Backscatter Observations Research Articles

Point-cloud clustering and tracking algorithm for radar interferometry

In data mining, density-based clustering, which entails classifying datapoints according to their distributions in some space, is an essential method to extract information from large datasets. With the advent of software-based radio, ionospheric radars are capable of producing unprecedentedly large datasets of plasma turbulence backscatter observations, and new automatic techniques are needed to sift through them. We present an algorithm to automatically identify and track clusters of radar echoes through time, using , a celebrated density-based clustering method for noisy point clouds. We demonstrate our algorithm's efficiency by tracking turbulent structures in the E-region ionosphere, the so-called radar aurora. Through conjugate auroral imagery, as well as satellite observations, we demonstrate that the observed turbulent structures generally track the motion of auroras. What is more, the radar aurora bulk motions exhibit key qualities of auroral electric field enhancements that have previously been observed with various instruments. We present preliminary statistical results using our method, and briefly discuss the method's limitations and potential future adaptations. Published by the American Physical Society 2024

Design and performance of the Climate Change Initiative Biomass global retrieval algorithm

The increase in Earth observations from space in recent years supports improved quantification of carbon storage by terrestrial vegetation and fosters studies that relate satellite measurements to biomass retrieval algorithms. However, satellite observations are only indirectly related to the carbon stored by vegetation. While ground surveys provide biomass stock measurements to act as reference for training the models, they are sparsely distributed. Here, we addressed this problem by designing an algorithm that harnesses the interplay of satellite observations, modeling frameworks and field measurements, and generated global estimates of above-ground biomass (AGB) density that meet the requirements of the scientific community in terms of accuracy, spatial and temporal resolution. The design was adapted to the amount, type and spatial distribution of satellite data available around the year 2020. The retrieval algorithm estimated AGB annually by merging estimates derived from C- and L-band Synthetic Aperture Radar (SAR) backscatter observations with a Water Cloud type of model and does not rely on AGB reference data at the same spatial scale as the SAR data. This model is integrated with functions relating to forest structural variables that were trained on spaceborne LiDAR observations and sub-national AGB statistics. The yearly estimates of AGB were successively harmonized using a cost function that minimizes spurious fluctuations arising from the moderate-to-weak sensitivity of the SAR backscatter to AGB. The spatial distribution of the AGB estimates was correctly reproduced when the retrieval model was correctly set. Over-predictions occasionally occurred in the low AGB range (<50 Mg ha−1) and under-predictions in the high AGB range (>300 Mg ha−1). These errors were a consequence of sometimes too strong generalizations made within the modeling framework to allow reliable retrieval worldwide at the expense of accuracy. The precision of the estimates was mostly between 30% and 80% relative to the estimated value. While the framework is well founded, it could be improved by incorporating additional satellite observations that capture structural properties of vegetation (e.g., from SAR interferometry, low-frequency SAR, or high-resolution observations), a dense network of regularly monitored high-quality forest biomass reference sites, and spatially more detailed characterization of all model parameters estimates to better reflect regional differences.

Multi-source machine learning and spaceborne remote sensing data accurately predict three-dimensional soil moisture in an in-service uranium disposal cell

