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

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

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.

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.

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.

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.

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.

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.

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).

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.

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.

Over one-third of the global land area undergoes a seasonal transition between predominantly frozen and non-frozen conditions each year, with the land surface freeze/thaw (FT) state a significant control on hydrological and biospheric processes over northern land areas and at high elevations. The NASA Soil Moisture Active Passive (SMAP) mission produced a daily landscape FT product at 3-km spatial resolution derived from ascending and descending orbits of SMAP high-resolution L-band (1.4 GHz) radar measurements. Following the failure of the SMAP radar in July 2015, coarser (36-km) footprint SMAP radiometer inputs were used to develop an alternative daily passive microwave freeze/thaw product. In this study, in situ observations are used to examine differences in the sensitivity of the 3-km radar versus the 36-km radiometer measurements to the landscape freeze/thaw state during the period of overlapping instrument operation. Assessment of the retrievals at high-latitude SMAP core validation sites showed excellent agreement with in situ flags, exceeding the 80% SMAP mission accuracy requirement. Similar performance was found for the radar and radiometer products using both air temperature and soil temperature derived FT reference flags. There was a tendency for SMAP thaw retrievals to lead the surface flags due to the influence of wet snow cover conditions on both the radar and radiometer signal. Comparison with other satellite derived FT products showed those derived from passive measurements (SMAP radiometer; Aquarius radiometer; Advanced Microwave Scanning Radiometer - 2) retrieved less frozen area than the active products (SMAP radar; Aquarius radar).

Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95 +/- 0.45 Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17 +/- 0.06 Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87 +/- 0.49 Gt) overestimated the national ground-based estimate by 7.5%. The comparable log -linear model result (63.29 +/- 1.36 Gt) overestimated ground results by 261%. All three national biomass GLAS estimates, LIN, LNI, and log -linear, are based on 241,718 pulses collected on 230 orbits. The US national forest inventory (ground) estimates are based on 119,414 ground plots. At the US state level, the average absolute value of the deviation of LNI GLAS estimates from the comparable ground estimate of total biomass was 18.8% (range: Oregon, -40.8% to North Dakota, 128.6%). Log-linear models produced gross overestimates in the continental US, i.e., >2.6x, and the use of this model to predict regional biomass using GLAS data in temperate, western hemisphere forests is not appropriate. The best model form, LNI, is used to produce biomass estimates in Mexico. The average biomass density in Mexican forests is 53.10 +/- 0.88 t/ha, and the total biomass for the country, given a total forest area of 688,096 km(2), is 3.65 +/- 0.06 Gt. In Mexico, our GLAS biomass total underestimated a 2005 FAO estimate (4.152 Gt) by 12% and overestimated a 2007/8 radar study's figure (3.06 Gt) by 19%. (C) Published by Elsevier Inc. NASA's Carbon Cycle Science Program within the Science Mission Directorate Earth Science Division [NNH10ZDA001N-CARBON(2010)] This research was funded by NASA's Carbon Cycle Science Program within the Science Mission Directorate Earth Science Division-NNH10ZDA001N-CARBON(2010).

Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.

Passive microwave datasets have been used to quantify the extent and duration of surface melt in Greenland and Antarctica from 1978 on with daily and near-daily intervals. These results have important implications for climate analysis and may help evaluate ice shelf stability. However, the accuracy of passive microwave methods used to detect melt is difficult to quantify, especially on the Antarctic Peninsula. Here four different melt detection methods are employed, including a new formulation of a statistical analysis of brightness temperature time series using a K-means clustering algorithm. Strikingly, two of the most widely used passive microwave melt detection methods are found to vary by 48% mean days of melt per year across six different locations on the Larsen C, Wilkins, and George VI Ice Shelves. In the absence of ground truth observations, time series of Sentinel-1 SAR observations from 2016 on provide a comparison dataset. In topographically flat regions where surface melt is spatially uniform, the passive microwave melt detection method based on a K-means analysis and the cross-polarization gradient ratio method demonstrate the highest agreement and correlation with active radar melt detection methods. One issue which has plagued passive microwave analysis is its coarse spatial resolution. High resolution SAR images are able to demonstrate and quantify the spatial variability of melt within individual passive microwave pixels. Melt is shown to be suitably uniform in space for passive microwave applications at the study sites on Antarctic Peninsula ice shelves, but not so in other regions of the Antarctic Peninsula. Spatial heterogeneity of surface melt on the sub-pixel scale is often related to varying surface topography.

