Multi$^{3}$Scat—A Helicopter-Based Scatterometer for Snow-Cover and Sea-Ice Investigations

A helicopter-based Doppler scatterometer (Multi$^{3}$Scat) is described. It allows simultaneous measurements of the surface radar backscatter at five different frequencies at co- and cross-polarization at incidence angles of 20$^{circ}$ –65$^{circ}$ from an altitude of 30–300 m. Video and infrared (IR) cameras simultaneously sense the surface in the scatterometers' footprint. The Multi $^{3}$Scat is calibrated using measurements carried out over corner reflectors. The stability of the Multi$^{3}$Scat's signal is found to be, on average, better than 0.5 dB. Typical signal-to-noise-ratio values for sigma-0 range between 10 and 20 dB for cross-polarization and between 15 and 25 dB for copolarization over snow and ice surfaces. The potential of the Multi$^{3}$Scat to acquire multifrequency multipolarization radar backscatter data and coincident video and IR temperature observations at different incidence angles over remote terrain such as the Arctic Ocean or the Alps is demonstrated.      

Super Cyclone Induces a Mesoscale Phytoplankton Bloom in the Bay of Bengal

Tropical cyclones when on land create havoc, but over the oceans they can trigger a very strong biological response, giving rise to phytoplankton blooms. The Super Cyclone (TC) 05B that occurred during October 25–29, 1999, in the Bay of Bengal over the tropical Indian ocean was one of the most significant tropical cyclones on record to affect India, with maximum winds of 240 km/h, and the worst since 1971. Using satellite data, it is found that this tropical super cyclone helped spawn a notable mesoscale phytoplankton bloom in the domain (17 $^{circ}$–20$^{circ} hbox{N}$; 87$^{circ}$–90 $^{circ} hbox{E}$), which persisted for over a month. The bloom spanned 440 km zonally and 330 km meridonally, enhanced the chlorophyll-$a$ concentrations to a maximum of 10 $hbox{mg/m}^{3}$ and the net primary productivity by 200%. Furthermore, a cyclonic eddy over the bloom region is revealed from an ocean general circulation model simulation, helping the bloom to last for over month.      

Seismic Attenuation Estimation From Instantaneous Frequency

An approach is proposed for quality ($Q$) factor estimation from the variation of envelope peak instantaneous frequency (EPIF). For a frequency-independent $Q$ model, assuming that the propagating wavelet can be modeled by a Gaussian function with constant phase, an approximate analytic relation between $Q$ and EPIF variation is derived. Synthetic tests show that the EPIF method has higher resolution and is less sensitive to noise and interference reflection than common methods. The field test of reflection seismic data indicates that the zone of lower $Q$ -factors corresponds well to the gas reservoir.      

Optimizing $sigma^{0}$ Information From the Jason-2 Altimeter

A radar altimeter's normalized backscatter, $sigma^{0}$, is used in many oceanographic applications to infer values of wind speed, wind stress, rain rate, and the presence of biogenic slicks. The waveform retracker used to estimate the key geophysical variables for the altimeters on the Jason-1 and Jason-2 satellites shows increased small-scale variability since the problem is ill-conditioned. A simple empirical adjustment to $sigma^{0}$ improves the separability between various parameters and also improves the along-track profiles of $sigma^{0}$. This leads to the following: 1) more realistic wind fields; 2) better discrimination of rain events; and 3) improved comparison between the Jason-1 and Jason-2 altimeters during their tandem mission.      

Satellite Observations on Cyclone-Induced Upper Ocean Cooling and Modulation of Surface Winds—A Study on Tropical Ocean Region