One reason arid and semi-arid environments have been used to store waste is due to low groundwater recharge, presumably limiting the potential for meteoric water to mobilize and transport contaminants into groundwater. The U.S. Department of Energy Office of Legacy Management (LM) is evaluating selected uranium mill tailings disposal cell covers to be managed as evapotranspiration (ET) covers, where vegetation is used to naturally remove water from the cover profile via transpiration, further reducing deep percolation. An important parameter in monitoring the performance of ET covers is soil moisture (SM). If SM is too high, water may drain into tailings material, potentially transporting contaminants into groundwater; if SM is too low, radon flux may increase through the cover. However, monitoring SM via traditional instrumentation is invasive, expensive, and may fail to account for spatial heterogeneity, especially over vegetated disposal cells. Here we investigated the potential for non-invasive SM monitoring using radar remote sensing and other geospatial data to see if this approach could provide a practical, accurate, and spatially comprehensive tool to monitor SM. We used theoretical simulations to analyze the sensitivity of multi-frequency radar backscatter to SM at different depths of a field-scale (3 ha) drainage lysimeter embedded within an in-service LM disposal cell. We then evaluated a shallow and deep form of machine learning (ML) using Google Earth Engine to integrate multi-source observations and estimate the SM profile across six soil layers from depths of 0–2 m. The ML models were trained using in situ SM measurements from 2019 and validated using data from 2014 to 2018 and 2020–2021. Model predictors included backscatter observations from satellite synthetic aperture radar, vegetation, temperature products from optical infrared sensors, and accumulated, gridded rainfall data. The radar simulations confirmed that the lower frequencies (L- and P-band) and smaller incidence angles show better sensitivity to deeper soil layers and an overall larger SM dynamic range relative to the higher frequencies (C- and X-band). The ML models produced accurate SM estimates throughout the soil profile (r values from 0.75 to 0.94; RMSE = 0.003–0.017 cm3/cm3; bias = 0.00 cm3/cm3), with the simpler shallow-learning approach outperforming a selected deep-learning model. The ML models we developed provide an accurate, cost-effective tool for monitoring SM within ET covers that could be applied to other vegetated disposal cell covers, potentially including those with rock-armored covers.

Multi‐Frequency SuperDARN Interferometer Calibration

AbstractThe ground‐based, high‐frequency radars of the Super Dual Auroral Radar Network (SuperDARN) observe backscatter from ionospheric field‐aligned plasma irregularities and features on the Earth's surface out to ranges of several thousand kilometers via over‐the‐horizon propagation of transmitted radio waves. Interferometric techniques can be applied to the received signals at the primary and secondary antenna arrays to measure the vertical angle of arrival, or elevation angle, for more accurate geolocation of SuperDARN observations. However, the calibration of SuperDARN interferometer measurements remains challenging for several reasons, including a 2π phase ambiguity when solving for the time delay correction factor needed to account for differences in the electrical path lengths between signals received at the two antenna arrays. We present a new technique using multi‐frequency ionospheric and ground backscatter observations for the calibration of SuperDARN interferometer data, and demonstrate its application to both historical and recent data.

Uncertainty estimates in the NISAR high-resolution soil moisture retrievals from multi-scale algorithm

It is important to know the amount of systematic and random uncertainties in any state variable to improve its geophysical application potential. The expected high-resolution (200 [m]) soil moisture product from the NASA-ISRO Synthetic Aperture Radar (NISAR) mission is no exception. Thus, knowing the quality of the soil moisture retrievals through the estimation of various error sources is imperative. The estimation error sources in soil moisture retrievals can be obtained by various methods. In situ measurements provide a reliable estimate of the uncertainty of soil moisture retrievals. However, in situ measurements are available only for limited locations, as they are typically very tedious and expensive to obtain. Thus, an analytical approach has been developed to obtain an estimate of the uncertainty in the soil moisture retrievals that vary in space and time across grid-cells. This uncertainty estimation is specifically developed for the multi-scale algorithm of the upcoming NISAR mission, which will provide soil moisture retrievals at 200 [m] resolution. The multi-scale algorithm for the NISAR mission disaggregates the coarser resolution soil moisture (∼9 [km]) to high-resolution (∼200 [m]) using NISAR L-band SAR measurements. However, uncertainty in high-resolution soil moisture retrievals might be introduced due to errors in input datasets (e.g., coarse resolution soil moisture, instrument error of SAR, etc.) and multi-scale algorithm parameters. Therefore, this study carried out a detailed sensitivity analysis of input datasets and algorithm parameters using the proposed approach. The sensitivity analysis shows that error in the input coarse resolution soil moisture is one of the primary drivers of uncertainty in the high-resolution soil moisture retrievals. The other portion of the uncertainty comes from errors in the algorithm parameters, and noise in SAR co-pol and cross-pol backscatter observations. Furthermore, the approach was tested on the UAVSAR L-band data time-series that had been simulated to closely match the expected characteristics of NISAR (e.g., spatial resolution and noise). The uncertainty estimates in UAVSAR-based high-resolution retrievals were compared with the SMAPVEX-12 in situ measurements. The uncertainties estimated for different crops were found to be close to the ubRMSE metric, which is also lower than the NISAR mission accuracy goal (0.06 [m3/m3]).

