李建, 周屈, 陈晓玲, 田礼乔, 李亭亭. 近岸/内陆典型水环境要素定量遥感空间尺度问题研究[J]. 武汉大学学报 ( 信息科学版), 2018, 43(6): 937-942. DOI: 10.13203/j.whugis20160174
引用本文: 李建, 周屈, 陈晓玲, 田礼乔, 李亭亭. 近岸/内陆典型水环境要素定量遥感空间尺度问题研究[J]. 武汉大学学报 ( 信息科学版), 2018, 43(6): 937-942. DOI: 10.13203/j.whugis20160174
LI Jian, ZHOU Qu, CHEN Xiaoling, TIAN Liqiao, LI Tingting. Spatial Scale Study on Quantitative Remote Sensing of Highly Dynamic Coastal/Inland Waters[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 937-942. DOI: 10.13203/j.whugis20160174
Citation: LI Jian, ZHOU Qu, CHEN Xiaoling, TIAN Liqiao, LI Tingting. Spatial Scale Study on Quantitative Remote Sensing of Highly Dynamic Coastal/Inland Waters[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 937-942. DOI: 10.13203/j.whugis20160174

近岸/内陆典型水环境要素定量遥感空间尺度问题研究

Spatial Scale Study on Quantitative Remote Sensing of Highly Dynamic Coastal/Inland Waters

  • 摘要: 近岸/内陆水环境遥感的空间尺度问题研究包括空间变异尺度及遥感监测空间尺度需求,以及多源多尺度遥感数据及定量产品的空间尺度误差两个方面。利用长时序高分一号16 m遥感数据集高时空分辨率的综合优势,采用空间半变异函数分析方法获取了中国近岸/内陆典型水环境要素(以悬浮颗粒物为例)的空间变异尺度。基于水环境要素空间变异的连续性和泰勒级数展开理论,定量化地描述了空间尺度误差解析函数。结果表明,近岸/内陆水体等高动态水体的空间变异尺度平均在150 m以下,而外海等相对稳定水体空间变异尺度在300 m以上。随着空间分辨率的降低,受到空间变异和水环境要素非线性定量反演模型的共同影响,悬浮颗粒物的空间尺度误差显著增大,亟需重点研究区域化的尺度误差校正方法。

     

    Abstract: Spatial scale issues of coastal and inland waters include spatial variation in the scale, requirements for remote sensing monitoring, and uncertainties in multi-source remote sensing data caused by inconsistent spatial resolution. Aiming at the highly dynamic water bodies, this study focuses on spatial scale issues in remote sensing. Taking advantage of the high spatial resolution GF-1 WFI (16 m) dataset, the scales in spatial variation of coastal water/inland lakes and open sea (taking suspended particulate matter (SPM) as an example) were demonstrated as less than 150 m and higher than 300 m respectively, using the spatial semivariation analysis. Based on the spatial variation continuity of water quality parameters and Taylor series expansion, the scale uncertainty function was quantified. Decreased spatial resolution, significantly increased the SPM errors caused by spatial scale variation, influenced by spatial variations and the non-linear retrieval model of water environmental parameters. This study highlights the crucial need for correction of spatial scale errors of coastal/inland waters, as well as a need for high-precision and consistent remote sensing monitoring from multi-resolution data.

     

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