In this letter, we evaluate the relationships between the sea-surface temperatures (SSTs) and meteorological parameters over the Bay of Bengal region, India, using microwave satellite remote sensing data. Most of the cyclones in this region occur during the premonsoon period in April–June and are associated with SSTs greater than 26 $^{circ}hbox{C}$. We particularly analyzed the data from two recent cyclonic events: Mala that occurred in April 24, 2006 and Tropical Cyclone 01B (TC 01B) that occurred in May 11, 2003. We used two different remote sensing data sets, sea surface temperature (SST) from the Tropical Rainfall Measurement Mission and the NASA QuikSCAT ocean surface wind vectors to characterize the ocean–atmosphere interactions in cold SST regions formed in the trail of the aforementioned two cyclone events. The results from the satellite data analysis suggested the systematic weakening of wind speed over the cold patch, along the trail of the cyclone. A cooling of around 4$^{circ}$–5 $^{circ}$ was observed to be associated with the passage of cyclone Mala. Wind speed gradually increased from 2 to 9 m/s from the center to the boundary of the cold patch and showed good correlation with SST $(r = 0.97)$. These observations have been validated with another cyclone data (TC 01B) over the Bay of Bengal region that occurred during May 2003. Our results were consistent with the Wallace hypothesis that SST modulates the surface winds via stability.      

Parameterization of the Land Parameter Retrieval Model for L-Band Observations Using the NAFE'05 Data Set

The Land Parameter Retrieval Model (LPRM) has been successfully applied to retrieve soil moisture from space-borne passive microwave observations at C-, X-, or Ku-band and high incidence angles (50 $^{circ}$–55$^{circ}$ ). However, LPRM had never been applied to lower angles or to L-band observations. This letter describes the parameterization and performance of LPRM using aircraft and ground data from the National Airborne Field Experiment 2005. This experiment was undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. It was found that model convergence could only be achieved with a temporally dynamic roughness. The roughness was parameterized according to incidence angle and soil moisture. These findings were integrated in LPRM, resulting in one uniform parameterization for all sites. The parameterized LPRM correlated well with field observations at 5-cm depth ($r = 0.93$ based on all sites) with a negligible bias and an accuracy of 0.06 $hbox{m}^{3}cdot hbox{m}^{-3}$. These results demonstrate comparable retrieval accuracies as the official SMOS soil-moisture retrieval algorithm (L-MEB), but without the need for the ancillary data that are required by L-MEB. However, care should be taken when using the proposed dynamic roughness model as it is based on a limited data set, and a more thorough evaluation is necessary to test the validity of this new approach to a wider range of conditions.      

Using Passive Microwave Response to Soil Moisture Change for Soil Mapping: A Case Study for the Livingstone Creek Catchment

The 46-$hbox{km}^{2}$ Livingstone Creek Catchment in southeastern Australia was flown with a passive microwave airborne remote sensor four times throughout the three-week National Airborne Field Experiment in 2006, with a spatial resolution of $sim$200 m. Both continuous and discrete measurements of soil moisture were taken to help with interpretation of results. The catchment was experiencing extreme drought conditions leading up to the experiment, and as a result, ground cover in the catchment was minimal with many paddocks consisting of sparse dry stubble and grass. During the experiment period of November 2006, 30 mm of rainfall occurred, with the catchment going from parched dry conditions to surface wet conditions and back to dry conditions again in a short period of time. Changes in moisture responses observed by the airborne passive microwave sensor were field verified to reflect the different geology, soil, and landform elements of the catchment. Consequently, this study suggests that passive microwave remote sensing has potential as a tool to assist with soil mapping, through detecting changes in soil moisture spatial and temporal patterns.      

Support-Based Implementation of Bayesian Data Fusion for Spatial Enhancement: Applications to ASTER Thermal Images

In this letter, a general Bayesian data fusion (BDF) approach is proposed and applied to the spatial enhancement of ASTER thermal images. This method fuses information coming from the visible or near-infrared bands (15 $times$ 15 m pixels) with the thermal infrared bands (90 $times$ 90 m pixels) by explicitly accounting for the change of support. By relying on linear multivariate regression assumptions, differences of support size for input images can be explicitly accounted for. Due to the use of locally varying variances, it also avoids producing artifacts on the fused images. Based on a set of ASTER images over the region of Lausanne, Switzerland, the advantages of this support-based approach are assessed and compared to the downscaling cokriging approach recently proposed in the literature. Results show that improvements are substantial with respect to both visual and quantitative criteria. Although the method is illustrated here with a specific case study, it is versatile enough to be applied to the spatial enhancement problem in general. It thus opens new avenues in the context of remotely sensed images.      

