A sea surface fractal model for ocean remote sensing

In this letter we propose and validate a sea surface fractal model suitably tailored for the application of ocean remote sensing by means of active sensors. The model includes both statistical and fractal properties of the surfaces and also accounts for the linear behaviour of sea waves' generation and propagation.     

A portable multiband radiometer for ground data collection in remote sensing

Abstract.

The design and calibration of a rugged, low-cost, portable multiband radiometer is described. The instrument measures simultaneously in four bands, the spectral coverage of each being determined by interchangeable absorption filters. Simple band-pass type radiometers are seen to be complementary to conventional spectroradiometers for ground data collection in remote sensing. They have the inherent advantages of portability and speed of operation which make them particularly suitable for fieldwork in areas of complex terrain. An example of the practical use of the instrument to measure the spectral reflectance of partially vegetated surfaces in an area of complex terrain in southern Italy is presented. The increased spectral and spatial resolution of second generation sensing systems will make remote sensing of complex areas more feasible and extensive ground-based spectral measurements in such areas are a necessary step to understanding and utilizing such remotely sensed data.     

Satellite passive microwave remote sensing for monitoring global land surface phenology

Vegetation phenology characterizes seasonal life-cycle events that influence the carbon cycle and land-atmosphere water and energy exchange. We analyzed global phenology cycles over a six year record (2003-2008) using satellite passive microwave remote sensing based Vegetation Optical Depth (VOD) retrievals derived from daily time series brightness temperature (T_b) measurements from the Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and other ancillary data inputs. The VOD parameter derives vegetation canopy attenuation at a given microwave frequency (18.7 GHz) and varies with canopy height, density, structure and water content. An error sensitivity analysis indicates that the retrieval algorithm can resolve the VOD seasonal cycle over a majority of global vegetated land areas. The VOD results corresponded favorably (p < 0.01) with vegetation indices (VIs) and leaf area index (LAI) information from satellite optical-infrared (MODIS) remote sensing, and phenology cycles determined from a simple bioclimatic growing season index (GSI) for over 82% of the global domain. Lower biomass land cover classes (e.g. savannas) show the highest correlations (R = 0.66), with reduced correspondence at higher biomass levels (0.03 < R < 0.51) and higher correlations for homogeneous land cover areas (0.41 < R < 0.83). The VOD results display a unique end-of-season signal relative to VI and LAI series, and may reflect microwave sensitivity to the timing of vegetation biomass depletion (e.g. leaf abscission) and associated changes in canopy water content (e.g. dormancy preparation). The VOD parameter is independent of and synergistic with optical-infrared remote sensing based vegetation metrics, and contributes to a more comprehensive view of land surface phenology.     

Sea surface effects on the sea surface temperature estimation by remote sensing

By using a sea surface temperature profiler buoy, the behaviour of the vertical temperature profile near the sea surface was observed in Mutsu Bay. In the daytime under calm and strong sunshine condition, there occurred a large temperature difference between the uppermost sea surface and the 1 m depth. The difference disappeared when the wind began to blow with a speed greater than 4ms^?1. Besides the atmospheric effects the inhomogeneity of the vertical temperature distribution near the sea surface must be another major error factor in the sea surface temperature estimation by satellite remote sensing.     

Zenith angle effects in multichannel infrared sea surface remote sensing

Several approaches to infrared multichannel sea surface temperature retrievals propose using a universal set of constants. It is shown that the single-channel multiangle technique (i.e., GOES and NOAA) and the multichannel single-angle technique (NOAA-n) are similar concepts with a common derivation from radiative transfer theory. It is also shown that the linear correlation factor between surface temperature minus satellite temperature in one channel versus the difference in satellite temperatures in two channels is not independent of the difference in satellite sensed equivalent blackbody temperature. The 3.7-μm, 11-μm, and 12-μm channels on the NOAA-n AVHRR can be used in combination to compute atmospheric transmissivity and average atmospheric temperature, but a better combination would be substituting three 0.5-μm-wide channels centered on 11.25 μm, 11.75 μm, and 12.25 μm. A triple window multispectral scanner in the 11–12.5 μm region allows determination of diffuse surface reflectance which can bias sea surface temperatures $\text{?0.4 K±0.3 K}$.     

