Lin Ren

GF-3 SAR Ocean Wind Retrieval: The First View and Preliminary Assessment

Gaofen-3 (GF-3) is the first Chinese civil C-band synthetic aperture radar (SAR) launched on 10 August 2016 by the China Academy of Space Technology (CAST), which operates in 12 imaging modes with a fine spatial resolution up to 1 m. As one of the primary users, the State Oceanic Administration (SOA) operationally processes GF-3 SAR Level-1 products into ocean surface wind vector and plans to officially release the near real-time SAR wind products in the near future. In this paper, the methodology of wind retrieval at C-band SAR is introduced and the first results of GF-3 SAR-derived winds are presented. In particular, the case of the coastal katabatic wind off the west coast of the U.S. captured by GF-3 is discussed. The preliminary accuracy assessment of wind speed and direction retrievals from GF-3 SAR is carried out against in situ measurements from National Data Buoy Center (NDBC) buoy measurements of National Oceanic and Atmospheric Administration (NOAA). Only the buoys located inside the GF-3 SAR wind cell (1 km) were considered as co-located in space, while the time interval between observations of SAR and buoy was limited to less the 30 min. These criteria yielded 56 co-locations during the period from January to April 2017, showing the Root Mean Square Error (RMSE) of 2.46 m/s and 22.22° for wind speed and direction, respectively. Different performances due to geophysical model function (GMF) and Polarization Ratio (PR) are discussed. The preliminary results indicate that GF-3 wind retrievals are encouraging for operational implementation.

The first quantitative remote sensing of ocean internal waves by Chinese GF-3 SAR satellite

Quantitative analysis and retrieval is given by the State Key Laboratory of Satellite Ocean Environment Dynamics (SOED), Second Institute of Oceanography (SIO), State Oceanic Administration (SOA), China, from the first batch of GF-3 synthetic aperture radar (SAR) data with ocean internal wave features in the Yellow Sea. It shows that the internal wave group appears in bright-and-dark bands and consists of three nonlinear wave trains (see the black line in Fig. 1). These nonlinear internal waves propagate to the northeast (see the arrow) with an average wavelength of about 250 m. The influence range of the internal wave group is more than 5 km (shown in red line). The maximum amplitude is about 5 m (located at the intersection of the red and black line). The water depth of this area is about 100 m and the average thermocline depth is 32 m in August. GF-3 is China’s first C band multi-polarization high resolution microwave remote sensing satellite, launched on August 10, 2016 in the Taiyuan Satellite Launch Center. This SAR image is acquired on August 18, 2016 and officially announced by the State Administration of Science, Technology and Industry for National Defense, China.

Significant wave height estimation using azimuth cutoff of C-band RADARSAT-2 single-polarization SAR images

This paper proposes two simple models, look-up table (LUT) model and empirical model, to directly retrieve significant wave height (Hs) using synthetic aperture radar (SAR) azimuth cutoff (λc). Both models aim at C-band VV, HH, VH, and HV single-polarization SAR images. The LUT model relates Hs to λc, while the empirical model relates Hs to both λc and SAR range-to-velocity (β). The LUT model coefficients are derived by simulation under different sea states and observation conditions, which depend on incidence angle (θ), wave direction (dw), and β but are independent of polarization. The empirical model coefficients are obtained by fitting the collocated data, which only depend on polarization. To fit empirical model coefficients and validate the two models, C-band RADARSAT-2 fine quad-polarization (VV+HH+VH+HV) single-look complex (SLC) SAR images and collocated buoy data are collected. Retrieved Hs, using Yang model and the two models proposed in this paper from four kinds of polarization SAR data, are compared with buoy Hs. Results show that both LUT and empirical models have the capacity of retrieving Hs from C-band RADARSAT-2 co-polarization SAR data, while Yang model is not suitable for these kinds of SAR data. Moreover, the empirical model is also valid for cross-polarization SAR data showing clear ocean wave stripes.

Retrieval Models of Water Vapor and Wet Tropospheric Path Delay for the HY-2A Calibration Microwave Radiometer

AbstractThis paper focuses on the development and validation of retrieval models of water vapor (WV) and wet tropospheric path delay (PD) for the calibration microwave radiometer (CMR) on board the first ocean dynamic environment satellite of China—Haiyang-2A (HY-2A). The reference data are those of Jason-1 microwave radiometer (JMR) and Jason-2 advanced microwave radiometer (AMR). The crossover points of the HY-2A and Jason-1/2 satellite tracks are extracted, and the data pairs at these points are divided into fitting and validation datasets. The retrieval models of WV and PD are built for the CMR using the fitting dataset and genetic algorithm, and validated using the validation dataset. The validation shows that the results of the retrieval models are consistent with the JMR and AMR data, and the root-mean-square differences of WV and PD are 1.86 kg m−2 and 11.4 mm, respectively. Finally, the retrieved results from the CMR brightness temperature along-track data using the retrieval models and the AMR a...

Empirical Algorithm for Significant Wave Height Retrieval from Wave Mode Data Provided by the Chinese Satellite Gaofen-3

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.

