An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data

Abstract

As one of the most important greenhouse gases, water vapor plays a vital role in various weather and climate processes. In recent years, a near-infrared ratio technique based on satellite images has become a research hotspot in the field of precipitable water vapor (PWV) monitoring. This study proposes a Level 2A PWV data retrieval method based on Sentinel-2 images (S2-L2A), which considers land-cover types and is more suitable for local areas. The radiative transfer model MODTRAN 5 is used to simulate the atmospheric radiative transfer process and obtain lookup tables (LUTs) for PWV retrieval. The spatial distribution of S2-L2A PWV is validated using Global Positioning System (GPS), Terra-MODIS PWV product (MOD05), and Level 2A product provided by ESA (ESA-L2A), while the time series results are evaluated using MOD05. Results show that the PWV retrieved by S2-L2A is both highly correlated and has low bias with the three PWV products, and is closer to the reference data than the MOD05 and ESA-L2A PWV. The relative PWV value in the morning is: bare soil > vegetation-covered area > construction land; as the elevation increases, the PWV value decreases. This study also analyzes the error distribution of the PWV data retrieved by S2-L2A, and finds that inversion error increases with AOT value, but decreases with elevation and normalized difference vegetation index (NDVI). Compared with the three water vapor products, the PWV data retrieved by the proposed method has high accuracy and can provide large-scale and high-spatial-resolution PWV data for many research fields, such as agriculture and meteorology.