Generating continuous fine-scale land cover mapping by edge-guided maximum a posteriori based spatiotemporal sub-pixel mapping

Abstract

Global land cover mapping activities are of great important for retrieving the environment we are living in. However, the trade-off between spatial and temporal resolution makes it difficult to obtain the continuous fine-scale land cover product for detail and frequent land surface analysis. To overcome the difficulty, this study proposed a modified maximum a posteriori (MAP) based spatiotemporal sub-pixel mapping (SSM), for a long-term and fine-scale land cover mapping. The proposed approach consists of three parts, i.e., data fidelity term, spatial regularization term, and enhanced temporal regularization term, which can make use of historical fine-scale image for a better super-resolution of the current coarse image. One regional experiment was implemented for the algorithm validation, and one real experiment was investigated, by generating continuous land cover mapping products (2010–2015) at Landsat-like spatial resolution from time series of MODIS images. Compared with traditional methods, the proposed approach reconstructed more pleasant spatial details and had greater quantitative accuracy, outperforming the state-of-the-art spatiotemporal sub-pixel mapping method by averagely 2.58% for all the inspected dates in OA metric. This study not only provides a generalized research framework for generating continuous fine-scale land cover product from daily available MODIS images, but also validates the usability of the time series products for detail land cover monitoring, and reveals a finding that the study area of Wuhan is a well practitioner of the sustainable development policy during its urbanization process.