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Received：29 August 2022，
Published：07 March 2023
移动端阅览
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。
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.
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