Research on Downscaling and Correction of TRMM Data in Central China

Abstract

The Central China has abundant rainfall, and rainfall is unevenly distributed in space and time, which can easily cause floods and soil erosion. It is of great significance to obtain precipitation information accurately and quickly. At present, remote sensing precipitation data has been widely used, but its spatial resolution and data accuracy still cannot meet actual application requirements. Therefore, this paper fully considers the applicability of TRMM 3B43 data from 2001 to 2019 in the Central China, based on a geographically weighted regression model, combined with NDVI, EVI, elevation, slope and aspect data. Different combinations were selected to downscale $TRMM_{EVI}$ data, and perform GDA and GRA corrections on the optimized TRMM data, and finally perform accuracy evaluation and result analysis on annual, quarterly, and monthly scales. The research results showed that: (1) The accuracy of the $TRMM_{EVI}$ data is better than the $TRMM_{NDVI}$ data when the spatial resolution is increased from 0.25° to 1 km, (2) The GDA correction result is more satisfactory than the GRA correction result and the data stability is better, so it is more suitable for TRMM data correction in the Central China. (3) The R² of $TRMM_{NDVI}^{GDA}$ data and the measured data of the site has high accuracy on the annual (0.91–0.986), quarter (0.704–0.88), and monthly (0.625–0.89) scales, and its detailed characteristics are better than TRMM data. (4) The better the downscaling and correction effect will be in the months with greater precipitation. Through downscaling and correction of TRMM data, it can better reflect the real precipitation information in the Central China, and provide reliable data support for agricultural production, optimal allocation of water resources, and flood prevention and disaster reduction.