Search

The papers in this special section examine the use of remote sensing technology to promote environmental sustainability in Asia-Pacific regions. Worldwide urbanization and deforestation are the two main interconnected ways that human activities are continually changing and reshaping the earth's surface. How earth observation and remote sensing technologies can contribute to improve the knowledge of the productivity and sustainability of natural and human ecosystems is an important theme in the global change community. In China, for instance, rapid economic growth and urbanization over the past three decades have resulted in dramatic changes in land use and land cover and have led to severe environmental consequences, which have made China's sustainable development a grand challenge. In the meantime, during the past few decades, environmental changes in the Asian–Pacific region have posed significant challenges to the scientific community. Therefore, the global problem of how earth observation and remote sensing technologies may be applied to assessing, monitoring, modeling, and simulating ecosystems, environments, and resources at various spatial and temporal scales translates into peculiar and very urgent questions and applications in this colossal and dynamic geographical region. [ABSTRACT FROM AUTHOR]

In this paper, the focus is on precipitation rate retrieval using 118.75 and 183.31-GHz channels from the microwave humidity and temperature sounder (MWHTS) onboard the FY-3C satellite. These channels provide not only atmospheric humidity profile measurements but they also provide temperature profile measurements. These measurements play important roles detecting and retrieving precipitation rates. Data from the MWHTS and the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) 3B42 are matched in time and space. Matched datasets are used to verify precipitation detection algorithms and precipitation rate retrieval algorithms. To retrieve the precipitation rate, the multiple linear regression algorithm and back propagation (BP) neutral network algorithm are applied in the ten models proposed in this paper. After detailed retrieval analysis between these two algorithms, advanced algorithms are presented this paper to improve the accuracy of the precipitation rate retrieval. In our algorithms, the correlation coefficient of the precipitation rate over the ocean can reach 0.82, the bias is approximately 0.07 mm/hr, and the root mean square error (RMSe) is less than 0.4 mm/hr. Over land, the correlation coefficient is approximately 0.73, the bias is 0.1 mm/hr, and the RMSe is less than 0.5 mm/hr. These results show that our retrieval algorithms improve the retrieval accuracy. Moreover, we study a number of typhoons and find that the FY-3C MWHTS works well to retrieve typhoon precipitation rates. The potential sensitivities of the temperature and humidity channels are analyzed, which can improve the retrieval accuracy and lay the foundation for further research. [ABSTRACT FROM AUTHOR]

The Bayesian hierarchical model has the outstanding capacity to combine prior knowledge with observations, as well as to generate rich spatiotemporal patterns. However, this advanced method has limited use in remotely sensed lattice data applications due to the large computational burden and unreasonable statistical inferences based on statistical units with a fixed scale. This paper presents a multiscale spatial homogeneous subdivision method and develops a Bayesian space–time hierarchical model (BSTHM) for remotely sensed lattice data. This can solve the above-mentioned limitations by constructing multiscale spatial homogeneous units with good statistical properties. The quantitative criteria for subdivision are provided. The outcome of the BSTHM is not only more reasonable in theory but also much easier to interpret; meanwhile, the computational efficiency is also considerably improved. This novel approach and its merits are illustrated by a case study that examines the spatiotemporal variation of PM2.5 pollution in Asia from 2000 to 2014 using remotely sensed data describing PM2.5 annual mean concentrations. Overall spatial pattern, common time trend, and local variation trend were decomposed and quantificationally estimated from an intricate spatiotemporal process. [ABSTRACT FROM AUTHOR]

Satellite remotely sensed products provide critical information to weather forecasters at the National Oceanic and Atmospheric Administration (NOAA) and greatly supplement spare or nonexistent surface observations. Low earth orbiting passive microwave sensors provide unique information related to atmospheric moisture and precipitation, as well as surface properties such as oceanic wind speed. This paper will focus on two microwave sensors that are part of NOAA's Joint Polar Satellite System—the Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Advanced Technology Microwave Sounder (ATMS)—which are flown on Japan's Global Change Observation Mission—Water (GCOM-W1) and the Suomi National Polar orbiting Partnership (S-NPP) satellites. The orbits of GCOM-W1 and S-NPP are such that AMSR2 and ATMS observe the same region of the earth nearly the same time of the day. This allows for comparison of similar products from the two sensors, which is of great interest to users such as NOAA's National Weather Service. In this paper, we focus on product comparisons for a historic flooding event in the U.S. associated with Hurricane Joaquin. The microwave products are presented and shown with other widely used in-situ observations. The performance of AMSR2 and ATMS products are compared. The higher spatial resolution of the AMSR2 sensor provides more detailed information on water vapor and rain rate compared to ATMS. On the other hand, the wider swath width of the ATMS provides a more continuous field of water vapor and precipitation, as well as water vapor retrievals over land. [ABSTRACT FROM PUBLISHER]

The global measurement of sea-surface salinity and more precise measurements of soil moisture from space has been enabled by L-band observations from the Soil Moisture and Ocean Salinity (SMOS) mission, the Aquarius mission, and the Soil Moisture Active Passive (SMAP) mission. These measurements are key components of the global water cycle and the ability to resolve climate scale variations in these variables is extremely valuable for improved understanding of how the hydrologic cycle varies in a changing climate system. It is therefore imperative that the radiometer systems making these measurements be calibrated to remove any spurious instrument temporal drifts. In this paper, models are developed over Antarctica and rainforest regions to track the gain and offset drifts in spaceborne L-band radiometer brightness-temperature (TB) measurements. The Antarctica region is found to be best for tracking small variations (0.1 K over 10 days) for vertically polarized observations near the Brewster angle. The rainforest regions are found to be best for tracking longer term variations ($ > $60 days) in all channels, due to larger uncertainty in the surface temperature knowledge, and excellent for tracking shorter term variations between channels ($ < $10 days). These reference regions were used to separate a long-term gain drift and a quasi-monthly offset variation in the Aquarius radiometer. These observations eventually led to the characterization of the root cause for the drift and are contributing to improved correction methods based on models for the hardware behavior. [ABSTRACT FROM PUBLISHER]

In this paper, we evaluate the near-surface air temperature datasets from the ERA-Interim (ERAI), Japanese 55-Year Reanalysis, Modern-Era Retrospective Analysis for Research and Applications Version 2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2 > 0.95) and that more modern reanalysis, such as ERAI, had lower errors [root-mean-square difference (RMSD) ∼0.9 °C than the older, lower resolution datasets, such as NCEP1 (RMSD ∼2.4 °C). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated growing degree day (GDD) during the growing season to estimate the peak normalized difference vegetation index signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season. [ABSTRACT FROM AUTHOR]

