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对盐湖水评估的大气校正算法的评估:准确性,带特异性效应和传感器一致性
Changjiang Liu1,2, Fei Zhang3, Chi-Yung Jim4
1Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Urumqi, China.
PloS one
|December 23, 2024
概括
阿科莱特-DSF算法最好用于纠正Ebinur湖的卫星图像,提供来自Landsat 8和Sentinel 2的准确水质数据. 这种方法推用于监测干旱的内陆盐湖.
科学领域:
- 遥感 遥感 遥感 遥感
- 环境监测 环境监测
- 地理空间分析是什么
背景情况:
- 大气调整对于精确的基于卫星的湖水质量评估至关重要.
- 内陆湖泊,特别是浅层和的湖泊,对大气校正算法构成独特的挑战.
- 选择合适的算法对于可靠的定量卫星水环境监测至关重要.
研究的目的:
- 评估和确定最适合的大气校正算法,用于Landsat 8和Sentinel 2在Ebinur湖的数据.
- 评估不同的大气校正算法 (FLAASH,QUAC,6S,Acolite-DSF,Acolite-EXP) 对内陆湖水的准确性和一致性.
- 在干旱地区对水体进行遥感的共同波段分析大气校正错误.
主要方法:
- 利用了来自中国Ebinur湖的Landsat 8和Sentinel 2一级数据 (2021年5月19日).
- 应用了五种大气校正算法:FLAASH,QUAC,6S,Acolite-DSF,以及Acolite-EXP. 这些算法包括:
- 使用Sentinel 2反射率产品和近似同时的超频谱数据验证了算法性能.
主要成果:
- 在Landsat 8和Sentinel 2数据中,Acolite-DSF算法表现出卓越的一致性和准确性.
- 使用Acolite-DSF校正的Landsat 8图像显示了0.3 (蓝色,绿色,红色频段) 和0.5 (NIR频段) 的相对误差.
- 用Acolite-DSF校正的Sentinel 2图像在所有评估的频段中显示出0.3的相对误差.
结论:
- Acolite-DSF是建议的算法,用于对Ebinur湖的Landsat 8和Sentinel 2数据进行大气校正.
- 该研究建议使用Landsat 8和Sentinel 2的蓝色,绿色,红色和NIR频段进行Ebinur湖的时间水环境监测.
- 这些发现特别适用于干旱环境中的内陆盐湖的遥感应用.
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