset. There is a slight improvement of about 8% for z-offset. Similarly, around 10% improvement in the STD for hardware delays can be achieved while the RMS values almost stay the same except for C40. Orbit determination from network solutions shows high accuracy compared to the public products for the satellite with good geometry configuration, which further validates our estimates for ISL PCOs and hardware delays.

Operating at L-band (24 cm wavelength) in wide-swath modes is one of the characteristics of the new and next generation satellite synthetic aperture radar (SAR) missions. After 3 years of operation, the Japan Aerospace Exploration Agency Advanced Land Observing Satellite-2 (ALOS-2) satellite has acquired a wealth of L-band wide-swath SAR data over many areas using its ScanSAR mode. We present interferometry SAR (InSAR) time series analysis results from ALOS-2 ScanSAR data. We analyze the possible error sources in the InSAR and correct them if possible. We present different time series analysis results including azimuth frequency modulation rate error, line of sight (LOS) ionospheric phase, azimuth shift caused by the ionosphere, and LOS displacement processed using both full-aperture and burst-by -burst workflows. The final InSAR LOS displacement time series result reveals both large-scale tectonic and small-scale anthropogenic deformation components. The results demonstrate the potential for measuring continental or even global-scale tectonic deformation and illustrate the promise of upcoming L-band wide-swath SAR missions, such as the NASA-ISRO SAR mission.

A wavelength-switchable L-band erbium-doped fiber laser (EDFL) assisted by an artificially controlled backscattering (ACB) fiber reflector is here presented. This random reflector was inscribed by femtosecond (fs) laser direct writing on the axial axis of a multimode fiber with 50 μm core and 125 μm cladding with a length of 17 mm. This microstructure was placed inside a surgical syringe to be positioned in the center of a high-precision rotation mount to accurately control its angle of rotation. Only by rotating this mount, three different output spectra were obtained: a single wavelength lasing centered at 1574.75 nm, a dual wavelength lasing centered at 1574.75 nm and 1575.75 nm, and a single wavelength lasing centered at 1575.5 nm. All of them showed an optical signal-to-noise ratio (OSNR) of around 60 dB when pumped at 300 mW. [ABSTRACT FROM AUTHOR]

The biodiversity loss in Southeast Asia indicates an urgent need for long-term monitoring, which is lacking. Much attention is being directed toward bird diversity monitoring using remote sensing, based on relation to forest structure. However, few studies have utilized space-borne active microwave remote sensing, which has considerable advantages in terms of repetitive observations over tropical areas. Here, we evaluate threatened bird occurrence from L-band satellite data explaining forest structure in Sumatra, Indonesia. First, we identified L-band parameters with strong correlations with the forest layer structure, defined as forest floor, understory, and canopy layers. Then, we analyzed the correlation between threatened bird occurrence and L-band parameters identified as explaining forest structure. The results reveal that several parameters can represent the layers of forest floor, understory, and canopy. Subsequent statistical analysis elucidated that forest-dependent and threatened bird species exhibit significant positive correlations with the selected L-band parameters explaining forest floor and understory. Our results highlight the potential of applying microwave satellite remote sensing to evaluate bird diversity through forest structure estimation, although a more comprehensive study is needed to strengthen our findings. [ABSTRACT FROM AUTHOR]

Theoretical studies have shown that the use of simultaneous mono- and bistatic synthetic aperture radar (SAR) data could be beneficial to agriculture and soil moisture monitoring. This study makes use of extensive ground-truth measurements and synchronous high-resolution fully polarimetric mono- and bistatic airborne SAR data in L-band to assess and compare the sensitivity of mono- and multistatic systems to the maize canopy row structure and biophysical variables, as well as to soil moisture and surface roughness in both vegetated and bare fields. The effect of the row structure of maize crops is assessed through the impact of the orientation of the planting rows relative to the sensor beam on microwave scattering measurements. The results of this analysis suggest that the row orientation of maize crops has a significant influence on both mono- and bistatic scattering measurements in both copolarizations, and especially, in HH, while the cross polarizations are not affected. Furthermore, the study also shows through a linear regression analysis that bistatic data, even with a very small bistatic baseline, can provide valuable additional information for maize crop biophysical variable retrieval, which however does not appear to be the case for soil moisture retrieval over bare soils.