The seasonal cycle of landscape freeze/thaw (FT) state across mid- to high latitudes influences critical processes such as the land surface energy balance, carbon cycle dynamics related to vegetation growth, and hydrological partitioning between surface runoff and infiltration. In this paper, we produce the first daily FT classification for the 2011–2014 period based on L-band radar measurements from Aquarius. The radar FT algorithm used in this paper is based on a seasonal threshold approach, which is also the baseline algorithm applied to higher-resolution (3 km) radar measurements from NASA’s Soil Moisture Active/Passive (SMAP) mission (Launched January 31, 2015). The lower frequency (L-band) radar backscatter measurements from Aquarius provide enhanced sensitivity to FT conditions in vegetation canopy, snow and surface soil layers, although the relative radar penetration depth and sensitivity of the FT signal to these landscape elements will vary according to surface moisture and vegetation biomass conditions, and underlying land cover and terrain heterogeneity [1], [2]. Evaluation of the seasonal threshold FT algorithm using Aquarius was performed using surface air and soil temperatures from selected stations in the Snow Telemetry (SnoTel) network. Analysis identified good agreement during the fall freeze-up period with flag agreement exceeding the 80% SMAP accuracy target when summarized on a monthly basis. Disagreement was greater during the spring thaw transition due in part to uncertainty in characterizing thaw from in situ measurements. Unlike the fall season, stronger agreement in the spring was identified when the reference state was characterized with air temperature compared to soil temperature. [ABSTRACT FROM PUBLISHER]

Aquarius (AQ)/SAC-D is a joint National Aeronautics and Space Administration (NASA)/Comisión Nacional de Actividades Espaciales (CONAE; Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using a L-band radiometer/scatterometer that measures ocean brightness temperature (Tb) and radar backscatter (sigma-0). The application of L-band radiometry to retrieve SSS is a difficult task; therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that “warms” the ocean Tb. The baseline approach, to provide this ocean roughness correction, uses the AQ radar scatterometer measurement of ocean sigma-0 to infer the radiometric excess ocean emissivity. In contrast, this paper develops an alternate approach for the AQ ocean roughness correction using the MicroWave Radiometer (MWR) Tb measurements at Ka-band. The theoretical basis of this MWR ocean roughness correction algorithm is described, which translates these Ka-band measurements to L-band to remove the AQ Tb errors that are caused by ocean wind speed and direction. MWR ocean roughness correction results are compared with corresponding results from the AQ scatterometer method. Also, AQ SSS retrievals are presented using both sets of roughness corrections that demonstrate the relative effectiveness of the MWR and AQ scatterometer approaches. [ABSTRACT FROM PUBLISHER]

This paper describes the technical approach to generate homogeneous warm brightness temperature scene binary mask over the earth for the intercalibration of microwave imagers. The objective is to identify homogeneous desert scenes as targets in radiometric intercalibration of multisource multisensor systems such as Global Precipitation Measurement (GPM) constellation. In this paper, a method is developed to generate mask over continental Australia using 10, 18, and 37 GHz vertical and horizontal polarized radiance observations from GPM Microwave Imager data. Major factors considered are diurnal variation, second Stokes parameter, spatial homogeneity, and surface emissivity. Major advantage of this tool is its minimal, almost negligible, dependence on any external ancillary modeled or observed data. [ABSTRACT FROM AUTHOR]

In this paper, an operational retrieval algorithm is described to infer sea surface temperature (SST) using measurements from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) aboard the Global Change Observation Mission-Water. The algorithm exploits AMSR-2 observations from 12 channels (6–36 GHz, horizontally and vertically polarized), after being corrected for residual calibration biases, to retrieve SST using a statistical based scheme. The Algorithm performance is assessed and results are compared to models and other independent data products. [ABSTRACT FROM PUBLISHER]

Boreal coniferous forests are a large and diverse biome, which has not been widely studied in scope of space borne soil moisture (SM) observations. This paper presents a series of airborne measurements that were made to explore microwave emissivity of a boreal coniferous forest in different moisture conditions. Ground observations of SM and vegetation were made in conjunction with the airborne measurements. The L-band microwave emission of the biosphere (L-MEB) model, which is used in the SM and ocean salinity (SMOS) SM algorithm, was used to model the emission of the forest scenes. The litter layer was included in the modeling because of the thick humus layer present in the study area. The measurements indicate that with sufficient parameters the L-MEB model can simulate the forest and soil-humus ensemble emission with reasonable accuracy (\textR = 0.75, \textRMSE = 4.4\text K). While SM retrieval is hampered by the complex behavior of humus layer, a correct parameterization of the layer is likely to improve accuracy of moisture retrieval for mixed pixels in SMOS observations. [ABSTRACT FROM PUBLISHER]

In this paper, the results of radiothermal remote sensing of soil temperature at a test site on the Yamal Peninsula using full-polarimetry multiangular brightness temperature (BT) observations at the frequency of 1.4 GHz are presented. The BT data were obtained from the Soil Moisture and Ocean Salinity (SMOS) satellite with the SMOS footprint near the Polar Weather Station Marresale, the Russia Federation. The SMOS data covered the period from January 1, 2013 to December 31, 2013. The method to retrieve the soil temperature was based on solving an inverse problem by minimizing the norm of the residuals between the observed and predicted values of the BTs. The calculation of the BT was performed using a semiempirical model of radiothermal emission, which incorporated an attenuation of the microwaves in the snow pack or the canopy and a temperature-dependent multirelaxation spectral dielectric model (TD MRSDM) for an organic-rich tundra soil. The TD MRSDM was specifically designed based on laboratory measurements of the complex permittivity of the organic-rich soil samples, which were collected at the test site on the Yamal Peninsula. As a result, the values of the root-mean-square error and the determination coefficient between the retrieved and measured soil temperatures were determined to be 2.2 °C and 0.70 and 3.5 °С and 0.52, respectively, for thawed frozen soil. These results indicate the perspectives of using the full-polarimetric multiangular BT observations in the L-band for the purpose of measuring the soil temperature in the Arctic region. [ABSTRACT FROM PUBLISHER]

This paper addresses the joint analysis of multisource and multiresolution remote sensing data for the interpolation of high-resolution sea surface geophysical fields. As case-study application, we consider the interpolation of sea surface temperature (SST) fields. We propose a novel statistical model that combines two key features: an exemplar-based prior and statistical priors. The exemplar-based prior, referred to as a nonlocal prior, exploits similarities between local patches (small field regions) to interpolate missing data areas from previously observed exemplars. This nonlocal prior also sets an explicit conditioning between the multisensor data. Two complementary statistical priors, namely a prior on the spatial covariance and a prior on the marginal distribution of the high-resolution details, are considered as sea surface geophysical fields that are expected to depict specific spectral and marginal features in relation to the underlying turbulent ocean dynamics. We report the experiments on both synthetic and real SST data. These experiments demonstrate the contributions of the proposed combination of nonlocal and statistical priors to interpolate visually consistent and geophysically sound SST fields from multisource satellite data. We further discuss the key features and parameterizations of this model as well as its relevance with respect to classical interpolation techniques. [ABSTRACT FROM PUBLISHER]

