Advanced Scatterometer Research Articles

Amery Ice Shelf surface snowmelt detected by ASCAT and Sentinel-1

ABSTRACTWidespread movement of meltwater has been found on the Amery Ice Shelf (AIS). Intensive surface snowmelt could accelerate ice loss and endanger the ice shelves under climate warming. In this study, the Advanced Scatterometer (ASCAT) was used to study the detailed melt patterns on the AIS, which was confirmed by the wet snow mapped by Sentinel-1 Synthetic Aperture Radar (SAR) images with a much higher spatial resolution. Melt seasons were longer than 50 days in the eastern part of AIS. Conversely, a large proportion of the western AIS only melted occasionally for several days with the exception of blue ice areas. Two obvious snowmelt patterns are found on the AIS. First, the opposing summer katabatic wind directions on the eastern and western sides of the AIS result in a warmer eastern AIS compared with the western side, which leads to the expansion of snowmelt from east to west on the AIS. Second, intense snowmelt was found in the grounding zone with low-albedo blue ice and rock outcrops. However, ASCAT fails to detect snowmelt in these places due to the dominated surface radar scattering. AIS melt patterns are determined by both meteorological conditions and the underlying surface.

Toward the Generation of a Wind Geophysical Model Function for Spaceborne GNSS-R

This paper presents a comprehensive procedure to improve the wind geophysical model function (GMF) for the Global Navigation Satellite System Reflectometry (GNSS-R) instrument onboard the TechDemoSat-1 satellite. The observable used to define the GMF is extracted from the measured delay-Doppler maps (DDMs) by correcting for the nongeophysical effects within the measurements. Besides the instrument and the geometric effects as provided in the bistatic radar equation, a calibration term that accounts for the uncalibrated receiver antenna gain and the unknown transmitter antenna gain is proposed to optimize the calculation of GNSS-R observables. Such calibration term is presented as a function of observing elevation and azimuth angles and is shown to remarkably reduce the measurement uncertainties. First, an empirical wind-only GMF is developed using the collocated Advanced Scatterometer (ASCAT) winds and European Centre for Medium-Range Weather Forecasts (ECMWF) model wind output. This empirical GMF agrees well with the model output. Then, the sensitivity of the observable to waves is analyzed using the collocated ECMWF wave parameters. The results show that it is difficult to include mean square slope (MSS) in the development of an empirical GMF, since the difference between ECMWF MSS and the MSS sensed by GNSS-R varies with incidence angle and wind speed. However, it is relevant to take significant wave height ( $H_{s}$ ) in account, particularly for low wind conditions. Consequently, a wind/ $H_{s}$ approach is proposed for improved wind retrievals.

Mapping soil moisture across the Tibetan Plateau plains using Aquarius active and passive L-band microwave observations

An algorithm is developed for retrieving soil moisture at plateau scale by combined usage of Aquarius active and passive L-band observations. In this algorithm, Look-Up-Tables (LUTs) are established for bare soil and vegetated areas by using the physical based Tor Vergata discrete electromagnetic model (hereafter, TV-DEM). In the case of vegetated area, the LUT is built based on simulations with varying soil moisture and Leaf Area Index (LAI). Only soil moisture is variable for the bare soil case, and values calibrated in previous works are adopted for the other TV-DEM parameters. Soil moisture is then retrieved by minimizing a squared difference object function based on the emissivity and backscatter coefficient observed by Aquarius and the corresponding TV-DEM simulations included in the LUT. The soil moisture retrievals are assessed at Aquarius footprint scale using in-situ measurements collected at three regional scale networks spread across the Tibetan Plateau. The unbiased root mean squared differences (ubRMSDs) from the three networks vary from 0.016 to 0.050 m3 m−3 and coefficients of determination (R2) are from 0.274 to 0.499 (-). This ubRMSD and R2 is in the same order of the passive-only official Aquarius product, Metop-A Advanced SCATterometer (ASCAT) L2 soil moisture product (hereafter ASCAT) as well as reanalysis data generated by European Centre for Medium-Range Weather Forecasts (hereafter, ERA-Interim). At Plateau-scale, all four soil moisture products capture the seasonal trend, whereby the dynamic range during the monsoon season captured by the ERA-Interim product is relatively small. Further, the northwest-southeast dry-wet gradient due to precipitation and evapotranspiration is well captured by the soil moisture spatial distributions produced by TV-DEM Aquarius, official Aquarius and ASCAT products, but less pronounced in the ERA-Interim product. This study demonstrates that the TV-DEM based algorithm can be used to retrieve soil moisture over a Plateau scale with robust results in terms of error statistics (e.g. bias, R2 and ubRMSD) and can generate realistic spatial patterns for soil moisture at Plateau-scale.