Mapping of settlement areas from space is entering a new era. With the recently developed Global Urban Footprint (based on radar data from TanDEM-X) and the Global Human Settlement Layer (based on optical data), two new initiatives that promise to map complex settlement patterns at global scales and unprecedented spatial resolutions are about to enter the scientific and map user community. However, comparative studies on these layers' strengths and weaknesses, especially in terms of their potential added value with regard to existing lower resolution maps, as well as their assessed accuracy are still absent. In this regard, we introduce a multi-scale cross-comparison framework that uses the best existing urban maps as a benchmark. To paint a complete picture, we simultaneously address several components of map accuracy including relative inter-map agreement, absolute accuracies and pattern-based classification differences. This framework is applied to present regionally representative results from two Central European test sites. In this, we find that the new base maps bring decisive advancements in preserving the small-scale complexity of global human settlement patterns beyond urban core areas. Relative inter-map comparison exposes low density settlement regions traditionally under-represented by lower resolution maps that are now recognized. Absolute metrics such as the Kappa coefficient of agreement ( K ) show that accuracies of the new high resolution layers ( K ¯ = 0.56–0.58) nearly double those of existing products. Beyond, they feature substantial consistency between urban ( K ¯ = 0.46–0.50) and rural landscapes ( K ¯ = 0.41–0.45). Results from pattern-based exploration further reveal significant correlation of accuracies with physical pattern variations such as settlement density and mark a clear shift of accuracies from large to medium and small patch sizes. This differentiated view on classification accuracies shows that the new generation of urban maps constitutes a significantly enhanced spatial representation of large-scale settlement patterns.

Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field scale using a data assimilation strategy.

Earth remote sensing using reflected GNSS signals is currently an emerging trend especially in ocean surface wind measurements. Unlike the existing scatterometer missions, GNSS-R uses L-Band navigation signals that can penetrate through clouds and rain. Rain may have a negligible impact on the transmitted signal in terms of path attenuation at this wavelength. However, there are other effects due to rain, such as changes in surface roughness and rain induced local winds, which can significantly alter the measurements. Currently, there is no observation-based characterization of all possible impacts of rain on radar forward scatter, which is the nature of operation of GNSS-R missions. In this study, we propose a 3-fold rain model which accounts for attenuation, surface effects of rain and rain induced local winds. We utilize the large dataset of measurements made by the CYGNSS mission to separate these different effects of rain. The attenuation model suggests that a total of at least 96% transmissivity exists at L-Band up to a rain rate of 30 mm/h. A perturbation model is used to characterize the other rain effects. It suggests that rain is accompanied by an overall reduction in the scattering cross-section of the ocean surface and, most importantly, this effect is observed only up to surface wind speeds of 15 m/s, beyond which the gravity capillary waves dominate the scattering in the quasi-specular direction. Observations also suggest that, at very low wind speeds, the lower bound on wavenumber of the portion of the surface roughness spectrum that influences the measurements deviates from the geometric optics approximation normally used. This work binds together several rain-related phenomena and enhances our overall understanding of rain effects on GNSS-R measurements.

Water Surface Elevation (WSE) is an important hydrometric observation, useful to calibrate hydrological models, predict floods, and assess climate change. However, the number of in-situ gauging stations is in decline worldwide. Satellite altimetry, including the recently launched satellite missions (e.g. the radar altimetry missions Cryosat 2, Jason 3, Sentinel 3A/B and the LIDAR mission ICESat-2), can determine WSE only in rivers which are more than ca. 100 m wide. WSE measurements in small streams currently remain limited to the few existing in-situ stations or to time-consuming in-situ surveys. Unmanned Aerial Systems (UAS) can acquire real-time WSE observations during periods of hydrological interest (but with flight limitations in extreme weather conditions), within short survey times and with automatic or semi-automatic flight operations. UAS-borne photogrammetry is a well-known technique that can estimate land elevation with an accuracy as high as a few cm, similarly UAS-borne LIDAR can estimate land elevation but without requiring Ground Control Points (GCPs). However, both techniques face limitations in estimating WSE: water transparency and lack of stable visual key points on the Water Surface (WS) complicate the UAS-borne photogrammetric estimates of WSE, while the LIDAR reflection from the water surface is generally not strong enough to be captured by most of the UAS-borne LIDAR systems currently available on the market. Thus, LIDAR and photogrammetry generally require extraction of the elevation of the “water-edge” points, i.e. points at the interface between land and water, for identifying the WSE. We demonstrate highly accurate WSE observations with a new radar altimetry solution, which comprises a 77 GHz radar chip with full waveform analysis and an accurate dual frequency differential Global Navigation Satellite System (GNSS) system. The radar altimetry solution shows the lowest standard deviation (σ) and RMSE on WSE estimates, ca. 1.5 cm and ca. 3 cm respectively, whilst photogrammetry and LIDAR show a σ and an RMSE at decimetre level. Radar altimetry also requires a significantly shorter survey and processing time compared to LIDAR and especially to photogrammetry.

