862 lines
40 KiB
Python
862 lines
40 KiB
Python
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import pandas as pd
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import numpy as np
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import re
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from typing import Dict, List, Optional, Union
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class WaterQualityIndexCalculator:
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"""
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水质光谱指数计算器
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为每个算法创建单独的函数，自动查找最接近的波长列
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"""
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def __init__(self):
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self.references = {}
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def find_closest_wavelength(self, df_columns: List[str], target_wl: int) -> str:
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"""
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在数据框列名中查找最接近目标波长的列
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Args:
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df_columns: 数据框的所有列名
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target_wl: 目标波长
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Returns:
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最接近的列名
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"""
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# 提取所有数值型波长
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numeric_wavelengths = []
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for col in df_columns:
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try:
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# 从列名中提取数字
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numbers = re.findall(r'\d+', col)
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if numbers:
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wavelength = int(numbers[0])
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numeric_wavelengths.append((col, wavelength))
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except:
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continue
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if not numeric_wavelengths:
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raise ValueError(f"无法从列名中提取波长信息: {df_columns}")
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# 找到最接近的波长
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closest_col, closest_wl = min(numeric_wavelengths,
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key=lambda x: abs(x[1] - target_wl))
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print(f"为波长 {target_wl}nm 找到最接近的列: {closest_col} ({closest_wl}nm)")
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return closest_col
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# =========================================================================
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# 叶绿素算法
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# =========================================================================
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def chl_Al10SABI(self, df: pd.DataFrame) -> pd.Series:
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"""
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Surface Algal Bloom Index (SABI) for chlorophyll detection
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参考文献: Alawadi, F. Detection of surface algal blooms using the newly
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developed algorithm surface algal bloom index (SABI). Proc. SPIE 2010, 7825.
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"""
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w857 = df[self.find_closest_wavelength(df.columns, 857)]
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w644 = df[self.find_closest_wavelength(df.columns, 644)]
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w458 = df[self.find_closest_wavelength(df.columns, 458)]
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w529 = df[self.find_closest_wavelength(df.columns, 529)]
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result = (w857 - w644) / (w458 + w529)
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return result
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def chl_Am092Bsub(self, df: pd.DataFrame) -> pd.Series:
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"""
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Baseline subtraction algorithm for chlorophyll
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参考文献: Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S.
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Novel optical techniques for detecting and classifying toxic dinoflagellate
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Karenia brevis blooms using satellite imagery. Opt. Express 2009, 17, 9126–9144.
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"""
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w681 = df[self.find_closest_wavelength(df.columns, 681)]
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w665 = df[self.find_closest_wavelength(df.columns, 665)]
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result = w681 - w665
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return result
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def chl_Be16FLHblue(self, df: pd.DataFrame) -> pd.Series:
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"""
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Fluorescence Line Height algorithm with blue baseline for chlorophyll
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参考文献: Beck, R.A. and 22 others; Comparison of satellite reflectance
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algorithms for estimating chlorophyll-a in a temperate reservoir using
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coincident hyperspectral aircraft imagery and dense coincident surface
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observations, Remote Sens. Environ., 2016, 178, 15-30.
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"""
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w529 = df[self.find_closest_wavelength(df.columns, 529)]
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w644 = df[self.find_closest_wavelength(df.columns, 644)]
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w458 = df[self.find_closest_wavelength(df.columns, 458)]
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result = w529 - (w644 + (w458 - w644))
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return result
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def chl_Be16FLHviolet(self, df: pd.DataFrame) -> pd.Series:
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"""
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Fluorescence Line Height algorithm with violet baseline for chlorophyll
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参考文献: Beck, R.A. and 22 others; Comparison of satellite reflectance
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algorithms for estimating chlorophyll-a in a temperate reservoir using
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coincident hyperspectral aircraft imagery and dense coincident surface
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observations, Remote Sens. Environ., 2016, 178, 15-30.
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"""
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w529 = df[self.find_closest_wavelength(df.columns, 529)]
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w644 = df[self.find_closest_wavelength(df.columns, 644)]
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w429 = df[self.find_closest_wavelength(df.columns, 429)]
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result = w529 - (w644 + (w429 - w644))
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return result
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def chl_Be16NDTIblue(self, df: pd.DataFrame) -> pd.Series:
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"""
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Normalized Difference Turbidity Index with blue band for chlorophyll
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
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for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
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a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
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and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 543.
