Spatiotemporal Heterogeneity of Multiple In Situ Observational Sites and Its Site Deployment Optimization Strategy

The validation of remote sensing land surface temperature (LST) data necessitates a comparison between satellite retrieval outcomes and in situ observations. The efficiency of in situ observations can be ameliorated via analysis and modeling, whereby the heterogeneity of in situ observations on temporal and spatial scales is central to the analysis. A fresh algorithm has been developed to optimize deployment by relying on the standard deviation (STD) of spatial heterogeneity. The validation outcomes indicated that the coefficient of determination (
$R^{2}$
) of the five typical surface features at three time points was 0.66, with a root mean square error (RMSE) of 1.99 °C and a mean absolute error (MAE) of 1.62 °C. Moreover, the spatiotemporal heterogeneity character of typical surface features displayed different features, and the LST variation curves of each typical surface feature displayed a similar pattern under sunny conditions. The application of the Savitzky–Golay filtering (S-G filtering) method reduced errors by 4% of the total errors caused by random errors in in situ observations. With the analysis of the spatiotemporal characteristics of in situ observation. First, the number of required sites (NRS) algorithm computed a minimum sampling number of 4. Second, the analysis of the means algorithm computed the five optimal points. Additionally, the multipoint in situ observations were regularized by standard scores. The optimization of the selected points could be executed to improve the results by eliminating the “distance” points, which are located further away from the multipoint in situ observed LST statistical mean. Our outcomes will deepen the comprehension of the spatiotemporal character of in situ observed LST and enhance the efficiency of equipment with equivalent accuracy.