Irrigation Quantification Through Backscatter Data Assimilation With a Buddy Check Approach

AbstractIrrigation is an important component of the terrestrial water cycle, but it is often poorly accounted for in models. Recent studies have attempted to integrate satellite data and land surface models via data assimilation (DA) to (a) detect and quantify irrigation, and (b) better estimate the related land surface variables such as soil moisture, vegetation, and evapotranspiration. In this study, different synthetic DA experiments are tested to advance satellite DA for the estimation of irrigation. We assimilate synthetic Sentinel‐1 backscatter observations into the Noah‐MP model coupled with an irrigation scheme. When updating soil moisture, we found that the DA sets better initial conditions to trigger irrigation in the model. However, DA updates to wetter conditions can inhibit irrigation simulation. Building on this limitation, we propose an improved DA algorithm using a buddy check approach. The method still updates the land surface, but now the irrigation trigger is not primarily based on the evolution of soil moisture, but on an adaptive innovation (observation minus forecast) outlier detection. The new method was found to be optimal for more temperate climates where irrigation events are less frequent and characterized by higher application rates. It was found that the DA outperforms the model‐only 14‐day irrigation estimates by about 20% in terms of root‐mean‐squared differences, when frequent (daily or every other day) observations are available. With fewer observations or high levels of noise, the system strongly underestimates the irrigation amounts. The method is flexible and can be expanded to other DA systems, also real‐world cases.

Synergistic Retrievals of Ice in High Clouds from Elastic Backscatter Lidar, Ku-Band Radar, and Submillimeter Wave Radiometer Observations

Abstract In this study, we investigate the synergy of elastic backscatter lidar, Ku-band radar, and submillimeter-wave radiometer measurements in the retrieval of ice from satellite observations. The synergy is analyzed through the generation of a large dataset of ice water content (IWC) profiles and simulated lidar, radar and radiometer observations. The characteristics of the instruments (frequencies, sensitivities, etc.) are set based on the expected characteristics of instruments of the Atmosphere Observing System (AOS) mission. A hold-out validation methodology is used to assess the accuracy of the IWC profiles retrieved from various combinations of observations from the three instruments. Specifically, the IWC and associated observations are randomly divided into two datasets, one for training and the other for evaluation. The training dataset is used to train the retrieval algorithm, while the evaluation dataset is used to assess the retrieval performance. The dataset of IWC profiles is derived from CloudSat reflectivity and CALIOP lidar observations. The retrieval of the ice water content IWC profiles from the computed observations is achieved in two steps. In the first step, a class, of 18 potential classes characterized by different vertical distribution of IWC, is estimated from the observations. The 18 classes are predetermined based on the k-means clustering algorithm. In the second step, the IWC profile is estimated using an ensemble Kalman smoother algorithm that uses the estimated class as a priori information. The results of the study show that the synergy of lidar, radar, and radiometer observations is significant in the retrieval of the IWC profiles. Nevertheless, it should be mentioned that this synergy was found under idealized conditions, and additional work might be required to materialize it in practice. The inclusion of the lidar backscatter observations in the retrieval process has a larger impact on the retrieval performance than the inclusion of the radar observations. As ice clouds have a significant impact on atmospheric radiative processes, this work is relevant to ongoing efforts to reduce uncertainties in climate analyses and projections.

Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments

Abstract Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments. Significance Statement The purpose of this study is to improve streamflow estimation by integrating soil moisture information from satellite observations into a hydrological modeling framework. This is important preparatory work for operational centers that are responsible for producing the most accurate flood forecasts for the society. Our results provide new insights into how and where streamflow forecasting could benefit from high-spatial-resolution Sentinel-1 radar backscatter observations.

Groundtruther: A QGIS plug-in for seafloor characterization

This work focuses on developing a software system for concurrently analyzing co-located multibeam echo sounder (MBES) datasets and seafloor imagery, fulfilling the need for a unified seafloor data exploration and analysis platform. The system comprises a graphical user interface where image browsing and geospatial data are linked and several toolboxes for extracting backscatter distribution, its angular response, and bathymetric derivatives to ultimately build detailed quantitative reports. The overall objective is to provide an efficient means of understanding the relationships between morphology, backscatter, and the observed biota and, thus, understanding the relationship between the physical and ecological elements of the seafloor. In addition, Groundtruther provides new ways to interpret remotely sensed information derived from MBES and aid the development of spatial distribution models. Furthermore, it could lead to the enrichment of ground-truth databases used to develop formal geophysical models that link acoustic backscatter observations to intrinsic properties of the seafloor.

Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France

In this work, Advanced SCATterometer (ASCAT) backscatter data are directly assimilated into the interactions between soil, biosphere, and atmosphere (ISBA) land surface model using Meteo-France’s global Land Data Assimilation System (LDAS-Monde) tool in order to jointly analyse soil moisture and leaf area index (LAI). For the first time, observation operators based on neural networks (NNs) are trained with ISBA simulations and LAI observations from the PROBA-V satellite to predict the ASCAT backscatter signal. The trained NN-based observation operators are implemented in LDAS-Monde, which allows the sequential assimilation of backscatter observations. The impact of the assimilation is evaluated over southwestern France. The simulated and analysed backscatter signal, surface soil moisture, and LAI are evaluated using satellite observations from ASCAT and PROBA-V as well as in situ soil moisture observations. An overall improvement in the variables is observed when comparing the analysis with the open-loop simulation. The impact of the assimilation is greater over agricultural areas.

An integrated monitoring system (IMS) for air quality: Observations of a unique ozone-exceedance event in Maryland

Ozone (O3) exceedance events in the mid-Atlantic states of the U.S. are typically observed during the summer months. They frequently result from local diurnal emissions of NO2 and VOCs where photochemical production of O3 is most prevalent. Occasionally, summertime O3-exceedances in the region are driven by long-range transport of pollutants or regional nocturnal dynamics. Discussed herein is the use of multiple observation platforms as an integrated monitoring system (IMS) to characterize the spatiotemporal confluence of long-range transport and nocturnal low-level jet induced O3-exceedances impacting air quality in the mid-Atlantic region. The IMS discussed is comprised of enhanced monitoring of vertically resolved observations of O3, atmospheric backscatter, winds, and state variables (e.g., temperature and relative humidity). This is in combination with surface-O3 observations, satellite-retrieved aerosol optical depth (by the moderate resolution imaging spectroradiometer (MODIS)), and simulations of air mass transport by NOAA's hybrid single-particle Lagrangian trajectory (HYSPLIT) model. Here we discuss an exceedance event that impacted the Mid-Atlantic region on May 20, 2021, where seven air monitoring sites in Maryland were driven by the combination of nocturnal low-level jet downmixing of pollutants and long-range transport of biomass-burning pollutants/O3-precursors. The most notable features of this case are this event's deviation from decadal O3-exceedance seasonality trends, the large spatial context of high surface-O3 impacting traditionally non-exceedance sites (sites that have not exceeded in years), the arrival of a well-mixed polluted air mass into the region, and nocturnal increases in surface-O3 induced by NLLJ.

Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations

In preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014–2018) with 1-km Sentinel-1 backscatter (γ°) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019–2021), the γ° simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 γ° remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil γ° holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors.

Comparison of structure and turbulent mixing between lateral and leading-edge river plume fronts: Microstructure observations from a T-REMUS AUV

The energetic frontal region of the tidally pulsed Connecticut River plume was sampled with the T-REMUS Autonomous Underwater Vehicle (AUV) which navigated ten cross-front transects at various depths. Synchronous and high resolution hydrographic, microstructure, velocity and backscatter observations allowed the creation of detailed views of frontal structure and turbulent mixing in the reference frame of the propagating front. The front was defined by a sharp horizontal density gradient of ∼18.5 kg m−4 across only 0.7 m in the horizontal, and strong downwelling velocities of 0.17 m/s. Three successive zones were consistently identified. The first O (10 m) wide downwelling zone in which buoyant plume water was forced downward, thereby forming a highly energetic frontal head that extended down to 6 m depth and had TKE dissipation rates (ε) of order 10−3 m2/s3. Beyond the downwelling zone to O (100 m), a transition zone was observed, characterized by substantial entrainment of ambient water. This zone had high amplitude and high frequency density anomaly (σθ) variability consistent with elevated but decreasing turbulent kinetic energy dissipation rates (ε) which ranged between 10−4 and 10−5 m2/s3. Beyond O (100 m) from the front, stable stratification, high Ri values, and low Reb values, suppressed the growth of instabilities and diminished turbulent mixing. In this stable interior region the plume base shoaled to 2 m depth and ε decreased further, from 10−5 to 10−7 m2/s3. In ambient Long Island Sound waters, ε ranged between 10−6 and 10−7 m2/s3. The cross-front distributions of σθ and ε did not change substantially as the front transformed from a lateral boundary, where plume flow was primarily in the along-front direction, to one that was at the leading edge of the plume, where plume flow was mostly in the cross-front direction. In both cases, a significant convergence was present at the front due to strong ambient tidal velocities. The observed ε values and those from four previous studies of large and mid-sized plumes were nondimensionalized using the velocity and length scales associated with the downwelling region at the front. The effective normalization suggests that local dynamics primarily control ε in the near-frontal region.

