Search

Over the last two decades, there has been a notable increase in the use of bistatic radar configurations for remote sensing, particularly employing Signals-of-Opportunity (SoOp), such as Global Navigation Satellite System - Reflectometry (GNSS-R). Unlike the more common monostatic backward radar configuration, GNSS-R forward scattering is dominated by different mechanisms. The forward scattering measurement provides valuable insights into regions where the backscatter is minimal or dominated by the double bounce effect caused by the vegetation. The Soil Moisture Active Passive (SMAP) mission carries an L-band radiometer and a radar. However, following a radar malfunction during its commissioning, the radar was repurposed to measure GPS L2C signals. This repurposing provides a unique opportunity to compare data from both the SMAP radar and GNSS-R (SMAP-R) collected by the same platform, using the same antenna, same incident angle, and similar frequencies. The study conducted offers a comprehensive analysis, both qualitative and quantitative, of the relationship between forward scattering at full polarization, and backward scattering at three polarizations (3-polarization: HH, VV and HV). Additionally, the study includes comparisons with ancillary geophysical measurements across three distinct areas with varying environmental characteristics, including dense forests and deserts. • There is a strong correlation between polarimetric GNSS-R and L-band radar. • Polarimetric GNSS-R provides complementary sensitivity over densely vegetated regions. • The relationship between forward and backward scattering is proportional but inverse. • Radar and reflectometry measurements complement each other over distinct areas. [ABSTRACT FROM AUTHOR]

ASCAT normalized backscatter (σ 40 o) and slope (σ ′) contain valuable information about soil moisture and vegetation. While σ 40 o has been assimilated to constrain soil moisture, sometimes together with Leaf Area Index (LAI), this study is the first to assimilate σ ′ directly to constrain vegetation states. Here, we assimilate σ 40 o and slope σ ′ into the ISBA-A-gs LSM using the Simplified Extended Kalman Filter (SEKF) using a Deep Neural Network (DNN) as the observation operator. The performances of the data assimilation (DA) and open loop (OL) are evaluated against in-situ soil moisture observations from the International Soil Moisture Network (ISMN), and LAI observations from the Copernicus Global Land Service (CGLS). Given an accurate and physically plausible observation operator, along with well-defined model and observation errors, the data assimilation system should yield improved estimates of the model states. However, results show that the DA performance is neutral compared to the OL in terms of the median unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (ρ) across all validation sites. In addition, an analysis of the residuals and innovations confirms that DA had limited or no impact. This poor performance is perplexing. Furthermore, given the growing interest in the use of machine-learning-based observation operators, it is essential to understand the role that the use of the DNN may be playing in this poor performance. While representativeness errors and error specification play some part, it is demonstrated that the key factor constraining the efficacy of the SEKF is the correct estimation of the Jacobians that control the degree to which the observations update the states in the SEKF. It is argued that the DNN relating model states to satellite observations must have physically-plausible and robust Jacobians for the DNN to be effective in a data assimilation framework. • ASCAT backscatter and slope are directly assimilated into the ISBA LSM. • A Deep Neural Network is used as the observation operator. • Validation against ISMN measurements reveals neutral performance. • The primary cause is related to the Jacobians of the DNN model. [ABSTRACT FROM AUTHOR]

Recent studies have shown that radar temporal coherence over tropical and boreal forests undergoes a diurnal cycle as a result of a combined effect of the wind-induced motion of scatterers and of the change and displacement of water within the plant in response to the transpiration process. Within this context, the objective of this paper is to investigate, for the first time, the diurnal cycle of temporal coherence over wheat crops in relation to its development and physiological functioning throughout the agricultural season. A ground-based experiment was installed in Morocco, targeting a wheat field during the 2020 agricultural season. The radar system, essentially based on a Vector Network Analyzer (VNA) connected to 6C-band antennas installed at the top of a 20 m tower, has enabled quad-polarimetric acquisitions every 15 min. In parallel, evapotranspiration, soil moisture and meteorological variables are automatically measured in addition to above-ground biomass and vegetation water content collected during field campaigns. The results show that the temporal coherence with a 15 min baseline follows a marked diurnal cycle characterized by variable amplitude according to the phenological stage, with high values during the night, a significant morning drop to reach the lowest values in the late afternoon followed by an increase to recover the high nighttime values. The rate of the drop at dawn is shown to be related to the increase of evapotranspiration (r = 0.80 at VV polarization) when the wheat is covering the soil and the transpiration dominate the evapotranspiration process. This supports the assumption of a physiological effect related to water movement entailing a decorrelation. By contrast, the daily minimum of temporal coherence occurring in the late afternoon correlates well to the daily maximum of wind (r = 0.7). Interestingly enough, the amplitude of the diurnal cycle exhibit a marked seasonal evolution characterized by an increase of 85% from tillering to maturity in relation to the wheat development. At the early start of the season when the soil is almost bare, irrigation events impact slightly the diurnal cycle of temporal coherence. Likewise, it is shown that the presence of dew in the early morning has led to a decrease of the decorrelation rate. Temporal coherence dynamic has also been investigated for longer baselines up to 22 days. Results indicate a stronger decorrelation than what has been observed on tropical and boreal forests by previous studies with values below 0.4 for baselines above 2 days. Taken together, the results of this work demonstrate the unique potential of sub-daily C-band data for monitoring crop water status by future geostationary radar missions such as Hydroterra. • Diurnal cycles of 15-min C-band temporal coherence over wheat crops are analyzed. • The morning drop of the temporal coherence is related to the transpiration process. • Temporal coherence exhibits a marked seasonal dynamic according to phenological stages. • Irrigation events and morning dew have an impact on sub-daily temporal coherence. • Early morning hours showed potential for monitoring the vegetation physiological functioning. [ABSTRACT FROM AUTHOR]

