Improvement in the GERB short wave flux estimations over snow covered surfaces

Because space-borne radiometers do not measure the Earth’s outgoing fluxes directly, angular distribution models (ADMs) are required to relate actual radiance measurement to flux at given solar angle, satellite-viewing geometries, surface, and atmospheric conditions. The conversion of one footprint broad-band radiance into the corresponding flux requires therefore one to first characterize each footprint in terms of surface type and cloud cover properties to properly select the adequate ADM.

A snow (and sea-ice) retrieval technique based on spectral measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat 8 is presented. It has been developed to improve the scene identification and thus the ADM selection in the near-real time processing of the Geostationary Earth Radiation Budget (GERB) data at the Royal Meteorological Institute of Belgium. The improvement in the GERB short wave flux estimations over snow covered scene types resulting from angular conversion using dedicated snow ADMs (e.g., empirical snow ADMs and/or pre-computed theoretical snow ADM) instead of empirical snow-free ADMs is discussed.     

Mercury’s Surface Composition and Character as Measured by Ground-Based Observations

Mercury’s surface is thought to be covered with highly space-weathered silicate material. The regolith is composed of material accumulated during the time of planetary formation, and subsequently from comets, meteorites, and the Sun. Ground-based observations indicate a heterogeneous surface composition with SiO₂ content ranging from 39 to 57 wt%. Visible and near-infrared spectra, multi-spectral imaging, and modeling indicate expanses of feldspathic, well-comminuted surface with some smooth regions that are likely to be magmatic in origin with many widely distributed crystalline impact ejecta rays and blocky deposits. Pyroxene spectral signatures have been recorded at four locations. Although highly space weathered, there is little evidence for the conversion of FeO to nanophase metallic iron particles (npFe⁰), or “iron blebs,” as at the Moon. Near- and mid-infrared spectroscopy indicate clino- and ortho-pyroxene are present at different locations. There is some evidence for no- or low-iron alkali basalts and feldspathoids. All evidence, including microwave studies, point to a low iron and low titanium surface. There may be a link between the surface and the exosphere that may be diagnostic of the true crustal composition of Mercury. A structural global dichotomy exists with a huge basin on the side not imaged by Mariner 10. This paper briefly describes the implications for this dichotomy on the magnetic field and the 3 : 2 spin : orbit coupling. All other points made above are detailed here with an account of the observations, the analysis of the observations, and theoretical modeling, where appropriate, that supports the stated conclusions.     

Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios. Image classification by multiple classifier system has been evolving as a promising method for enhancing accuracy and reliability of image classification. Because of the diversity in generalization capabilities of various dimensionality reduction methods, the classifier optimal to the problem and hence the accuracy of image classification varies considerably. The impact of including multiple dimensionality reduction methods in the MCS architecture for the supervised classification of a hyperspectral image for land cover classification has been assessed in this study. Multi-source airborne hyperspectral images acquired over five different sites covering a range of land cover categories have been classified by a multiple classifier system and compared against the classification results obtained from support vector machines (SVM). The MCS offers acceptable classification results across the images or sites when there are multiple dimensionality reduction methods in addition to different classifiers. Apart from offering acceptable classification results, the MCS indicates about 5% increase in the overall accuracy when compared to the SVM classifier across the hyperspectral images and sites. Results indicate the presence of dimensionality reduction method specific empirical preferences by land cover categories for certain classifiers thereby demanding the design of MCS to support adaptive selection of classifiers and dimensionality reduction methods for hyperspectral image classification.     

Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data

Present study focuses on the estimation of rainfall over Indian land and oceanic regions from the Special Sensor Microwave/Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP) F-13. Based on the measurements at 19.35, 22.235 and 85.5 GHz channels of SSM/I Satellite, scattering index (SI) has been developed for the Indian land and oceanic regions separately. These scattering indices were co-located against rainfall from Precipitation Radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) to develop a new regional relationship between the SI and the rain rate for the Indian land and oceanic regions. A non-linear fit between the rain rate and the SI is established for rain measurement. In order to have confidence in our method, we have also estimated rainfall using the global rainfall and scattering index relationship developed by Ferraro and Marks [Ferraro, R.R., Marks, G.F. The development of SSM/I rain rate retrieval algorithms using ground based radar measurements. J. Atmos. Ocean. Technol. 12, 755–770, 1995]. The validation with the rain-gauge shows that the present scheme is able to retrieve rainfall with better accuracy than that of Ferraro and Marks (1995). Further intercomparison with TRMM-2A12 and validation with rain-gauges rainfall showed that the present algorithm is able to retrieve the rainfall with reasonably good accuracy.     

