Search

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2021-08-07, 資料番号: PA2210062000

A network of High-Frequency radar (HFR) stations runs operationally in the Malta-Sicily Channel (MSC), Central Mediterranean Sea, providing sea surface current maps with high temporal (1 h) and spatial (3 × 3 km) resolutions since August 2012. Comparisons with surface drifter data and near-surface Acoustic Doppler Current Profiler (ADCP) observations, as well as radar site-to-site baseline analyses, provide quantitative assessments of HFR velocities accuracy. Twenty-two drifters were deployed within the HFR domain of coverage between December 2012 and October 2013. Additionally, six ADCP vertical current profiles were collected at selected positions during a dedicated field survey. External radio-frequency interferences lead to significant gaps in the HFR coverage, which were minimized by an interpolation technique applied to the surface current fields. The validation of HFR velocities was first performed for each individual station in the network, using data generated with both the ideal and the measured antenna patterns. The validation was then repeated with pairs of HFR stations using the site-to-site baseline error analysis. The analysis confirms that the baseline error estimations and the full validation metrics for this HFR frequency band are consistent, and in some aspects superior, when compared to results from similar studies in other regions. The validation with drifter tracks suggests that the adopted interpolation procedure does not in general impact on the comparison metrics, but may introduce biases when used to extrapolate currents in regions where radar geometrical constraints are suboptimal. The analysis of the percentage of contribution of individual HFR stations shown that the quality of HFR data is influenced by the network geometry.

Multi-temporal analysis has been widely acknowledged as a promising method to derive soil moisture from radar backscatter observations. The method assumes that only soil moisture varies in the period of interest, while all other parameters such as vegetation water content and soil surface roughness are sufficiently time invariant. However, this assumption is not easy to satisfy in agricultural areas where cultivation practices such as ploughing and irrigation are irregularly conducted between radar acquisitions. The paper has proposed an unsupervised change detection method to serve as a pre-processing procedure for multi-temporal retrieval. Briefly, the temporal ratio of HV and the temporal difference of HV/VV and VV polarizations were selected as the optimal feature space, using a genetic algorithm based feature selection algorithm and an extensive synthetic data set. The change map is determined from a two-step procedure with the first step producing multiple over-detected change maps for the period of interest using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. The second step merges the multiple change maps to remove the false alarms with a principle similar to the ensemble machine learning. Evaluation on a synthetic data set demonstrated that the proposed method can largely remove the error in multi-temporal soil moisture retrieval that is caused by abrupt roughness and vegetation changes. Evaluation on real radar data sets, including airborne L-band radar, RADARSAT-2 at C-band and COSMO SkyMed at X-band, demonstrated an accurate identification (>0.9) while yielding a low false-alarm rate (

The Soil Moisture Active Passive (SMAP) satellite can no longer directly deliver high-resolution (9 km) soil moisture products with the failure of the onboard L-band radar. Thus, an appropriate replacement sensor and new algorithms are urgently needed to compensate for the lost radar and estimate soil moisture at high-resolution. This paper presents a new downscaling approach, the Downscaling basEd oN gradient boosting deciSion trEe (DENSE) method to solve this problem. We analyzed 26 soil moisture related indices, derived from MODIS and a digital elevation model, to identify proxy variables for soil moisture variability. A gradient boosting decision tree regression links the aggregated soil moisture proxies and SMAP observations at a coarse scale to express the nonlinear relationships between them. High-resolution soil moisture products were generated by applying this built regression model to the optimal soil moisture proxies at a fine scale over the entire Tibetan Plateau during the years 2015–2017. In situ measurements were collected from the Ngari, Naqu, and Maqu networks, which represent different climatic and vegetation conditions. We evaluated the downscaled soil moisture against ground observations at the daily, point, and network scales. The results indicated that the DENSE method effectively infers the spatio-temporal variability of soil moisture; and at the same time, preserves SMAP soil moisture accuracy. The performance of the proposed method failed in the Maqu network as this area is covered by dense vegetation. Although further improvements are still needed to correct vegetation effects, nevertheless, the DENSE method improved the spatial resolution of SMAP soil moisture estimates, from 36 km to 1 km over the Tibetan plateau where high-resolution soil moisture products are necessary to support local and global hydrological applications.

Active and passive microwave signals over land co-vary depending on their shared scattering and emission characteristics by soil and vegetation media. Estimates of this covariation can be used beneficially to downscale coarse-resolution brightness temperatures with high-resolution backscatter for enhanced-resolution Earth observations. In this study, a forward model of covariation for vegetated soil is derived by combining two well-established models of active and passive microwave interactions. The covariation model is inverted to obtain a single-pass observation-driven estimation of active-passive microwave covariation (β) based on multi-channel radiometer and Synthetic Aperture Radar (SAR) scenes. A key feature of the estimation approach is that it is applicable to co-located spatial data scenes and does not rely on temporal information. We present applications of the estimation with combinations of SMAP (L-band) radiometry and both SMAP (L-band) and multi-angular Sentinel-1 (C-band) backscatter data. We first show that for the period of available SMAP L-band radiometer and radar data, the estimation approach for β, introduced in this study, yields similar results as the statistical time-series approach originally designed for SMAP. The new single-pass approach also allows estimation of active-passive covariation where the statistical approach cannot be applied because dynamic range of brightness temperature and backscatter are too limited to allow regression. We then apply the developed estimation method to SMAP L-band radiometer and multi-angular Sentinel-1 C-band SAR data. Here, the study quantifies the effects of microwave frequency and look-angles on the covariation by applying the estimation globally and analyzing the results as a function of vegetation cover – a key determinant of the strength of the covariation.

Hurricane Harvey caused unprecedented flooding and socioeconomic devastation in Eastern Texas with high winds, elevated storm tide, and record rainfall. The flooded area is mapped using the radar backscattering difference between Sentinel-1A/B satellite acquisitions spanning the event, which provides a snapshot of standing water at the time of image acquisition. We find vast areas outside of designated flood hazard zones are overwhelmed. Furthermore, we map pre-cyclone land subsidence using multitemporal interferometric processing of large SAR datasets acquired by Advanced Land Observation Satellite (ALOS) and Sentinel-1A/B satellites. We find that subsidence of up to 49 mm/yr and 34 mm/yr during the ALOS (Jul-2007–Jan-2011) and Sentinel-1A/B (Dec-2015 to Aug-2017) acquisition periods affect various parts of Houston-Galveston area. We conclude that 85% of the flooded area subsided at a rate > 5 mm/yr. We suggest that subsidence affected flood severity by modifying base flood elevations and topographic gradients, supported by the Chi-square test of independence. This work highlights the importance of incorporating InSAR measurements of land subsidence in flood resilience strategies.

