A method for estimating hourly photosynthetically active radiation (PAR) in China by combining geostationary and polar-orbiting satellite data

Photosynthetically active radiation (PAR) is an important parameter in ecosystem and land surface models. PAR represents the amount of solar radiation in the spectral range of 400-700 nm that travels through the atmosphere to the top of the vegetation canopy. In recent years, various methods using different input data to estimate PAR and produce different PAR products have been developed. However, most of the algorithms used in these state-of-the-art studies have not fully compensated for the low spatial and temporal resolution of the data, which affects the accuracy of the PAR estimates. In this study, we have developed a method for estimating hourly PAR based on a combination of geostationary and polar-orbiting satellite data. The Multifunctional Transport Satellite (MTSAT) was selected to retrieve cloud optical depth (COD) with a higher spatial resolution, and the polar orbit satellite data of the Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to derive surface parameters based on multispectral characteristics. A look-up table was established by the Second Simulation of a Satellite Signal in the Solar Spectrum-Vector (6SV) model and the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model consisting the following parameters: solar zenith angle, total water vapor, total ozone column, aerosol optical depth (AOD), COD, surface elevation, surface albedo and PAR. The instantaneous PAR was linearly interpolated from the input data for the selected parameters and the look-up table. The root mean square error (RMSE) between the estimated and observed instantaneous PAR at the Huailai station was 45.72 W/m2 for all sky conditions. The RMSE between the estimated and observed daily PAR at the meteorological stations was 17% in the eastern regions of China. The mean bias error (MBE) was between − 2.83 and 32.43 W/m2 for the Tibetan Plateau. These results indicated that the proposed method can significantly improve the accuracy of PAR estimates and can be used to produce PAR products with high spatial and temporal resolution. However, the method requires further improvement, especially with respect to cloudy conditions.