Water Multi-Parameter Sampling Design Method Based on Adaptive Sample Points Fusion in Weighted Space

Abstract

The spatial representativeness of the in-situ data is an important prerequisite for ensuring the reliability and accuracy of remote sensing product retrieval and verification. Limited by the collection cost and time window, it is essential to simultaneously collect multiple water parameter data in water tests. In the shipboard measurements, sampling design faces problems, such as heterogeneity of water quality multi-parameter spatial distribution and variability of sampling plan under multiple constraints. Aiming at these problems, a water multi-parameter sampling design method is proposed. This method constructs a regional multi-parameter weighted space based on the single-parameter sampling design and performs adaptive weighted fusion according to the spatial variation trend of each water parameter within it to obtain multi-parameter optimal sampling points. The in-situ datasets of three water parameters (chlorophyll a, total suspended matter, and Secchi-disk Depth) were used to test the spatial representativeness of the sampling method. The results showed that the sampling method could give the sampling points an excellent spatial representation in each water parameter. This method can provide a fast and efficient sampling design for in-situ data for water parameters, thereby reducing the uncertainty of inversion and the validation of water remote sensing products.

Remote Sensing Inversion of the Total Suspended Matter Concentration in the Nanyi Lake Based on Sentinel-3 OLCI Imagery

“…The in situ data for this study were collected using the Nanjing Remote Sensing Authenticity Validation Site. To obtain more accurate and comprehensive hyperspectral and TSM data, the measured points were selected according to adaptive sample points fusion in weighted space [26] and navigational water experiments were used.…”