Soil moisture (SM) is a key state variable in understanding the climate system through its control on the land surface energy, water budget partitioning, and the carbon cycle. Monitoring SM at regional scale has become possible thanks to microwave remote sensing. In the past two decades, several satellites were launched carrying on board either radiometer (passive) or radar (active) or both sensors in different frequency bands with various spatial and temporal resolutions. Soil moisture algorithms are in rapid development and their improvements/revisions are ongoing. The latest SM retrieval products and versions of products that have been recently released are not yet, to our knowledge, comprehensively evaluated and inter-compared over different ecoregions and climate conditions. The aim of this paper is to comprehensively evaluate the most recent microwave-based SM retrieval products available from NASA's (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) satellite, ESA's led mission (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) satellite, ASCAT (Advanced Scatterometer) sensor on board the meteorological operational (Metop) platforms Metop-A and Metop-B, and the ESA Climate Change Initiative (CCI) blended long-term SM time series. More specifically, in this study we compared SMAPL3 V4, SMOSL3 V300, SMOSL2 V650, ASCAT H111, and CCI V04.2 and the new SMOS-IC (V105) SM product. This evaluation was achieved using four statistical scores: Pearson correlation (considering both original observations and anomalies), RMSE, unbiased RMSE, and Bias between remotely-sensed SM retrievals and ground-based measurements from >1000 stations from 17 monitoring networks, spread over the globe, disseminated through the International Soil Moisture Network (ISMN). The analysis reveals that the performance of the remotely-sensed SM retrievals generally varies depending on ecoregions, land cover types, climate conditions, and between the monitoring networks. It also reveals that temporal sampling of the data, the frequency of data in time and the spatial coverage, affect the performance metrics. Overall, the performance of SMAP and SMOS-IC products compared slightly better with respect to the ISMN in situ observations than the other remotely-sensed products. ispartof: REMOTE SENSING OF ENVIRONMENT vol:224 pages:289-303 status: published

Prescribed burning is a technique commonly applied to reduce fire risk. Fire is introduced under specific environmental conditions, with explicit duration, intensity and rate of spread. Such conditions deviate from those encountered during the fire season. As a result, prescribed burns mostly affect surface fuels and understorey vegetation, an outcome markedly different when compared to wildfires. Information on prescribed burning outcomes is important for evaluating the extent to which land management objectives have been achieved. This study focused on the ability of Radar Burn Ratio (RBR), a Synthetic Aperture Radar (SAR)-based multi-temporal index, to appraise the outcome of prescribed burns in eucalypt forests of Western Australia. L-band datasets acquired by the Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) sensor were used to relate changes in radar backscatter coefficient with the effects of prescribed burning. Two modelling approaches based on post- to pre-fire ratios were used to evaluate prescribed burn impacts. Post-fire tree survival in areas affected by high fire impacts was inferred by assessing the recovery rates of the backscatter coefficient for the first year after the burn. The impact of prescribed burns was estimated with relatively high overall accuracy (79%) using cross-polarized backscatter (HV) under dry conditions. The HH polarization and the Normalized Difference Backscatter Intensity (NDBI) showed certain ability to detect burned areas under wet conditions. The results suggest that L-band backscatter coefficient may contribute to evaluating the success of prescribed burns in Western Australian eucalypt forests through its sensitivity to changes in vegetation structure. In addition, tree survival (as opposed to mortality) and recovery towards the pre-fire state might be inferred by analysing short-term post-fire trends of the backscatter coefficient in areas affected by high fire impacts.

A new approach is proposed in this study to maximize the skill of satellite altimeter-derived heights by utilizing river extent information from additional sensors. We apply this approach for JASON-3 nadir altimeter. First, locations were identified in South and South East Asia region as potential candidates for this approach based on river extent. Information on river extent composed of river width and course (i.e., path) from visible (Landsat) and Synthetic Aperture Radar (SAR) platform (Sentinel-1). The extent-based approach was applied to filter out non-water radar returns in two methods: 1) River Mask (RM) based K-means (KM) clustering (hence, RM + KM) and 2) K-means clustering embedded with River Mask (RM) (hence, KMRM). Both methods were validated over 12 locations with long records of in-situ height observations and with distinct river characteristics. The maximum benefit observed in utilizing dynamic river mask is during low flow season when the probability of non-water radar signal contamination is highest. For highly varying seasonal widths, satellite imagery on river conditions, as an ensemble of techniques, yields heights with higher accuracy. An average RMSE improvement of 40% in 8 out of 12 stations is observed. Overall, at least one of the two dynamic mask-based techniques studied consistently outperformed the conventional technique of altimeter height extraction based on a fixed river mask. Information on river mask or extent can play an important role in highly meandering rivers as the deviation between actual satellite altimeter passes and nominal passes can be considered during the extraction of heights. Compared to visible (Landsat) imagery, SAR was found to be much more effective at improving altimeter height extraction because of the all-weather capability during frequently cloudy conditions. The practicality of the river mask-based approach for altimeter height extraction needs to be carefully considered by the operational end user based on available computational resources and the latency that can be afforded. An ensemble of morphology based techniques using SAR imagery is likely the future of satellite altimeter-based river height extraction in South and South-East Asia where rivers are increasingly impacted by human regulation, encroachment and seasonality.

In the event of a flood disaster, first response agencies need inundation maps produced in near real time (NRT). Such maps can be generated using satellite-based information. In this study, we developed mapping techniques that rely on synthetic aperture radar (SAR) on-board earth-orbiting platforms. SAR provides valid ground surface measurements through cloud cover with high resolution and sampling frequency that has recently increased through multiple missions. Despite numerous efforts, automatic processing of SAR data to derive accurate inundation maps still poses challenges. To address them, we have developed an NRT system named RAdar-Produced Inundation Diary (RAPID). RAPID integrates four processing steps: classification based on statistics, morphological processing, multi-threshold-based compensation, and machine-learning correction. Besides SAR data, the system integrates multisource remote-sensing data products, including land cover classification, water occurrence, hydrographical, water type, and river width products. In comparison to expert handmade flood maps, the fully-automated RAPID system exhibited “overall,” “producer,” and “user” accuracies of 93%, 77%, and 75%, respectively. RAPID accommodates commonly encountered over- and under-detections caused by noise-like speckle, water-like radar response areas, strong scatterers, and isolated inundation areas—errors that are in common practice to ignore, mask out, or be filtered out by coarsening the effective resolution. RAPID can serve as the kernel algorithm to derive flood inundation products from satellites—both existing and to be launched—equipped with high-resolution SAR sensors, including Envisat, Radarsat, NISAR, Advanced Land Observation Satellite (ALOS)-1/2, Sentinel-1, and TerraSAR-X.

A semi-empirical model of the ocean surface is developed for the compact-polarimetry (CP) mode of Synthetic Aperture Radar (SAR) which will be a main product of the RADARSAT Constellation Mission (RCM), scheduled for launch in November 2018. The fundamental mechanism for the semi-empirical model is the interactions between sea surface waves and the radar microwave. Sea surface waves are generated by ocean surface winds and may be modulated by precipitation or oceanic processes, such as surface currents, internal waves (IWs), and surfactants (e.g. oil spill, and sea ice). Model results for normalized radar cross sections (NRCSs) induced by ocean winds are validated against a database we developed. The ocean wind vectors were observed by in situ buoys of the National Data Buoy Center (NDBC); the RCM NRCSs are obtained from a “CP simulator”, using composites of RADARSAT-2 quad-pol SAR images. We simulate the dependencies of the NRCSs on wind speeds and incidence angles, for up-wind (wind direction is 0°) and cross-wind (wind direction is 90°) conditions. And we report the potential abilities and limitations of ocean winds monitoring using the RCM CP mode, as well as suggest that the RV-pol should be suitable for hurricane monitoring in the future. As hurricanes containing high winds are generally associated with heavy rain conditions, we discuss the possible mechanisms for precipitation effects related to the RV- and RH- polarizations by simulating two effects on the ocean surface waves. Moreover, we suggest that the RCM CP mode is better able to monitor the ocean surface currents than the fully polarimetry SAR systems (e.g. RADARSAT-2), because of the associated large swath and the possible continuous images of three RCM satellites.

