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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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

In the past decades, important research has been carried out to map the natural disturbances in the Paraná River Delta. The benefits of the combined use optical and radar data are also known. The main objective of this paper is to assess the wetland fire cartography through a synergetic use of radar and optical data. We focus on integrating radar (SAOCOM) and Sentinel 1, as well as Sentinel 2 optical data, concerning the fires impact analyses in the wetland areas. The generation of water masks through the radar images can contribute to improve the burned wetland area estimations. The relationship between landforms, vegetation cover, and the spatial/temporal resolution imposed by the flood pulse, play a vital role in the results. Burnt areas represent a total of 2439.57 sq km, which is more than 85% of the wetland, during the winter and spring (Q3 and Q4) periods. Understanding the wetland heterogeneity and its recovery pattern after a fire, is crucial to improve the cartography of the burned areas; for this, biweekly or monthly image compositions periodicity are of crucial importance. The inclusion of different indexes, for optical and radar images, improve the precision for the final classification. The results obtained here are promising for post-flood and post-fire evaluation, even applying radar and optical data integration into the evaluation and the monitoring of wetland fires is far from being a uniform standardized process. [ABSTRACT FROM AUTHOR]

This study investigates the potential of L- and C- bands Polarimetric Synthetic Aperture Radar (PolSAR) data to monitor soil moisture over the forested sites of SMAP Validation Experiment 2012 (SMAPVEX12). The optimal backscattering coefficients and polarimetric parameters to characterize the soil moisture were determined based on L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), C-band RADARSAT-2, and ground measurements composed of soil and vegetation parameters collected during SMAPVEX12. Linear and circular backscattering coefficients (σ0) and polarimetric parameters such as correlation coefficients (ρHHVV) and phase difference (φHHVV) between HH and VV, pedestal height (PH), entropy (H), anisotropy (A), α angle, surface (Ps), and double bounce (Pd) powers were used to develop the relationships with soil moisture. The analysis of these relationships shows that over the forested sites of SMAPVEX12: (a) at L-band several optimal backscattering coefficients and polarimetric parameters allow the monitoring of soil moisture, particularly the linear and circular σ0 (r = 0.60–0.96), Ps (r = 0.59–0.84), Pd (r = 0.60–0.82), ρHHHV_30°, ρVVHV_30°, φHHHV_30° and φHHVV_30° (r = 0.56–0.81). However, compared to the results obtained with σ0, there is no added value of the polarimetric parameters for soil moisture retrievals. (b) at C-band, only a few polarimetric parameters φHHHV, φVVHV, and φHHVV are correlated with soil moisture (r = ~0.90). They can contribute to soil moisture retrievals over forested sites when L-band data are not available. [ABSTRACT FROM AUTHOR]

This paper proposes an improved model-based forest height inversion method for airborne L-band dual-baseline repeat-pass polarimetric synthetic aperture radar interferometry (PolInSAR) collections. A two-layer physical model with various volumetric scattering attenuation and dynamic motion properties is first designed based on the traditional Random Motion over Ground (RMoG) model. Related PolInSAR coherence functions with both volumetric and temporal decorrelations incorporated are derived, where the impacts of homogenous and heterogeneous attenuation and dynamic motion properties on the performance of forest height inversion were investigated by the Linear Volume Attenuation (LVA), Quadratic Volume Attenuation (QVA), Linear Volume Motion (LVM), and Quadratic Volume Motion (QVM) depictions in the volume layer. Dual-baseline PolInSAR data were acquired to increase the degree of freedom (DOF) of the coherence observations and thereby provide extra constraints on the forest parameters to address the underdetermined problem. The experiments were carried out on a boreal forest in Canada and a tropical one in Gabon, where physical models with LVA + QVM (RMSE: 3.56 m) and QVA + LVM (RMSE: 6.83 m) exhibited better performances on the forest height inversion over the boreal and tropical forest sites, respectively. To leverage the advantages of LVA, QVA, LVM, and QVM, a pixel-wise optimization strategy was used to obtain the best forest height inversion performance for the range of attenuation and motion profiles considered. This pixel-wise optimization surpasses the best-performing single model and achieves forest height inversion results with an RMSE of 3.21 m in the boreal forest site and an RMSE of 6.48 m in the tropical forest site. [ABSTRACT FROM AUTHOR]

SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types. [ABSTRACT FROM AUTHOR]