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"""
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w658 = df[self.find_closest_wavelength(df.columns, 658)]
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w458 = df[self.find_closest_wavelength(df.columns, 458)]
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result = (w658 - w458) / (w658 + w458)
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return result
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def chl_Be16NDTIviolet(self, df: pd.DataFrame) -> pd.Series:
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"""
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Normalized Difference Turbidity Index with violet band for chlorophyll
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
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for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
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a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
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and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 544.
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"""
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w658 = df[self.find_closest_wavelength(df.columns, 658)]
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w444 = df[self.find_closest_wavelength(df.columns, 444)]
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result = (w658 - w444) / (w658 + w444)
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return result
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def chl_De933BDA(self, df: pd.DataFrame) -> pd.Series:
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"""
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Band difference algorithm for chlorophyll
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参考文献: Dekker, A.; Detection of the optical water quality parameters for
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eutrophic waters by high resolution remote sensing, Ph.D. thesis, 1993,
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Free University, Amsterdam.
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"""
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w600 = df[self.find_closest_wavelength(df.columns, 600)]
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w648 = df[self.find_closest_wavelength(df.columns, 648)]
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w625 = df[self.find_closest_wavelength(df.columns, 625)]
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result = w600 - w648 - w625
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return result
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def chl_Gi033BDA(self, df: pd.DataFrame) -> pd.Series:
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"""
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Gitelson algorithm for chlorophyll estimation
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参考文献: Gitelson, A.A.; U. Gritz, and M. N. Merzlyak.; Relationships between
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leaf chlorophyll content and spectral reflectance and algorithms for
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non-destructive chlorophyll assessment in higher plant leaves. J. Plant Phys. 2003, 160, 271-282.
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"""
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w672 = df[self.find_closest_wavelength(df.columns, 672)]
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w715 = df[self.find_closest_wavelength(df.columns, 715)]
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w757 = df[self.find_closest_wavelength(df.columns, 757)]
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result = ((1 / w672) - (1 / w715)) * w757
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return result
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def chl_Kn07KIVU(self, df: pd.DataFrame) -> pd.Series:
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"""
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Kneubuhler algorithm for chlorophyll in Lake Kivu
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参考文献: Kneubuhler, M.; Frank T.; Kellenberger, T.W; Pasche N.; Schmid M.;
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Mapping chlorophyll-a in Lake Kivu with remote sensing methods. 2007,
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Proceedings of the Envisat Symposium 2007, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007).
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"""
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w458 = df[self.find_closest_wavelength(df.columns, 458)]
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w644 = df[self.find_closest_wavelength(df.columns, 644)]
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w529 = df[self.find_closest_wavelength(df.columns, 529)]
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result = (w458 - w644) / w529
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return result
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def chl_MM12NDCI(self, df: pd.DataFrame) -> pd.Series:
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"""
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Normalized Difference Chlorophyll Index
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参考文献: Mishra, S.; and Mishra, D.R. Normalized difference chlorophyll
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index: A novel model for remote estimation of chlorophyll-a concentration
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in turbid productive waters, Remote Sens. Environ., 2012, 117, 394-406
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"""
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w715 = df[self.find_closest_wavelength(df.columns, 715)]
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w686 = df[self.find_closest_wavelength(df.columns, 686)]
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result = (w715 - w686) / (w715 + w686)
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return result
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def chl_Zh10FLH(self, df: pd.DataFrame) -> pd.Series:
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"""
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Zhao fluorescence line height algorithm for chlorophyll
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参考文献: Zhao, D.Z.; Xing, X.G.; Liu, Y.G.; Yang, J.H.; Wang, L. The relation of
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chlorophyll-a concentration with the reflectance peak near 700 nm in
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algae-dominated waters and sensitivity of fluorescence algorithms for
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detecting algal bloom. Int. J. Remote Sens. 2010, 31, 39-48
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"""
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w686 = df[self.find_closest_wavelength(df.columns, 686)]
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w715 = df[self.find_closest_wavelength(df.columns, 715)]
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w672 = df[self.find_closest_wavelength(df.columns, 672)]
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w751 = df[self.find_closest_wavelength(df.columns, 751)]
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result = w686 - (w715 + (w672 - w751))
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return result
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# =========================================================================
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# 蓝藻/藻蓝蛋白算法 (BGA/PC)
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# =========================================================================
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def BGA_Am09KBBI(self, df: pd.DataFrame) -> pd.Series:
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"""
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Karenia Brevis Bloom Index for cyanobacteria/phycocyanin
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参考文献: Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S.;
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Novel optical techniques for detecting and classifying toxic dinoflagellate
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Karenia brevis blooms using satellite imagery, Optics Express, 2009, 17, 11, 1-13.