Over the temperature inversion regions, tropospheric temperature increases with height and clouds are unable to grow vertically. Over the western Arabian Sea, thermal inversion (TI) is seen at the lower troposphere during the Indian summer monsoon season of June to September. Characteristics of this TI are studied using radiosonde data collected over two stations viz., Muscat (23.5°N; 58.5°E) and Salalah (17.0°N; 54.1°E), located close to the TI region and compared with satellite data. Results suggest that the inversion pattern is highly variable on day-to-day basis over Muscat but is nearly constant over Salalah. Three distinct inversion patterns are identified over Muscat. They are: Type-1: temperature increases with height from surface to about 900 hPa and decreases thereafter; Type-2: temperature decreases with height in a shallow region close to the surface followed by an increase up to about 900 hPa and decreases thereafter; and Type-3: multiple inversions up to about 850 hPa. Salalah always showed type-2 inversion with peak temperature around 950 hPa. Statistics of the occurrence of different inversion types are computed for Muscat and Salalah. This paper also evaluates the ability of Infrared Atmospheric Sounding Interferometer (IASI) onboard MetOP satellite in identifying the TI over the Arabian Sea. IASI data are able to capture type-1 and type-2 inversions over Muscat but is unable to capture the type-3 inversions due to its coarser vertical resolution. IASI data are also unable to capture the sharp inversion over Salalah for the same reason. [ABSTRACT FROM PUBLISHER]

This paper provides the description of an ocean wind speed retrieval algorithm developed for the SAC-D/Aquarius microwave radiometer’s (MWR). Empirical model coefficients are derived using MWR brightness temperature at 36.5 GHz H- and V-pol with collocated numerical weather prediction (NWP) model estimates of oceanic and atmospheric environmental parameters. A unique feature of this algorithm is that it incorporates NWP estimates of wind direction to correct the MWR surface brightness temperatures before simultaneously retrieving atmospheric transmissivity, an isotropic ocean surface wind speed. Comprehensive on-orbit validation was conducted using 6 months of MWR wind speed measurements with collocated satellite ocean wind speed retrievals, provided by Remote Sensing Systems, Inc. These results indicate that MWR surface wind retrieval data are quite acceptable for scientific analysis. [ABSTRACT FROM PUBLISHER]

In this paper, we discuss the Aquarius scatterometer calibration, starting with the instrument calibration. We examine the stability of Aquarius as quantified using the loop-back power and estimated receiver gain to shown Aquarius has been extremely stable to order 0.1 dB since mission start. We show the temperatures of scatterometer components not contained in the loop-back path have been controlled precisely to 0.5°C to minimize any temperature-dependent losses. Combined, these results show Aquarius produces accurate $\sigma_0$ over the mission lifetime. In the next section, we discuss the stability as quantified using external models and again show stability to order 0.1 dB in very good agreement with instrument-only methods. Then, we discuss the methods used to absolutely calibrate Aquarius $\sigma_0$ with respect to previous L-band radar systems. We show that Aquarius is relatively calibrated to order 0.1 dB for copolarization channels and better than 0.2 dB for cross-polarization channels. Finally, we discuss the calibration of the Aquarius wind speed product. We compare the Aquarius wind speed with radiometer wind speed products, other radar scatterometers, and numerical weather products. We show that the Aquarius wind speed product is on par with previous scatterometers in data quality. [ABSTRACT FROM PUBLISHER]

The TanDEM-X SAR satellite formation permits improved ocean surface observations by means of bistatic along-track interferometry (ATI) when compared to single-satellite systems. The flexible imaging geometry of its two cooperating SAR sensors forms an interferometer that can achieve very high sensitivity to motions of objects on ground. This way, radar imaging of surface currents with unprecedented accuracy, high spatial resolution and wide coverage at the same time becomes possible. We demonstrate the capabilities of the sensor in the contexts of tidal current mapping, measurement of thermohaline and wind-driven ocean currents as well as detection of areas with surface films. We have developed a dedicated postprocessing system for TanDEM-X image products that allows extracting surface current information from the data. By this paper, we address bistatic data acquisition and processing aspects for sea surface imaging with TanDEM-X like interferometric baseline geometry, temporal decorrelation, and phase calibration. We present a variety of examples of data evaluation that clearly demonstrate the application potential of the methodology. [ABSTRACT FROM PUBLISHER]

The key role of oceans in the global climatic system can be accurately quantified by the measurement of CO $_{2}$ exchange across air–sea interface. The direction and magnitude of this CO $_{2}$ exchange are mainly governed by the gradient of partial pressure of carbon dioxide (pCO $_{2}$) at the air–sea interface. The sea surface pCO $_{2}$ is highly variable, primarily regulated by seasonal sea surface temperature (SST) and CO $_{2}$ (aqueous) concentration. In this paper, the moored pCO $_{2}$ observations from autonomous pCO $_{2}$ system deployed at 15  $^{\circ }$ N 90  $^{\circ }$ E in the Bay of Bengal (BOB) were used to derive CO $_{2}$ fluxes to examine variability over time scale at BOB. An attempt has also been made to develop a model using multiple linear regression (MLR) and support vector regression (SVR) to estimate the sea surface pCO $_{2}$ from space-based observations of SST and salinity. pCO $_{2}$ variability was primarily attributed to SST and CO $_{2}$ concentration changes. Derived fluxes showed that BOB is a net annual source (0.440 g-C/m $^{2}$ /year for year 2014) of CO $_{2}$ at mooring location with significant seasonality effects. For the estimation of surface pCO $_{2}$ , SVR model showed better results [root mean square error (RMSE) = 7.68 $\mu$ atm] in comparison to MLR model (RMSE = 12.36 $\mu$ atm). The seasonal climatological pCO $_{2}$ maps from September 2011 to February 2018 showed southward increase in pCO $_{2}$ in all seasons possibly due to southward increase in SST and less influence of river water. These pCO $_{2}$ maps can further be utilized in deriving CO $_{2}$ flux maps for BOB. [ABSTRACT FROM AUTHOR]

Modern weather satellites provide more detailed observations of cloud and precipitation processes. To harness these observations for better satellite data assimilations, a cloud-resolving model, known as the Goddard Cumulus Ensemble (GCE) model, was developed and used by the Goddard Satellite Data Simulator Unit (G-SDSU). The GCE model has also been incorporated as part of the widely used weather research and forecasting (WRF) model. The computation of the cloud-resolving GCE model is time-consuming. This paper details our massively parallel design of GPU-based WRF GCE scheme. With one NVIDIA Tesla K40 GPU, the GPU-based GCE scheme achieves a speedup of \bf 361 \times as compared to its original Fortran counterpart running on one CPU core, whereas the speedup for one CPU socket (four cores) with respect to one CPU core is only \bf 3.\bf 9 \times . [ABSTRACT FROM PUBLISHER]

A technique to sense sea surface features using the reflections from Global Navigation Satellite System (GNSS) satellites is presented in this paper, in which a novel method for compensating the distortions induced during the delay-Doppler map (DDM) deconvolution process of scattering coefficient retrieval is proposed. A distortion distribution generated using a clean ocean surface is used as a baseline for the inaccuracy due to the constrained least square (CLS) filter. This baseline distribution is used to correct the CLS distortions of “deblurred” DDMs generated from various ocean surface conditions. Subsequently, a spatial integration approach (SIA) is applied to retrieve the scattering coefficients unambiguously using the DDMs obtained by two separate antenna beams. Additionally, the proposed technique is then applied to oil slick detection and cyclone mapping. A performance characterization including retrieval accuracy and resolution is demonstrated with respect to wind speed (WS), oil slick size, and cyclone location. Various simulations show that the retrieval error can be reduced by the SIA after the distortion correction. The technique proposed here can be applied generically to produce physical surface maps of the bistatic scattering coefficient from multiple DDMs from a single space-based platform. [ABSTRACT FROM PUBLISHER]