Accurate quantification of iceberg populations is essential to inform estimates of Southern Ocean freshwater and heat balances as well as shipping hazards. The automated operational monitoring of icebergs remains challenging, largely due to a lack of generality in existing approaches. Previous efforts to map icebergs have often exploited synthetic aperture radar (SAR) data but the majority are designed for open water situations, require significant operator input, and are susceptible to the substantial spatial and temporal variability in backscatter that characterises SAR time-series. We propose an adaptive unsupervised classification procedure based on Sentinel 1 SAR data and a recursive Dirichlet Process implementation of Bayesian Gaussian Mixture Model. The approach is robust to inter-scene variability and can identify icebergs even within complex environments containing mixtures of open water, sea ice and icebergs of various sizes. For the study area in the Amundsen Sea Embayment, close to the calving front of Thwaites Glacier, our classifier achieved a mean pixel-wise F1 score against manual iceberg delineations from the SAR scenes of 0. 960 ± 0. 018 with a corresponding object-level F1 score of 0. 729 ± 0. 086. The method provides an excellent basis for estimation of total near-shore iceberg populations and has inherent potential for scalability that other approaches lack. [Display omitted] • Unsupervised method as basis for scalable operational iceberg monitoring and tracking. • Icebergs reliably segmented within open water, sea ice and adjacent to calving fronts. • Adaptive method accounts for inter-scene variability without prior knowledge. [ABSTRACT FROM AUTHOR]

Systematic Sentinel-1 acquisitions provide an unprecedented stream of SAR data which allows to describe forest temporal dynamics in detail, a powerful tool for phenological studies and forest type classification. Several studies have explored the temporal variation of backscatter intensity in this context, but none considered that scattering directionality of canopies may vary. Said directionality is related to target-sensor geometry (incidence angle), forest height, and optical depth, associated with leaf dynamics. This study explicitly models backscatter dependance on incidence angle by fitting a regression model for each Sentinel-1 image and forest type. Residuals are accumulated across the time series and used to classify pixels into the most likely forest type using the smallest accumulated residual. This modelling and classification strategy has been applied over a North-South transect across the Carpathian Mountains, including forests with different physiognomies, from deciduous broadleaf forest, to mixed broadleaf-needleleaf and pure perennial needleleaf forests. These forests were classified with increasing detail, assessing the results against in-situ forest stand data and satellite-based land cover classification products (Copernicus Forest type layer). The accuracy of our classification was K > 0.8, OA > 90% when separating broadleaf from needleleaf forest types. The accuracy decreased (K > 0.6, OA > 79%) when also separating mixed forest types. Our results suggest that incorporating directional effects into classification models can improve SAR-based forest classification of temperate forest over mountainous terrain. Furthermore, models fitted between backscatter and incidence angle provide an estimate of n , a parameter related to optical depth that has been shown to vary with leaf dynamics. n could be used to improve image normalization in studies aiming at the estimation of biomass, or to aid the estimation of fast-changing parameters such as leaf area index or leaf moisture content. • Backscatter/incidence angle relationship depends on the season. • Common γ 0 normalization may be inadequate for images acquired during the winter. • Forest types have a characteristic temporal signature for backscatter and opacity. • Temporal signatures separated broadleaf and needleleaf forest with 90% accuracy. • When separating mixed forest, the class had 47% omission and 72% commission error. [ABSTRACT FROM AUTHOR]