The Potential of Advanced Scatterometer (ASCAT) 12.5 km Coastal Observations for Offshore Wind Farm Site Selection in Irish Waters

The offshore wind industry has seen unprecedented growth over the last few years. In line with this growth, there has been a push towards more exposed sites, farther from shore, in deeper water with consequent increased investor risk. There is therefore a growing need for accurate, reliable, met-ocean data to identify suitable sites, and from which to base preliminary design and investment decisions. This study investigates the potential of hyper-temporal satellite remote sensing Advanced Scatterometer (ASCAT) data in generating information necessary for the optimal site selection of offshore renewable energy infrastructure, and hence providing a cost-effective alternative to traditional techniques, such as in situ data from public or private entities and modelled data. Five years of the ASCAT 12.5 km wind product were validated against in situ weather buoys and showed a strong correlation with a Pearson coefficient of 0.95, when the in situ measurements were extrapolated with the log law. Temporal variations depicted by the ASCAT wind data followed the same inter-seasonal and intra-annual variations as the in situ measurements. A small diurnal bias of 0.12 m s−1 was observed between the descending swath (10:00 to 12:00) and the ascending swath (20:30 to 22:30), indicating that Ireland’s offshore wind speeds are slightly stronger in the daytime, especially in the nearshore areas. Seasonal maps showed that the highest spatial variability in offshore wind speeds are exhibited in winter and summer. The mean wind speed extrapolated at 80 m above sea level showed that Ireland’s mean offshore wind speeds at hub height ranged between 9.6 m s−1 and 12.3 m s−1. To best represent the offshore wind resource and its spatial distribution, an operational frequency map and a maximum yield frequency map were produced based on the ASCAT wind product in an offshore zone between 20 km and 200 km from the coast. The operational frequency indicates the percentage of time during which the observed local wind speed is between cut-in (3 m/s) and cut-out (25 m/s) for a standard turbine. The operational frequency map shows that the frequency of the wind speed within the cut-in and cut-off range of wind turbines was between 92.4% and 97.2%, while the maximum yield frequency map showed that between 40.6% and 59.5% of the wind speed frequency was included in the wind turbine rated power range. The results showed that the hyper-temporal ASCAT 12.5 km wind speed product (five consecutive years, two observations daily per satellite, two satellites) is representative of wind speeds measured by in situ measurements in Irish waters, and that its ability to depict temporal and spatial variability can assist in the decision-making process for offshore wind farm site selection in Ireland.

Can We Improve Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data?

Parametric cyclonic wind fields are widely used worldwide for insurance risk underwriting, coastal planning, and storm surge forecasts. They support high-stakes financial, development and emergency decisions. Yet, there is still no consensus on a potentially “best” parametric approach, nor guidance to choose among the great variety of published models. The aim of this paper is to demonstrate that recent progress in estimating extreme surface wind speeds from satellite remote sensing now makes it possible to assess the performance of existing parametric models, and select a relevant one with greater objectivity. In particular, we show that the Cyclone Global Navigation Satellite System (CYGNSS) mission of NASA, along with the Advanced Scatterometer (ASCAT), are able to capture a substantial part of the tropical cyclone structure, and to aid in characterizing the strengths and weaknesses of a number of parametric models. Our results suggest that none of the traditional empirical approaches are the best option in all cases. Rather, the choice of a parametric model depends on several criteria, such as cyclone intensity and the availability of wind radii information. The benefit of using satellite remote sensing data to select a relevant parametric model for a specific case study is tested here by simulating hurricane Maria (2017). The significant wave heights computed by a wave-current hydrodynamic coupled model are found to be in good agreement with the predictions given by the remote sensing data. The results and approach presented in this study should shed new light on how to handle parametric cyclonic wind models, and help the scientific community conduct better wind, wave, and surge analyses for tropical cyclones.