Atmospheric fronts are an important feature of the marine atmospheric boundary layer. In this paper, India's first synthetic aperture radar (SAR) mission, RISAT-1, is used to detect the atmospheric fronts especially shear lines over the Indian Ocean by studying their imprints on the sea surface using backscatter radar echo. For this study, a multitude of SAR images were analyzed for detecting potential front structure over the Arabian Sea, Bay of Bengal, and South Indian Ocean. Frontal identification, analysis, and characterization are carried with the help of an ancillary satellite and atmospheric model data as provided by the sea-surface temperature, lower tropospheric stability, vertical wind shear, etc. The computation of the thermal front parameter and front points derived from them along with the associated wind speed exhibit a discernible demarcation at the frontal boundary, with the wind speed difference in the range of 2–4 m/s. The study is first of its kind over the Indian Ocean (IO), where separate zones of the IO are explored to emphasize different frontogenesis mechanisms, based on ocean–atmosphere coupling. [ABSTRACT FROM AUTHOR]

Image simulation of remote sensing systems is important for the development of new instruments and validations of data processing algorithms. In image simulation process, surface scene simulation is a fundamental issue and usually has the first priority. For two mid-infrared absorption bands near 2.7 µm and 4.3 µm, although there are a lot of applications in remote sensing field, relevant research on surface scene simulation is very limited. In these two mid-infrared ranges, surface radiance is a combination of reflected and emitted radiance. However, the radiance is generally reduced because of strong absorption by atmosphere. Therefore, analysis of surface reflected radiance is essential for simulation work. In this paper, we use a radiative transfer model MODTRAN to simulate proportions of surface reflected radiance for common ground materials under various observation conditions. The obtained results show that proportions of studied materials are 0.8%–99.8% in the band of 2.63–2.83 µm and 1.1%–94.8% in the band of 4.2–4.5 µm. The proportions of surface reflected radiance in both absorption bands are affected by surface reflectivity. In addition, in the band of 2.7 µm the proportion of surface reflected radiance is sensitive to solar geometry, water vapor content and surface temperature, whereas it is insensitive in the band of 4.3 µm. Based on these results, we conduct that both reflection and emission are important for surface scene simulations in the 2.7 µm and 4.3 µm absorption bands. [ABSTRACT FROM PUBLISHER]

In this paper, a hydrodynamic-electromagnetic model is proposed for analyzing the electromagnetic scattering characteristic of a multiscale deterministic sea surface with internal waves in a two-layer ocean system. A variable-coefficient Gardner model is applied to establish the profile of the internal waves, and the spectrum induced by a variable current is then derived by the action balance equation. The slope-deterministic facet-based two-scale method (SDFb-TSM) is used to calculate the scattering characteristics of the multiscale deterministic sea surface. The total coefficient distribution is then quantitatively obtained from this composite model considering Bragg resonance, tilting effect, and specular reflection effect. Numerical results show that the shape and modulated depth of normalized radar cross section induced by the internal soliton of the Gardner model agree quite well with the measured data. Thus, the Gardner model combined with the SDFb-TSM method performes better than the KdV model combined with statistical scattering methods for the prediction of scattering characteristics of a sea surface with internal waves. The variation in scattering signature characteristics for the depression internal wave are discussed for both one- and two-soliton cases. The influences on the scattering coefficients under different parameters of the internal wave, sea state, and radar system are analyzed. Furthermore, both overtaking-type and head-on-type internal waves are presented and the effect of the perturbed term on internal wave profiles and the scattering characteristics of both one- and two-soliton cases are discussed. [ABSTRACT FROM PUBLISHER]

This paper aims at determining the set of optimum frequencies to be used in the companion microwave radiometers in future synthetic aperture radar altimeters, to provide higher spatial resolution of the atmospheric water-vapor state, so as to improve the wet delay correction in the coastal regions. The channel selection is based on the study of the frequencies that provide the largest amount of information, as defined by the largest information entropy change from a prior knowledge state. It is found that four frequencies, one close to the 22-GHz peak and three other ones around 175.188 GHz, provide a near optimum compromise between the amount of information measured, and the instrument's complexity. [ABSTRACT FROM PUBLISHER]

Numerical weather prediction (NWP) and buoy ocean surface winds show some systematic differences with satellite scatterometer and radiometer wind measurements, both in statistical results and in local geographical regions. It is possible to rescale these reference winds to remove certain aspects of these systematic differences. Space-borne ocean surface winds actually measure ocean surface roughness, which is related more directly to stress. Air mass density is relevant in the air-sea momentum transfer as captured in the stress vector. Therefore, apart from the already common “neutral wind correction” for atmospheric stratification, also a “mass density wind correction” is investigated here to obtain a better correspondence between satellite stress measurements and buoy or NWP winds. The bicorrected winds are called stress-equivalent winds. Stress-equivalent winds do not strongly depend on the drag formulation used and provide a rather direct standard for comparison and assimilation in user applications. This paper presents details on how this correction is performed and first results that show the benefits of this correction mainly in the extratropical regions. [ABSTRACT FROM PUBLISHER]

Sea surface temperature (SST) plays a fundamental role in the exchange of heat, momentum, and water vapor between atmosphere and ocean. Therefore, measurement of SST has been done from ships, buoys, offshore platforms, and satellites. During past decades satellites are being increasingly used, because datasets over wide areas can be obtained. In this paper, moderate resolution imaging spectroradiometer (MODIS) on board Terra and Aqua SST products were examined and validated for coastal waters in the Yellow Sea by using the in situ buoy data. A strict match-up method was adopted in view of the complexity and variability of coastal area, resulting in 154 and 164 match-ups for Terra and Aqua, respectively. The MODIS SST agreed well with in situ buoy SST, with squared correlation coefficients R2 of 0.989 for Terra, and 0.987 for Aqua. Relative to in situ SSTs, the satellite-derived SSTs had a bias of 0.23 °C and 0.06 °C, a standard deviation of 0.79 °C and 0.85 °C, and a root mean square error of 0.83 °C and 0.85 °C, for Terra and Aqua. The differences between MODIS and in situ SST exhibited apparent seasonal variations. The accuracy of MODIS SST products for spring and summer were lower than those for autumn and winter, which could be caused by frequent sea fog along the southern coast of Shandong Peninsula. The SST bias approximately depended on wind velocity; low wind velocity could enhance the diurnal SST amplitude and the bulk-skin temperature difference. However, there is no significant dependence of the bias on air-sea temperature difference or surface flow velocity. [ABSTRACT FROM PUBLISHER]