L-band passive microwave remote sensing (RS) is an important tool for monitoring global soil moisture (SM) and freeze/thaw state. In recent years, progress has been made in its in-depth application and development in the Tibetan Plateau (TP) which has a complex natural environment. This paper systematically reviews and summarizes the research progress and the main applications of L-band passive microwave RS observations and associated SM retrievals on the TP. The progress of observing and simulating L-band emission based on ground-, aircraft-based and spaceborne platforms, developing regional-scale SM observation networks, as well as validating satellite-based SM products and developing SM retrieval algorithms are reviewed. On this basis, current problems of L-band emission simulation and SM retrieval on the TP are outlined, such as the fact that current evaluations of SM products are limited to a short-term period, and evaluation and improvement of the forward land emission model and SM retrieval algorithm are limited to the site or grid scale. Accordingly, relevant suggestions and prospects for addressing the abovementioned existing problems are finally put forward. For future work, we suggest (i) sorting out the in situ observations and conducting long-term trend evaluation and analysis of current L-band SM products, (ii) extending current progress made at the site/grid scale to improve the L-band emission simulation and SM retrieval algorithms and products for both frozen and thawed ground at the plateau scale, and (iii) enhancing the application of L-band satellite-based SM products on the TP by implementing methods such as data assimilation to improve the understanding of plateau-scale water cycle and energy balance. [ABSTRACT FROM AUTHOR]

Mangroves are a globally important ecosystem that provides a wide range of ecosystem system services, such as carbon capture and storage, coastal protection and fisheries enhancement. Mangroves have significantly reduced in global extent over the last 50 years, primarily as a result of deforestation caused by the expansion of agriculture and aquaculture in coastal environments. However, a limited number of studies have attempted to estimate changes in global mangrove extent, particularly into the 1990s, despite much of the loss in mangrove extent occurring pre-2000. This study has used L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA) for 11 epochs from 1996 to 2020 to develop a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. This approach was applied between all image-date pairs producing 10 maps for each epoch, which were summarised to produce the global mangrove time-series. The resulting mangrove extent maps had an estimated accuracy of 87.4% (95th conf. int.: 86.2–88.6%), although the accuracies of the individual gain and loss change classes were lower at 58.1% (52.4–63.9%) and 60.6% (56.1–64.8%), respectively. Sources of error included misregistration in the SAR mosaic datasets, which could only be partially corrected for, but also confusion in fragmented areas of mangroves, such as around aquaculture ponds. Overall, 152,604 km2 (133,996–176,910) of mangroves were identified for 1996, with this decreasing by −5245 km2 (−13,587–1444) resulting in a total extent of 147,359 km2 (127,925–168,895) in 2020, and representing an estimated loss of 3.4% over the 24-year time period. The Global Mangrove Watch Version 3.0 represents the most comprehensive record of global mangrove change achieved to date and is expected to support a wide range of activities, including the ongoing monitoring of the global coastal environment, defining and assessments of progress toward conservation targets, protected area planning and risk assessments of mangrove ecosystems worldwide. [ABSTRACT FROM AUTHOR]

The green area index (GAI) and the soil moisture under the canopy are two key variables for agricultural monitoring. The current most accurate GAI estimation methods exploit optical data and are rendered ineffective in the case of frequent cloud cover. Synthetic aperture radar (SAR) measurements could allow the remote estimation of both variables at the parcel level, on a large scale and regardless of clouds. In this study, several methods were implemented and tested for the simultaneous estimation of both variables using the water cloud model (WCM) and dual-polarized radar backscatter measurements. The methods were tested on the BELSAR-Campaign data set consisting of in-situ measurements of bio-geophysical variables of vegetation and soil in maize fields combined with multi-polarized C- and L-band SAR data from Sentinel-1 and BELSAR. Accurate GAI estimates were obtained using a random forest regressor for the inversion of a pair of WCMs calibrated using cross and vertical co-polarized SAR data in L- and C-band, with correlation coefficients of 0.79 and 0.65 and RMSEs of 0.77 m2 m−2 and 0.98 m2 m−2, respectively, between estimates and in-situ measurements. The WCM, however, proved inadequate for soil moisture monitoring in the conditions of the campaign. These promising results indicate that GAI retrieval in maize crops using only dual-polarized radar data could successfully substitute for estimates derived from optical data. [ABSTRACT FROM AUTHOR]

The 3 Cat-4 mission aims at demonstrating the capabilities of a CubeSat to perform Earth Observation (EO) by integrating a combined GNSS-R and Microwave Radiometer payload into a 1-Unit CubeSat. One of the greatest challenges is the design of an antenna that respects the 1-Unit CubeSat envelope while operating at the different frequency bands: Global Positioning System (GPS) L1 and Galileo E1 band (1575 MHz), GPS L2 band (1227 MHz), and the microwave radiometry band (1400–1427 MHz). Moreover, it requires between 8 and 12 dB of directivity depending on the band whilst providing at least 10 dB of front-to-back lobe ratio in L1 and L2 GPS bands. After a trade-off analysis on the type of antenna that could be used, a helix antenna was found to be the most suitable option to comply with the requirements, since it can be stowed during launch and deployed once in orbit. This article presents the antenna design from a radiation performance point of view starting with a theoretical analysis, then presenting the numerical simulations, the measurements in an Engineering Model (EM), and finally the final design and performance of the Flight Model (FM). [ABSTRACT FROM AUTHOR]

