测绘通报 ›› 2022, Vol. 0 ›› Issue (6): 66-70.doi: 10.13474/j.cnki.11-2246.2022.0173.

• 学术研究 • 上一篇    下一篇

基于Sentinel-3 OLCI卫星数据的海冰反照率研究

段博凯   

  1. 伦敦大学学院地理系, 英国 伦敦 WC1E 6BT
  • 收稿日期:2022-01-17 发布日期:2022-06-30
  • 作者简介:段博凯(1998-),男,硕士生,研究方向为环境遥感与监测。E-mail:bokai.duan.19@ucl.ac.uk

Study on sea ice albedo based on Sentinel-3 OLCI satellite data

DUAN Bokai   

  1. Department of Geography, University College London, London WC1E 6BT, United Kingdom
  • Received:2022-01-17 Published:2022-06-30

摘要: 监测海冰是一项全球任务,其中地表反照率的反演和建模是关键过程。与广泛使用的BRDF方法不同,本文重点研究了一种更直接的反演方法(编码在SNAP工具中)。首先,应用两个场景聚焦于Utqiagvik的OLCI低云量数据集,通过Rayleigh校正和IdePix云分类进行预处理,生成rBRR波段和云掩膜;然后,在雪属性处理器(snow processor)中对这两项数据与OLCI TOA数据进行综合处理,输出波段光谱反照率和宽谱反照率。将地表反照率产品与其他MISR和MODIS在沿海海域的反照率产品进行比较,结果表明,SNAP雪处理器工具高估了红光反照率,低估了近红外反照率;将陆地冰反照率与Utqiagvik基线站的塔式反照率产品进行比较,结果表明,该方法具有很高的准确率,达95%。

关键词: Sentinel-3 卫星, 反照率, 海冰, OLCI传感器, SNAP工具

Abstract: Monitoring sea ice is a global task. The key process is to invert and model the surface albedo. Rather than widely used BRDF method, this paper focuses on a new direct estimation method (encoded in the SNAP tool). In this research, two scenes of OLCI datasets focusing on Utqiagvik with low cloud cover are applied and pre-processed through Rayleigh correction and IdePix cloud classification and produce rBRR bands and cloud layers. The two bands are processed in the snow processor together with OLCI TOA and outputted products, including spectral and broadband albedo. As a result, surface albedo products are compared with other albedo products derived from MISR & MODIS at the coastal sea area. The results suggest that the SNAP tool has overestimated the red-light albedo and underestimated the NIR albedo. Also, in-land ice-albedo is compared with tower-based albedo product at Utqiagvik baseline station. The result shows a very high accuracy of 95%.

Key words: Sentinel-3 satellite, albedo, sea ice, OLCI, SNAP tool

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