The retrieval of vegetation parameters benefits significantly from the data fusion of optical and microwave signals. The integration of accurate forward models in both regions can play an important role in supporting these fusion approaches. Because of the different imaging mechanisms used in optical and microwave wavelength domains, the forward models in the two domains have been generally developed separately based on the different specifications of the scene. The inconsistencies between optical and microwave models, such as confusing input/output parameter definitions, different scattering theories and discrepant model complexity, make the data fusion difficult to conduct and lead to different results in terms of accuracy and computer time. Therefore, it is of great interest to develop a unified three-dimensional (3D) model using one scattering theory, identical input and similar complexity. To our knowledge, there are very few 3D models that can accomplish this task. By extending the Radiosity Applicable to Porous IndiviDual Objects (RAPID) model for the optical region, a general radiosity model (RAPID2) was proposed in this paper for the microwave region. This is the first time radiosity theory has been applied in microwave remote sensing, which invents a new way to solve the radar multiple scattering more efficiently. RAPID2 has four new functions: projecting translucent objects, tracking specular scattering, separating polarization components and imaging radar signals. The relationship between the radar cross section (RCS) and the bi-directional reflectance factor (BRF) is bridged. The modified Stokes vector and Mueller matrix are integrated into radiosity formulas to unify the scattering process between the optical and microwave regions. RAPID2 can simulate double-bouncing and multiple scattering effects over vegetated 3D scenes containing topography. The simulated radar images can well reflect the distinct radar geometric features, including layover, foreshortening and shadows. Validation over two forest sites shows good agreement with AIRSAR backscattering data (errors

Fresh water stored by on-farm reservoirs (OFRs) is an important component of surface hydrology and is critical for meeting global irrigation needs. Farmers use OFRs to store water during the wet season and for crop irrigation during the dry season, yet their seasonal and inter-annual variability and downstream impacts are not quantified. Therefore, OFRs' sub-weekly surface area changes are critical to understanding their dynamics and mitigating their downstream impacts. However, prior to the recent increase in satellite imagery availability and improvement in sensors' spatial resolution, monitoring the OFRs' sub-weekly surface area changes across space and time was challenging because OFRs occur in high numbers (i.e. hundreds) and are small water bodies ( 0.95) and small uncertainties (4–8%) when compared to the validation dataset. We found higher uncertainties (5–14%) when adding S1 to the Kalman filter—this is related to the higher uncertainties found for S1 (~20%). The algorithm can assimilate optical and radar satellite data to increase the OFRs' surface area time series cadence allowing us to investigate sub-weekly surface area changes. The algorithm is not sensor-specific, and it accounts for the uncertainties in both the sensors observations and the resulting surface areas, which are key advantages when compared to other algorithms used to combine satellite data. By improving the surface area observations cadence and providing the surface area uncertainties, the approach presented in this study has the potential to enhance water conservation plans by allowing better assessment and management of the OFRs.

Sinkhole activity in Florida is a major hazard for people and property. Its increasing frequency is often related to an accelerated use of ground-water and land resources in the region. In this work, we use a combined approach of radar interferometry and spatial clustering analysis over three selected sites in West-Central Florida to identify localized deformation that may be caused by sinkhole activity. The region of West-Central Florida is a densely active sinkhole region, where sinkholes tend to be small and land cover is mixed resulting in variable interferometric coherence that complicates Interferometric Synthetic Aperture Radar (InSAR) surveys. In this work, we present a combined methodology implementing InSAR and a Density-Based Spatial Clustering Analysis (DBSCAN) algorithm to detect unknown sinkhole activity and to alert to possible precursors of sinkhole collapse. The data used for the study consist of acquisitions from three TerraSAR-X frames covering time spans of ~1.7 and 2.5 years with spatial resolutions ranging from 25 cm up to 1 m. We applied the Persistent Scatterer Interferometry (PSI) technique using the Stanford Method for Persistent Scatterers (StaMPS) and confirmed the observed deformation signals by also processing the data using the SAR PROcessing tool (SARPROZ). Results show several areas of localized subsidence, from which the cluster with highest rates for each site was selected for detailed inspection. Locations of selected clusters were found in buildings with sizes ranging from 300 m2 to nearly 2000 m2, with subsidence trends ranging from −3 to −6 mm/yr. Results were compared with in-situ observations such as ground penetrating radar (GPR), electrical resistivity tomography (ERT) surveys, visual structural inspection and public county archive documents to help as ground truthing; subsiding locations were found to be related to sinkhole presence or development.

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. Among those, WaCM (Rodriguez et al., 2019), SKIM (Ardhuin et al., 2019) and the ESA Earth Explorer candidate Harmony (ESA, 2020) and SEASTAR (Gommenginger et al., 2019).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.

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

International audience; Reducing carbon emissions from deforestation and degradation (REDD) requires detailed insight into how the forest biomass is measured and distributed. Studies so far have estimated forest biomass stocks using rough assumptions and unreliable data. High-resolution data and robust methods are required to capture the spatial variability of forest biomass with sufficient precision. Here we aim to improve on previous approaches by using radar satellite ALOS PALSAR (25-m resolution) and optical Landsat-derived tree cover (30-m resolution) observations to estimate forest biomass stocks in Madagascar, for the years 2007'2010. The radar signal and in situ biomass were highly correlated (R2 = 0.71) and the root mean square error was 30% (for biomass ranging from 0 to 500 t/ha). Using our map at 25-m resolution for the entire island of Madagascar, we estimated the total above-ground forest carbon for the four years 2007, 2008, 2009 and 2010 to be 1.1173 ± 0.0304, 1.1029 ± 0.0303, 1.0916 ± 0.0301 and 1.0773 ± 0.0298 PgC, respectively. Carbon stocks were found to have decreased constantly over this period due to anthropogenic deforestation and likely also to climate change. The results are expected to serve as a more accurate benchmark for monitoring progress on REDD and to provide strong supports for current and future spaceborne missions such as ALOS-2, SAOCOM and BIOMASS.

Space borne radar altimeters often complement in-situ water levels to provide greater insights to hydrodynamic models in sparsely gauged catchments. However, 10-day or 35-day water levels derived from satellite radar altimetry are generally too infrequent for flood forecasting or hydrodynamic modelling purposes. This paper proposes a new approach to find daily water levels for areas where in-situ river heights are not available. The new approach is based on the relationship between river height and difference between daytime and night time land surface temperatures (LST). This relationship is first demonstrated using in-situ gauge data to explore appropriate statistical models to predict river height using LST differences. The approach is then applied to a number of virtual stations at the intersections between the Mekong River and the ground-tracks of Jason-2 satellite altimetry which gives 10-day water level time series. The LST difference from the thermal infrared (TIR) observations of Moderate Resolution Imaging Spectro-radiometer (MODIS) is shown to have a strong relationship with in-situ water levels and a good relationship with the Jason-2 water levels. The models included a simple linear regression model which was then extended to firstly include seasonal terms and secondly assimilate satellite altimetry data. A regression model tree (M5) was also considered but was found to be inferior to the seasonal linear model. The developed regression models were used to predict daily water levels to infill the 10-day Jason-2 altimeters. RMSE of modelled daily water levels at gauges is between 0.3 m to 0.6 m, while RMSE of modelled daily water levels without using in-situ data is higher varying from 1.4 m to 1.9 m. The temporal correlation between the modelled water levels using satellite altimeters at virtual stations and in-situ water levels at adjacent gauges ranged from 0.72 to 0.86. These results show the potential of the proposed approach to produce high temporal resolution water levels for flood models or other applications using only remotely sensed data.