Orbit Accuracy Requirement for ABYSS: The Space Station Radar Altimeter to Map Global Bathymetry

The Altimetric Bathymetry from Surface Slopes (ABYSS), which is the proposed science payload on the International Space Station (ISS), is a Johns Hopkins University Applied Physics Laboratory-developed flight-proved delay-Doppler phase-monopulse radar altimeter capable of measuring ocean surface slope in the 6–200-km half-wavelength frequency band range with an accuracy of 0.5 $muhbox{rad}$ , with autonomous gimbal control to compensate for the ISS structural motions. This measurement allows an improved mapping of the global bathymetry, enabling a wide range of scientific research works and applications. The nonrepeat ISS orbital ground track is ideal for ABYSS. This letter describes a simulation study on the effects of the Earth's gravity field and other errors, including thermal bending of the ISS, on the orbit determination of the altimeter instrument antenna phase center location, fulfilling the science objectives of ABYSS. Our study concluded that the error due to mean gravity field is no longer limiting due primarily to the recent Gravity Recovery and Climate Experiment gravity modeling and that the ABYSS/ISS radial orbit slope error budget in the presence of various force and measurement model errors is estimated at the 0.2-$mu hbox{rad}$ root-sum-squared (RSS) level, which satisfies the ABYSS orbit accuracy science requirement to provide an improved mapping of global bathymetry.      

Improved Fraunhofer Line Discrimination Method for Vegetation Fluorescence Quantification

This letter presents a modification to the established Fraunhofer line discrimination (FLD) method for improving the accuracy of the solar-induced chlorophyll fluorescence (ChF) retrieval over terrestrial vegetation. The FLD method relies on the decoupling of reflected and ChF emitted radiation by the evaluation of measurements inside and outside the absorption bands. The improved FLD method introduces two correction coefficients that relate the values of the fluorescence and the reflectance inside and outside the absorption band. The new method uses the full spectral information around the absorption band to derive these coefficients. A sensitivity analysis has been performed to evaluate the impact of the correction coefficients on the accuracy of the ChF estimation. The new formulation has been tested for the $hbox{O}_{2}$ A-band on synthetic data obtaining lower errors in comparison to the standard FLD and has been successfully applied to real measurements at canopy level.      

Dielectric-Covered Ground Reflectors in GPS Multipath Reception—Theory and Measurement

Global positioning system receivers sometimes operate under severe multipath conditions, including those which include a ground specularly reflected signal along with a direct signal. Here, such a case is discussed which involves a dielectric-covered flat ground reflector, with incident energy at 1.57542-GHz right-hand circularly polarized at elevation angles between 5$^{circ}$ and 50 $^{circ}$. The relative received powers produced by the sum of the specularly reflected and direct signals are computed from conventional theory and are measured for various thicknesses of dielectrics. Both theory and measurement show that the received powers produced by the sum of the specularly reflected and direct signals have the same behavior throughout a range of elevation angles. A snow case study shows potential for inferring snow depth by fitting the theory to the measurements.      

Assessing the SMOS Soil Moisture Retrieval Parameters With High-Resolution NAFE'06 Data

The spatial and temporal invariance of Soil Moisture and Ocean Salinity (SMOS) forward model parameters for soil moisture retrieval was assessed at 1-km resolution on a diurnal basis with data from the National Airborne Field Experiment 2006. The approach used was to apply the SMOS default parameters uniformly over 27 1-km validation pixels, retrieve soil moisture from the airborne observations, and then to interpret the differences between airborne and ground estimates in terms of land use, parameter variability, and sensing depth. For pastures (17 pixels) and nonirrigated crops (5 pixels), the root mean square error (rmse) was 0.03 volumetric (vol./vol.) soil moisture with a bias of 0.004 vol./vol. For pixels dominated by irrigated crops (5 pixels), the rmse was 0.10 vol./vol., and the bias was $-$0.09 vol./vol. The correlation coefficient between bias in irrigated areas and the 1-km field soil moisture variability was found to be 0.73, which suggests either 1) an increase of the soil dielectric roughness (up to about one) associated with small-scale heterogeneity of soil moisture or/and 2) a difference in sensing depth between an L-band radiometer and the in situ measurements, combined with a strong vertical gradient of soil moisture in the top 6 cm of the soil.      

