ON-SITE DATA-PROCESSING ALGORITHM AND OPTIMIZATION FOR AIRBORNE ICE SOUNDING RADAR CONFIGURED ON THE “SNOW EAGLE 601”

Airborne observation is an important approach to collect data in the remote, hostile Antarctica and study the relationship between the Antarctica and global climate. During airborne observations, it is necessary to conduct data processing and quality control on site, which can help to timely evaluate the status of airborne instruments, provide scientific clues, and develop ideal schemes for following airborne observations. As one critical component of airborne instruments, airborne ice sounding radar can delineate sub-ice bedrock topography and internal layers, which cannot be realized by other instruments. In this study, we present an on-site data processing algorithm for high-resolution and high signal-to-noise ratio (SNR) ice sounding radar data acquired by the “Snow Eagle 601”, the first fixed-wing airplane deployed by China for the Antarctic expeditions. In addition, the algorithm is further optimized in terms of static pre-allocated memory and parallel and block processing of data to enhance processing speed and meet the requirements for quality control and analysis of on-site data. Finally, we test the optimized algorithm with different volume of ice sounding radar data through implementing on different computer configurations, including i7, i5 CPU and 8G, 16G memory with the same disk. The results show that the average processing speed of the optimized algorithm is 5.143 times faster than the non-optimized algorithm on different computer configurations.

SATELLITE DATA PROCESSING ALGORITHM IN THE PROCESS OF FORMATION OF THE TIME SERIES OF VEGETATION INDEXES

The diverse spectral indexes computed from the satellite images are used extensively in the world practice of remote sensing of the Earth from space. This approach proved its validity for the satellite monitoring of the underlying terrain, detection of ongoing changes and trends of their dynamic patters. Accumulated prodigious amount of satellite data, the state-of-the-art methods of thematic interpretation gave rise to creation of services providing free access to both images and to image processing results. Notwithstanding the foregoing, in the furtherance of the local and regional scale it turns out that usage of the end products of thematic processing of space information supplied by the known available services was not efficient on all occasions. Consequently, we may need to generate our own archives of the long-term series of satellite indexes. The volume of files containing the digital index matrices computed based on the MODIS satellite low resolution data subject to the complete coverage of the territory of Kazakhstan surpasses 4 Gb. This often results in the delayed computations, and on frequent occasions in infeasibility of computation of a full matrix when the medium specs computers are employed. This article is focused on the satellite data processing algorithm in the process of formation of the time series of vegetation indexes. As a consequence, the multi-year archive of vegetation indexes (over a period of 2001-2020), which provided a basis for trend analysis of the underlying terrain, determination of their future trends and forecasting of their changes was created within the territory of the Republic.

The PPP data processing algorithm to create geodetic networks

The article deals with determination of coordinates using global navigation systems, and application of the PPP data processing algorithm to obtain coordinates. The authors conducted an experiment illustrating the algorithm accuracy.