Understanding Sentinel-1 backscatter response to sugarcane yield variability and waterlogging

Sentinel-1 observes the whole globe every 12 days (6 days when both satellites were operational) and provides a wealth of data relevant to agriculture. Sugarcane cultivators could potentially benefit from these data by using them to assist operational and management practices. However, first, thorough understanding is needed of Sentinel-1 backscatter and its behavior over sugarcane canopies. In this study, we aimed to improve understanding of how Sentinel-1 backscatter responds to sugarcane yield variability and waterlogging. In order to do so we focused on an irrigated sugarcane plantation in Xinavane, Mozambique. In the analysis presented, we assessed different polarizations, their ratio, and benchmarked them against optical indices and passive microwave observations in different seasons. With the help of a large sugarcane yield dataset, we analyzed how backscatter relates to sucrose yield variability in different seasons. We found VV backscatter related to the stalk development, the most important reservoir for sucrose accumulation. In addition, in a season with reported waterlogging, optical and radar observations showed a delay in sugarcane crop development. Further analysis showed the presence of water underneath the canopy caused an increase in all polarizations and the cross ratio (CR). The results imply that Sentinel-1 backscatter contains information on both waterlogging under the canopy as well as sucrose development in the stalk. By isolating and quantifying the impact of waterlogging on backscatter, it will be possible to further quantify sucrose development with backscatter observations and identify waterlogging simultaneously.

Observed aerosol‐layer depth at Station Nord in the high Arctic

AbstractThe depth of the aerosol layer at the Villum Research Station at Station Nord in the high Arctic is analysed based on 8 years of observations from a ceilometer and one full year from a wind lidar. The layer is of particular interest for aerosol process modelling and atmospheric chemistry studies. The depth of the aerosol layer is assigned to the inflection point in the attenuated backscatter profile by two methods; one is based on polynomial approximation of the profile and the other is direct numerical differentiation. The analysis is based on two types of hourly profiles; one consists of averaging the attenuated backscatter observations and the other by computing the median. Due to sporadic occurrence of outliers in the ranges around 50 m in the ceilometer observations, this part of the profile is not used in this study. Restricting the observations to heights above 100 m, the depths of the aerosol layer are found to be typically ≈230 m. It varies little between winter and summer, but the spread in the depth is larger during the winter as compared to summer. To extend the study of the aerosol‐layer depth below 100 m, a method is applied that combines the ceilometer measurements with the carrier‐to‐noise ratio from the wind lidar. The results are available for 2018 only, and they show aerosol‐layer depths below ≈80 m as well as depths around 230 m and they show few observations between ≈80 and ≈230 m. Near the ground, the observed backscatter exhibits a pronounced seasonal variation, having low values during the summer and high values during the winter. The strength of the seasonal variability decreases with height, especially above the aerosol‐layer depth, and is virtually absent at 1 km.

