A generalized split-window algorithm for retrieving land-surfacetemperature from space

Proposes a generalized split-window method for retrieving land-surface temperature (LST) from AVHRR and MODIS data. Accurate radiative transfer simulations show that the coefficients in the split-window algorithm for LST must vary with the viewing angle, if the authors are to achieve a LST accuracy of about 1 K for the whole scan swath range (±55° from nadir) and for the ranges of surface temperature and atmospheric conditions over land, which are much wider than those over oceans. The authors obtain these coefficients from regression analysis of radiative transfer simulations, and they analyze sensitivity and error over wide ranges of surface temperature and emissivity and atmospheric water vapor abundance and temperature. Simulations show that when atmospheric water vapor increases and viewing angle is larger than 45°, it is necessary to optimize the split-window method by separating the ranges of the atmospheric water vapor, lower boundary temperature, and the surface temperature into tractable subranges. The atmospheric lower boundary temperature and (vertical) column water vapor values retrieved from HIRS/2 or MODIS atmospheric sounding channels can be used to determine the range for the optimum coefficients of the split-window method. This new algorithm not only retrieves land-surface temperature more accurately, but is also less sensitive to uncertainty in emissivity and to instrument quantization error     

Impact of the atmospheric transmittance and total water vaporcontent in the algorithms for estimating satellite sea surfacetemperatures

Sea surface temperature (SST) algorithms for NOAA AVHRR data can determine SST with rms values of 0.7 K on a global basis. However, this figure is not compatible with the high accuracy of 0.3 K required by climate studies. Biases in the SST product, arising when the factors that increase the optical path-length (absorbents concentration in the atmosphere or viewing angles) are large, cause problems in the use of the split-window formulation for climate monitoring. The reason is that the split-window coefficients currently used are not adequate to cover for all the atmospheric variability. To show this, simulations of channels 4 and 5 of AVHRR/2 of NOAA-11 using a radiative transfer model have been made. The range of atmospheric conditions and surface temperatures introduced in the simulation covers the variability of these parameters on a worldwide scale. From these data, the authors present new split-window coefficients that take into account the atmospheric variability through the ratio of the channel transmittances, or else through the total water vapor content along the path. They also show, using simulated and actual data, that the proposed split-window algorithm has a real global character and represents an improvement over the conventional algorithms     

A simplified method for estimating the total water vapor contentover sea surfaces using NOAA-AVHRR channels 4 and 5

A simplified method for estimating the total amount of atmospheric water vapor, W, over sea surfaces using NOAA-AVHRR Channels 4 and 5 is presented. This study has been carried out using simulated AVHRR data at 11 and 12 μm (with MODTRAN 3.5 code and the TIGR database) and AVHRR, PODAAC, and AVISO databases provided by the Louis Pasteur University (Strasbourg-France), NASA-NOAA, and Meteo France, respectively. The method is named linear atmosphere-surface temperature relationship (LASTR). It is based on a linear relationship between the effective atmospheric temperature in AVHRR Channel 4 and sea surface temperature. The LASTR method was compared with the linear split-window relationship (LSWR), which is based on a linear regression between W and the difference of brightness temperature measured in the same channels (ΔT=T4-TS). The results demonstrate the advantage of the LASTR method, which is capable of estimating W from NOAA-14 afternoon passes with a bias accuracy of 0.5 g cm^-2 and a standard deviation of 0.3 g cm^-2, compared with the W obtained by the AVISO database. In turn, a global bias accuracy of 0.1 g cm^-2 and a standard deviation within 0.6 g cm^-2 have been obtained in comparison with the W included in the PODAAC database derived from the special sensor microwave/imager (SSM/I) instrument     

Improvements in the split-window technique for land surfacetemperature determination

