# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 1 Introduction  
Water vapor is one of the important sources of the greenhouse effect [1, 2] and a key factor affecting precipitation and severe weather [3, 4]. The main source of atmospheric water vapor is the evaporation of water and the transpiration of plants. Although water vapor makes up only a small part of the entire atmosphere, it plays important roles in the Earth's weather system and climate change. In addition, water vapor emits and absorbs radiation in a specific spectral range, which significantly affects the accuracy of quantitative remote sensing, such as aerosol property retrieval and atmospheric correction of remote sensing data [5, 6, 7]. Driven by thermodynamics, atmospheric water vapor shows distinct spatial and temporal differences in certain regions [8, 9]. Precipitable water vapor (PWV) can characterize the water vapor content in the atmosphere. The PWV is the water vapor content in a vertical column per unit cross-sectional area from the ground to the top of the atmosphere. A study found that the total water vapor in the atmosphere increases by about 1% every 10 years [10]. Therefore, the satellite remote sensing of PWV has great significancefor monitoring water vapor content on a large spatial scale and for supporting studies on climate change, weather systems, and atmospheric correction of remote sensing data. GaoFen-5 (02) (GF5-02) is a new Chinese satellite that was launched on 7 September 2021. The multi-view polarization camera Directional Polarimetric Camera (DPC) is one of the main payloads. The DPC has eight spectral observation channels in the spectral range from 0.4 \(\upmu\)m to 0.9 \(\upmu\)m, and it has enhanced observation information for aerosol remote sensing [11; 12]. Since the DPC aerosol retrieval channels are affected by water vapor absorption, PWV data are needed to -eliminate the water vapor absorbing effect before aerosol retrieval. At present, researchers have developed many types of high-precision PWV data for different satellite sensors, such as Moderate-resolution Imaging Spectroradiometer (MODIS) PWV data and Medium Resolution Spectral Imager-2 (MERSI-2) PWV data [13; 14; 15]. However, due to the different transit times of different satellites and the significant temporal difference in the distribution of atmospheric water vapor, it is difficult to achieve accurate water vapor absorption correction of DPC data using an external PWV dataset. To solve this problem, the water vapor absorption band (910 nm) for water vapor retrieval was set in the DPC. It allows PWV results to be produced by means of DPC observation directly. In addition, as a new satellite PWV source, DPC PWV data can also provide data support for research on, e.g., climate change and weather systems. At present, the DPC on the GF5-02 satellite is still in the on-orbit test period, and no public PWV data are available. Therefore, it is necessary and urgent to develop a DPC PWV retrieval algorithm for the production of a PWV dataset. We can roughly divide the existing water vapor retrieval algorithms into four categories according to the wavelength used: visible light algorithms, near-infrared algorithms, thermal infrared algorithms, and microwave algorithms. Visible light algorithms are developed based on the absorption characteristics of water vapor in the visible light spectrum. Such algorithms have been successfully used for water vapor retrieval with sensors such as Ozone Monitoring Instrument (OMI) and Global Ozone Monitoring Experiment-2 (GOME-2) [16; 17]. Since these algorithms require observation in the visible light spectrum, they can only be implemented in cloudless areas during the daytime. Near-infrared algorithms use data from the near-infrared band, which requires the sensor to have both the water vapor absorption band and the non-absorption band in the spectral range of 850-1250 nm. Near-infrared algorithms have been applied to water vapor retrieval using satellite data, such as MODIS and MERSI-2 [13; 14].