In this study, we set up a new drone-borne ground-penetrating radar (GPR) for soil moisture mapping. The whole radar system weighs 1.5 kg and consists of a handheld vector network analyzer (VNA) working as frequency domain radar, a lightweight hybrid horn-dipole antenna covering a wide frequency range (250–2800 MHz), a GPS for positioning, a microcomputer with the controlling application, and a smartphone for remote control. Soil moisture is derived from the radar data using full-wave inverse modeling based on the radar equation of Lambot et al. and multilayered media Green's functions. The inversion is performed in the time domain and focuses on the surface reflection. The antenna-drone system is characterized by global reflection and transmission functions which are determined through a calibration procedure. We performed drone-GPR measurements over three different agricultural fields in the loess belt region of Belgium. In this study, we used the 500–700 MHz range to avoid soil surface roughness effects and to focus on the top 10–20 cm of the soil. These fields present a range of landform conditions leading to specific soil moisture distributions. The soil moisture maps were constructed from the local measurements using kriging. The obtained soil moisture maps are in good agreement with the topographical conditions of the fields and aerial orthophotography observations. These results demonstrated the potential and benefits of drone-GPR for fast, high-resolution mapping of soil moisture at the field scale, and to support, e.g., precision agriculture and environmental monitoring.

Volcanoes with rugged terrain remain a challenging target for generating high-resolution digital elevation models (DEMs), especially in tropical areas with frequent cloud cover. Using Nevado del Ruiz volcano as an example, we combined DEMs from the TanDEM-X (TDX) satellite mission, terrestrial radar interferometry (TRI), and Structure from Motion (SfM), to generate a new DEM with 10-m spatial resolution. This is the first study combining satellite radar, ground-based radar, photography, and freely available global DEMs to generate a high-resolution DEM without data gaps. TDX data from ascending and descending orbits were combined to generate the base DEM. Instead of using a raster format to fuse DEMs generated from different data sets with different resolutions, we developed a methodology based on 3-D point clouds: 1) re-georeference the 5-m TRI and ~1-m SfM DEMs to the 10-m TDX DEM using the iterative closest point (ICP) algorithm to minimize the horizontal and vertical discrepancy between DEMs; then 2) merge the multiple point clouds to generate a final DEM without data gaps using an adaptive algorithm that uses two search distances to smooth the transition at the edges of different data sets. We assess the new 10-m DEM by comparing simulated inundation zones obtained with two volcano flow models, LaharZ (for lahars) and VolcFlow (for pyroclastic flows), and find significant differences with respect to the 30-m SRTM DEM. Our LaharZ simulation over the new DEM shows a longer lahar run-out distance. For pyroclastic flows, the VolcFlow simulation over the new DEM produces highly channelized flows over the steep portions of a river channel and gives a larger extent of thicker deposits compared to those obtained with the 30-m SRTM DEM. Quantitative and qualitative geomorphic analysis suggests that up-to-date DEMs with high spatial resolution (~ 10 m or even better) need to be generated to improve volcano hazard assessment for active volcanoes.

In the last years the Frequency Modulated Continuous Wave (FMCW) technology has been playing an ever greater role in the realization of compact, light and cheap Synthetic Aperture Radar (SAR) systems to be mounted onboard small, low altitude platforms such as airplanes, helicopters and drones. To correctly focus FMCW SAR images, it is necessary to accurately know some system parameters, including the frequency sweep rate of the signal transmitted by the radar. It may happen, however, that this frequency sweep rate is not very accurately measured by the radar provider, and thus an incorrect value of this parameter is used during the SAR data focusing procedure. This may produce serious geometric distortion effects in the focused FMCW SAR images. To circumvent these problems, in this work we present a procedure that estimates the frequency sweep rate actually employed by the FMCW radar, thus providing a key information that can be then profitably used to achieve the correct focusing of the SAR data acquired by the radar system at hand. More specifically, we propose an algorithm that exploits on one side the focused SAR images corrupted by the geometric distortion effects induced by the inaccurate knowledge of this radar parameter, and on the other side the very precise in-situ measurements of the positions of a limited number of Corner Reflectors (CRs) properly deployed over the observed scene. The effectiveness of the proposed algorithm has been tested on real data acquired by an airborne X-band FMCW SAR system.