Accurate modeling of surface emissivity is imperative for accurate radiative transfer simulation and forward modeling of satellite radiance observations. The Radiative Transfer for (A)TOVS (RTTOV) fast radiative transfer model uses the Infrared Surface Emissivity Model (ISEM) for the computation of sea surface emissivity in the infrared ( IR) spectrum. However, the model does not incorporate the effect of surface-emitted surface reflected (SESR) radiation and dependence of wind speed in the emissivity calculation. This paper investigates the uncertainty in the ISEM model caused by ignoring the SESR radiation and wind speed effects in the 3 IR bands, 3.7, 11, and 12 μm. First, we develop a new model called Surface E missivity Model in IR with SESR (SEMIS) that takes the SESR radiation and wind speed effects into account. The uncertainty in the ISEM model is then quantified by comparing the ISEM emissivity against SEMIS derived emissivity. The comparison results suggest that two models are in excellent agreement below $\sim 60^\circ $ emission angle, implying no notable uncertainty in the ISEM model at smaller angles. Nevertheless, uncertainty tends to significantly increase with increasing emission angle above \sim 60^\circ $, which is even more notable at high wind speed ( {\rm{\sim 15\, m/s}}). Two models are further compared against emissivity measurements from a radiometer. The ISEM model has produced large errors as opposed to the SEMIS. [ABSTRACT FROM PUBLISHER]

In this paper, we develop a novel observation-driven methodology to explore spatio-temporal dependencies between satellite-derived sea surface height (SSH) and sea surface temperature (SST) fields. Level-set-based and registration-based criteria are defined to evaluate and detect spatial links between SSH and SST anomaly fields. The method is applied to one-year SST and SSH time series in the highly dynamical Aghulas return current region. The analysis evidences a seasonal variation of the overall correlation between SST and SSH fields. As further revealed, the coldest SST anomalies are reported to efficiently trace the lowest SSH anomalies for all seasons, while the warmest SST anomalies solely match the largest SSH anomalies during winter. The second criterion relies on the registration of SSH and SST anomaly fields. The registration energy is shown to corresponds to the seasonal influence of the mixed-layer depth revealed here by atmospheric forcing. These results show us that the SST-derived SSH reconstruction using the surface quasigeostrophic approximation should take into account stratification effects, especially during summer. As discussed, the proposed methodology can enforce the reconstruction of sea surface current from a joint analysis of satellite altimetry data and high-resolution satellite-derived SST data. [ABSTRACT FROM PUBLISHER]

Ocean remote sensing is the only technique capable of quantitatively detecting long-term and global variability of marine primary production. However, because the spectral curves of light derived from satellite sensors have a limited capability to identify the physiological state of phytoplankton, the direct estimation of photoadaptive variables, such as daily maximum carbon fixation in a water column (P{opt^{b}}), from the spectra of satellite sensors is difficult. This difficulty results in most of the errors in ocean color-based primary productivity models. In this paper, algorithms were developed to estimate $P_{{{\rm opt}}}^{{\rm b}}$ using statistical models known as support vector machines (SVM). The algorithms were run using three different inputs: sea surface temperature (SST); both SST and sea surface chlorophyll a concentration; and SST, sea surface chlorophyll a concentration and photosynthetically active radiation (PAR) together. The results indicate that the algorithm using the three inputs (SST, sea surface chlorophyll a concentration, and PAR) had the best performance. The algorithms based on the SVM had better results than the general statistical regression method. Using the new $P_{{{\rm opt}}}^{{\rm b}}$ algorithm, the performances of the previous primary production models, including the vertically generalized production model and the statistical model developed in our earlier research, were improved. In contrast to the previous algorithm, the global primary productivity estimated using the new $P_{{{\rm opt}}}^{{\rm b}}$ algorithm showed regional changes, with higher primary productivity in most open ocean areas and lower primary productivity in coastal waters. [ABSTRACT FROM PUBLISHER]

The landscape freeze/thaw (F/T) state has an important impact on the surface energy balance, carbon fluxes, and hydrologic processes; the timing of spring melt is linked to active layer dynamics in permafrost areas. L-band (1.4 GHz) microwave emission could allow the monitoring of surface state dynamics due to its sensitivity to the pronounced permittivity difference between frozen and thawed soil. The aim of this paper is to evaluate the performance of both Aquarius and soil moisture and ocean salinity (SMOS) L-band passive microwave measurements using a polarization ratio (PR)-based algorithm for landscape F/T monitoring. Weekly L-band satellite observations are compared with a large set of reference data at 48 sites across Canada spanning three environments: 1) tundra; 2) boreal forest; and 3) prairies. The reference data include in situ measurements of soil temperature (\mathbf T\mathbf{soil}) and air temperature (\mathbf T\mathbf{air}), and moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and snow cover area (SCA) products. Results show generally good agreement between L-band F/T detection and the surface state estimated from four reference datasets. The best apparent accuracies for all seasons are obtained using \mathbfT\mathbf{air} as the reference. Aquarius radiometer 2 (incidence angle of 39.6°) data give the best accuracies (90.8%), while for SMOS, using the Aquarius temporal characteristics, the best results (87.8% of accuracy) are obtained at higher incidence angles (\mathbf55^\circ\!\!-\!\!\mathbf60^\circ ). The F/T algorithm identifies both freeze onset and end with a delay of about 1 week in tundra and 2 weeks in forest and prairies, when compared to \mathbfT\mathbf{air}. The analysis shows a stronger F/T signal at tundra sites due to the typically clean transitions between consistently frozen and thawed conditions (and vice versa) and the absence of surface vegetation. Results in the prairies were poorer because of the influence of vegetation growth in summer (which decreases the PR) and the high frequency of ephemeral thaw events during winter. Freeze onset and end maps created from the same algorithm applied to SMOS and Aquarius measurements characterize similar F/T patterns over Canada. This study shows the potential of using L-band spaceborne observations for F/T monitoring, but underlines some limitations due to ice crusts in the snowpack, liquid water content in snow cover during the spring freeze to thaw transition, and vegetation growth. [ABSTRACT FROM PUBLISHER]

The ocean covers over 70% of the surface of our planet and plays a key role in the global climate. Most ocean circulation is mesoscale (scales of 50–500 km and 10–100 days), and the energy in mesoscale circulation is at least one order of magnitude greater than general circulation; therefore, the study of mesoscale oceanic structures (MOS) is crucial to ocean dynamics, making it especially useful for analyzing global changes. The detection of MOS, such as upwellings or eddies, from satellites images is significant for marine environmental studies and coastal resource management. In this paper, we present an object-based image analysis (OBIA) system which segments and classifies regions contained in sea-viewing field-of-view sensor (SeaWiFS) and Moderate Resolution Imaging Spectro-radiometer (MODIS)-Aqua sensor satellite images into MOS. After color clustering and hierarchical data format (HDF) file processing, the OBIA system segments images and extracts image descriptors, producing primary regions. Then, it merges regions, recalculating image descriptors for MOS identification and definition. First, regions are labeled by a human-expert, who identifies MOS: upwellings, eddies, cool, and warm eddies. Labeled regions are then classified by learning algorithms (i.e., decision tree, Bayesian network, artificial neural network, genetic algorithm, and near neighbor algorithm) from selected features. Finally, the OBIA system enables images to be queried from the user interface and retrieved by means of fuzzy descriptors and oceanic structures. We tested our system with images from the Canary Islands and the North West African coast. [ABSTRACT FROM PUBLISHER]

