Spatial interpolation of marine environment data using P-MSN. Issue 3 (3rd March 2020)

• Record Type: Journal Article
• Authors: Gao, Bingbo; Hu, Maogui; Wang, Jinfeng; Xu, Chengdong; Chen, Ziyue; Fan, Haimei; Ding, Haiyuan
• Is Part Of: International journal of geographical information science. Volume 34:Issue 3(2020)
• Page Start: 577
• Page End: 603
• Publication Date: 2020-03-03
• Subjects: Stratified non-homogeneity -- best linear unbiased estimator (BLUE) -- spatial interpolation -- marine environment; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1683183
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: When a marine study area is large, the environmental variables often present spatially stratified non-homogeneity, violating the spatial second-order stationary assumption. The stratified non-homogeneous surface can be divided into several stationary strata with different means or variances, but still with close relationships between neighboring strata. To give the best linear-unbiased estimator for those environmental variables, an interpolated version of the mean of the surface with stratified non-homogeneity (MSN) method called point mean of the surface with stratified non-homogeneity (P-MSN) was derived. P-MSN distinguishes the spatial mean and variogram in different strata and borrows information from neighboring strata to improve the interpolation precision near the strata boundary. This paper also introduces the implementation of this method, and its performance is demonstrated in two case studies, one using ocean color remote sensing data, and the other using marine environment monitoring data. The predictions of P-MSN were compared with ordinary kriging, stratified kriging, kriging with an external drift, and empirical Bayesian kriging, the most frequently used methods that can handle some extent of spatial non-homogeneity. The results illustrated that for spatially stratified non-homogeneous environmental variables, P-MSN outperforms other methods by simultaneously improving interpolation precision and avoiding artificially abrupt changes along the strata boundaries.