An Assessment of QuikSCAT Ku-Band Scatterometer Data for Soil Moisture Sensitivity

The QuikSCAT enhanced (2.225-km) backscattering product is investigated for sensitivity to changes in soil moisture and its potential for spatial disaggregation of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture. Specifically, an active–passive methodology based on temporal change detection is tested using data from the 2006 National Airborne Field Experiment data set. This campaign was carried out from October 29 to November 20, 2006 in a 60 km $times$ 40 km area of the Murrumbidgee catchment, southeast Australia. Temporal change detection analysis and accuracy in terms of spatial pattern distribution throughout the domain were assessed using a passive microwave airborne product derived from the Polarimetric L-band Multibeam Radiometer at 1-km spatial resolution. QuikSCAT–AMSR-E intercomparisons indicated higher correlations when using C-band observations. The greatest sensitivity to soil moisture was observed when using V-polarized backscatter measurement. While backscattering data showed adequate temporal sensitivity to changes in soil moisture due to precipitation events, the spatial agreement was complicated by the presence of irrigation and standing water (rice fields). This resulted in low Cramer's Phi values (less than 0.06), which were used as a measure of spatial correspondence in terms of change in soil moisture and backscatter. In addition, the high QuikSCAT sensor frequency and existence of noise in the observed data contributed to the observed discrepancies.      

An Automatized Frequency Analysis for Vine Plot Detection and Delineation in Remote Sensing

The availability of an automatic tool for vine plot detection, delineation, and characterization would be very useful for management purposes. An automatic and recursive process using frequency analysis (with Fourier transform and Gabor filters) has been developed to meet this need. This results in the determination of vine plot boundary determination and accurate estimation of interrow width and row orientation. To foster large-scale applications, tests and validation have been carried out on standard very high spatial resolution remotely sensed data. About 89% of vine plots are detected corresponding to more than 84% of vineyard area, and 64% of them have correct boundaries. Compared with precise on-screen measurements, vine row orientation and interrow width are estimated with an accuracy of 1$^{circ}$ and 3.3 cm, respectively.      

Extinction Behavior of Soil at 26.5 to 110 GHz

Soil electromagnetic properties at the microwave frequencies have been extensively documented in the literature. However, similar information at the higher millimeter frequencies is not available. A laboratory experiment was conducted to investigate the extinction behavior of wet and dry soil at millimeter wavelengths (26.5–110 GHz). For dry soil, the extinction coefficient increased from 0.02 to 0.6 $hbox{cm}^{-1}$ as the frequency increased from 26.5 to 110 GHz. The presence of even a small amount of water in the soil (5% by weight) reduced the penetration of millimeter wave signals into soil by a factor of ten.      

Soft-Computing Modelling of Seismicity in the Southern Hellenic Arc

This letter investigates the possible coalition of time intervals and patterns in seismic activity during the preparation process of consecutive sizeable seismic events (i.e., $M_{S} geq 5.9$). During periods of low-level seismic activity, stress processes in the crust accumulate energy at the seismogenic area, while larger seismic events act as a decongesting mechanism that releases considerable amounts of that energy. Monthly mean seismicity rates have been introduced as a tool to monitor this energy management system and to divert this information into an adaptive neuro-fuzzy inference system. The purpose of the neuro-fuzzy model is to identify and to simulate the possible relationship between mean seismicity rates and time intervals among consecutive sizeable earthquakes. Successful training of the neuro-fuzzy model results in a real-time online processing mechanism that is capable of estimating the time interval between the latest and the next forthcoming sizeable seismic event.      