Land surface temperature (LST) retrievals obtained from NOAA Advanced Very High Resolution Radiometer (AVHRR) are of considerable importance for climatic research. However, the accurate evaluation of LST from space has been severely limited because of the difficulty in separating atmospheric from surface effects as the surface cannot be modeled as a black-body radiator. With this goal in mind, a novel extension of the split-window technique is presented in which the atmospheric contribution to the radiance measured by the satellite is investigated by the ratioing of covariance and variance of the brightness temperatures measured in channels 4 and 5 of AVHRR/2. Furthermore, the contribution of emissivity is evaluated from coefficients that depend on the spectral emissivities in both thermal channels. Using a wide range of simulations from an atmospheric radiative transfer model it is shown that the proposed algorithm provides an estimate of LST, to within 0.4 K if the spectral surface emissivity is known, which is better than that given by the currently used split-window algorithms for LST determination. Also the limitations on algorithm accuracy are discussed considering different values of noise equivalent temperature. Finally the authors present the preliminary results obtained using the proposed method from AVHRR data over a semi-arid region-of Northwestern Victoria in Australia provided by CSIRO, and a mountainous region of Northeast of France acquired in the frame of Regio Klimat Projekt     

Retrieval of cloud base heights from passive microwave and cloudtop temperature data

Water vapor profiling algorithms that treat liquid clouds explicitly yield a cloud base height as a byproduct. A single case of a water vapor profile retrieval using a combination of the SSM/T-2 on the DMSP satellite and cloud parameters from the AVHRR on the NOAA satellite retrieved a reasonable cloud base. While hardly definitive, this case is suggestive. The authors examine the cloud base signal in a combination of the SSM/1 and SSM/T-2 on the DMSP satellite from a theoretical point of view. It is shown that the signal is strong enough for a useful retrieval only over the ocean. For low altitudes, a cloud top temperature (CTT) constraint, as could be provided from an infrared radiometer, is required. While difficult with the DMSP-NOAA satellite combination, this has become much easier with the recent launch of NOAA-K with the AMSU-B and AVHRR. It is shown that the signal is acceptable over the relevant range of cloud liquid water content values. To achieve useful results, some local tuning of the algorithm will be necessary. This tuning could take the form of water vapor profile covariance matrices, climatological estimates of the cloud liquid water density, or purely empirical methods. Broken and multilayer clouds provide additional complications to the problem     

On-Orbit Calibration Assessment of AVHRR Longwave Channels on MetOp-A Using IASI

Accurate and precise satellite radiance measurements are important for data assimilations in numerical weather prediction models and for climate-change detection. After the successful launch of the Infrared Atmospheric Sounding Interferometer (IASI), several studies have indicated that the IASI radiance measurements are well calibrated and maintain superb spectral and radiometric calibration accuracy. Owing to its hyperspectral nature and high-quality measurements, the IASI radiance can serve as a relative reference to independently assess the radiance measurements of broad- or narrow-band instruments that share the same spectral region. In this paper, we demonstrate the utility of the IASI radiances to evaluate the Advanced Very High Resolution Radiometer (AVHRR) infrared (IR) channel measurements. The coregistered AVHRR pixels inside each IASI pixel are averaged spatially. We then compared the spatially averaged radiance from AVHRR IR channels with IASI by convolving the IASI-measured spectra with the AVHRR spectral response functions. It was found that, statistically, the temperature observed from AVHRR channels 4 and 5 is slightly warmer than that in IASI for the brightness temperature (BT) range of 200 K–300 K. The mean BT difference (IASI minus AVHRR) is less than 0.4 K with a standard deviation of $ sim$0.3 K for AVHRR channels 4 and 5. The BT difference between IASI and AVHRR IR channels is scene-temperature dependent for both channels 4 and 5, which is probably caused by the nonlinearity of the AVHRR detectors. Both AVHRR channels 4 and 5 show slight scan-dependent bias with maximum differences of approximately $sim$0.2 K (with AVHRR being warmer than IASI) at both ends of the scan.      

PIn. I. An operational nonlinear physical inversion algorithm forprecipitable and cloud liquid water estimate in nonraining conditionsover sea

For pt.II see ibid., vol.39, no.12, p.2575-86 (2001). An operational nonlinear physical inversion (PIn) algorithm for precipitable and cloud liquid water estimate is described. It is suited for a generic conical scanning satellite microwave radiometer acquisition over sea in nonraining conditions. The algorithm does not need any calibration phase and is independent of the availability of in situ data, being consistent in different geographical and climatological situations. Adopted formulation is addressed to provide observational data to help in validating water vapor and cloud fields produced by a numerical weather prediction model. Furthermore, such a technique can be utilized for the purpose of global reanalysis, improving estimates of primary fields of the hydrological cycle. A sensitivity study of the forward model and a comparison between output brightness temperatures and those from a robust numerical code are also reported. The discrepancies that result are considered acceptable with respect to instrumental constraints and computation time     