Mississippi River, the largest river in North America, carries a significant amount of sediment into the northern Gulf of Mexico coastal waters. In this region, recurring wind events associated with atmospheric cold fronts between fall and the following spring dominate resuspension and transport of the sediment. In this paper, based on a time series of the moderate-resolution imaging spectroradiometer satellite images with a moderate spatial resolution of 250 m during the passages of three atmospheric cold fronts in March 2013, considering the entire process of cold fronts, the distribution and variation of total suspended sediment (TSS) concentration and underlying hydrodynamic mechanism were analyzed. The TSS concentration distribution and sediment transport in the Atchafalaya Bay within the Mississippi River estuary were found to be positively correlated with the magnitude of local winds and waves, and negatively correlated with bathymetry. During the prefrontal stage, concentrations in the offshore water always decreased as a result of the wind-induced landward sediment transport. In the shallower water, the variations of TSS concentration are dominated by the intensity of wind stress. In the postfrontal period, the variations of TSS were controlled by wind-driven currents, resuspension, and vertical mixing. Although there were some exceptional situations due to the variations of the characteristics of wind and wind-induce wave, in general, the TSS concentration decreased in the entire region. [ABSTRACT FROM PUBLISHER]

Mare Orientale is the youngest and best-preserved multiring impact basin, whose microwave thermal emission (MTE) research has great significance for studying the internal thermal features and thermal structures of the lunar surface. In this paper, the microwave sounder (Chang'E Lunar Microwave Sounder, CELMS) data from Chang'E-2 satellite were used to study the MTE features of the Mare Orientale. The results indicate that the MTE features of the lunar regolith are much different between those in the superficial layer and in the substrate layer. Also, substrate temperature in the Orientale Basin is likely much higher than what we know. In addition, a new perspective is given to the Hevelius Formation, which is divided into the northeastern, northwestern, and southern parts, and has several potential linear structures. The results are of substantial geologic importance to study the thermal evolution of the Moon. [ABSTRACT FROM PUBLISHER]

Study of sea surface salinity (SSS) plays an important role in the marine ecosystem, estimation of global ocean circulation and observation of fisheries, aquaculture, coral reef, and sea grass habitats. Three statistical methods applied without considering the physical effects of the input parameters are proposed to calculate SSS from soil moisture and ocean salinity (SMOS)-measured brightness temperature (TB) values and associated auxiliary data. Using these three statistical methods, named multiple linear regression (MLR) model, principal component regression (PCR) model, and quadratic polynomial regression (QPR) model, the first predictions of daily and monthly averaged SSS are made with $1 ^\circ\times1 ^\circ$ spatial resolution in the South China Sea (SCS, in the study area of 4°N-25 °N, 105°E-125°E) during the period between April and June 2013. Results are compared with the corresponding SMOS SSS products and Aquarius SSS products and validated using Argo measurements. Validation results show that the root-mean-squared error (RMSE) of the QPR model is around 0.46 practical salinity units (psu) compared to 0.58 psu for Aquarius daily SSS products. World Ocean Atlas (WOA13) SSS data are also used for validation in the SCS and the QPR model gives a 0.54-psu value of RMSE, which may be compared with 0.69 psu, 0.73 psu for SMOS and Aquarius Level-3 (L3) SSS products, respectively. [ABSTRACT FROM PUBLISHER]

The Goddard Profiling Algorithm 2010 Version 2 (GPROF2010V2) was adapted for use for the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission–Water satellite. This study presents the validation and evaluation of the National Oceanic and Atmospheric Administration’s AMSR2 precipitation product. Rain rates calculated by GROF2010V2 meet mission accuracy requirements, with root–mean-squared error of 1.3\ \textmm\cdot \texth^-1 and 3.6\ \textmm\cdot \texth^-1 over ocean and land, respectively. In addition to meeting instantaneous rain rate requirements, GPROF2010V2 captures monthly and seasonal precipitation patterns relative to rain gauge analysis. Recurring problems with the algorithm over land surfaces, such as false alarms due to snow cover and surface contamination, are identified. These deficiencies can be mitigated using separate day/night screening procedures and ancillary information on daily snow cover. [ABSTRACT FROM PUBLISHER]

The Aquarius L-band microwave radiometer is a three-beam pushbroom instrument designed to measure sea-surface salinity. Results are analyzed for performance and systematic effects over three years of operation. The thermal control system maintains tight temperature stability promoting good gain stability. The gain spectrum exhibits expected orbital variations with 1/f noise appearing at longer time periods. The on-board detection and integration scheme coupled with the calibration algorithm produce antenna temperatures with NEDT < 0.16\;\textK for 1.44-s samples. Nonlinearity is characterized before launch and the derived correction is verified with cold-sky calibration (CSC) data. Finally, long-term drift is discovered in all channels with 1-K amplitude and 100-day time constant. Nonetheless, it is adeptly corrected using an exponential model. [ABSTRACT FROM PUBLISHER]

The Bayes Principal components Backpropagation Network (BPBN) is proposed to retrieve total precipitable water (TPW) from the AMSR2 instrument on-board recently launched GCOM-W1 satellite. The proposed algorithm is a physical inversion method, developed using a radiative transfer model to assure that the geophysical retrieval of the TPW is consistent with the radiative transfer theory. The algorithm is comprised of- a Bayes variational algorithm for bias correction, the principal components transformation of the bias-corrected radiometric brightness temperature, and finally, a Bayesian regulation backpropagation network to translate the principal components to TPW estimate in the geophysical space. The algorithm is applicable over ocean, and in clear and cloudy scenes. However, the rainy and sea ice scenes are excluded in the retrieval. A random forest classifier and NASA sea ice temperature retrieval algorithm are used to detect and suppress the rainy and sea ice scenes, respectively. On the whole, the BPBN is a “comprehensive” algorithm, from discarding the redundant scenes to transforming the radiometric information to TPW estimate, and doesn’t use any auxiliary data. This will make it very useful for assimilating into the numerical weather prediction models. The retrieval accuracy of the BPBN algorithm is around 2 \kg/m^2. [ABSTRACT FROM PUBLISHER]

A simple and new fog detection algorithm using any geostationary satellite data that is having at least one visible and one thermal channel is developed. The visible channel observation is used to detect fog, whereas the thermal infrared channel data are used to eliminate the medium/high clouds and snow area. This method is based on temporal differencing technique. Thresholds used in this technique are derived dynamically for each data. It also includes the spatial homogeneity test, which is a unique characteristic of fog. The algorithm is tested with half hourly data of INSAT-3D imager from 0400 to 0930 UTC of 2013–2014 winter months. The fog maps were validated with visibility data available at hourly interval for five different locations over the Indo-Gangetic plains of India for January 2014. The main advantage of this algorithm is its capability to detect day time fog without using the 3.9\-\upmu \text m channel, which is having both emissive and reflective components during day time. This technique is found to have potential for operational use. [ABSTRACT FROM PUBLISHER]