Featured Application: The novel, non-destructive technique of tooth dosimetry using L-band electron paramagnetic resonance (EPR) presented in this paper could be applied to obtain assessments of the environmental impact after possible radiation exposures from nuclear or radiological accidents. We applied a non-destructive tooth dosimetry technique using L-band electron paramagnetic resonance (EPR) to assess radiation doses in cattle due to the Fukushima Daiichi Nuclear Power Station (FDNPS) accident, which occurred 10 years ago. The radiation exposure of cattle in the area affected by the FDNPS accident was estimated retrospectively with X-band and L-band EPR devices. Characteristic radiation-induced EPR signals were obtained from the teeth of the cattle in Fukushima, confirming their exposure. The estimated doses to the teeth were found to be consistent with the dose trends estimated for individual cows, while considerable uncertainties were seen in the doses of some tooth samples. This variation might be due to errors in the accuracy of the method but also might reflect the actual exposure because the cattle may have been exposed to higher areas of radioactivity in their quest for food and/or due to irradiation from absorption of the isotopes with localization in or near the teeth. However, at a minimum, these results confirm that L-band EPR can be used for non-destructive qualitative assessment of radiation exposure to animals using their teeth, which could be very valuable. Possible causes of the uncertainties should be investigated to enhance the value of the use of this technique. [ABSTRACT FROM AUTHOR]

For InSAR topographic mapping, multi-baseline InSAR height estimation is known to be an effective way to facilitate phase unwrapping by significantly increasing the ambiguity intervals and maintaining good height measurement sensitivity, especially in mountainous areas. In this paper, an efficient multi-baseline SAR interferometry approach based on maximum likelihood estimation is developed for refined topographic mapping in mountainous areas. In the algorithm, maximum likelihood (ML) height estimation is used to measure the topographic details and avoid the complicated phase unwrapping process. In order to be well-adapted to the mountainous terrain conditions, the prior height probability is re-defined to take the local terrain conditions and neighboring height constraint into consideration in the algorithm. In addition, three strategies are used to optimize the maximum likelihood height estimation process to obtain higher computational efficiency, so that this method is more suitable for spaceborne InSAR data. The strategies include substituting a rational function model into the complicated conversion process from candidate height to interferometric phase, discretizing the continuous height likelihood probability, and searching for the maximum likelihood height with a flexible step length. The experiment with simulated data is designed to verify the improvement of the ML height estimation accuracy with the re-defined prior height distribution. Then the optimized processing procedure is tested with the multi-baseline L-band ALOS/PALSAR data covering the Mount Tai area in China. The height accuracy of the generated multi-baseline InSAR DEM can meet both standards of American DTED-2 and Chinese national 1:50,000 DEM (mountain) Level 2. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) provides the capability to detect surface deformation. Numerous processing approaches have been developed to improve InSAR results and overcome its limitations. Regardless of the processing methodology, however, temporal decorrelation is a major obstacle for all InSAR applications, especially over vegetated areas and dynamic environments, such as Interior Alaska. Temporal coherence is usually modeled as a univariate exponential function of temporal baseline. It has been, however, documented that temporal variations in surface backscattering due to the change in surface parameters, i.e., dielectric constant, roughness, and the geometry of scatterers, can result in gradual, seasonal, or sudden decorrelations and loss of InSAR coherence. The coherence models introduced so far have largely neglected the effect of the temporal change in backscattering on InSAR coherence. Here, we introduce a new temporal decorrelation model that considers changes in surface backscattering by utilizing the relative change in SAR intensity between two images as a proxy for the change in surface scattering parameters. The model also takes into account the decorrelation due to the change in snow depth between two images. Using the L-band Advanced Land Observation Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) data, the model has been assessed over forested and shrub landscapes in Delta Junction, Interior Alaska. The model decreases the RMS error of temporal coherence estimation from 0.18 to 0.09 on average. The improvements made by the model have been statistically proved to be significant at the 99% confidence level. Additionally, the model shows that the coherence of forested areas are more prone to changes in backscattering than shrub landscape. The model is based on L-band data and may not be expanded to C-band or X-band InSAR observations. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as forests, thus providing an opportunity to infer structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations of forest structure is addressed using simulated and experimental datasets. First, 3D reflectivity profiles were extracted by means of TomoSAR reconstruction based on a Compressive Sensing (CS) approach. Next, two complementary indices for the description of horizontal and vertical forest structure were defined and estimated by means of the distribution of local maxima of the reconstructed reflectivity profiles. To assess the sensitivity and consistency of the proposed methodology, variations of these indices for different types of forest changes in simulated as well as in real scenarios were analyzed and assessed against different sources of reference data: airborne Lidar measurements, high resolution optical images, and forest inventory data. The forest structure maps obtained indicated the potential to distinguish between different forest stages and the identification of different types of forest structure changes induced by logging, natural disturbance, or forest management. [ABSTRACT FROM AUTHOR]