Forward model studies of water vapor using scanning microwave radiometers, global positioning system, and radiosondes during the cloudiness intercomparison experiment

Brightness temperatures computed from five absorption models and radiosonde observations were analyzed by comparing them with measurements from three microwave radiometers at 23.8 and 31.4 GHz. Data were obtained during the Cloudiness Inter-Comparison Experiment at the U.S. Department of Energy's Atmospheric Radiation Measurement Program's (ARM) site in North-Central Oklahoma in 2003. The radiometers were calibrated using two procedures, the so-called instantaneous "tipcal" method and an automatic self-calibration algorithm. Measurements from the radiometers were in agreement, with less than a 0.4-K rms difference during clear skies, when the instantaneous method was applied. Brightness temperatures from the radiometer and the radiosonde showed a bias difference of less than 0.69 K when the most recent absorption models were considered. Precipitable water vapor (PWV) computed from the radiometers were also compared to the PWV derived from a Global Positioning System station that operates at the ARM site. The instruments agree to within 0.1 cm in PWV retrieval.     

Application of neural networks to AVHRR cloud segmentation

The application of neural networks to cloud screening of AVHRR data over the ocean is investigated. Two approaches are considered, interactive cloud screening and automated cloud screening. In interactive cloud screening a neural network is trained on a set of data points which are interactively selected from the image to be screened. Because the data variability is limited within a single image, a very simple neural network topology is sufficient to generate an effective cloud screen. Consequently, network training is very quick and only a few training samples are required. In automated cloud screening, where a general network is designed to handle all images, the data variability can be significant and the resulting neural network topology is more complex. The latitudinal, seasonal and spatial dependence of cloud screening large AVHRR data sets is studied using an extensive data set spanning 7 years. A neural network and associated feature set are designed to cloud screen this data set. The sensitivity of the thermal infrared bands to high atmospheric water vapor concentration was found to limit the accuracy of cloud screening methods which rely solely on data from these channels. These limitations are removed when the visible channel data is used in combination with the thermal infrared data. A post processing algorithm is developed to improve the cloud screening results of the network in the presence of high atmospheric water vapor concentration. Post processing also is effective in identifying pixels contaminated by subpixel clouds and/or amplifier hysteresis effects at cloud-ocean boundaries. The neural network, when combined with the post processing algorithm, produces accurate cloud screens for the large, regionally distributed AVHRR data set     

Atmospheric correction against algorithm for NOAA-AVHRR products:theory and application

Investigation of the effect of atmospheric constituents on NOAA Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared data is presented. The general remote sensing equation, including scattering, absorption, and bidirectional reflectance effects for the AVHRR solar bands, is described. The magnitude of the atmospheric effects for AVHRR solar bands with respect to their impact on the normalized difference vegetation index (NDVI) and the surface bidirection reflectance is examined. Possible approaches for acquiring atmospheric information are discussed, and examples of atmospheric correction of surface reflectance and NDVI are given. Invariant effects (ozone absorption and molecular scattering) and variant effects (water vapor absorption and aerosol scattering) are shown to dominate the atmospheric effects in the AVHRR solar bands     

Adjusting for Long-Term Anomalous Trends in NOAA's Global Vegetation Index Data Sets

The weekly 0.144 resolution global vegetation index from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) has a long history, starting late 1981, and has included data derived from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA-7, -9, -11, -14, -16, -17, and -18 satellites. Even after postlaunch calibration and mathematical smoothing and filtering of the normalized difference vegetation index (NDVI) derived from AVHRR visible and near-infrared channels, the time series of global smoothed NDVI (SMN) still has apparent discontinuities and biases due to sensor degradation, orbital drift [equator crossing time (ECT)], and differences from instrument to instrument in band response functions. To meet the needs of the operational weather and climate modeling and monitoring community for a stable long-term global NDVI data set, we investigated adjustments to substantially reduce the bias of the weekly global SMN series by simple and efficient algorithms that require a minimum number of assumptions about the statistical properties of the interannual global vegetation changes. Of the algorithms tested, we found the adjusted cumulative distribution function (ACDF) method to be a well-balanced approach that effectively eliminated most of the long-term global-scale interannual trend of AVHRR NDVI. Improvements to the global and regional NDVI data stability have been demonstrated by the results of ACDF-adjusted data set evaluated at a global scale, on major land classes, with relevance to satellite ECT, at major continental regions, and at regional drought detection applications.     