The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process ~2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP high-resolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m3/m3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of ~0.053 m3/m3. The SMAP L2SMAP product for ~2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined L-band radar-radiometer for proving high spatial resolution and accurate global soil moisture.

Late-winter lake ice regimes are controlled by water depth relative to maximum ice thickness (MIT). When MIT exceeds maximum water depth, lakes freeze to the bottom with bedfast ice (BI) and when MIT is less than maximum water depth lakes have floating ice (FI). Both airborne radar and space-borne synthetic aperture radar (SAR) imagery (Ku-, X-, C-, and L-band) have been used previously to determine whether lakes have a BI or FI regime in a given year, across a number of years, or across large regions. In this study, we use a combination of ERS-1/2, RADARSAT-2, Envisat, and Sentinel-1 SAR imagery for seven lake-rich regions in Arctic Alaska to analyze lake ice regime extents and dynamics over a 25-year period (1992–2016). Our interactive threshold classification method determines a unique statistic-based intensity threshold for each SAR scene, allowing for the comparison of classification results from C-band SAR data acquired with different polarizations and incidence angles. Additionally, our novel method accommodates declining signal strength in aging extended-mission satellite SAR instruments. Comparison of SAR ice regime classifications with extensive field measurements from six years yielded a 93% accuracy. Significant declines in BI regimes were only observed in the Fish Creek area with 3% of lakes exhibiting transitional ice regimes—lakes that switch from BI to FI during this 25-year period. This analysis suggests that the potential conversion from BI to FI regimes is primarily a function of lake depth distributions in addition to regional differences in climate variability. Remote sensing of lake ice regimes with C-band SAR is a useful tool to monitor the associated thermal impacts on permafrost, since lake ice regimes can be used as a proxy for of sub-lake permafrost thaw, considered by the Global Climate Observing System as an Essential Climate Variable (ECV). Continued winter warming and variable snow conditions in the Arctic are expected and our long-term analysis provides a valuable baseline for predicting where potential future lake ice regimes shifts will be most pronounced.

Precipitation is one of the most important hydrometeorological components of the Tibetan Plateau (TP). The relationships between precipitation and topography in the TP were investigated using 17 years of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) data and 2 years of Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) data. First, the retrieval and total errors of the two radars were quantified in the TP using ground-based precipitation datasets. The TRMM PR and GPM DPR both significantly underestimate the precipitation, with total errors up to 40% and 53%, respectively. Then, precipitation of the two radars was corrected prior to effective application. The correction was separately performed for each river basin in the TP by adding total errors. Finally, precipitation–topography relationships were documented using corrected radar data. The main findings on precipitation–topography relationships are: (1) precipitation generally decreases with increasing elevation from 2 to 6 km in the TP. However, the trend is inverse in the Qaidam Basin which is characterized by extremely low precipitation in the interior basin and high precipitation along the windward slopes of surrounding mountains; (2) quantitative relationships between precipitation and elevation can be depicted for most exorheic basins in the TP using exponential fitting. The coefficients of determination for the fitting are higher than 0.9 in six basins; (3) the relationships based on GPM DPR data are less robust due to its fewer samples, whereas the GPM DPR probably resolves the problem of overestimating precipitation in some grid pixels with high elevation, compared to the TRMM PR; (4) three typical mountains (i.e., Himalaya, Hengduan, and Tianshan mountains) exhibit significantly negative correlation between precipitation and elevation. Particularly, there are two bands of precipitation maxima along the southern slopes of the central Himalayas (78°E–88°E), corresponding to the two-step topography, which affects the vegetation distribution. This study provides insights into the precipitation–topography relationships in the TP and its surrounding regions based on data from spaceborne precipitation radars.

InSAR technology provides a powerful tool to detect potentially unstable slopes across wide areas and to monitor surface displacements of a single landslide. However, conventional time series InSAR methods such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) can rarely identify sufficient measurement points (MPs) in mountainous areas due to decorrelations caused by steep terrain and vegetation coverage. In this study, we developed a new InSAR approach, coherent scatterer InSAR (CSI), to map landslide surface displacements in the radar line-of-sight (LOS) direction by combining persistent scatterers (PS) and distributed scatterers (DS). The key ideas of CSI include the employment of the generalized likelihood ratio (GLR) test for the identification of statistically homogeneous pixels (SHPs) and the use of the phase linking algorithm to estimate optimal phase for each DS pixel. The joint exploitation of PS and DS targets dramatically increases the spatial density of MPs, which makes the phase unwrapping more reliable. To demonstrate the effectiveness of the CSI approach, we applied it to retrieve the historical displacements of the Jiaju landslide in Danba County of southwest China using 19 L-band ALOS PALSAR images (2006–2011) and nine C-band ENVISAT ASAR images (2007–2008). Multiple comparisons clearly illustrated the big advantages of CSI over PSI and SBAS in mapping landslide displacements with more details owing to much higher (> 10 times) MP density. Furthermore, the superiority of L-band SAR data over C-band for landslide investigation in rural environments was confirmed. Quantitative validation of the CSI results for PALSAR data against in-situ GPS measurements suggested an accuracy of about 10.5 millimeters per year (mm/year) in terms of root mean square error (RMSE). Afterwards, the spatial-temporal characteristics of the Jiaju landslide surface displacements were summarized, with a new upper boundary for the active northern part delineated. Particularly, the northern part of the landslide moved faster than the southern part, exhibiting a maximum LOS displacement rate of around 120 mm/year. Subsequently, the fluvial erosion by the Dajinchuan River was identified as the predominant impact factor for the instability of the Jiaju landslide. Finally, the major problems and challenges for the application of CSI method were discussed, and the conclusions were given.

We used 26 Radarsat-2 and TerraSAR-X single- and dual-co-polarization Synthetic Aperture Radar (SAR) images of a test site on the German North Sea coast to investigate the influence of imaging geometry and environmental conditions (wind speed, water level, and vegetation period) on the radar return from exposed intertidal flats. Multi-temporal analyses of single- (VV-) polarization SAR imagery indicate that the surface roughness is more variable at shorter scales (responsible for the X-band backscatter) than at longer scales (C-band). Less variation at both radar bands was found in sea-grass meadows. TerraSAR-X dual-co-polarization data were used for polarimetric analyses based on a decomposition of the Kennaugh matrix, whose elements provide information on the total intensity at both co-polarizations and on the relative strength of even- and odd-bounce backscattering. At steep incidence angles (around 30°) the radar backscatter from bare sand flats is similarly strong at both co-polarizations, while the vertically polarized radar return dominates at higher incidence angles (above 40°). At low water levels, resulting in lower moisture of the sandy sediments, strong single-bounce radar backscattering was observed, while higher water levels (and moisture) caused weak mixed (single- and double-bounce) backscattering. Apart from the absolute water level its history, e.g. the time and level of the closest low tide, must be considered. During the vegetation period, sea-grass meadows cause a stronger increase in horizontally (HH-) polarized radar backscatter, along with an increased double-bounce backscattering. We conclude that the Kennaugh element framework has potential to be used for classification purposes in intertidal areas, but also that, for a full interpretation of the SAR imagery, the exact topography and the surface roughness have to be known.

