Gaofen Data Download

Abstract:Analysis Ready Data (ARD) has been greatly recommended by the Committee on Earth Observation Satellites (CEOS) for simplifying and fostering long time series analysis at large scale with minimum additional user effort. Landsat ARD has been successfully made and widely used for large scale analysis. Subsequently, the Chinese satellite data similar to Landsat data have been processed and will be processed into ARDs to promote the use of the Chinese satellite data. At the first stage of the mission, the 4 Wide Field Viewing (WFV) data on GaoFen 1 (GF1) covering the whole of China and the surrounding areas have been processed into ARD. The ARD is provided as standard tiles under a common and unified projection with per pixel quality assurance and metadata for tracing back and further processing data, which are finally stored into a Hierarchical Data File (HDF); furthermore, all spectral bands are georegistered and radiometrically cross-calibrated as top of atmosphere (TOA) reflectance and are atmospherically corrected as surface reflectance (SR). Therefore, the ARD can be further used easily to produce land cover and land cover change maps and retrieve geophysical and biophysical parameters.Keywords: GaoFen; analysis ready data; WFV; reflectance; tiling

In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. To pre-train deep model specific to HRRS images, we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite images. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, Sentinel-2A, and Google Earth platform data, show encouraging results and demonstrate the applicability of the proposed scheme to land-cover classification with multi-source HRRS images.

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We construct a large-scale land-cover dataset with Gaofen-2 (GF-2) satellite images. This new dataset, which is named as Gaofen Image Dataset (GID), has superiorities over the existing land-cover dataset because of its large coverage, wide distribution, and high spatial resolution. GID consists of two parts: a large-scale classification set and a fine land-cover classification set. The large-scale classification set contains 150 pixel-level annotated GF-2 images, and the fine classification set is composed of 30,000 multi-scale image patches coupled with 10 pixel-level annotated GF-2 images. The training and validation data with 15 categories is collected and re-labeled based on the training and validation images with 5 categories, respectively.

Since 2019, China has adopted an open data policy for Gaofen (GF) satellites, which grants the global community free and open data access. As a first step, global data with 16-meter resolution from GF 1 and GF 6 satellites have been made publicly accessible. The released data are in the Level 1 processing stage, which means that end users will inevitably face different preprocessing steps for these data before they are able to explore domain application tasks. To improve the usability of the shared data for end users, Gaofen 16m Analysis Ready Data (ARD) is seriously needed in order to introduce a series of standard preprocessing procedures. In this Special Issue, we invite submissions related to Gaofen 16m ARD techniques, including, but not limited to:

The data, collected by the Gaofen-1 and Gaofen-6 earth observation satellites, respectively launched in 2013 and 2018, will be accessible on the platform www.cnsageo.com, CNSA revealed at the opening of the Group on Earth Observations (GEO) conference.

The platform allows users to search for and download data. It also carries information and functions such as policies and standards, data upgrades, data quality assessment and introduction of satellites, and displays satellite data applications.

By sharing the platform with the world, China is committed to building a global observation system that provides support for resource survey and observation, environmental monitoring and evaluation, emergency disaster forecasting and global climate change monitoring, and provides data for forestry and agriculture. (People's Daily Online)

Aerosol optical depth (AOD) is an important factor to estimate the effect of aerosol on light, and an accurate retrieval of it can make great contribution to monitor atmosphere. Therefore, retrieval of AOD has been a frontier topic and attracted much attention from researchers at home and abroad. However, the spatial resolution of AOD, based on Moderate-resolution Imaging Spectroradiometer (MODIS), is low, and hard to meet the needs of regional air quality fine monitoring. In 2018, China launched Gaofen-6 satellite, which set up a network with Gaofen-1 enabling two-day revisited observations in China's land area, improving the scale and timeliness of remote sensing data acquisition and making up for the shortcomings of lacking multi-spectral satellite with medium and high spatial resolution. Along with advancement of the Earth Observation System and the launch of high-resolution satellites, it is of profound significance to give full play to the active role of high-scoring satellites, in monitoring atmospheric environmental elements such as atmospheric aerosols and particulate matter concentrations, and achieve high-resolution retrieval of AOD through Gaofen satellites.In this paper the data of Gaofen-6 and Gaofen-1 was used to retrieve the AOD. based on the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm. This algorithm can retrieve the surface reflectance and AOD synchronously through constructing a closed equation based on double star observations. It can be applied to various types of surface reflectance which extends the coverage of the retrieval of AOD inversion effectively. Experimental data includes the satellite data of New South Wales and eastern Queensland on November 21, 2019, which have been suffered from unprecedented large-scale forest fires for over 2 months. The retrieval of AOD during the time with the satellite data is benefit for the prevention and monitoring of forest fire. The experimental results are compared with the AERONET ground observation data for preliminary validation. The correlation coefficient is about 0.7. The experimental results show that the method have higher accuracy, and further validation work is continuing.