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"""
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w686 = df[self.find_closest_wavelength(df.columns, 686)]
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w658 = df[self.find_closest_wavelength(df.columns, 658)]
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result = (w686 - w658) / (w686 + w658)
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return result
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def BGA_Be162B643sub629(self, df: pd.DataFrame) -> pd.Series:
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"""
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Band subtraction algorithm for phycocyanin
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
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for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
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a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
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and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538.
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"""
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w644 = df[self.find_closest_wavelength(df.columns, 644)]
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w629 = df[self.find_closest_wavelength(df.columns, 629)]
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result = w644 - w629
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return result
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def BGA_Be162B700sub601(self, df: pd.DataFrame) -> pd.Series:
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"""
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Band subtraction algorithm for phycocyanin
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
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for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
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a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
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and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539.
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"""
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w700 = df[self.find_closest_wavelength(df.columns, 700)]
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w601 = df[self.find_closest_wavelength(df.columns, 601)]
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result = w700 - w601
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return result
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def BGA_Be162BsubPhy(self, df: pd.DataFrame) -> pd.Series:
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"""
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Band subtraction algorithm for phytoplankton/phycocyanin
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
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for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
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a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
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and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540.
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"""
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w715 = df[self.find_closest_wavelength(df.columns, 715)]
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w615 = df[self.find_closest_wavelength(df.columns, 615)]
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result = w715 - w615
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return result
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def BGA_Be16FLHBlueRedNIR(self, df: pd.DataFrame) -> pd.Series:
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"""
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Fluorescence Line Height with Blue-Red-NIR baseline for phycocyanin
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
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for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
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a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
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and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538.
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"""
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w658 = df[self.find_closest_wavelength(df.columns, 658)]
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w857 = df[self.find_closest_wavelength(df.columns, 857)]
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w458 = df[self.find_closest_wavelength(df.columns, 458)]
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result = w658 - (w857 + (w458 - w857))
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return result
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def BGA_Be16FLHGreenRedNIR(self, df: pd.DataFrame) -> pd.Series:
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"""
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Fluorescence Line Height with Green-Red-NIR baseline for phycocyanin
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
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for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
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a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
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and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539.
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"""
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w658 = df[self.find_closest_wavelength(df.columns, 658)]
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w857 = df[self.find_closest_wavelength(df.columns, 857)]
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w558 = df[self.find_closest_wavelength(df.columns, 558)]
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result = w658 - (w857 + (w558 - w857))
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return result
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def BGA_Be16FLHVioletRedNIR(self, df: pd.DataFrame) -> pd.Series:
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"""
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Fluorescence Line Height with Violet-Red-NIR baseline for phycocyanin
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
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for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
|
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a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
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and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538.
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"""
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w658 = df[self.find_closest_wavelength(df.columns, 658)]
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w857 = df[self.find_closest_wavelength(df.columns, 857)]
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w444 = df[self.find_closest_wavelength(df.columns, 444)]
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result = w658 - (w857 + (w444 - w857))
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return result
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def BGA_Be16MPI(self, df: pd.DataFrame) -> pd.Series:
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"""
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Maximum Peak Index for phycocyanin
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
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for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
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a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
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and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539.
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"""
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w615 = df[self.find_closest_wavelength(df.columns, 615)]
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w601 = df[self.find_closest_wavelength(df.columns, 601)]
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w644 = df[self.find_closest_wavelength(df.columns, 644)]
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result = (w615 - w601) - (w644 - w601)
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return result
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def BGA_Be16NDPhyI(self, df: pd.DataFrame) -> pd.Series:
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"""
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Normalized Difference Phytoplankton Index for phycocyanin
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
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for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
|
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a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
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and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540.