Fields of Lightning Discharges in Typhoons

We report the results of the use of the World Wide Lightning Location Network data to analyze the core structure of super typhoons that occurred in the northwestern Pacific Ocean in 2012–2013. The distributions of lightning discharges are compared to the ocean-surface wind fields according to the data of the Advanced Scatterometer (ASCAT) and the infrared images of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the MetTop-A and Aqua satellites, respectively. In the central regions of all of the studied typhoons, the structures that are fragments of circular or spiral mesometeorological-scale forms, which can be related to the presence of the cloud eyewall of typhoons, are observed even a day before the typhoon’s maximum intensity. These circular structures become completely apparent on the day of the typhoon’s maximum intensity and then begin to break down. It is shown that the position of the center of a typhoon and its traveling speed can be estimated from the distribution of lightning; in addition, the geometric characteristics of an eyewall, which are listed in reports on storms and typhoons, can be determined.

Sea Surface Wind Speed Retrieval and Validation of the Interferometric Imaging Radar Altimeter Aboard the Chinese Tiangong-2 Space Laboratory

This study focuses on the retrieval and validation of sea-surface wind speeds using data from the interferometric imaging radar altimeter (InIRA) aboard the Chinese Tiangong-2 space laboratory. First, an empirical model, KuLMOD2, is proposed, which is a revised version of the former Ku-band low incidence model (KuLMOD) that directly relates the normalized radar cross section σ 0 to both the wind speed and incidence angle. The revised model extends the ranges of the incidence angle and wind speed and is thus applicable to the new InIRA data. The model coefficients are estimated by fitting precipitation radar data from the Tropical Rainfall Measuring Mission and collocated wind data from the European Centre for Medium-Range Weather Forecasts. The wind speeds are then retrieved from the InIRA data using KuLMOD2. For more efficient retrieval, a lookup table method is used to find the wind speed solution. Finally, the InIRA wind speeds are validated using collocated Advanced Scatterometer (ASCAT) wind speeds. The validation of the InIRA wind speed data shows that InIRA can capture regional changes and that the data have a bias of 0.02 m/s and a root-mean-square error of 1.58 m/s. This suggests that the InIRA data alone can provide valid wind speeds for future sea state bias correction of InIRA-derived sea-surface heights and that the InIRA data can act as a complement to data from other spaceborne wind sensors.

The AQUI Soil Moisture Network for Satellite Microwave Remote Sensing Validation in South-Western France

Global soil moisture (SM) products are currently available thanks to microwave remote sensing techniques. Validation of these satellite-based SM products over different vegetation and climate conditions is a crucial step. INRA (National Institute of Agricultural Research) has set up the AQUI SM and soil temperature in situ network (composed of three main sites Bouron, Bilos, and Hermitage), over a flat area of dense pine forests, in South-Western France (the Bordeaux–Aquitaine region) to validate the Soil Moisture and Ocean salinity (SMOS) satellite SM products. SMOS was launched in 2009 by the European Space Agency (ESA). The aims of this study are to present the AQUI network and to evaluate the SMOS SM product (in the new SMOS-IC version) along with other microwave SM products such as the active ASCAT (Advanced Scatterometer) and the ESA combined (passive and active) CCI (Climate Change Initiative) SM retrievals. A first comparison, using Pearson correlation, Bias, RMSE (Root Mean Square Error), and Un biased RMSE (ubRMSE) scores, between the 0–5 cm AQUI network and ASCAT, CCI, and SMOS SM products was conducted. In general all the three products were able to reproduce the annual cycle of the AQUI in situ observations. CCI and ASCAT had best and similar correlations (R~0.72) over the Bouron and Bilos sites. All had comparable correlations over the Hermitage sites with overall average values of 0.74, 0.68, and 0.69 for CCI, SMOS-IC, and ASCAT, respectively. Considering anomalies, correlation values decreased for all products with best ability to capture day to day variations obtained by ASCAT. CCI (followed by SMOS-IC) had the best ubRMSE values (mostly < 0.04 m3/m3) over most of the stations. Although the region is highly impacted by radio frequency interferences, SMOS-IC followed correctly the in situ SM dynamics. All the three remotely-sensed SM products (except SMOS-IC over some stations) overestimated the AQUI in situ SM observations. These results demonstrate that the AQUI network is likely to be well-suited for satellite microwave remote sensing evaluations/validations.

