最新刊期
卷 21 ， 期 6 ， 2017
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摘要：Regrouping refers to the process of resampling the backscattering coefficients that are stored according to the observation sequence and the corresponding parameters of wind vector cell observation geometry. Regrouping is important in scatterometer data processing because it directly affects the final measurement accuracy. In August 2011, China launched its first operational satellite-borne scatterometer HSCAT. Thus, designing a suitable regrouping algorithm for accurately retrieving wind speed and direction data from HACAT observations is necessary. On the basis of the regrouping method for QuikSCAT and Shenzhou-4 multi-mode microwave remote sensing scatterometer, the present work has designed a business operational regrouping algorithm for HSCAT in accordance with its observation characteristics. This study offers a re-designed regrouping algorithm that includes two critical steps: (1) the generation of a ground grid and (2) the resampling of observation results. To simplify the resampling process, HSCAT adopted a convenient grid model along the sub-track swath, which centered on the nadir track, with a coordinate system specifying the location of a given point in terms of “longitude” parallel to the nadir track and “latitude” perpendicular to the nadir track. The origin of the nadir track grid is at the southernmost orbit latitude (rev boundary), and the resolution is set to 25 km×25 km. After the sub-track grid was set, for each “egg” this study searched the nadir track point with the shortest spherical distance to the observed backscattering coefficient, and this shortest distance was used to calculate the cross-track indices (column_number) in the sub-track grid for the “egg”. Then, the distance between this nadir point and the nadir point at the southernmost orbit latitude (rev boundary) was calculated. Moreover, this distance was used to calculate the along-track indices (column_number)) in the sub-track grid for the “egg”. To improve calculation efficiency, we used coarse and fine search strategies when searching for the minimum spherical distance between the backscattering coefficient and the nadir track point. To verify the effectiveness of the proposed regrouping algorithm, we first analyzed the location distribution of wind vector cells and the spatial distribution of the total independent observation number of each wind vector cell in high-latitude and low-latitude areas. Results show that the wind vector cells are uniformly distributed, and a sufficient number of independently observed backscattering coefficients are included in each cell, which can satisfy the requirements of wind vector retrieval. Meanwhile, comparison of NDBC buoy wind and the retrieved wind shows that the standard deviations of wind speed and wind direction are 1.7 m s –1 and 23.4° for the retrieved wind, respectively, indicating that the proposed regrouping algorithm can meet the requirements for high-quality sea surface wind field retrieval. This article proposes a business operational regrouping method for HSCAT. To simplify the resampling process, this method adopts the sub-track grid in along-track and cross-track directions with grid cells having approximately 25 km resolution. Meanwhile, the shortest spherical distance between each independent backscattering coefficient measurement (“egg”) and the nadir track point was used to calculate the cross-track indices (column_number) in the sub-track grid for each “egg” and the distance between the nadir point with “shortest spherical distance” and the nadir point at the southernmost orbit latitude (rev boundary) was used to calculate the along-track indices (column_number) in the sub-track grid for the “egg” In this way, the grid cells corresponding to the observed backscattering coefficients in the along-track/cross-track coordinate network were determined, and the resampling was completed. We used coarse and fine search strategies when searching for the minimum spherical distance between the backscattering coefficient and the nadir track point, effectively improving calculation efficiency. According to the post-regrouping location distribution of wind vector cells and the spatial distribution of the observation number of each wind vector cell, the proposed regrouping algorithm can achieve effective resampling of backscattering coefficient. After resampling, the acquired wind vector cells are uniformly distributed, and a sufficient number of independently observed backscattering coefficients are included in each wind vector cell. These results indicate that the backscattering coefficients can be resampled effectively.关键词：regrouping;HY-2 satellite;HSCAT;wind vector cell;sub-track grid- 234
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发布时间：2021-06-07 - 摘要：As the development of space technology and the launch of new type microwave payloads, such as imaging microwave altimeter, more and more radars that observe the ocean at low incident will be in orbit, such as the Precipitation Radar (PR) (incident angle: 0°—18°) on Tropical Rainfall Measuring Mission (TRMM) satellite, the Imaging ALTimeter (IALT) (incident angle: 1°—7°) on the TG-2 space station which is lunched on September 15, 2016, and the Surface Ware Investigation and Monitoring (SWIM) (incident angle: 0°—10°) on China France Oceanography Satellite which is scheduled to launch in 2018. How to make use of the Normalized Radar Cross Section (NRCS) at low incident angle effectively is a very hot topic. Can the NRCS at low incident angle be used for wind speed inversion? This is also the motivation of our research. This paper uses the PR data on TRMM to research