Abstract
Abstract. Earth observation and land cover monitoring are among major applications for satellite data. However, the use of primary satellite information is often limited by clouds, cloud shadows, and haze, which generally contaminate optical imagery. For purposes of hazard assessment, for instance, such as flooding, drought, or seismic events, the availability of uncontaminated optical data is required. Different approaches exist for masking and replacing cloud/haze related contamination. However, most common algorithms take advantage by employing thermal data. Hence, we tested an algorithm suitable for optical imagery only. The approach combines a multispectral-multitemporal strategy to retrieve daytime cloudless and shadow-free imagery. While the approach has been explored for Landsat information, namely Landsat 5 TM and Landsat 8 OLI, here we aim at testing the suitability of the method for Sentinel-2 Multi-Spectral Instrument. A multitemporal stack, for the same image scene, is employed to retrieve a composite uncontaminated image over a temporal period of few months. Besides, in order to emphasize the effectiveness of optical imagery for monitoring post-disaster events, two temporal stages have been processed, before and after a critical seismic event occurred in Lombok Island, Indonesia, in summer 2018. The approach relies on a clouds and cloud shadows masking algorithm, based on spectral features, and a data reconstruction phase based on automatic selection of the most suitable pixels from a multitemporal stack. Results have been tested with uncontaminated image samples for the same scene. High accuracy is achieved.
Highlights
Today, the availability of remotely sensed data as provided by satellite missions is key information for Earth Observation (EO) and land use/land cover (LULC) monitoring
At any rate, when the objective is of analyzing more images in a time series, images refer to the same sensor and same spatial resolutions, images co-registration, based on selected Ground Control Points (GCP), should be performed (Gao, Zhang, & Gu, 2017; Scaioni, Barazzetti, & Gianinetto, 2018)
In order to assess the effectiveness of the method, we consider either the capability of the masking algorithm, as well as the goodness of both pre- and post-disaster cloudless image scenes, obtained by applying the whole process
Summary
The availability of remotely sensed data as provided by satellite missions is key information for Earth Observation (EO) and land use/land cover (LULC) monitoring. Multitemporal-based methods rely on temporal and spatial coherence to combine information from different time periods based either on threshold approach (Min Li, Soo Chin Liew, & Leong Keong Kwoh, 2003), regression tree (Helmer & Ruefenacht, 2005), or a contextual prediction approach to determine spectro-temporal relationships among image sequences (Benabdelkader & Melgani, 2008). This allows to get cloud-uncontaminated and non-shadowed pixels from images acquired at different times and reconstruct a cloud-free image scene. Some limitations such as handling large cloud covered and shadowed areas in a heterogeneous landscape, or small-scale applications, as well as issues related to thermal response, or the need for effective clouds detection and masking algorithms, make the matter still challenging and necessary
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