A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data

The split-window algorithm is the most commonly used method for land surface temperature (LST) retrieval from satellite data. Simplification of the Planck’s function, as an important step in developing the SWA, allows us to directly relate the radiance to the temperature toward solving the radiative transfer equation (RTE) set. In this study, Planck’s radiance relationship between two adjacent thermal infrared channels was modeled to solve the RTE set instead of simplification of the Planck’s function. A radiance-based split-window algorithm (RBSWA) was developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data. The performance of the RBSWA was assessed and compared with three most common brightness temperature-based split-window algorithms (BTBSWAs) by using the simulated data and satellite measurements. Simulation analysis showed that the LST retrieval using RBSWA had a Root Mean Square Error (RMSE) of 0.5 K and achieved an improvement of 0.3 K compared with three BTBSWAs, and the LST retrieval accuracy using RBSWA was better than 1.5 K considering uncertainties in input parameters based on the sensitivity analysis. For application of RBSWA to MODIS data, the results showed that: 1) comparison between LST from MODIS LST product and LST retrieved using RBSWA showed a mean RMSE of 1.33 K for 108 groups of MODIS image covering continental US, which indicates RBSWA is reliable and robust; 2) when using the measurements from US surface radiation budget network as real values the RMSE of the RBSWA algorithm was 2.55 K and was slightly better than MODIS LST product; and 3) through the cross validation using Advanced Spaceborne Thermal Emission and Reflection Radiometer LST product, the RMSE of the RBSWA algorithm was 2.23 K and was 0.28 K less than that of MODIS LST product. We conclude that the RBSWA for LST retrieval from MODIS data can attain a better accuracy than the BTBSWA.     

Using CERES Data to Evaluate the Infrared Flux Derived From Diffusivity Approximation

Based on the diffusivity approximation theory, the infrared flux at the top of atmosphere (TOA) can be obtained by multiplying a factor of pi on the infrared radiance that was measured at a viewing zenith angle (VZA) of 53^deg. This letter applies the diffusivity approximation on radiance measurements of the Clouds and the Earth's Radiant Energy System (CERES) to derive TOA infrared fluxes and compares these fluxes with the state-of-the-art CERES outgoing radiative fluxes. We find that the mean difference between the two kinds of instantaneous flux that were estimated at the window channel is ~ 1 Wmiddotm^-2, with a root-mean-square error of ~ 1.7 Wmiddotm^-2. This result shows that radiance measurement at a fixed VZA of 53 ^deg is a simple and effective method in the remote sensing of the infrared flux for satellite missions that monitor some specific climate processes and require longwave/window TOA fluxes, such as the Broad Band Radiometer instrument on EarthCARE; however, this approach may involve errors from an inhomogeneous scene or non-Lambertian emission of the surface. A careful design of the VZA and scan mode, such as a conical scan at 53^deg, would produce much more convenient infrared flux measurements for the Earth-atmosphere system than other designs.     

垄行作物玉米方向亮温野外测量中视场角影响的简单分析

基于透视原理、地面试验中对于较高目标的观测存在着一定的偏差。这种偏差随传感器高度、观测角度、视场角大小、观测位置等多个因素改变。由于垄行作物空间结构和温度分布的复杂性 ,在采用较大视场角测量方向亮温的地面实验中 ,将不可避免地存在着误差。采用一个简化的三分量二维结构模型对这种误差进行初步的分析与估算。亮温三分量分别为植被、被阳光照到的亮土和植被阴影下的暗土。作物的结构简化为剖面为矩形的无限长平行体。通过对这三个分量在传感器视场中面积权重的计算来模拟目标结构、传感器高度、位置、视场角大小、观测角度等因素对测量结果产生的影响。模拟结果表明 ,在垂直观测中 ,视场中的植被权重往往被高估 ,偏差随传感器高度的降低急剧增加。在倾斜观测中 ,由于一种互补效应的产生 ,偏差被限制在一个较低的范围内。经过分析 ,减小误差的最有效办法是提高传感器高度。最后 ,实验数据与模拟结果进行了比较。恰当地选取模型输入 ,两种数据能非常好的吻合。     

AAC算法的大气校正复合改进算法

利用模拟数据,评价Autonomous Atmospheric Compensation(AAC)算法的抗噪性,认为AAC算法的抗噪性较弱。基于TASI实测数据,利用AAC算法开展反演计算时,计算结果呈现出多样性问题。结合In-scene Atmospheric Compensation(ISAC)算法中黑体像元的标定方法,提出了一种复合改进算法。首先,利用ISAC算法反演的大气透过率和路径辐射,重新计算AAC算法中大气透过率之比(Tr)和相邻两强弱吸收通道的路径辐射之差(Pd),再次,运用经验公式获得稳定的大气反演结果(大气透过率和路径辐射),有效解决了计算结果多样性的问题。利用复合改进算法,开展的温度与发射率分离实验,证明反演得到的发射率波谱更接近野外实测波谱。