Here, we examined multitemporal behavior of fully polarimetric SAR (PolSAR) parameters at L-band in relation to the stem volume of boreal forests. The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence and suitability for forest stem volume estimation across several seasonal conditions (frozen, thaw and unfrozen). The satellite SAR data were represented by a time series of PolSAR images acquired during several seasons in the years 2006 to 2009 by the ALOS PALSAR sensor. The study area was in central Finland, and represented a managed area in typical boreal mixed forest land. Utility of different PolSAR parameters, their temporal stability and cross-correlations were studied along with reference stand-level stem volume data from forest inventory. Further, two polarimetric parameters, cross-polarization backscatter and co-polarization coherence, were chosen for further investigation and stem volume retrieval. A relationship between forest stem volume and PolSAR parameters was established using the kNN regression approach. Ways of optimally combining PolSAR images were evaluated as well. For a single scene, best results were observed with polarimetric coherence (RMSE ≈ 38.8 m3/ha) for scene acquired in frozen conditions. An RMSE of 40.8 m3/ha (42.9%, R2 = 0.66) was achieved for cross-polarization backscatter in the best case. Cross-polarization backscatter was a better predictor than polarimetric coherence for few summer scenes. Multitemporal aggregation of selected PolSAR scenes improved estimates for both studied PolSAR parameters. Stronger improvement was observed for coherence with RMSE down to 34 m3/ha (35.8%, R2 = 0.77) compared to 38.8–51.6 m3/ha (40.8–54.3%) from separate scenes. Finally, the accuracy statistics reached RMSE of 32.2 m3/ha (34%, R2= 0.79) when multitemporal HHVV coherence was combined with multitemporal HV-backscatter. [ABSTRACT FROM AUTHOR]

The DabusWetland complex in the highlands of Ethiopia is within the headwaters of the Nile Basin and is home to significant ecological communities and rare or endangered species. Its many interrelated wetland types undergo seasonal and longer-term changes due to weather and climate variations as well as anthropogenic land use such as grazing and burning. Mapping and monitoring of these wetlands has not been previously undertaken due primarily to their relative isolation and lack of resources. This study investigated the potential of remote sensing based classification for mapping the primary vegetation groups in the Dabus Wetlands using a combination of dry and wet season data, including optical (Landsat spectral bands and derived vegetation and wetness indices), radar (ALOS PALSAR L-band backscatter), and elevation (SRTM derived DEM and other terrain metrics) as inputs to the non-parametric Random Forest (RF) classifier. Eight wetland types and three terrestrial/upland classes were mapped using field samples of observed plant community composition and structure groupings as reference information. Various tests to compare results using different RF input parameters and data types were conducted. A combination of multispectral optical, radar and topographic variables provided the best overall classification accuracy, 94.4% and 92.9% for the dry and wet season, respectively. Spectral and topographic data (radar data excluded) performed nearly as well, while accuracies using only radar and topographic data were 82–89%. Relatively homogeneous classes such as Papyrus Swamps, Forested Wetland, and Wet Meadow yielded the highest accuracies while spatially complex classes such as Emergent Marsh were more difficult to accurately classify. The methods and results presented in this paper can serve as a basis for development of long-term mapping and monitoring of these and other non-forested wetlands in Ethiopia and other similar environmental settings. [ABSTRACT FROM AUTHOR]

Passive microwave measurements from space are known to be sensitive to the freeze/thaw (F/T) state of the land surface. These measurements are at a coarse spatial resolution (~15-50 km) and the spatial variability of the microwave emissions within a pixel can have important effects on the interpretation of the signal. An L-band ground-based microwave radiometer campaign was conducted in the Canadian Prairies during winter 2014-2015 to examine the spatial variability of surface emissions during frozen and thawed periods. Seven different sites within the Kenaston soil monitoring network were sampled five times between October 2014 and April 2015 with a mobile ground-based L-band radiometer system at approximately monthly intervals. The radiometer measurements showed that in a seemingly homogenous prairie landscape, the spatial variability of brightness temperature (TB) is non-negligible during both frozen and unfrozen soil conditions. Under frozen soil conditions, TB was negatively correlated with soil permittivity (εG). This correlation was related to soil moisture conditions before the main freezing event, showing that the soil ice volumetric content at least partly affects TB. However, because of the effect of snow on L-Band emission, the correlation between TB and εG decreased with snow accumulation. When compared to satellite measurements, the average TB of the seven plots were well correlated with the Soil Moisture Ocean Salinity (SMOS) TB with a root mean square difference of 8.1 K and consistent representation of the strong F/T signal (i.e., TB increases and decreases when soil freezing and thawing, respectively). This study allows better quantitative understanding of the spatial variability in L-Band emissions related to landscape F/T, and will help the calibration and validation of satellite-based F/T retrieval algorithms. [ABSTRACT FROM AUTHOR]