The observed relationship between the south pole 225-GHz atmospheric opacity and the water vapor column density

We compare our previously reported measurements of South Pole 225 GHz atmospheric opacity,τ, to the column of precipitable water vapor (PWV) which was derived from concurrent upper air soundings. From this comparison we found thatτ=(2.8±0.1)×10^?2+(6.9±0.2)×10^?2×PWV withτ in units of nepers/airmass andPWV in units of mm of precipitable H₂O. We compared our results to predictions from Grossman's AT atmospheric transparency model which is widely used in the radio astronomy community. The coefficient of the second term of the above relation, 0.069, was consistent with the predictions from the model; however, the first term, 0.028, which represents the dry air opacity, was about five to ten times larger than expected. Most of this discrepancy between the observed and the predicted dry air opacity can be accounted for by including contributions from continuum emission from N₂ and O₂ as is done in Liebe's MPM atmospheric model.     

Early Warning and Crop Condition Assessment Research

The Early Warning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicator models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U. S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.     

一种高效计算高光谱分辨率红外大气辐射传输的方法

传统的大气辐射传输计算方法受计算资源和效率的限制已无法满足星载高光谱分辨率红外大气遥感资料处理的需要,基于单色波长辐亮度计算加权组合的方法,开发了一种快速准确的高光谱分辨率红外大气辐射传输的计算模型FFRTM_IR.利用FFRTM_IR模型,针对研制中的FY-3D红外高光谱大气探测仪(HIRAS)的光谱指标,模拟计算了仪器的观测通道亮温,并用独立样本对模拟亮温进行了验证和比较,结果显示:FFRTM_IR对HIRAS所有通道的模拟亮温偏差均小于0.06 K,标准差有效控制在0.1 K以内;在同等计算精度下,FFRTM_IR的计算速度略快于目前国际上通用的快速辐射传输计算模型CRTM.利用FFRTM_IR模型,采用解析计算方法可以进一步得到温度廓线、水汽廓线、二氧化碳、臭氧廓线以及地表参数的雅克比矩阵,计算结果与扰动法计算结果一致性较好,有较高的计算精度.计算和分析结果表明,初步建立了一种高效的红外大气辐射传输计算模型,可用于星载高光谱红外大气探测仪器的观测仿真和资料处理.     

一种基于时频分布尺度变换的ISAR成像新方法

 空中或海面目标复杂运动所产生的方位二次相位项,会造成方位成像的严重散焦;而传统的RD成像方法、WVD瞬时成像方法、Radon-Wigner成像方法和DechirpClean成像方法由于成像效果差或运算效率低等因素,不适合复杂运动目标的ISAR成像;因此,针对这些问题,本文提出了一种基于时频分布尺度变换的ISAR成像新方法,即把包络对齐后的各距离单元数据变换到时频平面内,通过尺度变换,解信号瞬时时间和相关函数延迟量的耦合影响,把方位二次相位项所产生的时频平面内的斜线校正成平行于时间轴的直线,并沿时间轴进行能量积累,减少交叉项的影响,最终对复杂运动目标进行高分辨ISAR成像.该方法成像效果好,同时,可以通过快速变换算法实现,因此,运算效率较高,适合实时成像.最后,通过仿真和实测数据验证了该方法的有效性.     