Cloud base heights (CBHs) play an important role in weather and climate studies, but remain one of the most challenging properties to be inferred using passive satellite spectral instruments. This is because spectral signals detected from space are mainly from cloud tops and contain minimal information on cloud vertical structures owing to limited penetration. This paper presents a new algorithm for retrieving CBH from radiometer measurements, utilizing an improved characterization for mutable cloud water contents (CWCs). This was achieved by introducing a variable named effective CWC (ECWC) to represent the vertically varying CWC; and subsequently investigating the sensitivity of ECWC to both cloud and environmental properties. Considering these characteristics, a systematic ECWC lookup table (LUT) was created based on collocated active and passive satellite observations. We then converted the existing cloud water path (CWP; kg/m2) to cloud geometric thickness (CGT; km) via the developed LUT, and calculated CBH by subtracting the CGT estimates from conventional cloud top height (CTH) retrievals. The algorithm was tested using observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Himawari Imager (AHI) as examples, and was evaluated using active measurements from the CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites and a ground-based Ka-band radar. Overall, our CBH results agreed closely with the active observations, with mean biases of 0.11 ± 1.93 km for MODIS and − 0.12 ± 2.34 km for AHI in single-layer cloud cases. Despite the natural limitations on retrieving CBHs using passive radiometers, the proposed algorithm achieves retrieval accuracies comparable to those of operational CTH products. Moreover, this algorithm works for both single-layer and overlapping clouds, and can be extended to other passive radiometers by reconstructing the LUT. Such accurate CBH information derived from high-resolution passive radiometers would greatly improve our understanding of cloud vertical distributions; and could play an important role in studies of cloud radiative effects and the aerosol-cloud-precipitation interactions. • An algorithm was developed to independently retrieve cloud base height (CBH) using passive radiometer observations. • A systematic lookup table was developed to characterize the distribution of effective cloud water content. • The CBH retrieval algorithm is applicable for both single-layer and overlapping clouds. • Our CBH retrievals achieved accuracy comparable to operational cloud top height (CTH) retrievals. [ABSTRACT FROM AUTHOR]

Ground-based snowfall observations over Antarctica are rare due to the harsh environment and high logistical, equipment maintenance, and operational costs. Satellite measurements are crucial to provide continent-wide precipitation estimates, and this highlights the importance of validating the satellite estimates with measurements collected by ground-based Antarctic stations. The NASA CloudSat satellite, launched in 2006, is equipped with a 94 GHz (W-band) Cloud Profiling Radar (CPR) that provides measurements of reflectivity profiles of clouds and precipitation, whereas the incoming ESA/JAXA EarthCARE mission will add Doppler capability to a 94 GHz radar. This study explores how the synergy between two instruments available at most Antarctic stations, i.e., Micro Rain Radar (24 GHz, K-band) and laser disdrometer, can be used to validate satellite-borne W-band radar measurements, including Doppler estimates. A new validation methodology (K2W) was proposed to combine these instruments for simulating the 94 GHz reflectivity and Doppler measurements from Micro Rain Radar spectra. Assessment of the proposed K2W conversion methodology showed that the CloudSat Z e profiles can be simulated by the method with 0.2 dB mean difference at the lowest satellite radar range bin when time lag within ±12.5 min and the distance within 25 km around the CloudSat overpass were considered. With the K2W method, the 94 GHz Doppler velocity below 1 km altitude that would be observed by EarthCARE was obtained, and the standard deviation of the simulated Doppler velocity was found to be smaller than about 0.2 m s−1. The simulated 94 GHz Doppler radar profile information, which is less affected by attenuation compared to ground-based 94 GHz radar, will significantly improve the quantification of precipitation over Antarctica. This methodology will be applied to further assess the EarthCARE CPR Doppler velocity measurement accuracy as well as the Level 2 standard products for precipitation in Antarctica and at many other ground observation sites. • Synergy between 24 GHz Micro Rain Radar and disdrometer is used by K2W methodology • 94 GHz Doppler Spectra below 1 km were obtained in Antarctica • CloudSat radar reflectivity was evaluated within 0.2 dB by K2W • EarthCARE radar Doppler velocities are expected to be evaluated within 0.2 m s−1 • The wide applicability of K2W hugely raises opportunities for EarthCARE validation [ABSTRACT FROM AUTHOR]

Increased erosion related to climate and/or land cover change has adverse impacts on terrestrial and aquatic ecosystems. Mapping of erosion hotspots improves our ability to identify and potentially remediate the most active erosion sources. The topical savannas of the Great Barrier Reef catchments, in northeast Australia, are generating excessive sediment yields, primarily from gully erosion. To reduce the adverse impacts on marine ecosystems, there is an urgent need to identify priority erosion hotspots and implement mitigation measures. While repeat airborne LiDAR surveys allow subtle topographic change detection, they are cost-intensive for catchment-scale applications and their applicability is constrained by data avalability and detection treshold of ground level change. In contrast, satellite-based radar imagery can allow large scale tracking of geomorphic change at high temporal resolution. Here we apply a new method based on Sentinel-1 C-band radar images and Coherence Change Detection (CCD), where large stacks of interferometric coherence images are subdivided with rain gauge time-series for separation of erosion-rich and erosion-free coherence information. After correcting the former with the latter, the resulting corrected coherence maps are compared with differential elevation models derived from multitemporal LiDAR, regional scale gully delineation maps, maps of gullying potential and in-situ field verification. Our results demonstrate the promising potential of this technique in detecting gully erosion hotspots. The coherence loss indicating erosion/deposition is well detected in wide gully morphologies, however, the line-of-sight angle does not allow penetration into narrow linear gullies. Further, CCD detects sheetwash or rill erosion occurring in areas identified as at risk of gully expansion, which is commonly below detection threshold for multitemporal LiDAR datasets. When used with LiDAR-derived geomorphic change mapping and gullying potential maps, CCD allows identification of gully erosion dynamics and forecasting gully evolution and creation, which is critical for supporting mitigation measures. • Sentinel-1 radar coherence stacks map erosion hotspots in northeast Australia. • Erosion-free coherence is used to correct erosion-affected coherence images. • Comparison with DoD, gully maps, gullying potential maps and in situ observations. • Capability is indicated to detect early development and deepening of gullies. • A cost-effective, semi-quantitative mapping of large-scale erosion patterns. [ABSTRACT FROM AUTHOR]