Analysis of theoretical surface scattering models for polarimetric microwave remote sensing of bare soils

During the MAESTRO 1 Campaign, mulli-frequency polarimetric SAR data were acquired over the Dutch site ‘Flevoland’. At the same time, an extensive ground data collection took place in order to measure several different bio- and geophysical parameters for bare soils. The SAR data are used to assess the performance of several theoretical surface scattering models for rough surfaces. These models are extended in order to yield all the elements of the covariance matrix, particularly the phase and the correlation factor between the HHand W channels. Using the ground data, input elements such as the soil permittivity and the surface roughness are fed into the models to compute the elements of the covar ance matrix. A comparison for Land C band between the theoretical predictions and the measured backscattering coefficients is then presented and clearly shows that the models predict in a satisfactory manner the elements of the covariance matrix.     

A concurrent spectral-screening PCT algorithm for remote sensing applications

The paper presents a concurrent algorithm for remote sensing applications that provides significant performance and image quality enhancements over conventional uniprocessor principal component transform (PCT) techniques. The algorithm combines spectral angle classification, principal component transform, and human centered color mapping. It is evaluated from an image quality perspective using images collected with the hyper-spectral digital imagery collection experiment (HYDICE) sensor, an airborne imaging spectrometer. These images correspond to foliated scenes taken from an altitude of 2000–7500 m at wavelengths between 400 nm and 2.5 μm. The scenes contain mechanized vehicles sitting in open fields as well as under camouflage. The algorithm operates with close to linear speedup on shared memory multiprocessors and can be readily extended to operate on multiple, low-cost PC-style servers connected with high-performance networking. A simple analytical model is outlined that allows the impact on performance of practical, application-specific properties to be assessed. These properties include image resolution, number of spectral bands, increases in the number of processors, changes in processor technology, networking speeds and system clock rates.     

A fresh consideration on the choice of radiometer operating frequencyin atmospheric remote sensing

Following the assertion of Westwater, on the consideration of the radiometer operating frequency around 22GHz for remote sensing of water vapour distribution in the atmosphere, a comprehensive study has been made to re-examine the validity of considering frequency away from resonant frequency for both dry locations, like Antarctica and for tropical region, Calcutta. Radiosonde data have been utilized to calculate line shape functions and weightingfunctions at different operating frequencies. Results indicate that water vapour sensitive 22·235 GHz is still better for dry climatic regions like Antarctica, whereas, for wet air locations, choice of frequency will be different from the resonance peak.     

Passive microwave remote sensing of soil moisture

Results of radiometric measurements over bare soil obtained with a horizontally polarized microwave radiometer at 19·1 GHz frequency are presented. Radiometer measurements were made with incidence angles varying from 10 to 50°. Ground-truth acquisition of soil moisture in the 0–1 cm layer and of soil temperature near the surface was made concurrently with radiometer measurements. The measured brightness temperatures over a bare field are higher than those calculated from an emmissivity model.     

An algorithm for the retrieval of sea surface wind fields using X-band TerraSAR-X data

TerraSAR-X (TS-X) is a new, fully polarized X-band synthetic aperture radar (SAR) satellite, which is a successor of the Spaceborne Imaging Radar X-band Synthetic Aperture Radar (SIR-X-SAR) and the SRTM. TS-X has provided high-quality image products over land and oceans for scientific and commercial users since its launch in June 2007. In this article, a new geophysical model function (GMF) is presented to retrieve sea surface wind speeds at a height of 10 m (U ₁₀) based on TS-X data obtained with VV polarization in the ScanSAR, StripMap and Spotlight modes. The X-band GMF was validated by comparing the retrieved wind speeds from the TS-X data with in situ observations, the high-resolution limited area model (HIRLAM) and QuikSCAT scatterometer measurements. The bias and root mean square (RMS) values were 0.03 and 2.33 m s^?1, respectively, when compared with the co-located wind measurements derived from QuikSCAT. To apply the newly developed GMF to the TS-X data obtained in HH polarization, we analysed the C-band SAR polarization models and extended them to the X-band SAR data. The sea surface wind speeds were retrieved using the X-band GMF from pairs of TS-X images obtained in dual-polarization mode (i.e. VV and HH). The retrieved results were also validated by comparing with QuikSCAT measurements and the results of the German Weather Service (DWD) atmospheric model. The obtained RMS was 2.50 m s^?1 when compared with the co-located wind measurements derived from the QuikSCAT, and the absolute error was 2.24 m s^?1 when compared with DWD results.     