Understanding the morphology and internal layering of large aeolian dune fields through radar observations can provide unique insights into the climatic and geophysical conditions that led to their formation. In this study, we perform a large-scale characterization of the morphology and internal layering of linear dunes in hyper-arid areas on Earth, through utilizing multiple complementary radar datasets (SIR-C imaging, SRTM interferometry-derived elevations and radar sounding or Ground Penetrating Radar (GPR)). Linear dune fields in the Egyptian desert are of special interest, due to their significance as planetary analogs to dunes on Mars and Saturn's largest moon, Titan. Satellite radar imagery and elevation data of the region show significant variance in the geomorphology of different dune fields in Egypt. In addition, GPR probing of the first few meters suggests different inner settings in the layering of dunes of different ages in eastern and western Egypt, reflecting different paleo-climatic regimes that led to their formation. Furthermore, our radiometric analysis suggests that dunes with different inner layering arrangement also exhibit different radar backscatter returns as a function of their heights. For relatively younger dunes with a homogeneous inner structure, like the ones in eastern Egypt in the Qattaniya dune field, we observe that sigma0 does not change as a function of the dune height. For relatively older dunes in western Egypt like the Great Sand Sea (Northern (Siwa) and Southern dune fields), we observed a linear correlation between sigma0 and the dune height. Thus, surface properties of dunes like morphology and backscatter variation with height are related to inner characteristics like arrangement of internal layering, relative ages and can be used to infer their depositional history. Linear dunes discovered in the equatorial regions of Titan by the Cassini-Huygens mission are morphologically very similar to these linear dune fields in the Egyptian Sahara. Hence, assessing the variability of morphology and radar backscatter properties of Titan's dunes as a function of their heights can help constrain the ambiguities associated with their internal structure and formation history and provide insights into Titan's paleo-wind regimes.

The ability to measure real time surface elevation over a large spatial domain offers large benefits to multiple industries, including offshore operations and marine renewable applications. In combination with a real time short-term wave prediction model safety related marine operations windows can be increased. This focuses on Quiescent Period Prediction during large sea states, and the ability of pre-emptive wave-by-wave tuning of marine energy converters leads to improved efficiencies. This study presents a new method of converting X-band radar sequence into approximate surface elevation in near real time using synthetic wave trough profile estimations, forgoing the use of a linear stochastic model. The artificial wave trough method applies a second order Stokes wave to trough regions where radar shadows occur, these shadows are caused as a result of high surround wave crest height where no surface information is gathered. The calculated surface elevation is validated against in situ sensor data at two locations, a correlation (r) of 0.69 is shown for the Signature 1000 sensor location, while a lower correlation of 0.56 is observed at the wave buoy location. When the phase-averaged wave parameters (Hm0 and Tp) derived from 20 min wave spectra for the entire duration of the measurement campaign are compared, a reasonable agreement of 0.62 and 0.41 for the AD2CP and wave buoy location is achieved. The presented method shows a good approximation of wave profile shape, where the swell component is predominantly measured, indicating the potential of X-band radar surface elevation measurements in near real time for nearshore inhomogeneous seas.

Earthquakes can trigger numerous landslides and cause other significant changes in the landscape over large areas. This study presents a new processing scheme combining radar (Copernicus Sentinel-1) and optical satellite data (Copernicus Sentinel-2) to quickly and easily map landscape changes such as landslides, coastal uplift and changes in water bodies caused by a severe event such as an earthquake. The processing scheme has been tested for the 2016 Kaikoura earthquake (Mw 7.8), New Zealand, which impacted vast and mostly inaccessible areas, causing hundreds of landslides. The workflow combines the following change-detection methods: i) Sentinel-1 amplitude change detection ii) Sentinel-2-based detection of non-vegetated areas that occurred after the event using the atmospherically resistant vegetation index (ARVI). To get a more complex view of the surface changes caused by the Kaikoura earthquake, the available online services and tools were further tested (open source via the European Space Agency) allowing automatic detection of vertical displacements and deformations. It was concluded, that the above-mentioned approaches facilitated the assessment of earthquake-triggered changes in a comprehensive manner. The methodology is an example of how to detect earthquake landscape changes in an automatic and rapid manner. The new processing scheme for the synergic use of Sentinel-1 and Sentinel-2 data has high potential to be used for operational and scientific purposes, since it relies on globally available, free data and provides high spatial and temporal resolution. The results can be obtained and made available only a few days after an event, therefore providing significant insights into earthquake impact assessment and may also be helpful for prioritizing field work.

As an extension of wave period inversion models based on altimetry data by an artificial neural network approach, a Deep Neural Network approach (DNN) mean wave period model is developed by introducing new parameters as DNN inputs. In addition to conventional altimeter-derived parameters such as significant wave height (SWH) and the normalized radar cross-section (NRCS) sigma0, the spatial (along-track) SWH gradient and SWH standard deviation (STD) for standard 1-s altimetry data are assumed to be responsible for random wave field dynamics and, thus, for the observed characteristic mean wave period. A comparison with in situ measurements of wave buoys shows higher accuracy of the novel DNN models by using these new parameters. The wave period estimation from DNN model is also consistent with the latest wave reanalysis and indicates less bias when compared to the buoys. The global mean wave steepness distribution from the DNN model shows good agreement with those provided by the wave reanalysis. The sensitivity of input variables sigma0, SWH, SWH gradient, and SWH STD on the results of the DNN model are also investigated. Perspectives on the DNN method for developing universal mission-independent wave period models are discussed. Objectives of the work The mean wave period is an important parameter for characterizing wave properties and can be retrieved from altimeter observations. Considering random wave field dynamics, we present a deep neural network (DNN) model for mean wave period retrieval by introducing the SWH gradient and standard deviation as the inputs. The mean wave period estimation of the DNN model shows good agreement with the latest wave reanalysis and less bias compared to buoys. In an attempt to make the DNN mean wave period retrieval model universal, a method of applying DNN model cross-altimetry missions is presented.

Soil moisture (SM) retrieval in agricultural regions during the growing seasons is particularly challenging due to high spatial variability and dynamic vegetation conditions. The retrievals have been problematic even when the passive signatures at different spatial scales match well since they depend upon the accuracy of vegetation information such as the vegetation water content (VWC). The VWC used in the Soil Moisture Active Passive (SMAP) single channel retrieval algorithm (SCA) is derived from remotely sensed, climatologically-based Normalized Difference Vegetation Index (NDVI), which does not respond to real-time vegetation dynamics and is prone to saturation. This study explored the differences and seasonal trend in passive signatures and SM at satellite- and field-scales and investigated uncertainties in retrievals arising from different approaches used to estimate VWC from optical and radar indices. It used high temporal resolution, ground-based data collected during the SMAP Validation-Microwave Water, Energy Balance Experiment in 2016 (SMAPVEX16-MicroWEX) during a growing season of corn and soybean. Overall, the brightness temperatures (TB) from SMAP matched well with the upscaled, ground-based TB, with a root mean square differences (RMSDs) of about 5 K. In contrast, the SMAP SM retrievals were worse during rapid vegetation growth in the mid-season, with higher RMSDs compared to the upscaled in situ SM, than those in the late-season. In addition, the ground-based TB from corn and soybean were similar in the early and the late seasons, while their emission differences were > 40 K in the mid season. This indicates the importance of accurate VWC information, particularly during the early and late growing seasons, to account for sub-pixel heterogeneities in agricultural regions. VWC obtained from five optical and radar indices were used in the SMAP SCA for soil moisture retrieval for the entire growing season of corn. The NDVI-based VWC provided SM retrievals that were consistently lower compared to those using in situ VWC, with a higher RMSD of 0.030 m3/m3 and a negative bias of 0.020 m3/m3 for VWC > 4 kg/m2. The Normalized Difference Water Index (NDWI)-derived VWC resulted in lower SM retrieval RMSD of 0.022 m3/m3 when compared with in situ SM. Among the three radar indices, vertically polarized cross-pol ratio (CRvv)-derived VWC provided similar RMSDs in retrieved SM as the NDWI-derived VWC during the growing season. The radar vegetation index (RVI)-derived VWC improved in the late season compared to the in situ VWC and resulted in SM retrievals with RMSDs similar to the CRvv-derived retrievals. Results presented here suggest that SMAP SCA SM retrievals could be improved through the use of near-real time NDWI and CRvv-derived vegetation information. Microwave data are available regardless of cloud cover, so the guaranteed availability of CRvv to capture seasonal and interannual variability is advantageous.