Detailed maps of forest structure attributes are crucial for sustainable forest management, conservation, and forest ecosystem science at the landscape level. Mapping the structure of broad heterogeneous forests is challenging, but the integration of extensive field inventory plots with wall-to-wall metrics derived from synthetic aperture radar (SAR) and optical remote sensing offers a potential solution. Our goal was to map forest structure attributes (diameter at breast height, basal area, mean height, dominant height, wood volume and canopy cover) at 30-m resolution across the diverse 463,000 km2 of native forests of Argentina based on SAR Sentinel-1, vegetation metrics from Sentinel-2 and geographic coordinates. We modelled the forest structure attributes based on the latest national forest inventory, generated uncertainty maps, quantified the contribution of the predictors, and compared our height predictions with those from GEDI (Global Ecosystem Dynamics Investigation) and GFCH (Global Forest Canopy Height). We analyzed 3788 forest inventory plots (1000 m2 each) from Argentina's Second Native Forest Inventory (2015–2020) to develop predictive random forest regression models. From Sentinel-1, we included both VV (vertical transmitted and received) and VH (vertical transmitted and horizontal received) polarizations and calculated 1st and 2nd order textures within 3 × 3 pixels to match the size of the inventory plots. For Sentinel-2, we derived EVI (enhanced vegetation index), calculated DHIs (dynamic habitat indices (annual cumulative, minimum and variation) and the EVI median, then generated 1st and 2nd order textures within 3 × 3 pixels of these variables. Our models including metrics from Sentinel-1 and 2, plus latitude and longitude predicted forest structure attributes well with root mean square errors (RMSE) ranging from 23.8% to 70.3%. Mean and dominant height models had notably good performance presenting relatively low RMSE (24.5% and 23.8%, respectively). Metrics from VH polarization and longitude were overall the most important predictors, but optimal predictors differed among the different forest structure attributes. Height predictions (r = 0.89 and 0.85) outperformed those from GEDI (r = 0.81) and the GFCH (r = 0.66), suggesting that SAR Sentinel-1, DHIs from Sentinel-2 plus geographic coordinates provide great opportunities to map multiple forest structure attributes for large areas. Based on our models, we generated spatially-explicit maps of multiple forest structure attributes as well as uncertainty maps at 30-m spatial resolution for all Argentina's native forest areas in support of forest management and conservation planning across the country. • Nationwide mapping of multiple forest structure attributes at 30-m resolution. • Uncertainty maps of each forest structure attribute at 30-m resolution. • Best predictors were a combination of Sentinel-1 and 2 data plus coordinates. • Forest height predictions outperformed those from GEDI and the GFCH. • Reliable predictions to support sustainable forest management in Argentina. [ABSTRACT FROM AUTHOR]

Soil moisture is an important variable in agricultural and hydrological applications. Sentinel-1 (A/B) satellite provides high-resolution (∼10 m) synthetic aperture radar data, and its high revisit capability (6 days at the equator) with multi-track data provides great potential for global soil moisture monitoring. In this study, an algorithm was proposed for soil moisture retrieval using the Multi-Temporal Dual-Channel (MTDC) Sentinel-1 data. The MTDC algorithm first conducted an incidence angle normalization method based on time series data to normalize backscattering coefficients from multiple tracks. Subsequently, using the vegetation water content obtained through a vegetation index climatology, soil roughness and soil unfrozen water content could be estimated for the freezing period (unfrozen water content in the soil is assumed to be stable for January and December). The changes in total backscatter between successive observations were expressed as a function of the two-way vegetation transmissivity multiplied by the difference between soil backscatter, which was directly related to the changes in soil moisture. Considering the retrieved soil roughness as a constant for the entire year, and the average unfrozen water content as the initial soil moisture, the changes in and absolute values of soil moisture for each observation can be estimated by minimizing a cost function between the modeled and observed changes in total backscatter. Retrieval results were validated against the soil moisture network in a semi-arid watershed of Shandian River with an unbiased Root Mean Square Error (ubRMSE) of 0.06 cm3·cm−3, and a bias of −0.04 cm3·cm−3. With the initial soil moisture from in-situ measurements, a higher accuracy was achieved for the MTDC algorithm (ubRMSE = 0.06 cm3·cm−3; bias = −0.01 cm3·cm−3). Further, comparisons with Soil Moisture Active Passive (SMAP) enhanced products further verified the efficacy of the proposed algorithm (ubRMSE = 0.03 cm3·cm−3; bias = −0.004 cm3·cm−3), and demonstrated the feasibility of operational soil moisture products using the Sentinel-1 radar dataset. • A multi-temporal dual-channel algorithm is proposed for soil moisture retrieval. • The incidence angle normalization enables the use of Sentinel-1 multi-track data. • Soil roughness and soil unfrozen water content are initially retrieved for frozen period. • Sentinel-1 can map 1-km soil moisture effectively at a local to regional scale. [ABSTRACT FROM AUTHOR]