L-band active microwave remote sensing is one of the most important technical methods of ocean environmental monitoring and dynamic parameter retrieval. Recently, a unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range was observed. To study the directional features of L-band ocean surface backscattering, a new directional spectrum model is proposed and built into the advanced integral equation method (AIEM). This spectrum combines Apel's omnidirectional spectrum and an improved empirical angular spreading function (ASF). The coefficients in the ASF were determined by the fitting of radar observations so that it provides a better description of wave directionality, especially over wavenumber ranges from short-gravity waves to capillary waves. Based on the improved spectrum and the AIEM scattering model, L-band NUC asymmetry at low wind speeds and positive upwind-crosswind (PUC) asymmetry at higher wind speeds are simulated successfully. The model outputs are validated against Aquarius/SAC-D observations under different incidence angles, azimuth angles and wind speed conditions. [ABSTRACT FROM AUTHOR]

At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of soil water contents at various depths by classical methods are cumbersome and usually highly invasive. We propose an improved method for the estimation of vertical soil moisture profiles from multi-offset ground penetrating radar (GPR) data. A semi-automated data acquisition technique allows for very fast and robust measurements in the field. Advanced common mid-point (CMP) processing is applied to obtain quantitative estimates of the permittivity and depth of the reflecting soil layers. The method is validated against TDR measurements using data acquired in different environments. Depth and soil moisture contents of the reflecting layers were estimated with root mean square errors (RMSE) on the order of 5 cm and 1.9 Vol.-%, respectively. Application of the proposed technique for the validation of synthetic aperture radar (SAR) soil moisture estimates is demonstrated based on a case study using airborne L-band data and ground-based P-band data. For the L-band case we found good agreement between the near-surface GPR estimates and extended integral equation model (I2EM) based SAR retrievals, comparable to those obtained by TDR. At the P-band, the GPR based method significantly outperformed the TDR method when using soil moisture estimates at depths below 30 cm. [ABSTRACT FROM AUTHOR]

The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère). One of the main goals of this SMOS-INRA-CESBIO (SMOS-IC) product is to be as independent as possible from auxiliary data. The SMOS-IC product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010–2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and the northern mid-latitudes. [ABSTRACT FROM AUTHOR]

Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km x 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained. [ABSTRACT FROM AUTHOR]

Global surface soil moisture products are derived from passive L-band microwave satellite observations. The applied retrieval algorithms include dielectric models (relating soil water content to relative permittivity) developed for mineral soils. First efforts to generate equivalent models for areas where organic surface layers are present such as in the high-latitude regions have recently been undertaken. The objective of this study was to improve our still insufficient understanding of L-band emission of organic substrates in prospect of enhancing soil moisture estimations in the high latitudes undergoing most rapid climatic changes. To this end, L-band relative permittivity measurements using a resonant cavity were carried out on a wide range of organic surface layer types collected at different sites. This dataset was used to evaluate two already existing models for organic substrates. Some samples from underlying mineral layers were considered for comparison. In agreement with theory the bulk relative permittivity measured in organic substrate was decreased due to an increased bound water fraction (where water molecules are rotationally hindered) compared to the measured mineral material and corresponding output of the dielectric model for mineral soils used in satellite algorithms. No distinct differences in dielectric response were detected in the measurements from various organic layer types, suggesting a generally uniform L-band emission behavior. This made it possible to fit a simple empirical model to the data obtained from all collected organic samples. Outputs of the two existing models both based on only one organic surface layer type were found to lie within the spread of our measured data, and in close proximity to the derived simple model. This general consensus strengthened confidence in the validity of all these models. The simple model should be suitable for satellite soil moisture retrieval applications as it is calibrated on a wide range of organic substrate types and the entire wetness range, and does not require any auxiliary input that may be difficult to obtain globally. This renders it generically applicable wherever organic surface layers are present. [ABSTRACT FROM AUTHOR]