Seasonal variations in vegetation water content retrieved from microwave remote sensing over Amazon intact forests

Vegetation optical depth (VOD) is seasonally sensitive to plant water content and aboveground biomass. This index has a strong penetrability within the vegetation canopy and is less impacted by atmosphere aerosol contamination effects, clouds and sun illumination than optical vegetation indices. VOD is thus increasingly applied in ecological applications, e.g., carbon stock, phenology and vegetation monitoring. However, VOD retrieval over dense forests is subject to uncertainties caused by the thick canopy and complex multiple scattering effects. Thus, a comprehensive evaluation of VOD products over dense forests is needed for effective and accurate applications. This study evaluated the seasonal variations of eight recently developed/reprocessed VOD products at different frequencies (e.g., Ku-, X-, C- and L-band) over Amazon intact forests, supported by the ORCHIDEE-CAN-NHA model-simulated vegetation water content. Furthermore, we also explored the potential causes of VOD retrieval issues, in terms of retrieval algorithm uncertainties.We first confirmed that soil water availability dominated seasonal dynamics of vegetation water content over Amazon intact forests. This was verified by model-simulated vegetation water content and by C-band radar backscatter observations. Generally, evening or midday vegetation water content shows higher correlations with soil moisture than morning or midnight vegetation water content. In terms of ability of morning or midnight VOD products to follow the seasonality of soil moisture, active microwave ASCAT-IB C-VOD (median seasonal correlation with soil moisture (R) ∼ 0.50) outperforms the passive microwave VOD products, followed by passive microwave AMSR2 X-VOD (R ∼ 0.26) and VODCA X-VOD (R ∼ 0.16). However, SMOS-IC L-VOD (R ∼ −0.15) and AMSR2 C1-VOD (R ∼ −0.20) show obviously negative seasonal correlations with soil moisture across most pixels. This implausible behavior is likely to be caused by the inappropriate setting of time-invariant scattering effects in the passive microwave VOD retrieval algorithms, which could lead to an overestimation of the VOD amplitude during dry seasons. Thus, we recommend that the seasonal scattering effects be considered in the passive microwave VOD retrieval algorithms. These findings can contribute to the improvement of VOD retrieval algorithms and help with the development of their ecological applications over Amazon dense forests.

Assimilation of Backscatter Observations into a Hydrological Model: A Case Study in Belgium Using ASCAT Data

We investigated the possibilities of improving hydrological simulations by assimilating radar backscatter observations from the advanced scatterometer (ASCAT) in the hydrological model SCHEME using a calibrated water cloud model (WCM) as an observation operator. The WCM simulates backscatter based on soil moisture and vegetation data and can therefore be used to generate observation predictions for data assimilation. The study was conducted over two Belgian catchments with different hydrological regimes: the Demer and the Ourthe catchment. The main differences between the two catchments can be summarized in precipitation and streamflow levels, which are higher in the Ourthe. The data assimilation method adopted here was the ensemble Kalman filter (EnKF), whereby the uncertainty of the state estimate was described via the ensemble statistics. The focus was on the optimization of the EnKF, and possible solutions to address biases introduced by ensemble perturbations were investigated. The latter issue contributes to the fact that backscatter data assimilation only marginally improves the overall scores of the discharge simulations over the deterministic reference run, and only for the Ourthe catchment. These performances, however, considerably depend on the period considered within the 5 years of analysis. Future lines of research on bias correction, the data assimilation of soil moisture and backscatter data are also outlined.

Canyon Downwelling and Water Mass Influences on Winter Zooplankton Distributions in the Coastal Northeast Pacific

AbstractSubmarine canyons are considered biological hotspots due to enhanced mixing, upwelling, and other dynamic processes. There have been few observational surveys of canyon processes or zooplankton‐watermass relations during stormy downwelling seasons. In winter 2017, three autonomous underwater gliders provided highly‐resolved hydrographic and zooplankton backscatter observations in and around a canyon in the Northeast Pacific. While a glider's slow speed typically confounds spatial and temporal signals, the concurrent use of multiple gliders, sampling a canyon transect repeatedly, allowed us to explore the canyon system both spatially (i.e., canyon influence) and temporally (i.e., changing water type fractions). Canyon downwelling displaced the pycnocline by about 30 m depth over the scale of the canyon, which is ∼30% of the total pycnocline depth deviation observed over the course of the study. Diel vertically migrating (DVM) zooplankton generally stayed above the pycnocline and mean zooplankton volume scattering strength (Sv) in the upper water column (above the pycnocline) was higher when the pycnocline was deeper. At mid‐depths (150–200 m depth), zooplankton backscatter and DVM behavior were correlated with water type fractions, with higher Sv being associated with higher proportions of Pacific Equatorial Water and stronger DVM being associated with higher proportions of Pacific Subarctic Upper Water. Winter zooplankton distributions appear to be influenced by multiple, interacting drivers including canyon dynamics and water mass composition. In our study, the latter was found to have the stronger influence on zooplankton distribution.