A Deep Neural Network (DNN) is used to estimate the Advanced Scatterometer (ASCAT) C-band microwave normalized backscatter (σ 40 o ), slope (σ ′) and curvature (σ ″) over France. The Interactions between Soil, Biosphere and Atmosphere (ISBA) land surface model (LSM) is used to produce land surface variables (LSVs) that are input to the DNN. The DNN is trained to simulate σ 40 o , σ ′ and σ ″ from 2007 to 2016. The predictive skill of the DNN is evaluated during an independent validation period from 2017 to 2019. Normalized sensitivity coefficients (NSCs) are computed to study the sensitivity of ASCAT observables to changes in LSVs as a function of time and space. Model performance yields a near-zeros bias in σ 40 o and σ ′. The domain-averaged values of ρ are 0.84 and 0.85 for σ 40 o and σ ′, compared to 0.58 for σ ″. The domain-averaged unbiased RMSE is 8.6% of the dynamic range for σ 40 o and 13% for σ ′, with land cover having some impact on model performance. NSC results show that the DNN-based model could reproduce the physical response of ASCAT observables to changes in LSVs. Results indicated that σ 40 o is sensitive to surface soil moisture and LAI and that these sensitivities vary with time, and are highly dependent on land cover type. The σ ′ was shown to be sensitive to LAI, but also to root zone soil moisture due to the dependence of vegetation water content on soil moisture. The DNN could potentially serve as an observation operator in data assimilation to constrain soil and vegetation water dynamics in LSMs. • A Deep Neural Network was trained to predict ASCAT backscatter, slope and curvature. • Input variables describing the soil and vegetation were obtained from the ISBA LSM. • Excellent agreement was achieved between observed and predicted ASCAT observables. • Sensitivity of ASCAT observables to inputs is consistent with microwave theory. • The DNN can be used as a measurement operator to assimilate ASCAT data into ISBA. [ABSTRACT FROM AUTHOR]

The southeast Tibetan Plateau has become the region with the fastest rate of mass loss within High Mountain Asia (HMA), and the mass loss from lake-terminating glaciers in the region is the most significant. However, the evolution of the mass change of lake-terminating glaciers is still unclear, and accurate penetration depth estimates of C-band and X-band radar in glacierized areas are unknown, which greatly hinders the monitoring and understanding of the glacier evolution. Hence, in this study, taking the largest lake-terminating glacier in HMA (the Yanong Glacier) as the study object, we first evaluated the X-band penetration depth using Pléiades and TanDEM-X data, and further combined the penetration depth difference between the C-band and X-band radar (1.14 ± 0.14 m) to estimate the C-band penetration depth based on a newly proposed area-weighted strategy, according to surface categories. The results indicate a region-wide average penetration depth of 1.84 ± 0.59 m and 2.98 ± 0.61 m for the X-band and C-band radar, respectively, demonstrating either an underestimation of 60% or an overestimation of 202% for the previous C-band penetration corrections. On this basis, we determined the multi-temporal glacier mass balance for the Yanong Glacier, using KH-9, SRTM DEM, SPOT-7, and multi-orbit TanDEM-X data. The results show that the Yanong Glacier has been in a state of serious mass loss (at a rate of −0.73 ± 0.13 m w.e./a) during 1974–2015, and the rate of mass loss has doubled since 2000 (i.e., −0.48 ± 0.20 (1974–2000), −0.95 ± 0.20 (2000−2012), and − 1.02 ± 0.53 m w.e./a (2012–2015)). Moreover, the inter-annual mass change has shown a highly volatile and accelerating trend during 2012–2015 (i.e., −0.47 ± 0.85 (2011−2012), −0.87 ± 0.66 (2012−2013), −1.26 ± 0.72 (2013–2014), and − 1.58 ± 0.44 m w.e./a (2014–2015)). By further analyzing the ERA5 reanalysis data and the changes in the proglacial lake and glacier dynamics, we can conclude that, qualitatively: 1) the long-term acceleration of mass loss at the Yanong Glacier has mainly been controlled by climate change, but has also been affected by the glacier dynamics to a limited extent; and 2) the inter-annual acceleration of mass loss has, to some extent, been affected by subglacial/englacial melting. The findings of this study will not only provide an accurate penetration depth correction for future relevant studies, but will also significantly improve the understanding of the evolution of lake-terminating glaciers in HMA and other high mountain areas of the world. • A horizontal positioning error correction method for the TanDEM-X DEM is proposed. • Accurate penetration depth estimates for X- and C-band radar are provided. • The evolution of a lake-terminating glacier is revealed by high-resolution images. • The rate of mass loss of the Yanong Glacier has doubled since 2000. • An abnormal subglacial collapse event is captured at an inter-annual timescale. [ABSTRACT FROM AUTHOR]