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"""
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w700 = df[self.find_closest_wavelength(df.columns, 700)]
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w622 = df[self.find_closest_wavelength(df.columns, 622)]
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result = (w700 - w622) / (w700 + w622)
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return result
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def BGA_Be16NDPhyI644over615(self, df: pd.DataFrame) -> pd.Series:
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"""
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Normalized Difference Phytoplankton Index (644/615) for phycocyanin
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参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
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Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
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Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
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Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
|
||
for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
|
||
a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
|
||
and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 541.
|
||
"""
|
||
w644 = df[self.find_closest_wavelength(df.columns, 644)]
|
||
w615 = df[self.find_closest_wavelength(df.columns, 615)]
|
||
|
||
result = (w644 - w615) / (w644 + w615)
|
||
return result
|
||
|
||
def BGA_Be16NDPhyI644over629(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Normalized Difference Phytoplankton Index (644/629) for phycocyanin
|
||
参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
|
||
Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
|
||
Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
|
||
Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
|
||
for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
|
||
a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
|
||
and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 542.
|
||
"""
|
||
w644 = df[self.find_closest_wavelength(df.columns, 644)]
|
||
w629 = df[self.find_closest_wavelength(df.columns, 629)]
|
||
|
||
result = (w644 - w629) / (w644 + w629)
|
||
return result
|
||
|
||
def BGA_Be16Phy2BDA644over629(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Band ratio algorithm (644/629) for phycocyanin
|
||
参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
|
||
Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
|
||
Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
|
||
Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
|
||
for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
|
||
a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
|
||
and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 545.
|
||
"""
|
||
w644 = df[self.find_closest_wavelength(df.columns, 644)]
|
||
w629 = df[self.find_closest_wavelength(df.columns, 629)]
|
||
|
||
result = w644 / w629
|
||
return result
|
||
|
||
def BGA_Da052BDA(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Band ratio algorithm (714/672) for phycocyanin
|
||
参考文献: Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A.,
|
||
Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial
|
||
blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672.
|
||
"""
|
||
w714 = df[self.find_closest_wavelength(df.columns, 714)]
|
||
w672 = df[self.find_closest_wavelength(df.columns, 672)]
|
||
|
||
result = w714 / w672
|
||
return result
|
||
|
||
def BGA_Go04MCI(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Maximum Chlorophyll Index for phycocyanin
|
||
参考文献: Gower, J.F.R.; Brown,L.; Borstad, G.A.; Observation of chlorophyll
|
||
fluorescence in west coast waters of Canada using the MODIS satellite sensor.
|
||
Can. J. Remote Sens., 2004, 30 (1), 17–25.
|
||
"""
|
||
w709 = df[self.find_closest_wavelength(df.columns, 709)]
|
||
w681 = df[self.find_closest_wavelength(df.columns, 681)]
|
||
w753 = df[self.find_closest_wavelength(df.columns, 753)]
|
||
|
||
result = w709 - w681 - (w753 - w681)
|
||
return result
|
||
|
||
def BGA_HU103BDA(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Hunter algorithm for phycocyanin
|
||
参考文献: Hunter, P.D.; Tyler, A.N.; Willby, N.J.; Gilvear, D.J.; The spatial
|
||
dynamics of vertical migration by Microcystis aeruginosa in a eutrophic
|
||
shallow lake: A case study using high spatial resolution time-series
|
||
airborne remote sensing. Limn. Oceanogr. 2008, 53, 2391-2406
|
||
"""
|
||
w615 = df[self.find_closest_wavelength(df.columns, 615)]
|
||
w600 = df[self.find_closest_wavelength(df.columns, 600)]
|
||
w725 = df[self.find_closest_wavelength(df.columns, 725)]
|
||
|
||
result = (((1 / w615) - (1 / w600)) - w725)
|
||
return result
|
||
|
||
def BGA_Ku15PhyCI(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Kudela Phytoplankton Community Index for phycocyanin
|
||
参考文献: Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K.,
|
||
Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial
|
||
blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-10.
|
||
"""
|
||
w681 = df[self.find_closest_wavelength(df.columns, 681)]
|
||
w665 = df[self.find_closest_wavelength(df.columns, 665)]
|
||
w709 = df[self.find_closest_wavelength(df.columns, 709)]
|
||
|
||
result = -1 * (w681 - w665 - (w709 - w665))
|
||
return result
|
||
|
||
def BGA_Ku15SLH(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Kudela Scattering Line Height for phycocyanin
|
||
参考文献: Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K.,
|
||
Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial
|
||
blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-11.