the sea surface wind speed inversion method and analyze the inversion accuracy. In this paper, the empirical Geophysical Model Function (GMF) model is established by crossing the PRNRCS with the QuikScat wind speed, and the GMF Table is given out in section 2. The Maximum Likelihood Estimation (MLE) method is used for PR wind speed inversion, and the objective function of MLE is provided in section 3. At last, the retrieved wind speeds are compared with the buoy and advanced scatterometer (ASCAT) wind speed. The performance of MLE method and the wind speed inversion accuracy are analyzed. By the performance analysis, we find that the bias and standard deviation of retrieved wind speed is smaller than 0.28 m/s and 1.51 m/s respectively. Moreover, the wind speed retrieval performances versus wind speed and incident angle are analyzed. The wind speed retrieval performance at medium wind speed is better than that at low and high wind speed. The inversion accuracy with 0°—8° incident angles is better than that with 8°—12° incident angles. From the results of this paper we can concluded that the NRCS at low incident angle can be used for wind speed retrieval. And the wind speed retrieval accuracy is even higher than that of scatterometer (typically 2 m/s). Thus, for the radars that operate near nadir, such as the IALT and the SWIM on China France Oceanography Satellite, we can offer the product of wind speed.关键词：TRMM PR;wind speed inversion;low incident angle;inversion accuracy
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发布时间：2021-06-07 - 摘要：Total Precipitable Water (TPW) products, which are derived from satellite FY-3C Visible and Infrared Radiometer (VIRR) infrared split-window information, have been officially put into operation. High-quality FY-3C TPWs could be used in many fields, such as weather analysis, climate study, and atmospheric correction in land surface remote sensing. The retrieval method of TPW products has only been introduced briefly, and product assessments have yet to be conducted. Thus, we compared the product accuracies and stabilities of MODIS Terra infrared TPWs and radiosonde TPWs. The global distribution of FY-3C VIRR TPW was evaluated and compared with that of MODIS Terra monthly infrared TPW in March 2015. In addition, we selected two months (March to April 2015) of radiosonde TPWs to calculate FY-3C TPW errors and analyze the features of global errors, errors changed with water vapor, error day–night distributions, and error space distributions. At the same time, the same accuracy analyses for MODIS Terra daily TPWs were conducted by comparing them with the same radiosonde TPWs. The results can be used as reference for FY-3C TPW accuracy evaluations. Moreover, monthly RMSEs of FY-3C TPWs from January 2015 to July 2016, as well as monthly RMSEs of MODIS TPWs, were calculated by comparing with radiosonde data. The variations of two time series RMSEs were compared to assess FY-3C TPW stabilities relative to MODIS TPWs. FY-3C VIRR TPW is generally consistent with MODIS monthly TPW in space distribution. The differences between the two TPWs are smaller than 30° S to the south pole and 30° N to the north pole and relatively larger between 30° S and 30° N. The verifications of VIRR TPW, which were conducted by comparing with two-month radiosonde data, show that the bias, RMSE, and correlation coefficient are –0.16 mm, 5.36 mm, and 0.85, respectively, which are slightly better than the errors of MODIS TPWs, except in dry atmosphere (TPW<10 mm). Analyses of errors changed with water vapor indicate that the relative errors of VIRR TPWs are less than 20% when water vapor is more than 30 mm, which is much higher in dry atmosphere. In addition, in dry (TPW<10 mm) and wet (TPW>55 mm) atmospheres, the relative errors of VIRR TPWs are smaller than those of MODIS TPWs, whereas the differences between the two TPWs are small when TPWs are between 30 mm and 50 mm. The performances of VIRR and MODIS TPWs are better at night than at daytime. Moreover, the qualities of VIRR TPW are better than those of MODIS TPWs at daytime but slightly worse at night. Biases of VIRR TPWs mostly sit at ±2 mm. RMSEs are small in middle- and high-latitude areas and large in lower-latitude areas. Furthermore, most biases and RMSEs of VIRR TPWs in the maps are better than those of MODIS TPWs but worse near the coastlines. The mean and standard deviation of 19 VIRR TPW monthly RMSEs are 5.63 and 0.54 mm, whereas those of MODIS TPWs are 6.19 and 1.33 mm, respectively. These data indicate that the qualities of VIRR TPWs are more stable than those of MODIS TPWs. Given their good agreement with radiosonde data and higher precision than MODIS TPWs, FY-3C VIRR TPW products have good accuracies. In addition, FY-3C VIRR TPWs have better stability than MODIS TPWs. With high precision and stable qualities, FY-3C VIRR TPW products have wide application capabilities.关键词：FY-3C/VIRR;infrared split window;total precipitable water;radiosonde;MODIS infrared water vapor product
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发布时间：2021-06-07
Chinese-Built Satellite