The primary objective of this study was to assess the water productivity (WP) of the annual (wheat, barley, and corn) and biennial (alfalfa and Rhodes grass) crops cultivated under center-pivot irrigation located over desert areas of the Al-Kharj region in Saudi Arabia. The Surface Energy Balance Algorithm for Land (SEBAL) was applied to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images to obtain evapotranspiration (ET) for assessing WP and irrigation performance (IP) of crops. Crop productivity (CP) was estimated using Normalized Difference Vegetation Index (NDVI) crop productivity models. The predicted CP (\bfCPP) for corn varied from 12 690 to 14 060 kg/ha and from 6000 to 7370 kg/ha for wheat. The ${{\rm CP}_{\rm P}} for alfalfa and Rhodes grass was 42 450 and 58 210 (kg/ha/year), respectively. The highest predicted WP was observed in wheat (\bf 0.80-2.01\nbsp\kg/\m^3) and the lowest was in alfalfa (\bf 0.38-0.46\nbsp\kg/\m^3). The deviation between SEBAL predicted ET (\bf ETP) and weather station recorded ET (\bf ETW) was 10%. The performance of the prediction models was assessed against the measured data. The overall mean bias/error of the predictions of CP, ET, and WP was 9.4%, \bf - 2.68\% , and 9.65%, respectively; the root mean square error (RMSE) was 1996 (kg/ha), 2107 (\bf \m^3/\ha), and 0.09 (\bf\kg/\m^3) for CP, ET, and WP, respectively. When CP was converted into variations between the actual and predicted, the variations were 8% to 12% for wheat, 14% to 20% for corn, 17% to 35% for alfalfa, 3% to 38% for Rhodes grass, and 4% for barley. [ABSTRACT FROM PUBLISHER]

The Joint Polar Satellite System-1 (renamed NOAA-20 after reaching the polar orbit) was successfully launched on November 18, 2017, into an afternoon orbit with a local equator crossing time of ∼1:30 p.m., in the same orbital plane as the Suomi National Polar-orbiting Partnership (S-NPP) but with a time separation of 50 min. The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) will become the primary operational imager succeeding the VIIRS onboard S-NPP, which has been in orbit for more than six years. Although the VIIRS onboard S-NPP and NOAA-20 have identical designs, there are small differences in the relative spectral response (RSR) in most bands. Previous studies have shown that minor differences in the S-NPP RSRs at thermal emissive bands (TEBs) can lead to several effects at the detector level, such as striping in sensor data record (SDR) products. Such differences may explain the striping pattern found in S-NPP VIIRS sea surface temperature (SST) products. This article analyzes the detector-level and operational band-averaged RSRs for NOAA-20 VIIRS TEBs and examines the radiometric response to them using the line-by-line radiative transfer model at a very high spectral resolution for convolving with the RSRs. We also evaluate the impact of RSR differences between S-NPP and NOAA-20 for radiometric biases and potential striping in VIIRS TEB SDR brightness temperature. This article will contribute toward measurement consistency for long-term observations in the thermal infrared bands and ensure the quality of retrieval data produced by VIIRS, such as SST, fire, and other retrievals. [ABSTRACT FROM AUTHOR]

Chang'E lunar microwave sounder (CELMS) data provide a potential way to understand the thermophysical features of the shallow lunar crust. In this study, four typical Copernican craters, including Copernicus, Aristarchus, Tycho, and Jackson, have been selected and their brightness temperature $(T_{B})$ performances are evaluated with the CE-2 CELMS data. The results are as follows. First, the hot $T_{B}$ anomaly is reunderstood according to the $T_{B}$ behaviors. The cause to the anomaly is still in doubt, and we rule out the previous explanations as rock abundance or topography. Second, the existence of the cold anomaly indicates the shallow lunar crust is likely much colder than what we knew. Third, the $T_{B}$ performances indicate that the shallow lunar crust is likely homogeneous, and the materials here have very low (FeO+TiO2) abundance and thermal inertia. Also, the difference of the $T_{B}$ performances from the crater floors to far distance will provide a new constraint for the cratering formation study. [ABSTRACT FROM AUTHOR]

The Sulawesi Sea and the northern part of the Maluku Sea belong to the western equatorial Pacific that is influenced by the Mindanao Current. During the southeast (SE) monsoon, contrasting sea surface temperature (SST) distributions occur between these two seas. The purpose of this research is to investigate the mechanisms of contrasting SST distribution in the Sulawesi Sea and the northern part of the Maluku Sea under the condition of strong southerly wind during the SE monsoon season. Our analysis was based on satellite observations. Warmer (colder) SSTs are identified in the Sulawesi Sea (northern part of the Maluku Sea), which correspond to the weaker (stronger) southerly wind speed. Strong southerly winds in the northern part of the Maluku Sea generate eastward Ekman mass transport (EMT) that carries the water mass away from the coast, inducing upwelling. In contrast, the southerly winds from the Maluku Sea are blocked by the Gorontalo and North Sulawesi Mountains, creating low-wind areas in the Sulawesi Sea that are located on the lee side of those mountains. Furthermore, the existence of a valley between those mountains acts as a wind gap that channels the southerly wind to the Sulawesi Sea. Since the seaward EMT is weak and the latent heat release is not dominant, SSTs in front of the wind gap are only slightly reduced, possibly through the mechanisms of oceanic mixing. [ABSTRACT FROM AUTHOR]

The Soil Moisture Active/Passive (SMAP) microwave radiometer is a fully polarimetric L-band radiometer flown on the SMAP satellite in a 6 AM/6 PM sun-synchronous orbit at 685 km altitude. After the SMAP L1B_TB data product version 3 was released in 2016, the radiometer has been undergoing further calibration and validation with the goal of reducing both the bias in the cold-sky measurements and calibration drift in the global ocean measurements experienced during eclipse seasons in data product version 3. The post-launch calibration algorithm has been upgraded by using new estimates of the reflector emissivity as well as using multiple scenes to calibrate the radiometer internal reference sources and antenna gain simultaneously. In addition, a correction offset is applied to the ocean roughness model for horizontal polarization based on nadir observations. Test and validation results show that the goal is achieved (e.g., biases are removed and the calibration stability achieved for data release version 4 is 0.1 K (rms) over both the global ocean and CS). [ABSTRACT FROM AUTHOR]

In recent years, the intensity of Arctic cyclones has remarkably increased and the impact on the Arctic ecosystem has become more prominent. The Kara Sea is a Russian marginal sea with wide shelf, high productivity of phytoplankton, and abundant resources, and has a very important strategic position and research significance. We have used multiple datasets, including satellite remote sensing data and model reanalysis, to obtain the spatial–temporal distribution of Chlorophyll-a (Chl-a) concentration and sea surface temperature (SST) before and after the passage of the Arctic cyclone over the Kara Sea in summer, to explore basic processes and the ecosystem response to the cyclone. The results indicated that after the passage of the Arctic cyclone, the Chl-a concentration in different regions of the Kara Sea increased at different levels, and the SST decreased briefly. The increase of Chl-a concentration (0.49 mg/m3) caused by the Arctic cyclone happened in Kara Sea in July 2012. The nearshore Chl-a concentration increased more than that on the continental shelf, the continental shelf increases in turn being greater than the deep sea; however, the nearshore response time was shorter than that on the shelf and the deep sea. Compared with nearshore SST, which decreased more than 2 °C, the SST on the continental shelf and in the deep-sea area decreased slightly less. Besides, the left side of the cyclone cooled faster than the right side. [ABSTRACT FROM AUTHOR]

