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

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