基于希尔波特-黄变换的冲击无线电信号检测

冲击无线电信号具有低截获、瞬态特性。本文提出采用希尔波特-黄变换(HHT)方法从干扰和噪声中提取微弱的冲击无线电信号。通过HHT可以得到冲击无线电信号的时频谱,并与传统的小波时频谱和Wigner-Ville分布进行了比较。仿真表明：在信噪比低达-10dB的情况下进行冲击无线电信号检测,HHT方法优于传统的小波分析和Wigner-Ville分布。     

利用大洋浮标数据和NCEP再分析资料对FY-2C红外分裂窗通道的绝对辐射定标

介绍利用大洋浮标数据和NCEP再分析资料对FY-2C红外分裂窗通道进行在轨绝对辐射定标的方法,并选择了2006年10个时次的卫星数据进行辐射定标试验.将利用这种方法获得的定标结果与FY-2C卫星数据产品中提供的定标查找表进行比较分析,结果表明两套不同的定标系数反演的大气层顶(TOA)亮度温度的主要差别集中在云顶、冰雪覆盖区域等低温像元;而在常温区的陆表和海表像元定标结果差别较小,反演的TOA亮温差在2K左右.提出的替代定标方法可以极大地提高定标频次,为实现FY-2C红外分裂窗通道的实时绝对辐射定标提供了重要的方法基础.     

Soft Fault Feature Extraction in Nonlinear Analog Circuit Fault Diagnosis

Aiming at the problem to diagnose soft faults in nonlinear analog circuits, a novel approach to extract fault features is proposed. The approach is based on the Wigner–Ville distribution (WVD) of the subband Volterra model. First, the subband Volterra kernels of the circuit under test are cleared. Then, the subband Volterra kernels are used to obtain the WVD functions. The fault features are extracted from the WVD functions and taken as input data into the hidden Markov model (HMM). Finally, with classification of features using HMMs, the soft fault diagnosis of the nonlinear analog circuit is achieved. The simulations and experiments show that the method proposed in this paper can extract the fault features effectively and improve the fault diagnosis.     

An atmospheric correction algorithm for thermal infraredmultispectral data over land-a water-vapor scaling method

Thermal infrared (TIR) multispectral data over land can be atmospherically corrected by radiative transfer calculations combined with global assimilated data from a weather forecast system. This approach is advantageous to operational processing but is not accurate. A new atmospheric correction algorithm with global assimilated data, a water vapor scaling (WVS) method, has improved results. In this algorithm, the accuracy of global assimilated data is markedly improved on a pixel-by-pixel basis as follows: (1) selecting gray pixels from an image; (2) estimating the scaling factors for the water-vapor profiles of gray pixels by an improved multichannel algorithm; (3) estimating the scaling factors for the water-vapor profiles of nongray pixels by horizontal interpolation; and (4) improving the water-vapor profiles of all pixels with the scaling factors. The proposed method can be applied if the image has one or more gray pixels. The simulation results for the advanced spaceborne thermal emission and reflection radiometer (ASTER) TIR subsystem show that the proposed method reduces errors on air temperature profiles as well as on water-vapor profiles and is as accurate as atmospheric correction with radiosonde measurements     

Refined Physical Retrieval of Integrated Water Vapor and Cloud Liquid for Microwave Radiometer Data

Monitoring atmospheric water is essential for the understanding of the dynamic processes of the atmosphere and for the assessment of wave-propagation properties. Microwave radiometers, in combination with a thermal infrared channel, have the potential to fulfill these tasks. This paper is focused on the surface-based system TROWARA with microwave channels at 21.3 and 31.5 GHz. TROWARA has been used for tropospheric water measurements at Bern since 1994 together with a standard meteo station. So far, emphasis has been put on integrated water vapor (IWV) measurements, particularly for climate studies, but integrated liquid water (ILW) has been retrieved as well. We report on methodological advances with the data analysis. First, the original algorithm was replaced by a new statistical retrieval based on the simulations of TROWARA data using radiosonde profiles. Second, in a physical refinement, the cause of the variable ILW bias has been identified, and a method for its reduction to the level of 0.001 to 0.005 mm has been developed and tested. The bias is mainly a result of the variable water-vapor influence on absorption at 31 GHz. The bias correction also influences the IWV retrieval. The refined physical retrieval includes the temperature dependence of cloud absorption based on a recent dielectric model of water. The three algorithms (original, new, and refined) have been compared for two years of data. The applications of the refined algorithm are focused on physical processes, such as the development of supercooled clouds. Future advances will include precipitation measurements.