Geophysical model functions (GMFs) are developed which map the Level 1 observables made by the Cyclone Global Navigation Satellite System (CYGNSS) radar receivers to ocean surface wind speed. The observables are: 1) the normalized bistatic radar cross section (σo) of the ocean surface; and 2) the slope of the leading edge of the radar return pulse scattered by the ocean surface. GMFs are empirically derived from measurements by CYGNSS which are nearly coincident with independent estimates of the 10-m-referenced ocean surface wind speed (u10). Two different sources of “ground truth” wind speed are considered: numerical weather prediction model outputs and measurements by the NOAA P-3 hurricane hunter during eyewall penetrations of major hurricanes. The GMFs derived in each case have significant differences that are believed to result from differences in the state of development of the long wave portion of the ocean surface height spectrum that result from characteristic differences in wave age and fetch length near versus far from a hurricane. [ABSTRACT FROM AUTHOR]

The calibration algorithm used by the Cyclone Global Navigation Satellite System (CYGNSS) mission to produce version 2.1 of its Level 1 (L1) science data products is described. Changes and improvements have been made to the algorithm, relative to earlier versions, based on the first year of on-orbit result. The L1 calibration consists of two parts: first, the Level 1a (L1a) calibration converts the raw Level 0 delay Doppler maps (DDMs) of processed counts into received power in units of watts. Second, the L1a DDMs are then converted to Level 1b DDMs of bistatic radar cross section values by unwrapping the forward scattering model, which are then normalized by the surface scattering area to arrive an observation of $ \sigma _0$. An update to the bottom up term-by-term error analysis is also presented, using on-orbit results to better quantify the accuracy of the rolled-up L1 calibration. The error analysis considers uncertainties in all known input calibration parameters. Finally, a method for calibrating the time delay of CYGNSS measurements is presented. [ABSTRACT FROM AUTHOR]

We compare Soil Moisture Ocean Salinity Level 2 Soil Moisture (L2SM) retrievals to the five-year in situ soil moisture dataset available for the predominately agricultural South Fork Iowa River (SFIR) watershed in the U. S. Corn Belt. SMOS L2SM is 0.039 m $^3$  m $^{-3}$ drier than the SFIR network soil moisture and has an unbiased RMSE (ubRMSE) of 0.062 m $^3$  m $^{-3}$ for the period of April 2013 to November 2017 (excluding DJF). The bias is 11% of the range of in situ soil moisture. The largest monthly dry biases occur in April (0.052 m $^3$  m $^{-3}$), July (0.072 m $^3$  m $^{-3}$), and November (0.061 m $^3$  m $^{-3}$). Potential sources of the dry bias we discuss are: bias in auxiliary modeled temperatures; errors in soil texture maps; and nonrepresentative parameterizations of single scattering albedo and soil surface roughness. Auxiliary skin temperature was colder than expected and may explain why SMOS L2SM has a slightly drier bias for evening overpasses. Increasing the parameterized soil surface roughness produces wetter SMOS L2SM retrievals but also decreases the range of SMOS L2SM. Random error in auxiliary surface temperature, edge-of-field locations of in situ sensors, and differences in sensing volume between SMOS and in situ sensors contribute to the soil moisture ubRMSE. ubRMSE can be decreased by using a nonzero single scattering albedo more representative of a corn and soybean canopy at the cost of increasing the dry bias. [ABSTRACT FROM AUTHOR]

A new payload concept has been proposed: the microwave imager combined active/passive (MICAP). MICAP is a combined one-dimensional microwave interferometric radiometer that operates at 1.4, 6.9, 18.7, and 23.8 GHz and L-band (1.26 GHz) scatterometer. It has the capability to simultaneously remotely sense sea surface salinity (SSS), sea surface temperature (SST), and wind speed. MICAP will be a candidate payload onboard the Ocean Salinity Satellite led by the State Oceanic Administration of China to monitor SSS and reduce geophysical errors caused by surface roughness and SST. To provide an “all-weather” estimation of SSS with high accuracy from space, the errors of the simultaneous retrieval of multiparameters using MICAP are analyzed, and noise levels and stability requirements of instruments are estimated. Preliminary analysis shows that MICAP can provide SSS with an accuracy of 1 psu for single measurement and 0.1 psu over the global ocean for 200 × 200 km resolution pixels and one month at middle and low latitudes with default instrument noises (0.1 K, 0.3 K and 0.3 K for the L, C, and K band radiometers, respectively, and 0.1 dB for the L-band scatterometer) while uncertainties of the drift corrections are less than the radiometer sensitivities. [ABSTRACT FROM AUTHOR]

This study focuses on the role that African dust over the Atlantic had on the persistence of the tropical system that eventually became Hurricane Sandy in October 2012. On October 8, a Saharan dust event in the Mauritania region of West Africa transported significant amounts of mineral dust into the troposphere and along the path of an easterly wave created by a break in the Intertropical Convergence Zone (ITCZ). The Terra/Aqua-MODIS satellite observations clearly define the spatial distribution of the coarse/fine aerosols, while the CALIPSO observations of the total attenuated backscatter at 532 nm provide a detailed view of the vertical structure and aerosol types in the dust-laden layer. European Centre for Medium-Range Weather Forecasts and Modern-Era Retrospective Analysis for Research and Applications, Version 2 reanalysis data show the distribution of aerosols along the path of the pre-Sandy wave as well as a second wave that formed north of the ITCZ under different condition. The second wave, which started in an area of relatively larger aerosol optical depth (AOD), moved into an area with abnormally low convective available potential energy and AOD, subsequently dying out, while the wave that became Sandy had light aerosol loading (AOD between 0.15–0.5) along a majority of its path. The evidence suggests that aerosols played a nontrivial role in the maintenance of this system until it moved into an environment favorable for cyclogenesis. [ABSTRACT FROM PUBLISHER]

The surface albedo in urban environment represents one of the most influencing parameters in the formation of the Urban Heat Island (UHI). Its assessment is therefore strategic to undertake actions for the UHI mitigation: the increase of urban surface reflectivity is an example of cooling strategies increasingly proposed and developed. Spaceborne observations could give a significant contribution supplying albedo maps, and different retrieval algorithms estimating broadband albedo from narrow band reflectivities provided by satellite sensors were proposed and validated in literature. In this study, the accuracy of two well-known retrieval algorithms applied to Landsat 8 OLI reflectivities was assessed in a city of Central Italy (Perugia), selecting test sites having different land cover types. Since the root mean square error of the estimated albedo was quite high (0.07–0.08), a custom retrieval algorithm was developed to evaluate the potential improvement in the narrowband reflectivity-broadband albedo conversion for the case study. This analysis was made possible thanks to an in situ measurement campaign, carried out during the summer months of 2016. The selected sites, having quite homogenous features inside the urban environment, also demonstrated that the albedo values are not significantly affected by the hour of the day, if measurements are taken near noon (variation less than 7%). Spaceborne observations showed once again a powerful tool for monitoring large areas with a quite good spatial resolution (30 m), even if an improvement of the literature albedo retrieval algorithms is required and possible in an urban context. [ABSTRACT FROM PUBLISHER]