The use of the reflected Global Navigation Satellite Systems' (GNSS) signals in Earth observation applications, referred to as GNSS reflectometry (GNSS-R), has been already studied for more than two decades. However, the estimation precision that can be achieved by GNSS-R sensors in some particular scenarios is still not fully understood yet. In an effort to partially fill this gap, in this paper, we compute the Cramér-Rao bound (CRB) for the specific case of static ground-based GNSS-R receivers and scenarios where the coherent component of the reflected signal is dominant. We compute the CRB for GNSS signals with different modulations, GPS L1 C/A and GPS L5 I/Q, which use binary phase-shift keying, and Galileo E1 B/C and E5, using the binary offset carrier. The CRB for these signals is evaluated as a function of the receiver bandwidth and different scenario parameters, such as the height of the receiver or the properties of the reflection surface. The CRB computation presented considers observation times of up to several tens of seconds, in which the satellite elevation angle observed changes significantly. Finally, the results obtained show the theoretical benefit of using modern GNSS signals with GNSS-R techniques using long observation times, such as the interference pattern technique. [ABSTRACT FROM AUTHOR]

Soil moisture plays an important role in understanding climate change and hydrology, and L-band passive microwave radiometers have been verified as effective tools for monitoring soil moisture. This paper proposes a novel, simplified algorithm for bare surface soil moisture retrieval using L-band radiometer. The algorithm consists of two sub-algorithms: a surface emission model and a soil moisture retrieval model. In analyses of the advanced integral equation model (AIEM) simulated database, the surface emission model was developed to diminish the effects of surface roughness using dual-polarization surface reflectivity. The soil moisture retrieval model, which was calibrated using the Dobson simulated database, is based on the relationship between the adjusted real refractive index Nr and the volumetric soil moisture. Soil moisture can be determined via a numerical solution that uses several freely available input parameters: dual-polarization microwave brightness temperature, surface temperature, and the contents of sand and clay. The results showed good agreement with the input soil moisture values simulated by the AIEM model, with root mean square errors (RMSEs) lower than 3% at all incidence angles. The algorithm was then verified based on data from the four-year L-band experiments conducted at Beltsville Agricultural Research Center (BARC) test sites, achieving RMSEs of 4.3% and 3.4% at 40° and 50°, respectively. These results indicate that the simplified algorithm proposed in this paper shows a very good accuracy in soil moisture retrieval. Additionally, the algorithm exhibits a better performance for the large incidence angle radiometers in L-band such as those produced by the Soil Moisture Active and Passive (SMAP). [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a highly relevant variable for agriculture, the emergence of floods and a key variable in the global energy and water cycle. In the last years, several satellite missions have been launched especially to derive large-scale products of the SM dynamics on the Earth. However, in situ validation data are often scarce. We developed a new method to retrieve SM of bare soil from measurements of low-cost GPS (Global Positioning System) sensors that receive the freely available GPS L1-band signals. The experimental setup of three GPS sensors was installed at a bare soil field at the German Weather Service (DWD) in Munich for almost 1.5 years. Two GPS antennas were installed within the soil column at a depth of 10 cm and one above the soil. SM was successfully retrieved based on GPS signal strength losses through the integral soil volume. The results show high agreement with measured and modelled SM validation data. Due to its non-destructive, cheap and low power setup, GPS sensor networks could also be used for potential applications in remote areas, aiming to serve as satellite validation data and to support the fields of agriculture, water supply, flood forecasting and climate change. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than -15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than -30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics. [ABSTRACT FROM AUTHOR]

Thermokarst is the process of ground subsidence caused by either the thawing of ice-rich permafrost or the melting of massive ground ice. The consequences of permafrost degradation associated with thermokarst for surface ecology, landscape evolution, and hydrological processes have been of great scientific interest and social concern. Part of a tundra patch affected by wildfire in northern Alaska (27.5 km²) was investigated here, using remote sensing and in situ surveys to quantify and understand permafrost thaw dynamics after surface disturbances. A two-pass differential InSAR technique using L-band ALOS-PALSAR has been shown capable of capturing thermokarst subsidence triggered by a tundra fire at a spatial resolution of tens of meters, with supporting evidence from field data and optical satellite images. We have introduced a calibration procedure, comparing burned and unburned areas for InSAR subsidence signals, to remove the noise due to seasonal surface movement. In the first year after the fire, an average subsidence rate of 6.2 cm/year (vertical) was measured. Subsidence in the burned area continued over the following two years, with decreased rates. The mean rate of subsidence observed in our interferograms (from 24 July 2008 to 14 September 2010) was 3.3 cm/year, a value comparable to that estimated from field surveys at two plots on average (2.2 cm/year) for the six years after the fire. These results suggest that this InSAR-measured ground subsidence is caused by the development of thermokarst, a thawing process supported by surface change observations from high-resolution optical images and in situ ground level surveys. [ABSTRACT FROM AUTHOR]