The recent development of imaging geodesy, an advanced technique with a high spatial resolution and large-scale coverage, has enabled researchers to obtain multiple high-quality surface displacement estimates at low labor-cost, thereby improving the capability to monitor and manage geological disasters. The different sources (e.g., radar, optical and LiDAR sensors) and analysis approaches (e.g., differential interferometric synthetic aperture radar, DInSAR; multiple-aperture InSAR; pixel offset tracking; and iterative closest point, ICP) in imaging geodesy used to derive displacement estimates have unique benefits and drawbacks. However, the inherent differences among these data sources and methods in the construction of three-dimensional (3D) deformation maps, particularly in the near field, remain poorly understood and require further discussion. In this study, we acquired three pairs of ALOS-2 stripmap mode images, two pairs of Sentinel-1 TOPS mode images and pre- and post-event LiDAR data for the 2016 Kumamoto earthquake to explore the 3D near-field displacements using various imaging geodesy techniques with different types of image information, i.e., SAR phase data, SAR amplitude data and LiDAR point cloud data. Our results show that each image type is independently capable of producing a high-quality 3D deformation map for the 2016 Kumamoto earthquake with an on-fault accuracy of

Archaeological and cultural heritage (ACH), one of the core carriers of cultural diversity on our planet, has a direct bearing on the sustainable development of mankind. Documenting and protecting ACH is the common responsibility and duty of all humanity. It is governed by UNESCO along with the scientific communities that foster and encourage the use of advanced non-invasive techniques and methods for promoting scientific research into ACH and conservation of ACH sites. The use of remote sensing, a non-destructive tool, is increasingly popular by specialists around the world as it allows fast prospecting and mapping at multiple scales, rapid analysis of multisource datasets, and dynamic monitoring of ACH sites and their surrounding environments. The cost of using remote sensing is lower or even zero in practical applications. In this review, in order to discuss the advantages of airborne and spaceborne remote sensing (ASRS), the principles that make passive (photography, multispectral and hyperspectral) and active (synthetic aperture radar (SAR) and light detection and ranging radar (LiDAR)) imaging techniques suitable for ACH applications are first summarized and pointed out; a review of ASRS and the methodologies used over the past century is then presented together with relevant highlights from well-known research projects. Selected case studies from Mediterranean regions to East Asia illustrate how ASRS can be used effectively to investigate and understand archaeological features at multiple -scales and to monitor and assess the conservation status of cultural heritage sites in the context of sustainable development. An in-depth discussion on the limitations of ASRS and associated remaining challenges is presented along with conclusions and a look at future trends.

The rapid expansion of SAR data availability and advancements in InSAR processing methods has enabled the formation of ground displacement time series for many parts of the world where such research was previously hindered by decorrelation due to sparse temporal sampling and SAR operating frequency. In particular, free and open data access from the European Sentinel-1 constellation and the future NASA-ISRO SAR (NISAR) mission is encouraging the global community to move towards automated, cloud-based processing that can accommodate these rapidly growing data volumes and facilitate the use of a suite of corrections to the data. A key challenge is related to path delays introduced when the radar signal propagates through the troposphere. Tropospheric corrections estimated from empirical, phase-based methods and those using independent data from weather models, GPS, and radiometers have been included in open-source packages such as TRAIN, PyAPS and GACOS. Users within the InSAR community have reported varying degrees of success using these methods in a range of areas around the world. However, the various statistical metrics used to evaluate the reliability of tropospheric corrections are not consistently applied and often depend on the area and the spatial scale over which they are evaluated. Examination of a simple metric such as the overall reduction in phase variability within an interferogram does not allow the user to determine whether the improvement was at large or short length scales. We present a review of existing tropospheric correction methods and statistical performance metrics, providing guidelines for global assessment and verification of the efficacy of tropospheric correction methods. We summarize the assumptions and limitations for each correction method as well as each statistical performance metric. We examine two regions with different atmospheric characteristics - one Sentinel-1 swath covering the central United States and one swath covering south central Mexico, including part of the Pacific coast. As the SAR community moves towards reliance on global and automated InSAR processing platforms that incorporate tropospheric corrections, approaches such as those examined here can aid researchers in their efforts to evaluate such corrections and include their uncertainties in derived products such as surface displacement time series, coseismic offsets, processes that correlate with topography, and signals with smaller magnitude or larger spatial scales such as those associated with small earthquakes, aseismic creep and slow slip events. We found that the GACOS products (leveraging the operational high resolution ECMWF weather model) outperform the other correction methods explored here on average, but this result is highly dependent on location, acquisition time, and data availability. We found spatial structure functions to be most useful for performance assessment because of their ability to convey information about performance at discrete spatial scales.