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摘要：Clouds, being the most abundant and variable factor in the atmosphere, are critical to the modification of Earth–atmosphere energy balance. The effects of clouds on radiation should therefore be carefully and thoroughly considered. However, traditional atmospheric radiative transfer models only consider two extreme situations, namely, all clear and overcast. To understand the influence of clouds on surface shortwave radiation and improve the accuracy of shortwave radiative components derived from remote sensing dataset, we propose a novel radiative transfer model to analyze the cloud conditions in this work. Based on the traditional one-dimensional radiative transfer model Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART), this study first classifies the actual sun/cloud-viewing geometric conditions into nine subtypes, considering whether the directions of the sun and sensor are obscured by clouds. Then, the original formula of land surface downward radiative components is expanded. Two cloud fraction parameters (i.e., hemispherical effective cloud fraction and regional cloud fraction) are introduced to the formula to establish an improved shortwave radiative transfer model, namely, the SBDART-CF model. Based on the formula, the nine subtypes are summarized into two types, that is, the situations where the direction of the sun is either obscured by the cloud or not. Then, the atmospheric spherical albedo and atmospheric transmission of different cloud conditions are compared to narrow the range of cloud parameters. Other thirteen parameters, such as solar zenith angle, surface albedo, and cloud thickness, among others, are introduced to the following sensitive analysis. Finally, the effects of the above-mentioned model parameters on the surface shortwave radiative components under different circumstances are calculated and analyzed by using SimLab software, which employs a global quantitative sensitivity analysis method. The proposed shortwave radiative transfer model can efficiently describe the influence of clouds on the surface shortwave radiation by considering the cloud horizontal distribution in the sky. Solar zenith angle and surface albedo both play significant roles in the modification of downward shortwave radiation and surface net shortwave radiation. Hemispherical effective cloud fraction and regional cloud fraction also considerably affect the radiation components. Six factors that are also important to the model can be easily derived from the satellite products and therefore can contribute to the model application effectively. Water vapor, ozone, and carbon dioxide column volume exert minimal effects on the surface shortwave radiation components under all the considered conditions. The above analysis results show that the proposed SBDART-CF model can deal with shortwave radiative problems under different cloud conditions. Therefore, the model can effectively accomplish the radiative component estimation from remotely sensed datasets.关键词：surface shortwave radiation;cloud parameter;radiative transfer model;SBDART;SimLab;sensitivity analysis- 304
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发布时间：2021-06-07
Fundamental Research
- 摘要：Lunar is the second largest field-of-angle object near the Earth, and its reflectance can be considered stable to 10–8 per year. Thus, lunar is selected as the radiometric standard for the calibration of Earth-orbiting satellite-borne instruments and as the radiance source at night similar to solar at daytime for ground-based and satellite remote sensing. However, the precision of existing lunar irradiance models is insufficient to satisfy application requirements. To improve lunar irradiance models and verify the accuracy of the models, a three-month Earth-based observation of lunar was conducted on December 2015 by the National Satellite Meteorological Center, China Meteorological Administration. The hyper-spectral irradiance of lunar ranging from 399 nm to 1060 nm was retrieved by an imaging spectrometer in Lijiang, Yunnan. This study compared Earth-based lunar observations and other models (ROLO and MT2009 models). Results show that the observations are more similar to the ROLO model than to the MT2009 model, and the difference between the two models is larger in the shortwave infrared region than in the visible region. The average relative difference between the ROLO model and ground-based observations is approximately 5.86%. Causes of this difference are discussed in this study, which provides a theoretical foundation for establishing an accurate model of lunar irradiance.关键词：ROLO;MT2009;lunar irradiance model;Earth-based observation;radiometric calibration
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发布时间：2021-06-07 -
摘要：Geostationary satellites can perform high-frequency observation in the same area of the Earth. To extend the area of observation, we use a two-dimensional pointing mirror combined with a frame sensor. A two-dimensional pointing mirror can acquire image information in a short time, and its small volume and low weight render it an appropriate device loaded on the satellite. However, imaging theory of the pointing mirror indicates that image rotation and non-linear error reduce the geometric accuracy of image and may cause missing scans. To address the problem, we first analyze the imaging characteristics of each slot and calculate the rotation angle. We numerically analyze different types of angular error introduced from pitch and azimuth rotation. Then, we propose a mechanism by which the pointing angle affects image coverage. We provide a flow chart of cover rate analysis and figures showing the cover area of different pointing angles. Boundary diagrams of slots are drawn to display the relationship between angular variations and cover rate. After numerical analysis, we find a better way to observe the Northern Hemisphere. Then, we develop two judging methods of seamless stitching: grid method and geometric intersection method. The grid method is used to grid the pointing plane and label cover time on each grid point, whereas the geometric intersection method is used to determine whether the image information is missing on the basis of intersecting relations of the slot envelope. Different