The most prevalent global surface models are derived from space-based technologies. The spatial and temporal coverage of radar and imaging systems have significant advantage over other sensors and platforms. However, these systems are often are challenged in certain environmental conditions to produce accurate elevations as compared to what might achieved with laser altimetry. Radar derived DEM (Digital Elevation Model) elevation accuracy is often less in vegetated regions, as the wavelengths associated with radar mapping missions do not fully penetrate the canopy. Elevation errors are also substantial over dynamic topography. Correction models using airborne lidar reference datasets can be effective for localized studies to improve the DEM but often the data is not readily available or seasonally/temporally irrelevant. This work presents an automated method for correcting digital terrain models derived from the Shuttle Radar Topographic Mission (SRTM) elevation products using NASA's newest Earth observing laser altimeter, the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2). ICESat-2 elevations, in concert with Landsat 8 (global imagery), create a model based correction strategy for the SRTM derived elevations using geographically correlated canopy cover and surface slope information. The results are validated at the study site using high-resolution, high fidelity airborne lidar datasets as a reference surface. The application of the correction model on the radar measurements results in nearly 50% improvement in elevation accuracy for this region. Additionally this established proof of concept provides a starting point for further research in how this method, with ICESat-2 data, can be extended to other environmental regions, other radar or image derived elevation products and inform future techniques for similar application at the global scale.

Interferometric processing of series of data acquired over time by synthetic aperture radar (SAR) satellites makes it possible to measure millimetric deformations (typically due to landslides, subsidence and earthquake or volcanic phenomena) and to monitor the stability of terrain and infrastructures. Despite the unique capability to observe very large areas, this technology has been typically applied to the analysis of relatively small sites or specific geophysical phenomena. In this work, we present the first application of this technology to a national scale project, which required the processing, through advanced persistent scatterer interferometry (PSI) techniques, of about 20,000 SAR images acquired from 1992 to 2014 over the whole Italian territory by the ERS, Envisat, and COSMO-SkyMed satellites. The obtained results provide a huge database of surface deformation measurements covering the last 20 years and the whole Italian territory, which represents an extremely useful and pioneering service for geo-hazards mapping and prevention.

Criteria for discriminating between radar signatures of oil films and biogenic slicks visible on synthetic aperture radar (SAR) images of the sea surface as dark patches are critically reviewed. We question the often claimed high success rate of oil spill detection algorithms using single-polarization SARs because the SAR images used to train these algorithms are based usually on subjective interpretation and are not validated by on-site inspections or multi-sensor measurements carried out from oil pollution surveillance planes. Furthermore, we doubt that polarimetric parameters derived from fully-polarimetric SAR data, like entropy, anisotropy, and mean scattering angle, are beneficial for discriminating between mineral oil films and biogenic slicks. We challenge the often-made claim that another scattering mechanism than Bragg scattering applies for radar backscattering from mineral oil films than from biogenic slicks. This view is supported by data acquired by the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) of NASA/JPL, which operates at L-band and has an extremely low noise floor. We suspect that opposing results obtained from previous analyses of spaceborne polarimetric SAR data are caused by the high noise floors of the spaceborne SARs. However, most of the analyzed spaceborne polarimetric data were not acquired at L-band, but at C-and X-band. On the other hand, differences in the statistical behavior of the radar backscattering could be real due to the fact that, other than biogenic surface films, mineral oil films, can form multi-layers, whose thickness can vary within an oil patch. Radar backscattering from emulsion layers can also fluctuate due to variations of the oil/water mixture ratio. These effects could cause an increase of the standard deviation (STD) of the co-polarized phase difference (CPD) for scattering at mineral oil films and emulsions. In the special case of thick oil layers or oil/water emulsion layers, where the radar is sensitive to the dielectric constant of the oil, discrimination becomes possible due the fact that Bragg scattering depends on the dielectric constant of the scattering medium.

Full polarimetric L-band PALSAR data facilitated one of the first empirical assessments of the relationships between radar polarization properties (RPPs) and ecological pattern properties (EPPs) in desert fringe ecosystems. Correlations between radar parameters (polarized backscattering intensities, their ratios, entropy, and degrees of polarization) and remotely sensed ecological parameters (shrub cover, edge parameter, average shrub height, and biomass information) were analyzed for 52 least-disturbed sites along a climatic gradient between semi-arid and arid zones in the south-eastern Mediterranean. The degree of horizontal polarization (DOHP) was the RPP that was most highly correlated with all EPPs, while NDVIR biomass (NDVI multiplied by relative rainfall) was the EPP most highly correlated with all RPPs. Combinations of NDVIR biomass and DOHP data exhibited consistent inverse relationships for both the 52 least-disturbed sites as well as for the entire gradient. Mapping these combinations along the transition zone enabled differentiation of desert fringe areas and dense shrub and tree areas, with three transition typologies between the two. Although DOHP is instrumental in detecting changes in tall shrub coverage, it was found to be insufficiently sensitive to surface conditions in areas with dwarf shrubs and shrubs of biomass lower than 0.7 kg/m 2 at the end of the summer. Differentiation between the desert, desert fringe, and semi-arid margins of high DOHP values (> 0.9) was facilitated by differences in their NDVIR biomass. Areas of high anthropogenic influence on the landscape, i.e. abandoned terraces and low-density pine plantations, deviate significantly from the inverse DOHP and NDVIR biomass relationship, as both were characterized by low values in these parameterizations. The wide extent of DOHP and NDVIR combinations representing desert fringe ecosystems and arid lands indicated a continuation of degradation processes in areas with rainfalls > 500 mm/year. The technique developed, which utilizes DOHP, provides new means for mapping desert fringe ecosystems and for exploring desertification over wide regions.