TDS-1 GNSS Reflectometry: Development and Validation of Forward Scattering Winds

This study presents the development and a systematic evaluation study of GNSS reflectometry wind speeds. After establishing a wind speed retrieval algorithm, UK TechDemoSat-1 (TDS-1) derived winds, from May 2015 to July 2017, are compared to the Advanced Scatterometer (ASCAT). ERA-Interim wind fields of the European Centre for Medium-range Weather Forecasts (ECMWF) and in situ observation from Tropical Atmosphere Ocean buoy array in the Pacific are taken as reference. One-year averaged TDS-1 global winds demonstrate small differences with ECMWF in a majority of areas as well as discuss under- and overestimations. The pioneering TDS-1 winds demonstrate a root-mean-squared error (RMSE) and bias of 2.77 and $-$ 0.33 m/s, which are comparable to the RMSE and bias derived by ASCAT winds, as large as 2.31 and 0.25 m/s, respectively. Using buoys measurements as reference, RMSE and bias of 2.23 and $-$ 0.03 m/s for TDS-1 as well as 1.40 and $-$ 0.68 m/s for ASCAT are obtained. Utilizing rain microwave–infrared estimates of the Tropical Rainfall Measuring Mission, rain-affected observation of both ASCAT and TDS-1 are collected and evaluated. Although ASCAT winds show a significant performance degradation resulting in an RMSE and bias of 3.16 and 1.03 m/s, respectively, during rain condition, TDS-1 shows a more reliable performance with an RMSE and bias of 2.94 and $-$ 0.21 m/s, respectively, which indicates the promising capability of GNSS forward scattering for wind retrievals during rain. A decrease in TDS-1-derived bistatic radar cross sections during rain events, at weak winds, is also demonstrated.

The Potential Utility of Satellite Soil Moisture Retrievals for Detecting Irrigation Patterns in China

Climate change and anthropogenic activities, including agricultural irrigation have significantly altered the global and regional hydrological cycle. However, human-induced modification to the natural environment is not well represented in land surface models (LSMs). In this study, we utilize microwave-based soil moisture products to aid the detection of under-represented irrigation processes throughout China. The satellite retrievals used in this study include passive microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and its successor AMSR2, active microwave observations from the Advanced Scatterometer (ASCAT), and the blended multi-sensor soil moisture product from the European Space Agency (i.e., ESA CCI product). We first conducted validations of the three soil moisture retrievals against in-situ observations (collected from the nationwide agro-meteorological network) in irrigated areas in China. It is found that compared to the conventional Spearman’s rank correlation and Pearson correlation coefficients, entropy-based mutual information is more suitable for evaluating soil moisture anomalies induced by irrigation. In general, around 60% of uncertainties in the anomaly of “ground truth” time series can be resolved by soil moisture retrievals, with ASCAT outperforming the others. Following this, the potential utility of soil moisture retrievals in mapping irrigation patterns in China is investigated by examining the difference in probability distribution functions (detected by two-sample Kolmogorov-Smirnov test) between soil moisture retrievals and benchmarks of the numerical model ERA-Interim without considering the irrigation process. Results show that microwave remote sensing provides a promising alternative to detect the under-represented irrigation process against the reference LSM ERA-Interim. Specifically, the highest performance in detecting irrigation intensity is found when using ASCAT in Huang-Huai-Hai Plain, followed by advanced microwave scanning radiometer (AMSR) and ESA CCI. Compared to ASCAT, the irrigation detection capabilities of AMSR exhibit higher discrepancies between descending and ascending orbits, since the soil moisture retrieval algorithm of AMSR is based on surface temperature and, thus, more affected by irrigation practices. This study provides insights into detecting the irrigation extent using microwave-based soil moisture with aid of LSM simulations, which has great implications for numerical model development and agricultural managements across the country.

Spatial disaggregation of ASCAT soil moisture under all sky condition using support vector machine

With recent advances in downscaling methodologies, soil moisture (SM) estimation using microwave remote sensing has become feasible for local application. However, disaggregation of SM under all sky conditions remains challenging. This study suggests a new downscaling approach under all sky conditions based on support vector regression (SVR) using microwave and optical/infrared data and geolocation information. Optically derived estimates of land surface temperature and normalized difference vegetation index from MODerate Resolution Imaging Spectroradiometer land and atmosphere products were utilized to obtain a continuous spatio-temporal input datasets to disaggregate SM observation from Advanced SCATterometer in South Korea during 2015 growing season. SVR model was compared to synergistic downscaling approach (SDA), which is based on physical relationship between SM and hydrometeorological factors. Evaluation against in situ observations showed that the SVR model under all sky conditions (R: 0.57 to 0.81, ubRMSE: 0.0292 m3 m−3 to 0.0398 m3 m−3) outperformed coarse ASCAT SM (R: 0.55 to 0.77, ubRMSE: 0.0300 m3 m−3 to 0.0408 m3 m−3) and SDA model (mean R: 0.56 to 0.78, ubRMSE: 0.0324 m3 m−3 to 0.0436 m3 m−3) in terms of statistical results as well as sensitivity with precipitation. This study suggests that the spatial downscaling technique based on remote sensing has the potential to derive high resolution SM regardless of weather conditions without relying on data from other sources. It offers an insight for analyzing hydrological, climate, and agricultural conditions at regional to local scale.