situations of geometric intersection are listed. The two methods are also compared. The grid method can determine the coverage time, and the geometric method has great advantages of speed and accuracy. Finally, the two methods have been used to obtain the cover situation of the edge of the Earth at different scanning angles, to find the maximum pitch and azimuth angle interval of the proposed optical system, which can cover the global area and seamless splicing images. When the point mirror is rotating in azimuth, image rotation is more obvious with an increase in motor angle. When the mirror is rotating in pitch, the rotation angle increases with a decrease in pitch angle. Thus, the relative location of the follow-up optical elements should be considered to obtain a low rotation angle of key observation area. This process provides the theoretical basis for setting the turning step of the motor and ensures that the time resolution of the load is increased without geometric information leakage.关键词：image rotation;seamless;geostationary;two-dimensional pointing mirror;missing scan- 263
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发布时间：2021-06-07 -
摘要：Subsurface thermal structure of the global ocean is a key factor that reflects the impact of global climate variability and change. Accurately determining and describing the global subsurface and deeper ocean thermal structure from satellite measurements are becoming even more important for understanding the ocean interior anomaly and dynamic processes during recent global warming and hiatus. The extent to which such surface remote sensing observations can be used to develop information about the global ocean interior is essential but challenging. This work proposes a Support Vector Regression (SVR) method, a popular machine learning method for data regression used to estimate Subsurface Temperature Anomaly (STA) in the global ocean. The SVR model can well estimate the global STA upper 1000 m through a suite of satellite remote sensing observations of sea surface parameters [including Sea Surface Height Anomaly (SSHA), Sea Surface Temperature Anomaly (SSTA), Sea Surface Salinity Anomaly (SSSA), and Sea Surface Wind Anomaly (SSWA)] with in situ Argo data for training and testing at different depth levels. In this study, we employed the Mean Squared Error (MSE) and squared correlation coefficient ( R2) to assess the performance of SVR on STA estimation. Results from the SVR model were validated to test the accuracy and reliability using the worldwide Argo STA data (upper 1000 m depth). The average MSE and R2 of the 15 levels are 0.0090/0.0086/0.0087 and 0.443/0.457/0.485 for two attributes (SSHA, SSTA)/three attributes (SSHA, SSTA, SSSA)/four attributes (SSHA, SSTA, SSSA, SSWA) SVR, respectively. The estimation accuracy was improved by including SSSA and SSWA for SVR input (MSE decreased by 0.4%/0.3% and R2 increased by 1.4%/4.2% on average). The estimation accuracy gradually decreased with the increase in depth from 500 m. With the increase in depth, the absolute value of STA became smaller, i.e., it became more indistinctive in the spatial heterogeneity. The STA became less intensive in the deeper ocean due to the water stratification and stability. Results showed that SSSA and SSWA, in addition to SSTA and SSHA, are useful parameters that can help estimate the subsurface thermal structure and improve the STA estimation accuracy. Moreover, an obvious advantage for SVR is the absence of limitation on the input of sea surface parameters. Therefore, we can figure out more potential and useful sea surface parameters from satellite remote sensing as input attributes to further improve the STA sensing accuracy from SVR machine learning. This study provides a helpful technique for studying thermal variability in the ocean interior, which has played an important role in recent global warming and hiatus from satellite observations over global scale.关键词：multisource satellite observation;subsurface temperature anomaly;support vector regression;information extraction;global ocean- 283
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发布时间：2021-06-07 - 摘要：In order to obtain quantitative information from satellite measurements, the satellite radiometer must first be calibrated. Calibration is a critical step to ensure data quality and to meet the needs of quantitative remote sensing in a broad range of scientific applications. One of the least expensive and increasingly popular methods of on-orbit calibration has been the use of pseudo invariant calibration sites. However, most of the researchers are tend to use one of these sites to monitor the multi-temporal stability of satellite sensors using time series analysis. A wide dynamic absolute calibration method by using multiple stable earth targets is presented here. This method relies on calculated Top-Of-Atmosphere (TOA) radiances over bright desert and salt lake sites as well as dark ocean targets. These simulated radiances represent the “reference” and are done using the 6S radiative transfer code with MODerate-resolution Imaging Spectroradiometer (MODIS) products and National Centers for Environmental Prediction (NCEP) reanalysis data for surface reflectance, aerosol optical depth, ozone amount and water vapor load estimation. When associating the simulated radiances with the sensor observed Digital Number (DN) using quadratic polynomial, the calibration coefficients can be obtained. The accuracy of the calibration results are determined by the accuracy of the “reference”, and this “reference” accuracy are assessed through using MODIS L1B data, which is characterized by high calibration accuracy (<2%), during one year period. Then, this