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Estimation of water quality parameters of GF-1 WFV in turbid water based on soft classification

  • role： First author第一作者
  • Affiliation：

    International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：zhangff07@radi.ac.cn
  • Introduction：张方方，研究方向为水环境遥感。E-mail：zhangff07@radi.ac.cn
ZHANG Fangfang12,  
  • role： Corresponding author通信作者
  • Affiliation：

    International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：lijs@radi.ac.cn
  • Introduction：李俊生，研究方向为水环境遥感。E-mail：lijs@radi.ac.cn
LI Junsheng123*,  
  • Affiliation：

    Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China

WANG Chao4,  
  • Affiliation：

    International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

WANG Shenglei12

resumen

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.

palabra clave

GF-1 WFV;water types (OWTs);chlorophyll a;total suspended solids;transparency

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