本文面向由于互联网遥感业务和PB级遥感影像数据的发展，遥感影像文件逐渐转至云端存储的场景，为提升云端遥感影像文件的读取性能、节省云端存储成本，提出面向云端存储的遥感影像文件优化技术。本文通过优化数据存储格式，在遥感影像文件中预先生成金字塔模型，无损压缩后整理为云端存储格式，读取时利用HTTPRange请求云端遥感影像文件内区域数据，为高性能云端遥感影像文件读取提供了支撑，使其更适合于海量遥感影像文件存储在云端的场景，提升遥感影像文件在云端的读取效率，进一步为互联网遥感业务打下基础。

一、前言 随着社会快速发展和航天科技事业的进步,我国遥感卫星无论在数量或质量上都有了跨越式发展.遥感卫星数量提升的同时,严格把控卫星数据质量,做好卫星数据服务是当下面临的重要问题.

Analysis of built-up areas-the most significant artificial urban areas-reveals physical development processes. Unlike previous research involving medium-resolution remote sensing (RS) images, this study used built-up area maps generated from multiple high-resolution RS images with abundant built-up area edge information to analyze development in Zhengzhou. A transferable built-up area extraction (TBUAE) algorithm was developed to map the built-up area maps. The algorithm allows the developed deep learning model used on a certain satellite image to be eligible for other types of satellite images by altering the data distribution with adaptive Wallis filtering (DT-AWF). The proposed method alleviates the pressure of deep learning on the demand for new satellite image samples that are time-consuming and laborious. Additionally, the accuracy of built-up area mapping using this method exceeds 90%. Quantitative and qualitative analyses were conducted on the map results to observe the urban development of Zhengzhou from 2016 to 2020. We found that Zhengzhou has expanded rapidly since it was defined as a central city in central China in 2016. Additionally, the suburban built-up area has expanded rapidly and developed together with the central city. Further, affected by the policy, the built-up areas in different regions of Zhengzhou has changed differently, the urban edge is more simplified, and the urban internal structure is more compact.

GaoFen6 (GF-6), successfully launched on June 2, 2018, is the sixth satellite of the High-Definition Earth observation system (HDEOS). Although GF-6 is the first high-resolution satellite in China to achieve precise agricultural observation, it will be widely used in many other domains, such as land survey, natural resources management, emergency management, ecological environment and so on. The GF-6 was not equipped with an onboard calibration instrument, so on-orbit radiometric calibration is essential. This paper aimed at the on-orbit radiometric calibration of the wide field of view camera (WFV) onboard GF-6 (GF-6/WFV) in multispectral bands. Firstly, the radiometric capability of GF-6/WFV is evaluated compared with the Operational Land Imager (OLI) onboard Landsat-8, Multi Spectral Instrument (MSI) onboard Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra, which shows that GF-6/WFV has an obvious attenuation. Consequently, instead of vicarious calibration once a year, more frequent calibration is required to guarantee its radiometric consistency. The cross-calibration method based on the Badain Jaran Desert site using the bi-directional reflectance distribution function (BRDF) model calculated by Landsat-8/OLI and ZY-3/Three-Line Camera (TLC) data is subsequently applied to GF-6/WFV and much higher frequencies of calibration are achieved. Finally, the cross-calibration results are validated using the synchronized ground measurements at Dunhuang test site and the uncertainty of the proposed method is analyzed. The validation shows that the relative difference of cross-calibration is less than 5% and it is satisfied with the requirements of cross-calibration.

Although many attempts have been made, it has remained a challenge to retrieve the aerosol optical depth (AOD) at 550 nm from moderate to high spatial-resolution (MHSR) optical remotely sensed imagery in arid areas with bright surfaces, such as deserts and bare ground. Atmospheric correction for remote-sensing images in these areas has not been good. In this paper, we proposed a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from moderate to high spatial-resolution imagery in arid areas with bright surfaces. Land surface in arid areas is usually bright and stable and the variation of atmosphere in these areas is also very small; consequently, the land-surface characteristics, specifically the bidirectional reflectance distribution factor (BRDF), can be retrieved easily and accurately using time series of satellite images with relatively lower spatial resolution like the Moderate-resolution Imaging Spectroradiometer (MODIS) with 500 m resolution and the retrieved BRDF is then used to retrieve the AOD from MHSR images. This algorithm has three advantages: (i) it is well suited to arid areas with bright surfaces; (ii) it is very efficient because of employed lower resolution BRDF; and (iii) it is completely automatic. The derived AODs from the Multispectral Instrument (MSI) on board Sentinel-2, Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Gao Fen 1 Wide Field Viewer (GF-1/WFV), Gao Fen 6 Wide Field Viewer (GF-6/WFV), and Huan Jing 1 CCD (HJ-1/CCD) data are validated using ground measurements from 4 stations of the AErosol Robotic NETwork (AERONET) around the world.