Abstract
FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.
Highlights
The land–atmosphere interactions at the regional or large scale have both been a focus and challenge in geoscientific research [1,2,3], and the acquisition of research data plays a crucial role in further investigating such interactions
The results show that the Kerr algorithm only involves the calculation of vegetation coverage, while the calculation of vegetation coverage involves the determination of NDVIv and normalized vegetation index (NDVI)
A larger NDVIv value corresponds to a smaller correlation coefficient and a larger root mean square errors (RMSEs) between the land surface temperature (LST) estimated by the Kerr algorithm and the measured value in Northwest China
Summary
The land–atmosphere interactions at the regional or large scale have both been a focus and challenge in geoscientific research [1,2,3], and the acquisition of research data plays a crucial role in further investigating such interactions. With the improvement of satellite detectors and the development of remote sensing technology, remote sensing and retrieval have become important techniques for collecting regional-scale research data [4,5,6]. With the launch of the TIROS-IIsatellite in the middle of last century, surface temperature retrieval using satellite thermal infrared data began to develop [7]. With the improvement of the atmospheric radiation transfer model, and the atmospheric radiation transfer equation simplification by the approximation and hypothesis, as well as the abundance of NOAA/Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and other remote sensing data, the deviation of sea surface temperature retrieved by the split-window method was reduced to less than 1.0 K. After the successful retrieval of sea surface temperature, it became more attractive to use satellite thermal infrared data to retrieve land surface temperature [12,13], and research has shown that there are obvious diurnal variations in land surface emissivity [14,15]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call