method are applied to Medium-Resolution Imager (MERSI) onboard the second Chinese Polar Orbital Meteorological Satellite of FY-3A and FY-3C to study the radiometric response characteristics, so that the best calibration strategy can be determined. Finally, the determined calibration strategy is applied to FY-3A/MERSI and FY-3C/MERSI respectively, and the calculated calibration coefficients are validated based on the field observation data obtained at China Dunhuang Calibration Test Site on August 16, 2014. With the field observation data and 6S radiative transfer code, the TOA reflectance for MERSI solar bands are simulated and are used to validate the TOA reflectance calculated with the calibration coefficients obtained through the calibration method using Multiple Stable earth Targets (MST) proposed here. The comparison results demonstrate that calibration “reference” has high accuracy with relative bias between simulation and observation less than ±2%. Study of radiometric response characteristics FY-3A/MERSI and FY-3C/MERSI shows that radiometric response of FY-3A/MERSI is linear, while FY-3C/MERSI is non-linear. Hence, during their calibration processes, linear calibration method using MST is applied to FY-3A/MERSI, and non-linear calibration for FY-3C/MERSI. Validation results show that the relative differences of TOA reflectance between the one obtained from the calibration method using MST and the one obtained from 6S simulation using In-situ measurements are within ±%5 for most solar bands of MERSI. Compared with the traditional two-point calibration method, calibration trough using MST has the following advantages: (1) covering a wider dynamic range of satellite sensors, hence be good at characterize their radiometric response characteristic, (2) be helpful to reduce the calibration uncertainty with a large amount of calibration samples, and (3) can achieve efficient, real-time absolute radiation calibration for satellite sensors.关键词：radiometric calibration;stable targets;MERSI;MODIS;nonlinear
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发布时间：2021-06-07 - 摘要：Soil moisture estimation from mountainous areas is important in many applications, such as vegetation growth monitoring, wildfire warning, and landslide prediction. Synthetic Aperture Radar (SAR) has been widely used to measure soil moisture in recent years due to its capacity to operate under different weather conditions, relatively strong penetrability, and sensitivity to soil moisture. However, the application of SAR to mountainous areas introduces two problems. On the one hand, geometric distortion and polarimetric rotation occur in SAR images. One the other hand, vegetation reduces the sensitivity of SAR images to soil moisture. Therefore, the retrieval of soil moisture from mountainous areas covered with low vegetation is still intractable. To this end, we present a soil moisture estimation method for mountainous areas covered with low vegetation. To solve the problem of geometric distortion and polarimetric rotation caused by undulate terrain in SAR images, the images are classified as measured and unmeasured areas in terms of incident, slope, and aspect angles. Second, backscattering coefficients are corrected in terms of the irradiated area and local coordinates with respect to horizontal and vertical polarizations. Furthermore, a scattering model for low vegetation is developed based on the Michigan microwave canopy scattering model. Under the assumption that the characteristics of vegetation and soil are fixed, the change in backscattering coefficients is only related to the change in soil moisture. Consequently, a method of soil moisture estimation is established by combining the proposed scattering model with the previously corrected data. Two spots with different levels of soil moisture are selected as the experimental sites from Maoxian, Sichuan, China. A field investigation is then carried out to collect the characteristics of vegetation and soil over the study areas. The soil moisture levels measured by two methods from the two experimental sites are 11.45% and 15.80% respectinely. The estimated results from the corresponding Radarsat-2 SAR image are 14% and 15%, with absolute errors of 2.55% and 0.80% and relative errors of 22% and 5%. The experimental results demonstrate that the proposed method is suited for general applications. Moreover, a soil moisture distribution map over the study area is generated, assuming that a similar vegetation observed from the two experimental sites covers the whole study area. Thus, the variation range of soil moisture is acceptable. Different from the current studies focusing on the soil moisture estimation from plain areas covered with monotype vegetation such as crops and grassland, the proposed method can estimate soil moisture from mountainous areas covered with low vegetation. The proposed method monitors the soil moisture in mountains. Our next work will focus on the classification of mountainous areas. The integration of the classification result of the proposed method and the employment of multi-temporal SAR images can provide accurate and continuous monitoring over the observed areas.关键词：soil moisture;SAR;mountainous terrain;vegetation scattering model;inversion
