高分一号卫星浑浊水体水质参数软分类反演

张方方; 李俊生; 王超; 王胜蕾

doi:10.11834/jrs.20232442

辐射与水参量反演与产品 | 浏览量 : 0 下载量: 2123 CSCD: 5

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高分一号卫星浑浊水体水质参数软分类反演
Estimation of water quality parameters of GF-1 WFV in turbid water based on soft classification
2023年27卷第3期页码：769-779
收稿：2022-08-29，

纸质出版：2023-03-07
DOI： 10.11834/jrs.20232442
稿件说明：

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张方方，李俊生，王超，王胜蕾.2023.高分一号卫星浑浊水体水质参数软分类反演.遥感学报，27（3）： 769-779 DOI： 10.11834/jrs.20232442.

Zhang F F，Li J S，Wang C and Wang S L. 2023. Estimation of water quality parameters of GF-1 WFV in turbid water based on soft classification. National Remote Sensing Bulletin， 27（3）：769-779 DOI： 10.11834/jrs.20232442.

摘要

高分一号卫星宽幅盖相机（GF-1 WFV）拥有高空间和高时间分辨率，在水环境遥感应用方面有较大潜力，现有研究以特定区域算法为主，缺少可用于大范围的水质参数反演算法。基于以上问题，本研究在全国开展了28次共计68个航次的水面测量与采样实验，获取了具有较好典型性和代表性的647个采样点数据用于水质参数反演建模和验证。为了满足光学特性复杂的浑浊水体大范围水质参数反演的需求，发展了基于软分类的GF-1 WFV水质参数反演算法。算法首先将水体分为4类，并计算各类型水体的质心光谱；然后为各类型水体优选和优化适用的叶绿素a浓度、总悬浮物浓度和透明度反演模型，并用距离权重进行加权融合获取最终的水质参数反演结果。经星地同步实验数据验证，水体叶绿素a浓度、总悬浮物浓度和透明度反演的相对误差分别为33.1%、28.6%和17.6%，且类别边界过渡平滑，避免了不同模型导致的数值跳变问题。结果表明，本算法具有大范围水质参数反演产品生产的能力。

Abstract

The GF-1 wide-field-of-view cameras (GF-1 WFV) has high spatial and temporal resolution

and has great potential in the application of water environment remote sensing. Existing studies mainly focus on region-specific algorithms

and lack of water-quality parameter estimation algorithms that can be used in a large range. Based on the above problems

this study carried out 28 water surface measurement and sampling experiments with 68 voyages in China

and obtained 647 typical and representative sampling point data for water quality parameter estimation modeling and validation. The study area included Taihu Lake

Chaohu Lake

Dianchi Lake

Three Gorges Reservoir

Guanting Reservoir

Yuqiao Reservoir

Shandong Pingyin Small Water Body

Shaanxi Yulin Small Water Body

Ningxia Ningdong Base Small Water Body. The GF-1 WFV images were used the relative atmospheric correction algorithm based on Sentinel2-MSI data of uniform invariant ground object spectral database to obtain the water remote sensing reflectance data. In order to meet the needs of large-scale estimation of water quality parameters in turbid water with complex optical characteristics

a GF-1 WFV water quality parameter estimation algorithm based on soft classification was developed. Firstly

the algorithm divided the water into four types (OWTs) by a stepwise iterative K-mean clustering method and calculated the centroid spectra of each type of water by the average of all spectra of this category

among them

OWT1 was jointly dominated by phytoplankton and non-algae particles

OWT2 was dominated by non-algae particles

OWT3 was dominated by phytoplankton

OWT4 was bloom (no water quality inversion in this water type); Then

the Spectral Angular distance (SAD) was used to calculate the distance from each pixel to each type of centroid spectra and the SAD was converted into distance weight

and the suitable estimation models of chlorophyll a concentration

total suspended solids concentration and transparency were selected and optimized for each type of water body

and the final estimation results of water quality parameters were obtained by weighted fusion with distance weight. In this paper

several band ratio and difference models were investigated. Chlorophyll a used the blue green ratio model in OWT1

the red green ratio model in OWT2

and the red near-infrared ratio model in OWT3. The total suspended concentration was applicable to the red green ratio model in OWT1

the green near-infrared ratio model in OWT2

and the red near-infrared ratio model in OWT3. The transparency models of the three types of water bodies all used the green band and match the blue and red band to constructed ratio model. The mean relative errors of chlorophyll a concentration

total suspended solids concentration and transparency estimation were 33.1%

28.6% and 17.6% verified by satellite earth synchronization experiment data

and the transition of category boundary was smooth

which avoiding the numerical jump caused by different models. The results showed that this algorithm had the ability to generate water quality parameter production of wide range area. Due to the limitation of the GF-1 WFV sensor band setting (only four broad bands from visible light to near-infrared)

the quantification processing and models have great limitations

and the applicability and scalability of the model need to be further improved.

关键词

Keywords

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