# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 1 Introduction  
With the development of human society, more and more people settle down near inland lakes, which causes the problems of water pollution and consequent water eutrophication [1]. The fertile water is a breeding ground for plankton, and algal bloom will occur under this circumstance [2]. Algal bloom consumes a considerable amount of oxygen and produces toxins in the water, which greatly affects the drinking water safety of the surrounding cities [3, 4]. Meanwhile, the frequent emergence of algal bloom will also cause serious damage to the water environment, leading to the massive death of aquatic organisms [5] and undermining the local aquaculture and fishery resources. To reduce the threats of algal bloom to the lakes and the coastal areas, many monitoring methods have been applied. Traditional field investigations directly measure the concentration of different kinds of algae and the toxins they have produced in the water, but the number and range of the samples are quite limited [6]. The cost of field investigation is also very high, so it is difficult to obtain bloom information about the whole lake and monitor its change in a rapid way. Remote sensing technology has been successfully applied in the field of algal bloom monitoring and research during recent decades; it has theadvantages of low expense and ability to observe targets at a large scale [7]. Optical remote sensing data in visible light and infrared band can distinguish the algal bloom-covered area from lake water well for the reason that the reflectance of bloom in different bands differs from that of water, especially in the near-infrared band. Meanwhile, the optical remote sensing method can also be used to perform regression analysis of the algal bloom density. Commonly used spaceborne optical data from the Earth Observing System (EOS), Landsat and Sentinel satellites are suitable for algal bloom observation [6]. However, on cloudy or rainy days, optical sensors cannot obtain sufficiently high-quality data [8], leading to gaps in the observation sequences. Moreover, the temporal resolution of spaceborne optical data is usually not very high, for example, Landsat-8 and Sentinel-2 satellites need at least 8 days to pass the same area again [9]. Monitoring algal bloom in the microwave band has also proved to be effective. Compared with optical sensors, it can observe targets day and night, and in bad weather. Synthetic aperture radar (SAR) is an active microwave remote sensing method that mainly relies on the backscattered radiation of the electromagnetic wave transmitted by itself to detect the scattering surface. [PERSON] et al. [10] showed that the bloom-covered lake surface could suppress radar wave backscattering and result in a 'dark area' in SAR images. Although SAR can overcome many of the shortcomings of the optical method, the temporal resolution of most spaceborne SAR data still fails to meet the requirement of daily monitoring [9]. The cost of SAR is relatively high among other remote-sensing instruments, as SAR satellites need to carry the transmitter onboard, and the preprocessing methods for SAR images are also complicated [11]. Airborne optical and SAR remote sensing methods are relatively flexible in observation time and spatial resolution [12], but they are poor in both spatial coverage and platform stability. Global navigation satellite system-reflectometry (GNSS-R) is an emerging technology in the field of remote sensing. It is an active remote sensing method, which collects the forward reflected L-band signals transmitted by GNSS satellites from the earth's surface to infer information about the specular point (SP). Similar to other microwave remote sensing methods, GNSS-R can avoid the signal attenuation caused by cloud and dust to a great extent. So far, GNSS-R has been employed in many research domains, for instance, altimetry [13], sea ice detection and thickness measurement [14; 15], flood and inland waterbody mapping [11; 16; 17], sea surface wind retrieving [18; 19] and soil moisture inversion [20; 21; 22].