Assessment of the offshore wind resource in Japan with the ASCAT microwave scatterometer

ABSTRACTWe analysed wind speed and direction off the coast of Japan using data from the satellite-borne Advanced Scatterometer (ASCAT) and the Weather Research and Forecasting model (WRF), validated these data using in situ wind measurements from 20 buoys, and evaluated the effect of the long time intervals from ASCAT observations on wind resource assessment. More than 25 km from the coast, and at heights of 10 m, the ASCAT wind speed has negative biases of up to 3.4% and root mean square errors of up to 18.5%; its wind direction has 11° to 27° of mean absolute error compared to buoy measurements at a height of 10 m. These accuracies are better than either the expected accuracies reported in the technical manual or those simulated with WRF with its spatial resolution of 10 km. We also evaluated long-term average ASCAT wind speeds in comparison to 4- and 5-year averages of in situ buoy wind speeds measured at three buoys, with resulting differences of –0.3%, –6.3%, and – 1.6%. Furthermore, wind roses show that appearance frequencies of the ASCAT wind direction for the long term are in a good agreement with those of the measurements at the three buoys. Our results show that the ASCAT-derived wind speed and direction are appropriate more than 25 km from the coast, and that the long time interval between ASCAT observations has an insignificant effect on wind resource assessment, if at least 4 or 5 years of averaged ASCAT data are used.

Using sentinel-1A SAR wind retrievals for enhancing scatterometer and radiometer regional wind analyses

ABSTRACTScatterometer surface wind speed and direction observations in combination with radiometer wind speeds allow to generate surface wind analyses with high space and time resolutions over global as well as at regional scales. Regarding scatterometer sampling schemes and physics, the resulting surface wind analyses suffer from lack of accuracy in areas near coasts. The use of the synthetic aperture radar (SAR) onboard the Sentinel-1A satellite attempts to address the enhancement of surface wind analyses issues. In this study, SAR wind speeds and directions retrieved from backscatter coefficients acquired in interferometric wide (IW) swath mode are used. Their accuracy is determined through comprehensive comparisons with moored buoy wind measurements. SAR and buoy winds agree well at offshore and nearshore locations. The statistics characterizing the comparison of SAR and buoy wind speeds and directions are of the same order as those obtained from scatterometer (Advanced SCATterometer (ASCAT) and RapidScat) and buoy wind comparisons. The main discrepancy between SAR and buoy data are found for high wind speeds. SAR wind speeds exceeding 10 m s–1 tend to be underestimated. A similar conclusion is drawn from SAR and scatterometer wind speed comparisons. It is based on the underestimation of SAR backscatter coefficient (σ°) with respect to σ° estimated from scatterometer winds and the geophysical model function (GMF) named CMOD-IFR2 (Ifremer C band MODel). New SAR wind speeds are retrieved using CMOD-IFR2. The corrected SAR retrievals allow better determination of the spatial characteristics of surface wind speeds and of the related wind components in near-coast areas. They are used for enhancing the determination of the spatial structure function required for the estimation of wind fields gridded in space and time at the regional scale. The resulting wind fields are only determined from scatterometer wind observations in combination with radiometer retrievals. Their qualities are determined through comparisons with SAR wind speeds and directions, and through their application for determination of wind power off Brittany coasts.