Preliminary Analysis of Chinese GF-3 SAR Quad-Polarization Measurements to Extract Winds in Each Polarization

This study analyzed the noise equivalent sigma zero (NESZ) and ocean wind sensitivity for Chinese C-band Gaofen-3 (GF-3) quad-polarization synthetic aperture radar (SAR) measurements to facilitate further operational wind extraction from GF-3 data. Data from the GF-3 quad-polarization SAR and collocated winds from both NOAA/NCEP Global Forecast System (GFS) atmospheric model and National Data Buoy Center (NDBC) buoys were used in the analysis. For NESZ, the co-polarization was slightly higher compared to the cross-polarization. Regarding co-polarization and cross-polarization, NESZ was close to RadarSAT-2 and Sentinel-1 A. Wind sensitivity was analyzed by evaluating the dependence on winds in terms of normalized radar cross-sections (NRCS) and polarization combinations. The closest geophysical model function (GMF) and the polarization ratio (PR) model to GF-3 data were determined by comparing data and the model results. The dependence of co-polarized NRCS on wind speed and azimuth angle was consistent with the proposed GMF models. The combination of CMOD5 and CMOD5.N was considered to be the closest GMF in co-polarization. The cross-polarized NRCS exhibited a strong linear relationship with moderate wind speeds higher than 4 m·s−1, but a weak correlation with the azimuth angle. The proposed model was considered as the closest GMF in cross-polarization. For polarization combinations, PR and polarization difference (PD) were considered. PR increased only with the incidence angle, whereas PD increased with wind speed and varied with azimuth angle. There were three very close PR models and each can be considered as the closest. Preliminary results indicate that GF-3 quad-polarization data are valid and have the ability to extract winds in each polarization.

An Improved Local Gradient Method for Sea Surface Wind Direction Retrieval from SAR Imagery

Sea surface wind affects the fluxes of energy, mass and momentum between the atmosphere and ocean, and therefore regional and global weather and climate. With various satellite microwave sensors, sea surface wind can be measured with large spatial coverage in almost all-weather conditions, day or night. Like any other remote sensing measurements, sea surface wind measurement is also indirect. Therefore, it is important to develop appropriate wind speed and direction retrieval models for different types of microwave instruments. In this paper, a new sea surface wind direction retrieval method from synthetic aperture radar (SAR) imagery is developed. In the method, local gradients are computed in frequency domain by combining the operation of smoothing and computing local gradients in one step to simplify the process and avoid the difference approximation. This improved local gradients (ILG) method is compared with the traditional two-dimensional fast Fourier transform (2D FFT) method and local gradients (LG) method, using interpolating wind directions from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data and the Cross-Calibrated Multi-Platform (CCMP) wind vector product. The sensitivities to the salt-and-pepper noise, the additive noise and the multiplicative noise are analyzed. The ILG method shows a better performance of retrieval wind directions than the other two methods.

Wind speed retrieval from Ku-band Tropical Rainfall Mapping Mission precipitation radar data at low incidence angles

Abstract. A Ku-band low incidence backscatter model (KuLMOD) for retrieving wind speeds from Tropical Rainfall Mapping Mission (TRMM) precipitation radar (PR) data is proposed. The data set consisted of TRMM PR observations and collocated National Data Buoy Center (NDBC) and Tropical Ocean Global Atmosphere program buoy-measured wind and wave data. The TRMM PR data properties were analyzed with regard to their dependence on spatial resolution, wind speed, relative wind direction, and significant wave height. The KuLMOD model was developed using incidence angles (0.5 to 6.5 deg) and wind speeds (1.5 to 16.5  m/s) as inputs. The model coefficients were derived by fitting the collocated data. The KuLMOD-derived normalized radar cross section, σ0, was compared with those obtained from the TRMM PR observations and a quasi-specular theoretical model and showed good agreement. With the KuLMOD, the wind speeds were retrieved from the TRMM PR data using the least squares method and validated with the buoy measurements, yielding a root mean square error of 1.45  m/s. The retrieval accuracies for the different incidence angles, wind speeds, and spatial resolutions were obtained.

Wave effects on the retrieved wind field from the advanced scatterometer (ASCAT)

To improve retrieval accuracy, this paper studies wave effects on retrieved wind field from a scatterometer. First, the advanced scatterometer (ASCAT) data and buoy data of the National Data Buoy Center (NDBC) are collocated. Buoy wind speed is converted into neutral wind at 10 m height. Then, ASCAT data are compared with the buoy data for the wind speed and direction. Subsequently, the errors between the ASCAT and the buoy wind as a function of each wave parameter are used to analyze the wave effects. Wave parameters include dominant wave period (dpd), significant wave height (swh), average wave period (apd) and the angle between the dominant wave direction (dwd) and the wind direction. Collocated data are divided into sub-datasets according to the different intervals of each wave parameter. A root mean square error (RMSE) for the wind speed and a mean absolute error (MAE) for the wind direction are calculated from the sub-datasets, which are considered as the function of wave parameters. Finally, optimal wave conditions on wind retrieved from the ASCAT are determined based on the error analyses. The results show the ocean wave parameters have correlative relationships with the RMSE of the retrieved wind speed and the MAE of the retrieved wind direction. The optimal wave conditions are presented in terms of dpd, swh, apd and angle.

The first quantitative ocean remote sensing by using Chinese interferometric imaging radar altimeter onboard TG-2

Foundation item: The National Key RD the National Natural Science Foundation of China under contract No. 41621064; China Manned Space Program under contract No. 921-2.