The Gaofen-3 (GF-3) satellite is Chinas first civilian quad-polarized C-band imaging microwave satellite that can provide high-resolution marine and land observations. Also, it is China's first low earth orbit remote sensing satellite with a design life of 8 years. During the design and development of this satellite, many major challenges have been overcome, such as the design of complex RF channels, difficult system integration, high quantitative requirements, and strict component life requirements. In this paper, a series of independent technological innovations in the development of GF-3 is presented. Meanwhile, the on-orbit operation of the satellite in the past five years is introduced, as well as the application of satellite data in marine observation, emergency relief, disaster reduction, surveying and mapping, water conservancy, etc. The further advancement and improvement of the GF-3 satellite are described at the end of this paper.

Gaofen-3 (GF-3), the first Chinese civil C-band synthetic aperture radar (SAR), was successfully launched by the China Academy of Space Technology on 10 August 2016. Among its 12 imaging modes, wave mode is designed to monitor the ocean surface waves over the open ocean. An empirical retrieval algorithm of significant wave height (SWH), termed Quad-Polarized C-band WAVE algorithm for GF-3 wave mode (QPCWAVE_GF3), is developed for quad-polarized SAR measurements from GF-3 in wave mode. QPCWAVE_GF3 model is built using six SAR image and spectrum related parameters. Based on a total of 2576 WaveWatch III (WW3) and GF-3 wave mode match-ups, 12 empirical coefficients of the model are determined for 6 incidence angle modes. The validation of the QPCWAVE_GF3 model is performed through comparisons against independent WW3 modelling hindcasts, and observations from altimeters and buoys from January to October in 2017. The assessment shows a good agreement with root mean square error from 0.5 m to 0.6 m, and scatter index around 20%. In particular, applications of the QPCWAVE_GF3 model in SWH estimation for two storm cases from GF-3 data in wave mode and Quad-Polarization Strip I mode are presented respectively. Results indicate that the proposed algorithm is suitable for SWH estimation from GF-3 wave mode and is promising for other similar data.

The purpose of our work is to determine the feasibility and effectiveness of retrieving sea surface wind speeds from C-band cross-polarization (herein vertical-horizontal, VH) Chinese Gaofen-3 (GF-3) SAR images in typhoons. In this study, we have collected three GF-3 SAR images acquired in Global Observation (GLO) and Wide ScanSAR (WSC) mode during the summer of 2017 from the China Sea, which includes the typhoons Noru, Doksuri and Talim. These images were collocated with wind simulations at 0.12° grids from a numeric model, called the Regional Assimilation and Prediction System-Typhoon model (GRAPES-TYM). Recent research shows that GRAPES-TYM has a good performance for typhoon simulation in the China Sea. Based on the dataset, the dependence of wind speed and of radar incidence angle on normalized radar cross (NRCS) of VH-polarization GF-3 SAR have been investigated, after which an empirical algorithm for wind speed retrieval from VH-polarization GF-3 SAR was tuned. An additional four VH-polarization GF-3 SAR images in three typhoons, Noru, Hato and Talim, were investigated in order to validate the proposed algorithm. SAR-derived winds were compared with measurements from Windsat winds at 0.25° grids with wind speeds up to 40 m/s, showing a 5.5 m/s root mean square error (RMSE) of wind speed and an improved RMSE of 5.1 m/s wind speed was achieved compared with the retrieval results validated against GRAPES-TYM winds. It is concluded that the proposed algorithm is a promising potential technique for strong wind retrieval from cross-polarization GF-3 SAR images without encountering a signal saturation problem.

Sea surface current is a research hotspot in oceanography. Space-borne along-track interferometric synthetic aperture radar (along-track InSAR, ATI) is a promising sensor for measuring high-resolution sea surface current field, and there is no operational system in orbit yet. To support future space-borne ATI systems, based on the ATI experimental mode of the Gaofen-3 (GF-3) satellite, the first sea surface current observing experiment was conducted in the Jiaozhou Gulf in China, in 2019. Meanwhile, SAR observations and in situ instrument measurements of the current are obtained in the experiments. The data is first preprocessed by a processor specially developed for the GF-3 ATI data. Then, the current is extracted based on the M4S mode. The retrieved current of the Jiaozhou Gulf is compared with ground-based high frequency surface wave radar data. The results show that the root mean square error of the surface current observed by the GF-3 satellite is less than 0.2 m/s.

With the increase in extreme climate events, forest fires burn in much larger areas. Therefore, it is important to accurately predict forest fire frequencies. Precipitation is an important factor that affects the probability of future forest fires. Previous models used average precipitation values, but the attenuation of precipitation was not considered. In this study, a time-decaying precipitation algorithm was used to calculate the comprehensive precipitation index. This method can better represent the effect of precipitation in predicting the occurrence of forest fires. Moreover, observed fire spots were converted into a continuous density of fire spots. The structure of the prediction model is more realistic, which is conducive to obtaining higher-precision prediction results. Additionally, the support vector machine (SVM) regression model was used to construct a forest fire warning model. When the comprehensive precipitation index was compared with the average precipitation value, the accuracy of the four forest areas in central and northern China in the test set was improved by approximately 10%. The findings are relevant to forest ecologists and managers for future mitigation of forest fires, and also for successful prediction of other fire-prone areas.