|
||
"""
|
||
w715 = df[self.find_closest_wavelength(df.columns, 715)]
|
||
w658 = df[self.find_closest_wavelength(df.columns, 658)]
|
||
|
||
result = (w715 - w658) + (w715 - w658)
|
||
return result
|
||
|
||
def BGA_MI092BDA(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Mishra band ratio algorithm (700/600) for phycocyanin
|
||
参考文献: Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for
|
||
predicting PC concentrations in cyanobacteria: A proximal hyperspectral
|
||
remote sensing approach. Remote Sens., 2009, 1, 758–775.
|
||
"""
|
||
w700 = df[self.find_closest_wavelength(df.columns, 700)]
|
||
w600 = df[self.find_closest_wavelength(df.columns, 600)]
|
||
|
||
result = w700 / w600
|
||
return result
|
||
|
||
def BGA_MM092BDA(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Mishra band ratio algorithm (724/600) for phycocyanin
|
||
参考文献: Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for
|
||
predicting PC concentrations in cyanobacteria: A proximal hyperspectral
|
||
remote sensing approach. Remote Sens., 2009, 1, 758–776.
|
||
"""
|
||
w724 = df[self.find_closest_wavelength(df.columns, 724)]
|
||
w600 = df[self.find_closest_wavelength(df.columns, 600)]
|
||
|
||
result = w724 / w600
|
||
return result
|
||
|
||
def BGA_MM12NDCIalt(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Alternative Normalized Difference Chlorophyll Index for phycocyanin
|
||
参考文献: Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to
|
||
quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett.,
|
||
2014, 9 (11), DOI:10.1088/1748-9326/9/11/114003
|
||
"""
|
||
w700 = df[self.find_closest_wavelength(df.columns, 700)]
|
||
w658 = df[self.find_closest_wavelength(df.columns, 658)]
|
||
|
||
result = (w700 - w658) / (w700 + w658)
|
||
return result
|
||
|
||
def BGA_MM143BDAopt(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Optimized band algorithm for phycocyanin
|
||
参考文献: Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to
|
||
quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett.,
|
||
2014, 9 (11), DOI:10.1088/1748-9326/9/11/114004
|
||
"""
|
||
w629 = df[self.find_closest_wavelength(df.columns, 629)]
|
||
w659 = df[self.find_closest_wavelength(df.columns, 659)]
|
||
w724 = df[self.find_closest_wavelength(df.columns, 724)]
|
||
|
||
result = ((1 / w629) - (1 / w659)) * w724
|
||
return result
|
||
|
||
def BGA_SI052BDA(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Simis band ratio algorithm (709/620) for phycocyanin
|
||
参考文献: Simis, S. G. H.; Peters, S.W. M.; Gons, H. J.; Remote sensing of
|
||
the cyanobacteria pigment phycocyanin in turbid inland water. Limn. Oceanogr., 2005, 50, 237–245
|
||
"""
|
||
w709 = df[self.find_closest_wavelength(df.columns, 709)]
|
||
w620 = df[self.find_closest_wavelength(df.columns, 620)]
|
||
|
||
result = w709 / w620
|
||
return result
|
||
|
||
def BGA_SM122BDA(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Mishra band ratio algorithm (709/600) for phycocyanin
|
||
参考文献: Mishra, S. Remote sensing of cyanobacteria in turbid productive
|
||
waters, PhD Dissertation. Mississippi State University, USA. 2012.
|
||
"""
|
||
w709 = df[self.find_closest_wavelength(df.columns, 709)]
|
||
w600 = df[self.find_closest_wavelength(df.columns, 600)]
|
||
|
||
result = w709 / w600
|
||
return result
|
||
|
||
def BGA_SY002BDA(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Schalles-Yacobi band ratio algorithm (650/625) for phycocyanin
|
||
参考文献: Schalles, J.; Yacobi, Y. Remote detection and seasonal patterns of
|
||
phycocyanin, carotenoid and chlorophyll-a pigments in eutrophic waters.
|
||
Archiv fur Hydrobiologie, Special Issues Advances in Limnology, 2000, 55,153–168
|
||
"""
|
||
w650 = df[self.find_closest_wavelength(df.columns, 650)]
|
||
w625 = df[self.find_closest_wavelength(df.columns, 625)]
|
||
|
||
result = w650 / w625
|
||
return result
|
||
|
||
def BGA_Wy08CI(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Wynne Cyanobacteria Index for phycocyanin
|
||
参考文献: Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A.,
|
||
Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial
|
||
blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672.