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发布时间：2021-06-07 - 摘要：Spatial clustering is important for spatial data mining and spatial analysis. Spatial objects in the same cluster should be similar in the spatial and attribute domains. Tendency and heterogeneity are important characteristics of geographic phenomena. Currently, most spatial clustering algorithms only consider either tendency or heterogeneity, failing to obtain satisfied clustering results. To overcome these limitations, a spatial clustering method based on graph theory and information entropy is developed in this work. The proposed algorithm involves two main steps: construct spatial proximity relationships and cluster spatial objects with similar attributes. Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships among objects. To obtain satisfactory results in spatial clustering with attribute similarity, the information entropy is introduced to overcome the defects of similarity measure with binary relation, which can reflect the clustering tendency of geographical phenomena. Furthermore, a local parameter measurement method based on the principle of “equal probability maximum entropy” is designed to adapt to the local change information of attribute distribution. The performance of the proposed algorithm was evaluated experimentally by comparing the leading state-of-the-art alternatives: DDBSC and multi-constraint algorithms. Results showed that our method outperformed the two other algorithms as attributes are unevenly distributed in space. The sensitivity analysis of these algorithms showed that our method was the least sensitive to outliers. The effectiveness and practicability of the proposed algorithm were validated using simulated and real spatial datasets. Two experiments were performed to illustrate the three advantages of our algorithm: (1) It can reflect the tendency of the entity attribute in the spatial distribution. (2) It can meet the requirement that attributes are unevenly distributed in space. (3) It can discover clusters with arbitrary shape and is robust to outliers.关键词：spatial clustering;Delaunay triangulation;information entropy;tendency;heterogeneity
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发布时间：2021-06-07
Technology and Methodology
- 摘要：Lunar surface temperature may be of interest in the radiometric calibration of space-based sensors. It is an essential parameter for exploring the Moon and a necessary boundary condition for studying lunar thermal evolution. This study presents a real-time model of lunar surface temperature based on the lunar surface temperature steady-state model of Racca. The effective solar irradiance can be calculated accurately and in real time. The proposed model can nearly accurately describe variations in Moon phases based on qualitative comparisons with lunar infrared images provided by the FY-2G satellite. A heat flow experiment provides the time series of the lunar surface temperature at the Apollo 15 landing site since the experiment started until its end in 1974. Result from the temperature model fits well with the measurement when the solar elevation angle is greater than zero. Furthermore, the differences between the calculated and measured temperature results are within ±1 K for the fifth day up to the tenth day since the elevation angle of the Sun is above the surface.关键词：lunar surface;temperature;real-time model;radiometric calibration
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发布时间：2021-06-07
技术方法
- 摘要：Global sea surface salinity (SSS) is retrieved using satellite microwave radiometers, Currently. However, SSS remote sensing with an L-band radiometer is still challenging due to the low sensitivity of its brightness temperature to SSS variation. Results show that the sensitivities of vertically (Tv) and horizontally (Th) polarized brightness temperature range from 0.4 K/psu to 0.8 K/psu and from 0.2 K/psu to 0.6 K/psu, respectively, at different observing angles and sea surface temperatures (SSTs). Hence, high-accuracy measurements are required. However, the quantitative effect of Sea Surface Temperature (SST) on the satellite retrieval of SSS remains unknown. In this study, we investigate the effect of SST on the accuracy of salinity retrieval from the Soil Moisture and Ocean Salinity (SMOS). The dielectric constant model proposed by Klein and Swift has been used to estimate theTv and Th of a flat sea water surface at L-band and obtain the derivatives of Tv and Th as a function of SSS to show the relative sensitivity at different incident angles (12.5° and 42.5°). Moreover, the Generalized Additive Model (GAM) and the Partial Least Squares (PLS) regression method were used to investigate the effect of SST on the accuracy of salinity retrieval from the SMOS. Furthermore, SMOS data are compared with Argo data to assess the quality of satellite-derived SSS data at different SSTs by calculating the root-mean-square error (RMSE) of two regions of the Pacific Ocean far from land and ice. Results show that satellite-measured brightness temperature has high sensitivity to SSS variation and good accuracy of SSS retrieval with high SST. For most open oceans where surface salinity is typically greater than 32 psu, the sensitivity is approximately 0.2–0.25 K/psu for Tv and Th when the SST is 5 ℃, and the brightness temperature is more sensitive to the SSS for Tv than Th with increasing SST. When the SST increases to 30 ℃, the sensitivity is approximately 0.8 K/psu for Tv. Moreover, the RMSEs of SMOS-derived SSS data are approximately 0.9, 0.7, and 0.4 psu in regions of the Pacific Ocean where the SSTs are approximately 16 ℃, 23 ℃, and 30 ℃, respectively. Results of the GAM and the PLS model indicate that satellite-measured brightness temperature highly correlates with in situ SSS at high SSTs. In addition, validation results of Argo data suggest that water temperature significantly affectsSSS retrieval accuracy and that accurate SSS retrieval can be achieved at high SSTs. These results indicate that SST can significantly influence the retrieval accuracy of SSS. Hence, the development of a new SSS retrieval algorithm that adapts to low SSTs is necessary.关键词：sea surface salinity;satellite remote sensing;water temperature;retrieval accuracy