A data-distributed parallel algorithm for wavelet-based fusion of remote sensing images

With the increasing importance of multiplatform remote sensing missions, the fast integration or fusion of digital images from disparate sources has become critical to the success of these endeavors. In this paper, to speed up the fusion process, a Data-distributed Parallel Algorithm for wavelet-based Fusion (DPAF for short) of remote sensing images which are not geo-registered remote sensing images is presented for the first time. To overcome the limitations on memory space as well as the computing capability of a single processor, data distribution, data-parallel processing and load balancing techniques are integrated into DPAF. To avoid the inherent communication overhead of a wavelet-based fusion method, a special design called redundant partitioning is used, which is inspired by the characteristics of wavelet transform. Finally, DPAF is evaluated in theory and tested on a 32-CPU cluster of workstations. The experimental results show that our algorithm has good parallel performance and scalability.     

Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing

Polar ice masses and sheets are sensitive indicators of climate change. Small-scale surface roughness significantly impacts the microwave emission of the sea ice/snow surface; however, published results of surface roughness measurements of sea ice are rare. Knowing the refractive index is important to discriminate between objects. In this study, the small-scale roughness and refractive index over sea ice are estimated with AMSR-E observations and a unique method. Consequently, the small-scale surface roughness of 0.25 cm to 0.5 cm at AMSR-E 6.9 GHz shows reasonable agreement with the results of known observations, ranging from 0.2 cm to 0.6 cm for the sea ice in the Antarctic and Arctic regions. The refractive indexes are retrieved from 1.6 to 1.8 for winter, from 1.2 to 1.4 for summer in the Arctic and the Antarctic, which are similar to those of the sea ice and results from previous studies. This research shows the physical characteristics of the sea ice edges and melting process. Accordingly, this investigation provides an effective procedure for retrieving the small-scale roughness and refractive index of sea ice and snow. Another advantage of this study is the ability to distinguish sea ice from the sea surface by their relative small-scale roughness.     

A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations

The backscattering and emission measured simultaneously by radar and radiometer show promise for the estimation of surface variables such as near-surface soil moisture and vegetation characteristics. In this paper, the 10.7 GHz Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) channel and 13.8 GHz precipitation radar (PR) observations are simultaneously used for the estimation of the near-surface soil moisture and vegetation properties. The Fresnel model for soil and a simple model for vegetation are used to simulate the passive microwave emission at 10.7 GHz. To determine the PR backscatter signal from a land surface, a theoretical approach is used based on the Geometric Optics Model for simulating bare soil and a semi-empirical water-cloud model for vegetation. The model parameters required in specifying the nature of the soil and vegetation are calibrated on the basis of in situ soil moisture data combined with remotely sensed observations. The calibrated model is subsequently used to retrieve near-surface soil moisture and leaf area index for assumed values of surface roughness and temperature. Algorithm assessment using synthetic passive and active microwave data shows a nonlinearity effect in the system inversion, which results in a varying degree of error statistics in soil wetness and vegetation characteristics retrieval. The technique was applied on TRMM radar/radiometer observations from three consecutive years and evaluated against in situ near-surface (5 cm) soil moisture measurements from the Oklahoma Mesonet showing a consistent performance.     

