# Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
## 1 Introduction
In the early stage of airborne GNSS-R experiments, only a small number of observations was obtained, and the wind speed was inferred mainly through optimal correlation fitting between the observed time delay waveform and the simulation waveform from the Z-V model [3]. However, the current on-orbit UK Technology Demonstration Satellite-1 (TDS-1) satellite and CYGNSS constellation provide massive observation data. The sea surface wind speed retrieval method, similar to the backward microwave scatterometer [17], is commonly employed, which regresses so-called DDM observables against the collocated wind from other observing data sources, and quantifies the mapping relationship to form the empirical geophysical model function (GMF) for future wind predicting. Previous studies have demonstrated that compared with the DDM model-fitting method, the formed empirical GMF can obtain better performance [18; 19]. Before establishing a retrieval model through empirical regression, it is necessary to further calibrate DDM to remove the influence of non-geophysical effects. The UK TDS-1 mission provided DDM calibrated to SNR [20], while for the CYGNSS mission, the DDM can be calibrated to the bistatic radar cross-section (BRCS) with more comprehensive ancillary parameters [15; 21]. However, the calibrated DDM still cannot be used directly for wind speed inversion but needs to further extract the feature quantity, it should sensitive to wind speed in a specific size of the DDM window determined in terms of the requirement of the inversion spatial resolution. [PERSON] and [PERSON] studied a fitting method by using CYGNSS DDM observables to build empirical GMF, adaptively performed minimum variance (MV) estimates based on DDM range-corrected gain (RCG) [22], and the Bayesian estimate was also adopted to calculate the weighting coefficient for each observable [23]. The results of these studies show that when the real wind speed over sea surface is less than 20 m/s, the wind speed retrieval error is less than 2 m/s, but a larger error appears at a higher wind speed range [24]. The latest studies have carried out some new attempts: [25] proposed sequential processing based on the extended Kalman filter, which is evaluated by the simulated wind field data; and [26] presented a method to train the neural network for wind speed prediction by using the entire DDM and different feature information. Both showed promising results, but need to further validate with a large amount of measured data. Although scatterometers, radiometers, buoys, the NWP product, and even ship observations can be used to construct a robust empirical GMF model, the accuracy and spatial-temporal resolution of the reference wind speed provided by different data sources are different. Therefore, it is important to evaluate the effect of the reference wind and the reliability of the inversion model by the retrieval results obtained from different ground truth winds. In this paper, a refined wind speed retrieval algorithm was used to establish the GMF from three different wind data sources, and the inversion results were analyzed and evaluated by multi-dataset. The rest of this manuscript is organized as follows. Section 2 introduces the GNSS-R remote sensing theory and the improved wind speed inversion algorithm. Section 3 depicts the dataset and presents the performance of the wind speed retrieval algorithm compared to different ground truth wind speeds. The discussion is given in Section 4, and Section 5 summarizes the main conclusions.