Gaofen (Chinese: ; pinyin: Gofn; lit. 'high resolution') is a series of Chinese high-resolution Earth imaging satellites launched as part of the China High-resolution Earth Observation System (CHEOS) program.[1][2] CHEOS is a state-sponsored, civilian Earth-observation program used for agricultural, disaster, resource, and environmental monitoring. Proposed in 2006 and approved in 2010, the CHEOS program consists of the Gaofen series of space-based satellites, near-space and airborne systems such as airships and UAVs, ground systems that conduct data receipt, processing, calibration, and taskings, and a system of applications that fuse observation data with other sources to produce usable information and knowledge.[2][3]

In 2003, the China National Space Administration (CNSA) agreed with Roscosmos to share Gaofen data for data from Russia's Earth observation satellites of similar capability. This agreement was expanded in August 2021 when leaders from BRICS space agencies agreed to share space-based remote sensing data.[8]

BEIJING, April 4 (Xinhua) -- China Wednesday received the first package of data from the three high-resolution Gaofen-1 satellites launched on March 31, according to the Institute of Remote Sensing and Digital Earth of the Chinese Academy of Sciences.

The development of the Gaofen-7 has achieved a breakthrough in sub-meter level 3D mapping camera technology, meeting the highest mapping accuracy requirement among the Gaofen series Earth observation satellites. The satellite can obtain high-resolution optical 3D observation data and high-precision laser altimetry data and can realize 1:10,000 scale satellite 3D mapping for civil use in China and will meet the needs of users in basic mapping, global geographic information, monitoring and evaluation in urban and rural construction, agricultural survey and statistics, etc.

The new satellite will work together with other Gaofen satellites to form an Earth observation system with high resolution and high positioning accuracy, which will help promote international sci-tech industrial cooperation through data sharing and support the Belt and Road initiative.

The platform shares three types of data: historical records, daily updated Wide-Field-of-View (WFV) images and global coverage data. It also makes available two PIE (Pixel Information Expert) software developed by Chinese company PIESAT, which can be used to better view and process of WFV data with a swath of 800 km. The platform provides two visual browsing interfaces, Globeview and Swath.

Wide-field-of-view (WFV) imager that observes the earth environment with four solar reflective bands in a spatial resolution of 16 m is equipped on board Gaofen-1 (GF-1) satellite. Chlorophyll-a (Chl-a) concentration in Lake Taihu, China from 2018 to 2019 is collected and collocated with GF-1 satellite data. This study develops a general and reliable estimation of Chl-a concentration from GF-1 WFV data under turbid inland water conditions. The collocated data are classified according to season and used in random forest (RF) regression to train models for retrieving the lake Chl-a concentration. A composite index is developed to select the most important variables in the models. The models trained for each season show a better performance than the model trained by using the whole year data in terms of the coefficient of determination (R2) between retrievals and observations. Specifically, the R2 values in spring, summer, autumn, and winter are 0.88, 0.88, 0.94, and 0.74, respectively; whereas that using the whole year data is only 0.71. The Chl-a concentration in Lake Taihu exhibits an obvious seasonal change with the highest in summer, followed by autumn and spring, and the lowest in winter. The Chl-a concentration also displays an obvious spatial variation with season. A high concentration occurs mainly in the northwest of the lake. The temporal and spatial changes of Chl-a concentration are almost consistent with the changes in the areas and times of cyanobacteria blooms based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The proposed algorithm can be operated without a priori knowledge on atmospheric conditions and water quality. Our study also demonstrates that GF-1 data are increasingly valuable for monitoring the Chl-a concentration of inland water bodies in China at a high spatial resolution. e24fc04721