L-band synthetic aperture radar (SAR) backscatter intensity is sensitive to land cover and can be used to estimate vegetation measures such as basal area (BA) and biomass. However, the estimation of BA, and especially change in BA, can be hampered by the influences upon backscatter of external factors such as imaging geometry, terrain topology, prevailing moisture conditions and even SAR sensor characteristics. This paper describes a method of reducing the adverse effects of such extraneous influences on vegetation and change estimates derived from single-channel SAR data. Empirical corrections for terrain slope and cross-track tendencies were applied and linear least squares difference minimization used to normalize the backscatter differences between scenes. The method was applied to state-wide coverage of L-band, fine-mode, HV polarization Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data over New South Wales (NSW), Australia. The data were acquired with different sensors over two "observational epochs": ALOS PALSAR in 2009 and ALOS-2 PALSAR-2 in 2016/17. The SAR datasets presented significant variations in backscatter intensity beyond those attributable to changes in vegetation cover. The corrective procedures resulted in improved uniformity of observed backscatter dependence on vegetation. Variations in backscattering coefficient between swaths were reduced by as much as 1.75 dB and 25% of the standard deviation in mean backscattering coefficients in common areas and at near- and far-range. This corresponded to a correction in BA estimate of 4.4 m2 ha−1. The method was observed to reduce ambiguities in regrowth estimates at swath boundaries and correct estimates of BA change by as much as 30% over large areas. The resulting estimates of 7-year change in BA provide spatially explicit forest structural information that is assisting in monitoring changes in woody vegetation across NSW. • Residual terrain correction ensures the same mean backscatter for similar slopes. • Internal matching reduces the variations in backscatter due to terrain and moisture. • Cross-sensor matching places data for each observational epoch on a common footing. • Corrections make possible wide-area spatially consistent estimates of basal area. • Observed increase in basal area provides a surrogate measure of regrowth. [ABSTRACT FROM AUTHOR]

• Surface Canopy Water (SCW) affects radar backscatter, cross ratio and RVI. • SCW affects the relations between L-band observables and crop biophysical variables. • SCW has a confounding influence on VOD estimation. • Overpass time and daily patterns in SCW affect the retrieval of crop parameters. The presence of surface water on the canopy affects radar backscatter. However, its influence on the relationship between radar backscatter and crop biophysical parameters has not been investigated. The aim of this study was to quantify the influence of surface canopy water (SCW) on the relationship between L-band radar backscatter and biophysical variables of interest in agricultural monitoring. In this study, we investigated the effect of SCW on the relationship between co- and cross-polarized radar backscatter, cross ratios (VH/VV and HV/HH), and radar vegetation index (RVI) and dry biomass, vegetation water content (VWC), plant height and leaf area index (LAI). In addition, the effect of SCW on estimated vegetation optical depth (VOD) and its relationship with internal VWC was investigated. The analysis was based on data collected during a field experiment in Florida, USA in 2018. A corn field was scanned with a truck-mounted, fully polarimetric, L-band radar along with continuous monitoring of SCW (dew, interception) and soil moisture every 15 min for 58 days. In addition, pre-dawn destructive sampling was conducted to measure internal vegetation water content and dry biomass. Results showed that the presence of SCW can increase the radar backscatter up to 2 dB and this effect was lower for cross ratios (CRs) and RVI. The Spearman's rank correlations between radar observables and biophysical parameters were, on average, 0.2 higher for dry vegetation compared to wet vegetation. The estimated VOD from wet vegetation was generally higher than those from dry vegetation, which led to different fitting parameter (so-called b) values in the linear fit between VOD and VWC. The results presented here underscore the importance of considering the influence of SCW on the retrieval of biophysical variables of interest in agricultural monitoring. In particular, they highlight the importance of overpass time, and the impact that daily patterns in dew and interception can have on the retrieval of biophysical variables of interest. [ABSTRACT FROM AUTHOR]

Glaciers in polar regions are sensitive to climate and ocean changes and can thin rapidly as a consequence of global warming. Digital Elevation Models (DEMs) from remote sensing observations have been widely used to detect changes in polar glaciers. DEMs from Terrestrial Radar Interferometer (TRI) have recently been used for high frequency glacier change and glacier-ocean interaction studies. However, it is unclear whether TRI DEM over a large study area can be combined directly with remote sensing observations to investigate glacier changes as well as the accuracy of TRI DEM at far range. In this study, we deployed a TRI close to Helheim Glacier, East Greenland and generated DEMs using TRI and satellite laser altimetry. We analyzed the accuracy of the TRI DEM using theoretical calculations, comparisons based on repeat observations, and comparisons with a high accurate ArcticDEM. The validation results suggest that for stable ground surfaces, the uncertainty (standard deviation) is <5 m at range < 9.8 km. Averaging across time (e.g. one hour) decreases the uncertainty almost linearly with range, over 0.5 m to 1.2 m when the range increases from 7.0 km to 10.0 km. Increasing the correlation coefficient threshold for phase unwrapping does not significantly reduce uncertainty. TRI DEMs are influenced by systematic error at far range primarily due to coarse azimuth resolution and phase unwrapping difficulties in discontinuous interferograms. As the absolute accuracy of TRI DEMs is not uniformly distributed in the range direction (farther points have worse uncertainty), our findings indicate that TRI DEMs within range of 10 km can reach <5 m uncertainty, which can be compared with DEMs obtained from remote sensing satellites to detect glacier thinning. • A method to generate TRI DEM referring to WGS-84 ellipsoid is presented. • Averaging across time decreases the DEM uncertainty almost linearly with range. • Increasing correlation coefficient threshold does not significantly improve TRI DEM. • Large uncertainty at far range is caused by coarser azimuth resolution and discontinuous interferogram. • TRI DEMs with range < 10 km have <5 m uncertainty and can match satellite observations. [ABSTRACT FROM AUTHOR]

• Sentinel-1 L2 radial velocity products suffer from uncorrected biases up to 2 m/s. • Simple land calibration method is described and applied to 78 snapshots. • Corrected Sentinel-1 radial current has a precision of 0.25 m/s against HF radar. • Better results when sea state is taken into account for WASV correction in this coastal area. Direct sensing of total ocean surface currents with microwave Doppler signals is a growing topic of interest for oceanography, with relevance to several new ocean mission concepts proposed in recent years. Since 2014, the spaceborne C-band SAR instruments of the Copernicus Sentinel-1 (S1) mission routinely acquire microwave Doppler data, distributed to users through operational S1 Level-2 ocean radial velocity (L2 OCN RVL) products. S1 L2 RVL data could produce high-resolution maps of ocean surface currents that would benefit ocean observing and modelling, particularly in coastal regions. However, uncorrected platform effects and instrument anomalies continue to impact S1 RVL data and prevent direct exploitation. In this paper, a simple empirical method is proposed to calibrate and correct operational S1 L2 RVL products and retrieve two-dimensional maps of surface currents in the radar line-of-sight. The study focuses on the German Bight where wind, wave and current data from marine stations and an HF radar instrumented site provide comprehensive means to evaluate S1 retrieved currents. Analyses are deliberately limited to Sentinel-1A (S1A) ascending passes to focus on one single instrument and fixed SAR viewing geometry. The final dataset comprises 78 separate S1A acquisitions over 2.5 years, of which 56 are matched with collocated HF radar data. The empirical corrections bring significant improvements to S1A RVL data, producing higher quality estimates and much better agreement with HF radar radial currents. Comparative evaluation of S1A against HF radar currents for different WASV corrections reveal that best results are obtained in this region when computing the WASV with sea state rather than wind vector input. Accounting for sea state produces S1 radial currents with a precision (std of the difference) around 0.3 m/s at ∼1 km resolution. Precision improves to ∼0.24 m/s when averaging over 21 × 27 km2, with correlations with HF radar data reaching up to 0.93. Evidence of wind-current interactions when tides and wind align and short fetch conditions call for further research with more satellite data and other sites to better understand and correct the WASV in coastal regions. Finally, 1 km resolution maps of climatological S1A radial currents obtained over 2.5 years reveal strong coastal jets and fine scale details of the coastal circulation that closely match the known bathymetry and deep-water coastal channels in this region. The wealth of oceanographic information in corrected S1 RVL data is encouraging for Doppler oceanography from space and its application to observing small scale ocean dynamics, atmosphere and ocean vertical exchanges and marine ecosystem response to environmental change. [ABSTRACT FROM AUTHOR]