After reflecting on the past decomposition studies using the polarimetric synthetic aperture radar (PolSAR) technique and data in Earth observation (EO) studies, three primary issues are identified. Elements C12 and C32 of a covariance matrix, [C], are essential in the decomposition and cannot be ignored. Existing algorithms cannot adequately distinguish urban targets with large azimuth orientation angles from vegetation. The algorithms are complex in the formulation and procedure. To resolve the issues and envision future algorithm development, we have articulated three key modules. They are a separation factor to separate an azimuthally symmetric or asymmetric target, a diplane to model an asymmetric target in an urban area, and a procedure to derive an equivalent azimuth-orientation angle for the diplane. Then, a four-component decomposition algorithm was developed. The algorithm has been applied to multiple airborne and spaceborne PolSAR C- and L-band datasets covering areas in Canada, France, Morocco, and the USA. The primary radar target types included trihedral and dihedral corner reflectors (CRs), airport runway/taxiway, urban targets with azimuth-orientation angles ranging between 0° and 45°, ocean and inland water surfaces, city parks, grassland, forests, farmland, and desert. The separation factor delineates a symmetric or asymmetric target at a correct average rate of 92.7%. The diplane coupled with the derived equivalent azimuth-orientation angles correctly modeled radar backscatter from dihedral CRs and urban asymmetric targets. The algorithm delineated each type of ground target with an appropriate and dominant single, double, or volume scattering. Furthermore, the algorithm has four readily interpretable components, and its mathematical expression is not complicated. Therefore, the primary objectives to resolve the above three issues and to have an algorithm with well-balanced usability in EO studies and complexity in formulation and procedure are achieved.

Ground observation absence in many parts of the world highlights the importance of merged satellite precipitation products. In this study, we aim to evaluate the effect of different sources of data in the uncertainties of a merged satellite product, by comparing the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final product (V06B) with a ground-based radar product, Multi-Radar Multi-Sensor (MRMS), using both pixel-based and object-based approaches. This study is focused on the eastern United States (land-only) during the hurricane days that occurred in 2016–2018. The results showed that IMERG had better agreement in terms of the average precipitation intensity and area with a bias reduction of 75% and 65%, respectively, when the passive microwave (PMW) sensor overpass is matched instantaneously with MRMS compared to temporally averaged MRMS data (MRMS-Averaged). PMW observations tend to show storms with smaller areas in the IMERG Final product in comparison with MRMS, possibly due to the effect of light precipitation not detected properly by PMW sensors. However, by removing the light precipitation (less than 1 mm/h) in the object-based approach, hurricane objects in the IMERG Final product tend to be larger during the PMW observations, which might be related to different viewing angles of sensors contributing to MRMS and IMERG products. Precipitation estimates have smaller areas with higher average intensity during the PMW observations in the IMERG Final product compared to data estimated by Morphed or IR (morph/IR) observations, which is probably related to the effect of morphing technique, leading to homogenization of the varying rainstorm characteristics. In addition, with the longer absence of PMW observations, the quality of morph/IR estimates in IMERG Final product deteriorates with a decreasing correlation coefficient, a growth in precipitation area and a downward trend in average precipitation intensity. Finally, the inter-comparison of PMW sensors showed the priority of imagers over sounders with GMI as the best among imagers and MHS as the best among sounders in terms of correlation and average intensity compared to MRMS; however, SSMIS was the best in capturing the precipitation area.

Ground-clutter is a significant cause of missed-detection and underestimation of precipitation in complex terrain from space-based radars such as the Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR). This research proposes an Artificial Intelligence (AI) framework consisting of a precipitation detection model (PDM) and a precipitation regime classification model (PCM) to improve orographic precipitation retrievals from GPM-DPR using machine learning. The PDM is a Random Forest Classifier using GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and low-level precipitation mixing ratios from the High-Resolution Rapid Refresh (HRRR) analysis as inputs. The PCM is a Convolutional Neural Network that predicts the precipitation regime class, defined independently based on quantitative features of ground-based radar reflectivity profiles, using GPM DPR Ku-band (Ku-PR) reflectivity profiles and GMI Tbs. The AI framework is demonstrated for warm-season precipitation in the Southern Appalachian Mountains over three years (2016–2019), achieving large reductions in false alarms (77%) and missed detections (82%) relative to GPM Ku-PR precipitation products. The spatial distribution of predicted precipitation classes within the GPM overpass reflects the complex interactions between storms and topography that determine orographic precipitation regimes. For each GPM pixel, the local precipitation class informs on the vertical structure of rainfall microphysics aiming to capture low-level processes missed in GPM DPR reflectivity profiles contaminated by ground-clutter (i.e., the radar blind-zone).

This study evaluated the possibility of studying mesoscale surface circulation in coastal areas, as is the Gulf of Cadiz, Spain, using high-resolution altimetry data (20-Hz of posting rate) along with the use of wind and bottom friction ageostrophic corrections. Absolute cross-track surface zonal current velocities, derived from filtered along-track CryoSat-2 SIRAL-SARM Absolute Dynamic Topography (ADT) measurements, are compared with high-frequency radar (HFR) data in the coastal area of the Gulf of Cadiz. The filter strategy followed in this study for the altimetry data is dependent on the HFR measurements for each track. Absolute surface geostrophic velocities obtained from 20-Hz altimetry data agree well HFR further than 25 km from the coast. Close to land ([3–25] km) the ageostrophic component of the surface current (due to the wind drag and the bottom friction) needs to be considered in the altimeter data. On average, the correlation between altimetry and HFR improved from 0.61 (no correction) to 0.72 (correcting these effects). The root mean square error (RMSE) lowered from 12.54 cm·s−1 to 7.35 cm·s−1. Furthermore, it has also been demonstrated that corrected altimeter measurements are useful for the study of dynamics and patterns of coastal areas.

Near-real-time flood maps are essential to organize and coordinate emergency services' response actions during flooding events. Thanks to its capacity to acquire synoptic and detailed data during day and night, and in all weather conditions, Synthetic Aperture Radar (SAR) satellite remote sensing is considered one of the best tools for the acquisition of flood mapping information. However, specific factors contributing to SAR backscatter in urban environments, such as shadow and layover effects, and the presence of water surface–like radar response areas, complicate the detection of flood water pixels. This paper describes an approach for near-real-time flood mapping in urban and rural areas. The innovative aspect of the approach is its reliance on the combined use of very-high-resolution SAR satellite imagery (C-band, HH polarization) and hydraulic data, specifically flood return period data estimated for each point of the floodplain. This approach was tested and evaluated using two case studies of the 2011 Richelieu River flood (Canada) observed by the very-high-resolution RADARSAT-2 sensor. In both case studies, the algorithm proved capable of detecting flooding in urban areas with good accuracy, identifying approximately 87% of flooded pixels correctly. The associated false negative and false positive rates are approximately 14%. In rural areas, 97% of flooded pixels were correctly identified, with false negative rates close to 3% and false positive rates between 3% and 35%. These results highlight the capacity of flood return period data to overcome limitations associated with SAR-based flood detection in urban environments, and the relevance of their use in combination with SAR C-band imagery for precise flood extent mapping in urban and rural environments in a crisis management context.

Quantifying freeze/thaw status of the landscape (soils and vegetation) is important for identifying the availability of liquid water in the high latitudes to support ecosystem processes controlling carbon sequestration and release. A better understanding of the seasonal transition of high latitude landscapes between frozen and thawed states is therefore critical for predicting carbon cycle feedbacks in a warming climate. However, observations of freeze/thaw state are not at sufficient temporal and spatial resolution to observe fast (~ days–weeks) transitions in highly heterogeneous regions. Here, we present new evidence that reflected Global Navigation Satellite System (GNSS) signals captured by the SMAP radar receiver have the potential to quantify changes in high-latitude soil freeze/thaw state at kilometer-scale resolution. Boreal wetland observations indicate a ~ 10 dB seasonal difference in the signal-to-noise ratio (SNR), in agreement with simulations from a simple reflectivity model. Our analysis indicates that ground-reflected GNSS signals as a form of L-band bistatic radar could be a robust observable to quantify soil freeze/thaw state at process-relevant scales. We describe the caveats and potential limitations of this approach, and highlight areas for continuing research.

Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and reporting. The conventional approach of using visible and near-infrared based vegetation index (VI) observations has prevailed for decades since the onset of the global satellite era. However, other satellite data encompass diverse spectral ranges that may contain complementary information on crop growth and yield, but have been largely understudied and underused. Here we conducted one of the first attempts at synergizing multiple satellite data spanning a diverse spectral range, including visible, near-infrared, thermal and microwave, into one framework to estimate crop yield for the U.S. Corn Belt, one of the world's most important food baskets. Specifically, we included MODIS Enhanced VI (EVI), estimated Gross Primary Production based on GOME-2 solar-induced fluorescence (SIF-GPP), thermal-based ALEXI Evapotranspiration (ET), QuikSCAT Ku-band radar backscatter, and AMSR-E X-band passive microwave Vegetation Optical Depth (VOD) in this study, benchmarked on USDA county-level crop yield statistics. We used Partial Least Square Regression (PLSR), an effective statistical model for dimension reduction, to distinguish commonly shared and unique individual information from the various satellite data and other ancillary climate information for crop yield estimation. In the PLSR model that includes all of the satellite data and climate variables from 2007 to 2009, we assessed the first two major PLSR components and found that the first component (an integrated proxy of crop aboveground biomass) explained 82% variability of modelled crop yield, and the second component (dominated by environmental stresses) explained 15% variability of modelled crop yield. We found that most of the satellite derived metrics (e.g. SIF-GPP, radar backscatter, EVI, VOD, ALEXI-ET) share common information related to aboveground crop biomass (i.e. the first component). For this shared information, the SIF-GPP and backscatter data contain almost the same amount of information as EVI at the county scale. When removing the above shared component from all of the satellite data, we found that EVI and SIF-GPP do not provide much extra information; instead, Ku-band backscatter, thermal-based ALEXI-ET, and X-band VOD provide unique information on environmental stresses that improves overall crop yield predictive skill. In particular, Ku-band backscatter and associated differences between morning and afternoon overpasses contribute unique information on crop growth and environmental stress. Overall, using satellite data from various spectral bands significantly improves regional crop yield predictions. The additional use of ancillary climate data (e.g. precipitation and temperature) further improves model skill, in part because the crop reproductive stage related to harvest index is highly sensitive to environmental stresses but they are not fully captured by the satellite data used in our study. We conclude that using satellite data across various spectral ranges can improve monitoring of large-scale crop growth and yield beyond what can be achieved from individual sensors. These results also inform the synergistic use and development of current and next generation satellite missions, including NASA ECOSTRESS, SMAP, and OCO-2, for agricultural applications.

Two-dimensional high-resolution maps of total surface current vectors obtained for the first time with an airborne demonstrator of the innovative Wavemill instrument concept are validated against HF radar data and compared with output from the POLCOMS high-resolution coastal ocean circulation model. Wavemill is a squinted along-track interferometric SAR system optimized for ocean surface current vector retrieval that operates at moderate incidence angles (∼30°) and is compatible with spaceborne implementation. This paper represents the first comprehensive validation of the current retrieval capabilities of squinted along-track SAR interferometry in support of its development as a future European Space Agency Earth Explorer mission. Wavemill airborne data were acquired in October 2011 in Liverpool Bay off the west coast of Great Britain in light southerly wind (5.5 m/s) and maximum tidal ebbing flow (0.7 m/s) conditions. Contributions to the measured SAR interferometric phase by surface gravity waves, known as the Wind-wave induced Artefact Surface Velocity (WASV), were removed using our best estimate of wind conditions and the (Mouche et al., 2012) empirical correction derived from Envisat ASAR. Validation of the 1.5 km resolution Wavemill current vectors against independent current measurements from HF radar gives very encouraging results, with Wavemill biases and precisions typically better than 0.05 m/s and 0.1 m/s for surface current speed, and better than 10° and 7° for current direction. The sensitivity of the current retrieval to the wind vector used to compute the WASV is estimated. A ± 1 m/s error (bias) in wind speed has minimal impact on the quality of the retrieved currents. In contrast, the choice of wind direction is critical: a bias of ± 15° in the direction of the wind vector degrades the accuracy of the airborne current speed against the HF radar by about ± 0.2 m/s. This highlights the need for future instruments to provide calibrated SAR Normalised Radar Cross Section data to support retrieval of wind and current vectors simultaneously. Comparisons of POLCOMS surface currents with HF radar data indicate that the model reproduces well the overall temporal evolution of the tidal current (correlation of spatial fields against HF radar over two tidal cycles of 0.9) but that the model features a systematic 1-h delay in the timing of the maximum ebbing flow in eastern parts of the domain near the Mersey Bar Light buoy. At the maximum ebb flow, the model underestimates the current speed (bias of −0.2m/s) with respect to the HF radar and Wavemill data at the time of the flights. Both the HF radar and Wavemill data reflect much greater snapshot spatial variability of the ocean surface current field than is present in the model, resulting in poor correlation of instantaneous spatial fields (< 0.5) between POLCOMS and the HF radar data. The Wavemill data reveal high spatial variability of ocean surface currents at fine scales, which are not visible in the 4km resolution HF radar data. Wavemill detects several strong (1–1.5m/s) localized current jets associated with deeper bathymetry channels in shallow waters (< 10 m) that are too narrow or too close to land to be observed by the HF radar. The study confirms the value of synoptic wide-swath maps of high-resolution ocean surface current vectors for coastal applications and to validate and develop high-resolution ocean circulation models.

Monitoring surface soil moisture (SSM) variability is essential for understanding hydrological processes, vegetation growth, and interactions between land and atmosphere. Due to sparse distribution of in-situ soil moisture networks, over the last two decades, several active and passive radar satellite missions have been launched to provide information that can be used to estimate surface conditions and subsequently soil moisture content of the upper few cm soil layers. Some recent studies reported the potential of satellite altimeter backscatter to estimate SSM, especially in arid and semi-arid regions. They also pointed out some difficulties of such technique including: (i) the noisy behavior of the backscatter estimations mainly caused by surface water in the radar foot-print, (ii) the assumptions for converting altimetry backscatter to SSM, and (iii) the need for interpolating between the tracks. In this study, we introduce a new inversion framework to retrieve soil moisture information from along-track altimetry measurements. First, 20Hz along-track nadir radar backscatter is estimated by post-processing waveforms from Jason-2 (Ku- and C-Band during 2008–2014) and Envisat (Ku- and S-Band during 2002–2008). This provides backscatter measurements every ∼300m along-track within every ∼10 days from Jason, and every ∼35days from Envisat observations. Empirical orthogonal base-functions (EOFs) are then derived from soil moisture simulations of a hydrological model, and used as constraints within the inversion. Finally, along-track altimetry reconstructed surface soil moisture (ARSSM) storage is inverted by fitting these EOFs to the altimeter backscatter. The framework is tested in arid and semi-arid Western Australia, for which a high resolution hydrological model (the Australian Water Resource Assessment, AWRA model) is available. Our ARSSM products are also validated against Soil Moisture and Ocean Salinity (SMOS) L3 products, for which maximum correlation coefficients of bigger than 0.8 are found. Our results also indicate that ARSSM can validate the simulation of hydrological models at least at seasonal time scales.