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发布时间：2021-06-07 - 摘要：As the main medium of radio wave propagation, atmospheric profiles influence the operating range of radio systems and the accuracy of navigation and location systems. Therefore, the research on the techniques of microwave remote sensing for atmospheric profiles, especially their abnormal structure, is important for evaluating the performance of radio information systems and improving the accuracy of radio refraction correction in complex atmospheric environment. A microwave radiometer provides continuous thermodynamic (temperature, water vapor and moisture) soundings during clear and cloudy conditions. The radiometric profiler observes radiation intensity along with zenith infrared and surface meteorological measurements. Historical radiosonde and neural network or regression methods are used for profile retrieval. The southeast coastal area of China has always been a research hot spot. In this study, radiosonde data of Qingdao, Sheyang, Xiamen and Haikou were selected to compute the average height and thickness of the atmospheric waveguide. We carried out a preliminary exploration to retrieve algorithms. We put forward the combined neural network algorithm to discriminate and retrieve the atmospheric waveguide. Meanwhile, the feasibility of this method was demonstrated with the results simulated by historical data in Qingdao, Sheyang, Xiamen and Haikou and measured data in Qingdao. This method is used for remote sensing the atmospheric abnormal structure, and the radiometer can retrieve the atmospheric waveguide. In this study, a new idea is provided for the following atmosphere waveguide. However, improvement is still warranted, considering the lack of depth of the study. Two problems are discussed in this study. First, inversion accuracy is low because of the few samples of atmospheric waveguides extracted from the radiosonde data. Second, the type of atmospheric waveguide is not judged, and the model time is long because of the high resolution. We hope to overcome these problems by improving and adjusting the algorithm in the future. Radiometric profiling provides continuous temperature and humidity soundings up to 10 km height in clear and cloudy conditions and low-resolution cloud liquid soundings. The accuracy of the radiometric temperature and humidity soundings is equivalent to that of the radiosonde soundings. Ground-based multi-channel microwave radiometry has been proven to be a powerful tool for sensing atmospheric waveguide as compared with past methods. It can retrieve continuous atmospheric waveguide and provide environmental information for radar, communication, and navigation systems. The issue of atmospheric waveguide involves electromagnetic fields, radio meteorology, and numerical weather forecasts. Future work should use multiple devices and study atmospheric duct inversion in combination with numerical weather prediction methods to retrieve the waveguide and improve the accuracy of duct prediction.关键词：atmospheric waveguide;learning vector quantization neural network algorithm;radical basis function;ground-based multi-channel microwave radiometer-brightness temperature
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发布时间：2021-06-07 -
摘要：Accurately obtaining building damage situation in disaster areas provides decision-making support for disaster relief and post-disaster reconstruction. The existing evaluation methods using polarization featuresare greatly influenced by the orientation angle of buildings, and the intact buildings with large orientation anglesare easily misclassified for the collapsed buildings. The combination of multiple features is an effective way to improve the accuracy of building damage assessment, in which the texture features play an important role. This paper proposed a new building damage assessment method which applies the texture features of polarization decomposition components. There are three key procedures in this method. Firstly,the non-building areasare removed by thefiltered π/4 double-bounce scattering component of Pauli decomposition. Secondly,thestrategy of extracting collapsed buildings is built.For building areas, the Variance and Contrast features of Gray-Level Co-occurrence Matrix (GLCM) are calculated on the Pauli decomposition parameters. Specifically, the Variance and Contrast textures are obtained by the π/4 double-bounce scattering component of Pauli decomposition; the Variance texture is given by the odd scattering component of Pauli decomposition. Then, the appropriatethresholds for distinguishing collapsed buildings from intact buildings are determined by these texture features.When the texture feature value of the pixel is smaller than the threshold, then the pixel is classified as collapsed building, otherwise, the pixel is identified as intact building. At last,building damage assessment is implemented. The building damage indexes computed by the three texture features are averaged to get the final building damage index, and then the result of building damage assessment is obtained. This method was validated on RADARSAT-2 fine-mode polarimetric SAR imagery from the Yushu earthquakeacquired on April 21, 2010. The ground-truth map of the buildings in Yushu city, interpreted on high-resolution optical data, was used as a reference for comparison. Compared with the H-α-ρ method, the method with homogeneous texture of span image and the method with integrated Normalized Circular-pol Correlation Coefficient (NCCC) and homogeneous texture feature, the building