A cloud-based remote sensing data production system

The data processing capability of existing remote sensing system has not kept pace with the amount of data typically received and need to be processed. Existing product services are not capable of providing users with a variety of remote sensing data sources for selection, either. Therefore, in this paper, we present a product generation programme using multisource remote sensing data, across distributed data centers in a cloud environment, so as to compensate for the low productive efficiency, less types and simple services of the existing system. The programme adopts “master–slave” architecture. Specifically, the master center is mainly responsible for the production order receiving and parsing, as well as task and data scheduling, results feedback, and so on; the slave centers are the distributed remote sensing data centers, which storage one or more types of remote sensing data, and mainly responsible for production task execution. In general, each production task only runs on one data center, and the data scheduling among centers adopts a “minimum data transferring” strategy. The logical workflow of each production task is organized based on knowledge base, and then turned into the actual executed workflow by Kepler. In addition, the scheduling strategy of each production task mainly depends on the Ganglia monitoring results, thus the computing resources can be allocated or expanded adaptively. Finally, we evaluated the proposed programme using test experiments performed at global, regional and local areas, and the results showed that our proposed cloud-based remote sensing production system could deal with massive remote sensing data and different products generating, as well as on-demand remote sensing computing and information service

Sea ice-atmosphere interaction. Application of multispectral satellite data in polar surface energy flux estimates

Satellite data for the estimation of radiative and turbulent heat fluxes is becoming an increasingly important tool in large-scale studies of climate. One parameter needed in the estimation of these fluxes is surface temperature. To our knowledge, little effort has been directed to the retrieval of the sea ice surface temperature (IST) in the Arctic, an area where the first effects of a changing climate are expected to be seen. The reason is not one of methodology, but rather our limited knowledge of atmospheric temperature, humidity, and aerosol profiles, the microphysical properties of polar clouds, and the spectral characteristics of the wide variety of surface types found there. We have developed a means to correct for the atmospheric attenuation of satellite-measured clear sky brightness temperatures used in the retrieval of ice surface temperature from the split-window thermal channels of the advanced very high resolution radiometer (AVHRR) sensors on-board three of the NOAA series satellites. These corrections are specified for three different 'seasons' and as a function of satellite viewing angle, and are expected to be applicable to the perennial ice pack in the central Arctic Basin

On the remote sensing of oceanic and atmospheric convection in the Greenland Sea by synthetic aperture radar

In this paper we discuss characteristic properties of radar signatures of oceanic and atmospheric convection features in the Greenland Sea. If the water surface is clean (no surface films or ice coverage), oceanic and atmospheric features can become visible in radar images via a modulation of the surface roughness, and their radar signatures can be very similar. For an unambiguous interpretation and for the retrieval of quantitative information on current and wind variations from radar imagery with such signatures, theoretical models of current and wind phenomena and their radar imaging mechanisms must be utilized. We demonstrate this approach with the analysis of some synthetic aperture radar (SAR) images acquired by the satellites ERS-2 and RADARSAT-1. In once case, an ERS-2 SAR image an a RADARSAT-1 ScanSAR image exhibit pronounced cell-like signatures with length scales on the order of 10-20 km and modulation depths of about 5-6 dB and 9-10 dB, respectively. Simulations with a numerical SAR imagaing model and various input current and wind fields reveal that the signatures in both images can be expained consistently by wind variations on the order of±2.5 ms, but not by surface current variations on realistic orders of magnitude. Accordingly, the observed features must be atmospheric convection cells. This is confirmed by visible typical cloud patterns in a NOAA AVHRR image of the test scenario. In another case, the presence of an oceanic convective chimney is obvious from in situ data, but no signatures of it are visible in an ERS-2 SAR image. We show by numerical simulations with an oceanic convection model and our SAR imaging model that this is consistent with theoretical predictions, since the current gradients associated with the observed chimney are not sufficiently strong to give rise to significant signatures in an ERS-2 SAR image under the given conditions. Further model results indicate that it should be generally difficult to observe oceanic convection features in the Greenland Sea with ERS-2 or RADARSAT-1 SAR, since their signatures resulting from pure wave-current interaction will be too weak to become visible in the noisy SAR images in most cases. This situation will improve with the availability of future high-resolution SARs such as RADARSAT-2 SAR in fine resolution mode (2004) and TerraSAR-X (2005) which will offer significantly reduced speckle noise fluctuations at comparable spatial resolutions and thus a much better visibility of small image variations on spatial scales on the order of a few hundred meters

Comparison of forest attributes derived from two terrestrial lidar systems.

Abstract Terrestrial lidar (TLS) is an emerging technology for deriving forest attributes, including conventional inventory and canopy characterizations. However, little is known about the influence of scanner specifications on derived forest parameters. We compared two TLS systems at two sites in British Columbia. Common scanning benchmarks and identical algorithms were used to obtain estimates of tree diameter, position, and canopy characteristics. Visualization of range images and point clouds showed clear differences, even though both scanners were relatively high-resolution instruments. These translated into quantifiable differences in impulse penetration, characterization of stems and crowns far from the scan location, and gap fraction. Differences between scanners in estimates of effective plant area index were greater than differences between sites. Both scanners provided a detailed digital model of forest structure, and gross structural characterizations (including crown dimensions and position) were relatively robust; but comparison of canopy density metrics may require consideration of scanner attributes

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Linear mixing model applied to coarse resolution satellite data

A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies

The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed