Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations

Abstract

Many previous studies have attempted to distinguish fog from clouds using low-orbit and geostationary satellite observations from visible (VIS) to longwave infrared (LWIR) bands. However, clouds and fog have often been misidentified because of their similar spectral features. Recently, advanced meteorological geostationary satellites with improved spectral, spatial, and temporal resolutions, including Himawari-8/9, GOES-16/17, and GeoKompsat-2A, have become operational. Accordingly, this study presents an improved algorithm for detecting daytime sea fog using one VIS and one near-infrared (NIR) band of the Advanced Himawari Imager (AHI) of the Himawari-8 satellite. We propose a regression-based relationship for sea fog detection using a combination of the Normalized Difference Snow Index (NDSI) and reflectance at the green band of the AHI. Several case studies, including various foggy and cloudy weather conditions in the Yellow Sea for three years (2017–2019), have been performed. The results of our algorithm showed a successful detection of sea fog without any cloud mask information. The pixel-level comparison results with the sea fog detection based on the shortwave infrared (SWIR) band (3.9 μm) and the brightness temperature difference between SWIR and LWIR bands of the AHI showed high statistical scores for probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS). Consequently, the proposed algorithms for daytime sea fog detection can be effective in daytime, particularly twilight, conditions, for many satellites equipped with VIS and NIR bands.

Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China

“…To identify more features, NDSI [11] is constructed with data from bands 2 and 5 (i.e., 0.51 μm and 1.6 μm), calculated using Equation (1) as:…”

“…Based on the spectral analysis, we selected bands 1, 2, 3, 5, and 14 and NDSI as classification features to construct the sea fog sample set. To identify more features, NDSI [11] is constructed with data from bands 2 and 5 (i.e., 0.51 µm and 1.6 µm), calculated using Equation (1) as:…”

“…The detailed classification made here allows us to further analyze the algorithm's accuracy. This paper compares and analyzes the results obtained with the proposed MLP method with those obtained by the NDSI threshold method proposed by [11], and the specific results are shown in Section 4.1.1.…”

“…Himawari-8 is a geostationary orbit satellite launched by Japan in 2014, which can provide stable remote sensing images at intervals of 10 min [10], but few sea fog detection studies based on individual Himawari-8 satellite data have been conducted. The authors of [11] proposed a normalized difference snow index (NDSI) threshold algorithm for sea fog detection, but they noted that distinguishing between low clouds and sea fog could be further improved. Therefore, establishing a sea fog identification method based on Himawari-8 satellite data, as well as deriving a navigation risk estimation model based on remote sensing data, are great challenges for both research fields.…”

Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms

“…The generation of LW is caused by water intercepted by the leaf during a rainfall or fog event, overhead irrigation, and dew formed on a leaf. Information about these formation mechanisms of LW can be indirectly obtained through satellite observations [19][20][21][22][23]. Particularly, many studies showed that satellite observation could be a good alternative to monitor surface moisture [24][25][26].…”