부산대학교 도서관

Estimating reliably chlorophyll-a (Chl-a) concentration from remote sensing images constitutes a vastly superior alternative to field measurements. To this end, spectral pixel signatures are used commonly for developing regression models. Spatial information has been traditionally ignored in this context, as Chl-a concentration is a spatially localized measurement, and sensors’ spatial resolutions have been relatively low in the past. However, the increased spatial resolution of newer satellites and a recent study have given strong indications that spatial–spectral description can boost estimation performance. Consequently, in this letter, we address the problem of Chl-a estimation from remote sensing images using attribute profiles, one of the paramount spatial–spectral description tools. We further propose an original technique to remove their cumbersome threshold requirement via operating on each pixel’s “attribute lineage.” We validate our approach with multispectral Sentinel-2 images, and a data set formed by field measurements spanning almost two years over Lake Balik (Turkey). We show that the proposed method outperforms various alternatives in terms of regression performance, across multiple experimental setups, and finally, we highlight a validation malpractice encountered often in the field of water quality estimation.