一种基于深度学习的遥感图像目标检测算法

遥感图像分析在国土资源管理、海洋监测等领域有着极为广阔的应用前景。深度学习技术已在图像处理领域取得突破性进展,然而,遥感图像固有的尺寸大、目标小而密集等特点,使得将面向普通图像的深度学习方法用于遥感目标检测普遍存在定位不准确、小目标检测难、大图检测精度差等问题。针对上述难题, 提出了一种新型遥感图像目标检测算法DFS。与传统机器学习方法相比,DFS 设计了新的维度聚类模块、定制损失函数和滑动窗口分割检测机制。其中,维度聚类模块通过设计聚类机制优化定制先验框,提高定位精度;定制损失函数提高对船只等小目标的检测精度;滑动窗口分割检测解决大图检测精度低的问题。在经典遥感数据集上开展的实验对比表明,与YOLOv2相比,DFS算法的mAP提高了256%,小目标检测效率及大图检测效能大幅提高。     

Automatic hierarchical clustering algorithm for remote sensing data

A histogram clustering algorithm is suggested, which builds the hierarchy of distributions better in cluster separability. The algorithm optimizes the average cluster separability choosing the system of the data subdomain quantization grid and allows a significant decrease in the number of clusters. Application of the algorithm for uncontrolled Earth’s surface classification by satellite spectral data is shown.     

A remote sensing study of a surface ship wake

Abstract

The turbulent wake of the USNS Hayes, a twin hulled ship, was imaged simultaneously by a thermal infrared scanner, an X-band coherent microwave radar and a 35 mm strip camera mounted in an NRL RP-3A aircraft. Thermal surface effects and centimetre-scale surface roughness characteristics were determined for both natural ship wakes and those treated with oleyl alcohol, an organic material which produced a monomolecular film on the surface of the turbulent wake. Extensive hydrographic, oceanographic and meteorological data were obtained during the surface wake study. The turbulent motions and the presence of the monomolecular film at the wake surface strongly influenced the centimetre-scale roughness. This influence appeared as a significant reduction in the microwave backscatter from the wake compared to the ambient surface. The persistence of this reduction in backscattered power increased when the film was present. Various computer codes were employed to analyse the digitized IR video data. They generated temperature contour plots and temperature profiles across the wake at various locations behind the ship. These computer plots along with the original 70 mm photographic representation of the data and the corresponding water temperature data indicated that all the wakes were significantly cooler than the surrounding ambient surface water. The thermal signatures of the wakes treated with the surface film were more persistent than the natural wakes and the coo! surface was maintained over a broader cross section of the treated wakes. These observations can be explained on the basis of changes in water surface properties, the ship wake hydrodynamics, wind stress considerations and surface film physics.     

SMOS first data analysis for sea surface salinity determination

Soil Moisture and Ocean Salinity (SMOS), launched on 2 November 2009, is the first satellite mission addressing sea surface salinity (SSS) measurement from space. Its unique payload is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at the L-band frequency. This article presents a summary of SSS retrieval from SMOS observations and shows initial results obtained one year after launch. These results are encouraging, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation.     

Technical note: Relief effects for passive microwave remote sensing

The signal of a microwave radiometer observing a land surface from space is composed of surface and atmospheric contributions, both of which depend on the relief. For proper interpretation of the data these effects should be quantified and, if necessary, taken into account. Relief effects are twofold. First, the path through the atmosphere between the surface and the sensor depends on the altitude of the emitting surface, thus leading to a height-dependent atmospheric influence. The effect can be taken into account by standard atmospheric radiative transfer models if the elevation of the surface and the atmospheric state are known. Second, more relevant for the present discussion is the variable topography of land surfaces, consisting of slopes, ridges and valleys, sometimes with characteristic alignments, and surfaces surrounded by elevated terrain. These surfaces interact radiatively, not only with the atmosphere, but also with each other, leading to the tendency to enhance the effective emission. Under such circumstances, deviations occur from the standard hemispheric emission of a horizontal surface. The interactions do not only depend on topography and emissivity, but also on the bistatic scattering behaviour. Special attention will be paid to the radiation enhancement in a landscape of Lambertian surfaces with elevated horizons. As an example, simulated data for southern Norway are shown.     

Hierarchical cluster algorithm for remote sensing data of earth

A new automatic, hierarchical, multidimensional, histogram-based clusterization algorithm is considered. A method for choosing the clusterization detailedness in different regions of the vector space of spectral features depending on the average separability of clusters is proposed. The algorithm is applied for the automatic classification of multispectral satellite data in recognizing the land cover.