Factors Controlling a Synthetic Aperture Radar (SAR) Derived Root-Zone Soil Moisture Product over The Seward Peninsula of Alaska

Root-zone soil moisture exerts a fundamental control on vegetation, energy balance, and the carbon cycle in Arctic ecosystems, but it is still not well understood in vast, remote, and understudied regions of discontinuous permafrost. The root-zone soil moisture product (30 m resolution) used in this analysis was retrieved from a time-series P-Band (420–440 MHz) synthetic aperture radar (SAR) backscatter observations (August 2017 &amp; October 2017). While similar approaches have been taken to retrieve surface (0 cm to 5 cm) soil moisture from L-Band (1.2 GHz) SAR backscatter, this is one of the first known attempts at reaching the root-zone in permafrost regions. Here, we analyze secondary factors (excluding primary factors, such as precipitation) controlling summer (August) soil moisture at depths of 6 cm, 12 cm, and 20 cm over a 4500 km2 area on the Seward Peninsula of Alaska. Using a random forest model, we quantify the impact of topography, vegetation, and meteorological factors on soil moisture distributions. In developing the random forest model, we explore a variety of feature scales (30 m, 60 m, 90 m, 120 m, 180 m, and 240 m), tune hyperparameters (the structure of individual decision trees making up the ensemble including the number and depth of trees), and perform the final feature selection using cross-validated recursive feature elimination. Results suggest that root-zone soil moisture on the Seward Peninsula is primarily controlled by vegetation at 6 cm, but deeper in the soil column topography and meteorological factors, such as predominant winter wind direction and summer insolation, play a larger role. The random forest model accounts for 40% to 60% of the variation observed (R2 = 0.44 at 6 cm, R2 = 0.52 at 12 cm, R2 = 0.58 at 20 cm). These results indicate that vegetation is the dominant control on soil moisture shallow in the soil column, but the impact of vegetation does not extend to deeper layers retrieved from P-Band SAR backscatter.

Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP

Abstract. In recent years, the amount of water used for agricultural purposes has been rising due to an increase in food demand. However, anthropogenic water usage, such as for irrigation, is still not or poorly parameterized in regional- and larger-scale land surface models (LSMs). By contrast, satellite observations are directly affected by, and hence potentially able to detect, irrigation as they sense the entire integrated soil–vegetation system. By integrating satellite observations and fine-scale modelling it could thus be possible to improve estimation of irrigation amounts at the desired spatial–temporal scale. In this study we tested the potential information offered by Sentinel-1 backscatter observations to improve irrigation estimates, in the framework of a data assimilation (DA) system composed of the Noah-MP LSM, equipped with a sprinkler irrigation scheme, and a backscatter operator represented by a water cloud model (WCM), as part of the NASA Land Information System (LIS). The calibrated WCM was used as an observation operator in the DA system to map model surface soil moisture and leaf area index (LAI) into backscatter predictions and, conversely, map observation-minus-forecast backscatter residuals back to updates in soil moisture and LAI through an ensemble Kalman filter (EnKF). The benefits of Sentinel-1 backscatter observations in two different polarizations (VV and VH) were tested in two separate DA experiments, performed over two irrigated sites, the first one located in the Po Valley (Italy) and the second one located in northern Germany. The results confirm that VV backscatter has a stronger link with soil moisture than VH backscatter, whereas VH backscatter observations introduce larger updates in the vegetation state variables. The backscatter DA introduced both improvements and degradations in soil moisture, evapotranspiration and irrigation estimates. The spatial and temporal scale had a large impact on the analysis, with more contradicting results obtained for the evaluation at the fine agriculture scale (i.e. field scale). Above all, this study sheds light on the limitations resulting from a poorly parameterized sprinkler irrigation scheme, which prevents improvements in the irrigation simulation due to DA and points to future developments needed to improve the system.