Decline in Surface Melt Duration on Larsen C Ice Shelf Revealed by The Advanced Scatterometer (ASCAT)

Surface melting has been contributing to the surface lowering and loss of firn air content on Larsen C Ice Shelf since at least the mid‐1990s. Where the amount of melting and refreezing is significant, the firn can become impermeable and begin to support ponds of surface meltwater such as have been implicated in ice shelf collapse. Although meteorological station data indicated an increase in melt on the Antarctic Peninsula over the second half of the 20th century, the existing Ku‐band Quick Scatterometer (QuikSCAT) time series is too short (1999–2009) to detect any significant 21st century trends. Here we investigate a longer 21st century period by extending the time series to 2017 using the C‐band Advanced Scatterometer (ASCAT). We validate our recent observations with in situ weather station data and, using a firn percolation model, explore the sensitivity of scatterometry to water at varying depths in the firn. We find that active microwave C‐band (5.6‐cm wavelength) instruments can detect water at depths of up to 0.75 m below a frozen firn layer. Our longer scatterometry time series reveals that Larsen C Ice Shelf has experienced a decrease in melt season length of 1–2 days per year over the past 18 years consistent with decreasing summer air temperatures. Only in western inlets, where föhn winds drive melt, has the annual melt duration increased during this period.

Constraining Conceptual Hydrological Models With Multiple Information Sources

AbstractThe calibration of hydrological models without streamflow observations is problematic, and the simultaneous, combined use of remotely sensed products for this purpose has not been exhaustively tested thus far. Our hypothesis is that the combined use of products can (1) reduce the parameter search space and (2) improve the representation of internal model dynamics and hydrological signatures. Five different conceptual hydrological models were applied to 27 catchments across Europe. A parameter selection process, similar to a likelihood weighting procedure, was applied for 1,023 possible combinations of 10 different data sources, ranging from using 1 to all 10 of these products. Distances between the two empirical distributions of model performance metrics with and without using a specific product were determined to assess the added value of a specific product. In a similar way, the performance of the models to reproduce 27 hydrological signatures was evaluated relative to the unconstrained model. Significant reductions in the parameter space were obtained when combinations included Advanced Microwave Scanning Radiometer ‐ Earth Observing System and Advanced Scatterometer soil moisture, Gravity Recovery and Climate Experiment total water storage anomalies, and, in snow‐dominated catchments, the Moderate Resolution Imaging Spectroradiometer snow cover products. The evaporation products of Land Surface Analysis ‐ Satellite Application Facility and MOD16 were less effective for deriving meaningful, well‐constrained posterior parameter distributions. The hydrological signature analysis indicated that most models profited from constraining with an increasing number of data sources. Concluding, constraining models with multiple data sources simultaneously was shown to be valuable for at least four of the five hydrological models to determine model parameters in absence of streamflow.

Combining ASCAT and NEXRAD Retrieval Analysis to Explore Wind Features of Mesoscale Oceanic Systems

AbstractOceanic mesoscale systems often produce heavy rainfall and can cause significant hazards in coastal regions and aviation. Surface features that are important in the evolution of these systems were examined combining the Advanced Scatterometer (ASCAT) on board the polar‐orbiting meteorological satellite series Metop‐A and ‐B and ground instrumentation; that is, Next Generation Weather Radars, soundings, buoys. Two case studies were analyzed, over the coastal United States, by reprojecting ASCAT wind and ground radar precipitation and wind retrievals over a common grid domain. ASCAT retrievals were in agreement with buoy and sounding measurements at distances greater than 30 km from the precipitation region. Enhanced uncertainty in ASCAT wind retrievals in heavy precipitation was represented by greater wind direction variability and increases in wind speed. Nevertheless, ASCAT was able to identify regions of convergence and divergence within the systems, as well as outflow boundaries associated with downdrafts, which agreed with wind fields retrieved from the single‐Doppler analysis. Quality flags reported by ASCAT were also examined against polarimetric radar retrievals. Thresholds for rain rate and median volume diameter (D0), corresponding to the boundary between flagged and unflagged ASCAT data, were variable among different cases. However, analysis of ice water path and liquid water path revealed that unflagged ASCAT data rarely exceeded approximately 0.5 kg/m2 in either parameter.

Evaluation of Wind Retrieval from Co-Polarization Gaofen-3 SAR Imagery Around China Seas

