A Multi-Parameter Collaborative Ground Site Optimization Method by Synchronously Optimizing Regional and Satellite Pixel-Scale Representativeness

Ground observation data are critical for quantitative remote sensing, yet current sampling is hindered by scale mismatch, regional bias, and parameter isolation. This study proposes a multiparameter collaborative ground site optimization method to synchronously enhance representativeness at both regional and pixel scales. At the regional scale, principal component analysis (PCA) and K-means clustering are integrated to select representative pixels characterized by attribute typicality and spatial independence. At the pixel scale, an exhaustive combinatorial optimization algorithm identifies the most representative points by minimizing a comprehensive multiparameter spatiotemporal objective function. Case study results from the Heihe River Basin demonstrate that at the regional scale, the covariance determinant of the selected sample areas reaches 149.44% of the entire study area, indicating superior feature space coverage. At the pixel scale, deploying no more than three points maintains spatiotemporal representativeness errors (REs) within 3% for most parameters. Compared with single-parameter methods, this approach achieves a multifold increase in sampling efficiency while maintaining comparable accuracy and costs. The proposed method realizes an optimized configuration for one-time deployment, multiscale synergy, and multiparameter sharing. Without relying on prior spatial assumptions, it provides a universal and robust ground-truth foundation for intelligent inversion models and multisource product validation.