Examining LiDAR – WorldView-3 data synergy to generate a detailed stand map in a mixed forest in the north-west of Turkey

Although stand delineation approach based on aerial photographs and field survey produces high accuracy maps, it is labour-intensive and time consuming. Furthermore, conventional forest stand maps may have some uncertainties that can hardly be verified due to the experiments and skills of photo-interpreters. Therefore, researchers have been seeking more objective and cost-effective methods for forest mapping. LiDAR (Light Detection and Ranging) data have a high potential to automatically delineate forest stands. Unlike optical sensors, LiDAR height data provides information about both the horizontal and vertical structural characteristics of forest stands. However, it deprives of spectral data that may be successfully used in separating tree species. In this study, we investigate the potential of LiDAR – WorldView-3 data synergy for the automatic generation of a detailed forest stand map which can be used for a tactical forest management plan. Firstly, image segmentation was applied to LiDAR data alone and LiDAR/WorldView-3 data set in order to obtain the most suitable image objects representing forest stands. Visual inspection of the segmentation results showed that image objects based on the LiDAR/WorldView-3 data set were more compatible with the reference forest stand boundaries. After the segmentation process, the LiDAR and LiDAR/WorldView-3 data sets were independently classified using object-based classification method. We tested two levels of classification. The first was a detailed classification with 14 classes considering reference stand types. The second was the rough classification with 9 classes where some stand types were combined. The mean, standard deviation and texture features of LiDAR metrics and spectral information were used in the classification. The accuracy assessment results of LiDAR data showed that the Overall Accuracy (OA) was calculated as 0.31 and 0.43, and the Kappa Index (KIA) was calculated as 0.26 and 0.32 for the detailed and rough classifications, respectively. For the LiDAR/WorldView-3 data set, the OA values were calculated as 0.50 and 0.61, and the KIA were calculated as 0.46 and 0.55 for the detailed and rough classifications, respectively. These results showed that the forest stand map derived from the LiDAR/WorldView-3 data synergy is more compatible with the reference forest stand map. In conclusion, it can be said that the forest stand maps produced in this study may provide strategic forest planning needs, but it is not sufficient for tactical forest management plans.     

Earth remote sensing as an effective tool for the development of advanced innovative educational technologies

Current educational system is facing a contradiction between the fundamentality of engineering education and the necessity of applied learning extension, which requires new methods of training to combine both academic and practical knowledge in balance. As a result there are a number of innovations being developed and implemented into the process of education aimed at optimizing the quality of the entire educational system. Among a wide range of innovative educational technologies there is an especially important subset of educational technologies which involve learning through hands-on scientific and technical projects. The purpose of this paper is to describe the implementation of educational technologies based on small satellites development as well as the usage of Earth remote sensing data acquired from these satellites. The increase in public attention to the education through Earth remote sensing is based on the concern that although there is a great progress in the development of new methods of Earth imagery and remote sensing data acquisition there is still a big question remaining open on practical applications of this kind of data. It is important to develop the new way of thinking for the new generation of people so they understand that they are the masters of their own planet and they are responsible for its state. They should desire and should be able to use a powerful set of tools based on modern and perspective Earth remote sensing. For example NASA sponsors “Classroom of the Future” project. The Universities Space Research Association in United States provides a mechanism through which US universities can cooperate effectively with one another, with the government, and with other organizations to further space science and technology, and to promote education in these areas. It also aims at understanding the Earth as a system and promoting the role of humankind in the destiny of their own planet. The Association has founded a Journal of Earth System Science Education. Authors describe an effective model of educational technology developed in the Center for Earth Remote Sensing of Bauman Moscow State Technical University and based on scientific and educational organizations integration in the field of applied studies. The paper also presents how students are being trained to acquire and process satellite imagery data from Terra and Aqua satellites. It also reveals the results of space monitoring for Russia's ecologically complex regions conducted by Bauman Moscow State Technical University students in cooperation with specialists from the Laboratory for Aerospace Methods of Moscow State University named after M. Lomonosov.     

嵌入式大气数据传感系统的改进算法

简要介绍了嵌入式大气数据传感系统及其空气动力学模型,提出了求解动、静压和修正参数的改进算法.改进算法首先应用Moore-Penrose广义逆矩阵对非线性方程组进行简化.然后采用改进迭代算法和BP神经网络求解动、静压和修正系数.改进算法在精度、可靠性和实时性上都能满足系统的要求.应用BP神经网络,达到系统要求的精度所需要的计算时间只相当于原有算法的5%,具有更大的实时性优势.