The mangroves of the Rufiji Delta are an important habitat and resource. The mangrove forest reserve is home to an indigenous population and has been under pressure from an influx of migrants from the landward side of the delta. Timely and effective forest management is needed to preserve the delta and mangrove forest. Here, we investigate the potential of polarimetric target decomposition for mangrove forest monitoring and analysis. Using three ALOS PALSAR images, we show that L-band polarimetry is capable of mapping mangrove dynamics and is sensitive to stand structure and the hydro-geomorphology of stands. Entropy-alpha-anisotropy and incoherent target decompositions provided valuable measures of scattering behavior related to forest structure. Little difference was found between Yamaguchi and Arii decompositions, despite the conceptual differences between these models. Using these models, we were able to differentiate the scattering behavior of the four main species found in the delta, though classification was impractical due to the lack of pure stands. Scattering differences related to season were attributed primarily to differences in ground moisture or inundation. This is the first time mangrove species have been identified by their scattering behavior in L-band polarimetric data. These results suggest higher resolution L-band quad-polarized imagery, such as from PALSAR-2, may be a powerful tool for mangrove species mapping. [ABSTRACT FROM AUTHOR]

The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for Synthetic Aperture Radar (SAR) data at C- and X-bands to SAR data at L-band. A large dataset of radar signal and in situ measurements (soil moisture and surface roughness) over bare soil surfaces were used. This dataset was collected over numerous agricultural study sites in France, Luxembourg, Belgium, Germany and Italy using various SAR sensors (AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR). Results showed slightly better simulations with exponential autocorrelation function than with Gaussian function and with HH than with VV. Using the exponential autocorrelation function, the mean difference between experimental data and Integral Equation Model (IEM) simulations is +0.4 dB in HH and -1.2 dB in VV with a Root Mean Square Error (RMSE) about 3.5 dB. In order to improve the modeling results of the IEM for a better use in the inversion of SAR data, a semi-empirical calibration of the IEM was performed at L-band in replacing the correlation length derived from field experiments by a fitting parameter. Better agreement was observed between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM (RMSE about 2.2 dB). [ABSTRACT FROM AUTHOR]

In this study, an algorithm to retrieve the gravimetric vegetation water content (GVWC, %) of corn was developed. First, the method for obtaining the optical depth from L-band (1.4 GHz) bi-angular, dual-polarized brightness temperatures (TB) for short vegetation was investigated. Then, the quantitative relationship between the corn optical depth, corn GVWC and corn leaf area index (LAI) was constructed. Finally, using the Polarimetric L-band Microwave Radiometer (PLMR) airborne data in the 2012 Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, the Global Land Surface Satellite (GLASS) LAI product, the height and areal density of the corn stalks, the corn GVWC was estimated (corn GLASS-GVWC). Both the in situ measured corn GVWC and the corn GVWC retrieved based on the in situ measured corn LAI (corn LAINET-GVWC) were used to validate the accuracy of the corn GLASS-GVWC. The results show that the GVWC retrieval method proposed in this study is feasible for monitoring the corn GVWC. However, the accuracy of the retrieval results is highly sensitive to the accuracy of the LAI input parameters. [ABSTRACT FROM AUTHOR]

Freeze-thaw (FT) and moisture dynamics within the soil active layer are critical elements of boreal, arctic and alpine ecosystems, and environmental change assessments. We evaluated the potential for detecting dielectric changes within different soil layers using combined L- and P-band radar remote sensing as a prerequisite for detecting FT and moisture profile changes within the soil active layer. A two-layer scattering model was developed and validated for simulating radar responses from vertically inhomogeneous soil. The model simulations indicated that inhomogeneity in the soil dielectric profile contributes to both L- and P-band backscatter, but with greater P-band sensitivity at depth. The difference in L- and P-band responses to soil dielectric profile inhomogeneity appears suitable for detecting associated changes in soil active layer conditions. Additional evaluation using collocated airborne radar (AIRSAR) observations and in situ soil moisture measurements over alpine tundra indicates that combined L- and P-band SAR observations are sensitive to soil dielectric profile heterogeneity associated with variations in soil moisture and FT conditions. [ABSTRACT FROM AUTHOR]

The amount of liquid water in snow characterizes the wetness of a snowpack. Its temporal evolution plays an important role for wet-snow avalanche prediction, as well as the onset of meltwater release and water availability estimations within a river basin. However, it is still a challenge and a not yet satisfyingly solved issue to measure the liquid water content (LWC) in snow with conventional in situ and remote sensing techniques. We propose a new approach based on the attenuation of microwave radiation in the L-band emitted by the satellites of the Global Positioning System (GPS). For this purpose, we performed a continuous low-cost GPS measurement experiment at the Weissfluhjoch test site in Switzerland, during the snow melt period in 2013. As a measure of signal strength, we analyzed the carrier-to-noise power density ratio (C/N0) and developed a procedure to normalize these data. The bulk volumetric LWC was determined based on assumptions for attenuation, reflection and refraction of radiation in wet snow. The onset of melt, as well as daily melt-freeze cycles were clearly detected. The temporal evolution of the LWC was closely related to the meteorological and snow-hydrological data. Due to its non-destructive setup, its cost-efficiency and global availability, this approach has the potential to be implemented in distributed sensor networks for avalanche prediction or basin-wide melt onset measurements. [ABSTRACT FROM AUTHOR]