|
||
"""
|
||
w686 = df[self.find_closest_wavelength(df.columns, 686)]
|
||
w672 = df[self.find_closest_wavelength(df.columns, 672)]
|
||
w715 = df[self.find_closest_wavelength(df.columns, 715)]
|
||
|
||
result = -1 * (w686 - w672 - (w715 - w672))
|
||
return result
|
||
|
||
# =========================================================================
|
||
# 浊度算法
|
||
# =========================================================================
|
||
|
||
def Turb_Be16GreenPlusRedBothOverViolet(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Turbidity algorithm: (Green + Red) / Violet
|
||
参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
|
||
Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
|
||
Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
|
||
Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
|
||
for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
|
||
a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
|
||
and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538
|
||
"""
|
||
w558 = df[self.find_closest_wavelength(df.columns, 558)]
|
||
w658 = df[self.find_closest_wavelength(df.columns, 658)]
|
||
w444 = df[self.find_closest_wavelength(df.columns, 444)]
|
||
|
||
result = (w558 + w658) / w444
|
||
return result
|
||
|
||
def Turb_Be16RedOverViolet(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Turbidity algorithm: Red / Violet
|
||
参考文献: Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.;
|
||
Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.;
|
||
Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.;
|
||
Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms
|
||
for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in
|
||
a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery
|
||
and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539
|
||
"""
|
||
w658 = df[self.find_closest_wavelength(df.columns, 658)]
|
||
w444 = df[self.find_closest_wavelength(df.columns, 444)]
|
||
|
||
result = w658 / w444
|
||
return result
|
||
|
||
def Turb_Bow06RedOverGreen(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Turbidity algorithm: Red / Green
|
||
参考文献: Bowers, D. G., and C. E. Binding. 2006. "The Optical Properties of
|
||
Mineral Suspended Particles: A Review and Synthesis." Estuarine Coastal
|
||
and Shelf Science 67 (1–2): 219–230. doi:10.1016/j.ecss.2005.11.010
|
||
"""
|
||
w658 = df[self.find_closest_wavelength(df.columns, 658)]
|
||
w558 = df[self.find_closest_wavelength(df.columns, 558)]
|
||
|
||
result = w658 / w558
|
||
return result
|
||
|
||
def Turb_Chip09NIROverGreen(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Turbidity algorithm: NIR / Green
|
||
参考文献: Chipman, J. W.; Olmanson, L.G.; Gitelson, A.A.; Remote sensing
|
||
methods for lake management: A guide for resource managers and decision-makers. 2009.
|
||
"""
|
||
w857 = df[self.find_closest_wavelength(df.columns, 857)]
|
||
w558 = df[self.find_closest_wavelength(df.columns, 558)]
|
||
|
||
result = w857 / w558
|
||
return result
|
||
|
||
def Turb_Dox02NIRoverRed(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Turbidity algorithm: NIR / Red
|
||
参考文献: Doxaran, D., Froidefond, J.-M.; Castaing, P. ; A reflectance band
|
||
ratio used to estimate suspended matter concentrations in sediment-dominated
|
||
coastal waters, Remote Sens., 2002, 23, 5079-5085
|
||
"""
|
||
w857 = df[self.find_closest_wavelength(df.columns, 857)]
|
||
w658 = df[self.find_closest_wavelength(df.columns, 658)]
|
||
|
||
result = w857 / w658
|
||
return result
|
||
|
||
def Turb_Frohn09GreenPlusRedBothOverBlue(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Turbidity algorithm: (Green + Red) / Blue
|
||
参考文献: Frohn, R. C., & Autrey, B. C. (2009). Water quality assessment in
|
||
the Ohio River using new indices for turbidity and chlorophyll-a with
|
||
Landsat-7 Imagery. Draft Internal Report, US Environmental Protection Agency.
|
||
"""
|
||
w558 = df[self.find_closest_wavelength(df.columns, 558)]
|
||
w658 = df[self.find_closest_wavelength(df.columns, 658)]
|
||
w458 = df[self.find_closest_wavelength(df.columns, 458)]
|
||
|
||
result = (w558 + w658) / w458
|
||
return result
|
||
|
||
def Turb_Harr92NIR(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Turbidity algorithm: NIR reflectance
|
||
参考文献: Schiebe F.R., Harrington J.A., Ritchie J.C. Remote-Sensing of
|
||
Suspended Sediments—the Lake Chicot, Arkansas Project. Int. J. Remote Sens. 1992;13:1487–1509
|
||
"""
|
||
w857 = df[self.find_closest_wavelength(df.columns, 857)]
|
||
|
||
result = w857
|
||
return result
|
||
|
||
def Turb_Lath91RedOverBlue(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Turbidity algorithm: Red / Blue
|
||
参考文献: Lathrop, R. G., Jr., T. M. Lillesand, and B. S. Yandell, 1991.