damage assessment accuracy of the proposed method was the highest, and its overall accuracy was improved to 74.39%.Particularly, the detection rate for serious damage buildings is 100%, and the false alarm rate is 15.62%. In addition, the intact buildings with large orientation angles in northeastern corner of the city were also extracted correctly. Based on the experimental analysis, the main factor of affecting the accuracy is the similarity of Pauli polarization features, the Variance and Contrast texture features betweenthe flat and dense intact building areas and serious damaged building areas. In addition, the effectiveness of the proposed method was verified by the ALOS-1 data after the 2011east Japan earthquake and tsunami, and its overall accuracy was improved to 80.26%. A damage assessment method based on texture features from GLCM of Pauli decomposition components is proposed in this paper. In this method, Pauli decomposition parameter was utilized to remove non-building areas, which was capable of removing the non-building areaseffectively, such as rivers, roads, bare ground and so on. By the comprehensiveutilization of the GLCM texture features of three Pauli decomposition components, the collapsed buildings can be extracted more accurately. By comparison with other methods, the results confirm the validity of the proposed method.关键词：polarimetric SAR;earthquake damage assessment;building;pauli decomposition;gray-level co-occurrence matrix- 329
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发布时间：2021-06-07
Remote Sensing Applications
- 摘要：Land surface evapotranspiration (ET) and its partitioning between evaporation (E) and transpiration (T) is a significant component of water and energy cycles at all scales, from field and watershed to regional and global, and is essential to many applications in climate, weather, hydrology, and ecology. The land surface ET and its components E and T can be produced conveniently at a range of spatial and temporal scales by combining the advanced remotely sensed data and its land surface products such as land surface temperature, leaf area index, and landcover, among others. This work aims to evaluate and summarize available remotely sensed models currently used to determine ET and components E and T. The remotely sensed-based model of land surface E and T has undergone several stages of development, including series and parallel energy balance models, spatial variability model, remote sensing and meteorological combination model, and data assimilation technology divided based on diverse model mechanisms. However, these models provide wide ranges of E and T, whose uncertainty may be limited by the unreasonable component temperatures partitioned from land surface temperature, parameterization of the stress factors of T from vegetation and E from soil surface, and uncertainty of the reproduced meteorological data as model input data. Future studies should improve model performance under heterogeneous surface and upscale the point or patch ground measurements of E and T to satellite pixel scale to validate remotely sensed model simulations.关键词：soil evaporation;vegetation transpiration;remotely sensed model;estimation;validation
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发布时间：2021-06-07 - 摘要：Aside from sonar systems, Airborne LIDAR Bathymetry (ALB) has become the most reliable depthometer. With the commercialization of ALB, several companies have produced powerful ALB, and research institutions have obtained the full-waveform data of water. Meanwhile, several algorithms have been proposed to process the signal of ALB. In this work, we introduce the theoretical basis of ALB and then review algorithms of correction for pulse stretching and peak finding of full waveform. Then, we analyze the main influencing factors of accuracy, including water depth, water color, and reflectance of substrates. We also provide an overview of the new application of ALB in substrate classification. In specific, ALB can help retrieve information from full waveform to the maximum extent. We draw the following conclusions: (1) Pulse stretching is mainly caused by the topography of substrate, whereas algorithms for correcting pulse stretching have been developed under the consideration of general terrain slope or incident angle of pulse. The complex topography of substrate should be considered, especially for the application in coral reef, where substrates distribute inhomogeneity. (2) Algorithms of peak finding of full waveform can be separated into three kinds: echo detection, mathematical approximation, and deconvolution. Echo detection methods run fast but are influenced by environmental noise more easily. The object function of mathematical approximation methods is difficult to be solved, but environmental parameters such as water attenuation coefficient can be derived. Deconvolution methods are stable but need to take effective measures to suppress noise. (3) The proposed algorithm cannot work well for extremely shallow water, especially at depths within centimeter level, but polarization lidar may solve the problem in the future. Low water quality and low reflectance of substrate reduce signal and noise ratio. Thus, new algorithms need to be developed for these conditions in the future. (4) The new application of ALB in substrate classification and data fusion with hyperspectral image indicates that further information in full waveform of water should be retrieved in the future.关键词：airborne LiDAR bathymetry;water depth;substrate classification;full waveform;hyperspectral
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发布时间：2021-06-07
Review
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