Sentinel-2 and Sentinel-3 Intersensor Vegetation Estimation via Constrained Topic Modeling

Abstract

This letter presents a novel intersensor vegetation estimation framework, which aims at combining Sentinel-2 (S2) spatial resolution with Sentinel-3 (S3) spectral characteristics in order to generate fused vegetation maps. On the one hand, the multispectral instrument (MSI), carried by S2, provides high spatial resolution images. On the other hand, the Ocean and Land Color Instrument (OLCI), one of the instruments of S3, captures the Earth's surface at a substantially coarser spatial resolution but using smaller spectral bandwidths, which makes the OLCI data more convenient to highlight specific spectral features and motivates the development of synergetic fusion products. In this scenario, the approach presented here takes advantage of the proposed constrained probabilistic latent semantic analysis (CpLSA) model to produce intersensor vegetation estimations, which aim at synergically exploiting MSI's spatial resolution and OLCI's spectral characteristics. Initially, CpLSA is used to uncover the MSI reflectance patterns, which are able to represent the OLCI-derived vegetation. Then, the original MSI data are projected onto this higher abstraction-level representation space in order to generate a high-resolution version of the vegetation captured in the OLCI domain. Our experimental comparison, conducted using four data sets, three different regression algorithms, and two vegetation indices, reveals that the proposed framework is able to provide a competitive advantage in terms of quantitative and qualitative vegetation estimation results.