Lake ice is an important feature of the physical landscape at northern latitudes. Not only does the presence of lake ice help modulate weather and climate but it also plays an important role in travel between northern communities. Furthermore, the thickness of ice cover can impact overwintering fish habitat and access to fresh water during winter months. Lake ice cover and lake ice thickness are two notable climate proxies identified by the Global Climate Observing System (GCOS) as Essential Climate Variables (ECVs). Changes in ice thickness can impact lakes freezing to bed which in turn affect permafrost thaw while longer periods of open water can impact heat transfer to the atmosphere. With recent climate warming in the Northern Hemisphere, there are general trends towards thinner ice and an increase in the length of the open water season. However, the paucity of in-situ observations in many countries has led to remote sensing playing an ever-increasing role for lake ice monitoring. This paper reviews progress in lake ice research conducted using active microwave remote sensing over the last 50 years and highlights areas where future developments are needed. Analysis of the literature found that the diversity of study areas where this work is taking place has increased, incorporating more lake sites from the mid-latitudes in North America, Europe, and Asia. Additionally, clear connections can be made between the launch of synthetic aperture radar (SAR) satellites and patterns in the use of certain radar frequencies in scientific studies. This review also discusses evolving theories concerning the scattering mechanisms associated with lake ice as well as the current state of lake ice cover and ice thickness retrieval methods. The paper concludes with a suite of recommendations for future research, highlighting work that is needed on: 1) advancing our understanding of the response of active microwave signals to lake ice properties; 2) applying machine learning algorithms to lake ice classification; 3) further exploring the retrieval of ice thickness from imaging SAR and radar altimetry data; and 4) other considerations such as data availability and the topic of snow on lake ice. • Active microwave remote sensing is the most common technology for studying lake ice. • Our understanding of how active data interacts with lake ice properties has changed. • A variety of methods exist for monitoring lake ice cover using SAR data. • Research is needed on interactions between active signals and lake ice properties. • Data availability will help develop algorithms to monitor essential ice properties. [ABSTRACT FROM AUTHOR]

Around 350 million hectares of land are affected by wildfires every year influencing the health of ecosystems and leaving a trail of destruction. Accurate information over burned areas (BA) is essential for governments and communities to prioritize recovery actions. Prior research over the past decades has established the potentials and limitations of space-borne earth observation for mapping BA over large geographic areas at various scales. The operational deployment of Sentinel-1 and Sentinel-2 constellations significantly improved the quality and quantity of the imagery from the microwave (C-band) and optical regions on the spectrum. Based on that, this study set to investigate whether the existing coarse BA products can be further improved by the synergy of optical surface reflectance (SR), radar backscatter coefficient (BS), and/or radar interferometric coherence (COR) data with higher spatial resolutions. A Siamese Self-Attention (SSA) classification strategy is proposed for the multi-sensor BA mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by test sites, feature sources, and classification strategies to appraise the improvements achieved by the proposed method. • Sentinel-1 and Sentinel-2 data are synergistically used for burned area mapping. • A small object dataset is constructed over 12 California fires in 2018–2019. • A deep-learning-based method is applied to discriminate burns and unburns. • Performance is assessed over different data sources, test sites, and classifiers. • The flexibility of the trained classifier is evaluated over independent sites. [ABSTRACT FROM AUTHOR]

Radar altimeters are important tools to monitor the volume of the ice sheets. The penetration of radar waves in the snowpack is a major source of uncertainty to retrieve surface elevation. To correct this effect, a better understanding of the sensitivity of the radar waveforms to snow properties is needed. Here, we present an extension of the Snow Model Radiative Transfer (SMRT) to compute radar waveforms and conduct a series of simulations on the Antarctic ice sheet. SMRT is driven by snow and surface roughness properties measured over a large latitudinal range during two field campaigns on the Antarctic Plateau. These measurements show that the snowpack is rougher, denser, less stratified, warmer, and has smaller snow grains near the coast than on the central Plateau. These simulations are compared to satellite observations in the Ka, Ku, and S bands. SMRT reproduces the observed waveforms well. For all sites and all sensors, the main contribution comes from the surface echo. The echo from snow grains (volume scattering) represents up to 40% of the amplitude of the total waveform power in the Ka band, and less at the lower frequencies. The highest amplitude is observed on the central Plateau due to the combination of higher reflection from the surface, higher scattering by snow grains in the Ka and Ku bands, and higher inter-layer reflections in the S band. In the Ka band, the wave penetrates in the snowpack less deeply on the central Plateau than near the coast because of the strong scattering caused by the larger snow grains. The opposite is observed in the S band, the wave penetrates deeper on the central Plateau because of the lower absorption due to the lower snow temperatures. The elevation bias caused by wave penetration into the snowpack show a constant bias of 10 cm for all sites in the Ka band, and a bias of 11 cm, and 21 cm in the Ku band for sites close to the coast and the central Plateau, respectively. Now that SMRT is performing waveform simulations, further work will address how the snowpack properties affect the parameters retrieved by more advanced retracking algorithms such as ICE-2 for different snow cover surfaces. • The snow radiative transfer model (SMRT) is extended for altimetry applications. • Waveform simulations are in good agreement with satellite observations in Antarctica. • The surface echo is the most important contribution in the Ka, Ku and S bands. • Smoother surfaces at the southern sites lead to higher waveform amplitudes. • In the Ka (resp. S) band, grain size (temperature) regulates the wave penetration depth. [ABSTRACT FROM AUTHOR]