Gaofen-3 (GF-3) is the first Chinese space-borne satellite to carry the C-band multi-polarization synthetic aperture radar (SAR). Marine applications, i.e., winds and waves retrieved from GF-3 SAR images, have been operational since January 2017. In this study, we have collected more than 1000 quad-polarization (vertical-vertical (VV); horizontal-horizontal (HH); vertical- horizontal (VH); horizontal-vertical (HV)) GF-3 SAR images, which were acquired around the China Seas from September 2016 to September 2017. Wind streaks were visible in these images in co-polarization (VV and HH) channel. Geophysical model functions (GMFs), including the CMOD5N together with polarization ratio (PR) model and C-SARMOD, were used to retrieve winds from the collected co-polarization GF-3 SAR images. Wind directions were directly obtained from GF-3 SAR images. Then, the SAR-derived wind speeds were compared with the measurements at a 0.25° grid from the Advanced Scatterometer on board the Metop-A/B and microwave radiometer WindSAT. Based on the analysis, empirical corrections are proposed to improve the performance of the two GMFs. Results of this study show that the standard deviation of wind speed is 1.63 m s−1 with a 0.19 m s−1 bias and 1.71 m s−1 with a 0.26 m s−1 bias for VV- and HH-polarization GF-3 SAR, respectively. Our work not only systematically evaluates wind retrieval by using the two advanced GMFs and PR models but also proposes empirical corrections to improve the accuracy of wind retrievals from GF-3 SAR images around the China Seas and thus enhance the accuracy of near real-time operational SAR-derived wind products.

How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products

Knowledge of irrigation is essential for ensuring food and water security, and to cope with the scarcity of water resources, which is expected to exacerbate under the pressure of climate change and population increase. Even though irrigation is likely the most important direct human intervention in the hydrological cycle, we have only partial knowledge on the areas of our planet in which irrigation takes place, and almost no information on the amount of water that is applied for irrigation.In this study, we developed a new approach exploiting satellite soil moisture observations for quantifying the amount of water applied for irrigation. Through the inversion of the soil water balance equation, and by using satellite soil moisture products as input, the amount of water entering into the soil, and hence irrigation, is determined. Through synthetic experiments, we first assessed the impact of soil moisture measurement uncertainty and temporal resolution, also as a function of climate, on the accuracy of the method. Second, we applied the proposed approach to currently available coarse resolution satellite soil moisture products retrieved from the Soil Moisture Active and Passive mission (SMAP), the Soil Moisture and Ocean Salinity (SMOS) mission, the Advanced SCATterometer (ASCAT), and the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Nine pilot sites in Europe, USA, Australia and Africa were used as case study to test the method in a real-world application.The synthetic experiment showed that the method is able to quantify irrigation, with satisfactory performance from satellite data with retrieval errors lower than ∼0.04 m³/m³ and revisit times shorter than 3 days. In the case studies based on real satellite data, qualitative assessments (due to missing in situ irrigation observations) showed that over regions in which satellite soil moisture products perform well, and which are characterized by prolonged periods without rainfall, the method shows good results in quantifying irrigation. However, at sites in which rainfall is sustained throughout the year, the proposed method fails in obtaining reliable performances. Similarly, low performances are obtained in areas where satellite products uncertainties are too large, or their spatial resolution is too coarse with respect to the size of the irrigated fields.

A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

AbstractWith satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The data sets used in this paper include the Noah land surface model‐based SM estimations, Atmosphere‐Land‐Exchange‐Inverse model‐based Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEM‐derived root‐mean‐square‐errors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSE‐weighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010‐Russia, 2011‐USA, 2013‐New Zealand droughts and other reported agricultural drought occurrences over the 2009–2014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts.

Intercomparison of Downscaling Techniques for Satellite Soil Moisture Products

During recent decades, various downscaling methods of satellite soil moisture (SM) products, which incorporate geophysical variables such as land surface temperature and vegetation, have been studied for improving their spatial resolution. Most of these studies have used least squares regression models built from those variables and have demonstrated partial improvement in the downscaled SM. This study introduces a new downscaling method based on support vector regression (SVR) that includes the geophysical variables with locational weighting. Regarding the in situ SM, the SVR downscaling method exhibited a smaller root mean square error, from 0.09 to 0.07 m3·m−3, and a larger average correlation coefficient increased, from 0.62 to 0.68, compared to the conventional method. In addition, the SM downscaled using the SVR method had a greater statistical resemblance to that of the original advanced scatterometer SM. A residual magnitude analysis for each model with two independent variables was performed, which indicated that only the residuals from the SVR model were not well correlated, suggesting a more effective performance than regression models with a significant contribution of independent variables to residual magnitude. The spatial variations of the downscaled SM products were affected by the seasonal patterns in temperature-vegetation relationships, and the SVR downscaling method showed more consistent performance in terms of seasonal effect. Based on these results, the suggested SVR downscaling method is an effective approach to improve the spatial resolution of satellite SM measurements.