For a foam-covered sea surface, it is difficult to retrieve sea surface salinity (SSS) with L-band brightness temperature (1.4 GHz) because of the effect of a foam layer with wind speeds stronger than 7 m/s, especially at low sea surface temperature (SST). With foam-controlled experiments, emissivities of a foam-covered water surface at low SST (-1.4 °C to 1.7 °C) are measured for varying SSS, foam thickness, incidence angle, and polarization. Furthermore, a theoretical model of emissivity is introduced by combining wave approach theory with the effective medium approximation method. Good agreement is obtained upon comparing theoretical emissivities with those of experiments. The results indicate that foam parameters have a strong influence on increasing emissivity of a foam-covered water surface. Increments of experimental emissivities caused by foam thickness of 1 cm increase from about 0.014 to 0.131 for horizontal polarization and 0.022 to 0.150 for vertical polarization with SSS increase and SST decrease. Contributions of the interface between the foam layer and water surface to the foam layer emissivity increments are discussed for frequencies between 1 and 37 GHz. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop models based on both optical and L-band Synthetic Aperture Radar (SAR) data for above ground dry biomass (hereafter AGB) estimation in mountain forests. We chose the site of the Loveh forest, a part of the Hyrcanian forest for which previous attempts to estimate AGB have proven difficult. Uncorrected ETM+ data allow a relatively poor AGB estimation, because topography can hinder AGB estimation in mountain terrain. Therefore, we focused on the use of atmospherically and topographically corrected multispectral Landsat ETM+ and Advanced Land-Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) to estimate forest AGB. We then evaluated 11 different multiple linear regression models using different combinations of corrected spectral and PolSAR bands and their derived features. The use of corrected ETM+ spectral bands and GLCM textures improves AGB estimation significantly (adjusted R2 = 0.59; RMSE = 31.5 Mg/ha). Adding SAR backscattering coefficients as well as PolSAR features and textures increase substantially the accuracy of AGB estimation (adjusted R2 = 0.76; RMSE = 25.04 Mg/ha). Our results confirm that topographically and atmospherically corrected data are indispensable for the estimation of mountain forest's physical properties. We also demonstrate that only the joint use of PolSAR and multispectral data allows a good estimation of AGB in those regions. [ABSTRACT FROM AUTHOR]

Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7/ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines (SVM), neural networks (NN), random forest (RF) and one traditional classifier (i.e., maximum likelihood classification (MLC)) as a benchmark. The non-topographically corrected ETM+ data failed to differentiate among different forest stand age classes (average classification accuracy (OA) = 65%). This confirms the need to reduce relief effects prior data classification in mountain regions. SAR backscattering alone cannot properly differentiate among different forest stand age classes (OA = 62%). However, textures and PolSAR features are very efficient for the separation of forest classes (OA = 82%). The highest classification accuracy was achieved by the joint usage of SAR and ETM+ (OA = 86%). However, this shows a slight improvement compared to the ETM+ classification (OA = 84%). The machine learning classifiers proved t o be more robust and accurate compared to MLC. SVM and RF statistically produced better classification results than NN in the exploitation of the considered multi-source data. [ABSTRACT FROM AUTHOR]

The forests of the Russian Taiga can be described as an enormous biomass and carbon reservoir. Therefore, they are of utmost importance for the global carbon cycle. Large-area forest inventories in these mostly remote regions are associated with logistical problems and high financial efforts. Remotely-sensed data from satellite platforms may have the capability to provide such huge amounts of information. This study presents an application-oriented approach to derive aboveground growing stock volume (GSV) maps using the annual large-area L-band backscatter mosaics provided by the Japan Aerospace Exploration Agency (JAXA). Furthermore, a multi-temporal map has been created to improve GSV estimation accuracy. Based on information from Russian forest inventory data, the maps were generated using the machine learning algorithm, Random Forest. The results showed the high potential of this method for an operational, large-scale and high-resolution biomass estimation over boreal forests. An RMSE from 55.2 to 63.3 m3/ha could be obtained for the annual maps. Using the multi-temporal approach, the error could be slightly reduced to 54.4 m3/ha. [ABSTRACT FROM AUTHOR]