|
||
Testing the utility of simple multi-date Thematic Mapper calibration
|
||
algorithms for monitoring turbid inland waters. International Journal of Remote Sensing
|
||
"""
|
||
w658 = df[self.find_closest_wavelength(df.columns, 658)]
|
||
w458 = df[self.find_closest_wavelength(df.columns, 458)]
|
||
|
||
result = w658 / w458
|
||
return result
|
||
|
||
def Turb_Moore80Red(self, df: pd.DataFrame) -> pd.Series:
|
||
"""
|
||
Turbidity algorithm: Red reflectance
|
||
参考文献: Moore, G.K., Satellite remote sensing of water turbidity,
|
||
Hydrological Sciences, 1980, 25, 4, 407-422
|
||
"""
|
||
w658 = df[self.find_closest_wavelength(df.columns, 658)]
|
||
|
||
result = w658
|
||
return result
|
||
|
||
def calculate_all_indices(
|
||
self,
|
||
input_file: str,
|
||
output_file: str = None,
|
||
selected_indices: Optional[List[str]] = None
|
||
) -> pd.DataFrame:
|
||
"""
|
||
计算所有水质指数
|
||
|
||
Args:
|
||
input_file: 输入CSV文件路径
|
||
output_file: 输出CSV文件路径（可选）
|
||
selected_indices: 可选的算法列表，仅计算指定的指数
|
||
|
||
Returns:
|
||
包含计算结果的数据框
|
||
"""
|
||
# 读取数据
|
||
df = pd.read_csv(input_file)
|
||
print(f"成功读取数据，形状: {df.shape}")
|
||
print(f"数据列: {list(df.columns)}")
|
||
|
||
results = df.copy()
|
||
|
||
# 获取所有算法方法
|
||
algorithm_methods = [
|
||
method for method in dir(self)
|
||
if not method.startswith('_') and method not in ['find_closest_wavelength', 'calculate_all_indices']
|
||
]
|
||
|
||
print(f"\n找到 {len(algorithm_methods)} 个算法")
|
||
|
||
if selected_indices is not None:
|
||
filtered = []
|
||
missing = []
|
||
for name in selected_indices:
|
||
if name in algorithm_methods:
|
||
filtered.append(name)
|
||
else:
|
||
missing.append(name)
|
||
|
||
if missing:
|
||
print(f"警告: 以下算法未找到，将被忽略: {', '.join(missing)}")
|
||
|
||
algorithm_methods = filtered
|
||
|
||
if not algorithm_methods:
|
||
raise ValueError("未找到可用算法，请检查 selected_indices 参数")
|
||
|
||
# 按算法类型分类计算
|
||
algorithm_categories = {
|
||
'chlorophyll': [],
|
||
'BGA/PC': [],
|
||
'turbidity': []
|
||
}
|
||
|
||
for method_name in algorithm_methods:
|
||
if method_name.startswith('Turb'):
|
||
algorithm_categories['turbidity'].append(method_name)
|
||
elif any(keyword in method_name for keyword in ['BDA', 'FLH', 'ND', 'sub', 'CI', 'SLH', 'MPI', 'Phy']):
|
||
if method_name not in algorithm_categories['turbidity']:
|
||
algorithm_categories['BGA/PC'].append(method_name)
|
||
else:
|
||
algorithm_categories['chlorophyll'].append(method_name)
|
||
|
||
# 计算每个类别的算法
|
||
for category, algorithms in algorithm_categories.items():
|
||
print(f"\n=== 计算 {category} 相关指数 ({len(algorithms)}个算法) ===")
|
||
|
||
for algo_name in algorithms:
|
||
try:
|
||
print(f"计算: {algo_name}")
|
||
method = getattr(self, algo_name)
|
||
results[algo_name] = method(df)
|
||
print(f"✓ 成功计算 {algo_name}")
|
||
|
||
except Exception as e:
|
||
print(f"✗ 计算 {algo_name} 时出错: {str(e)}")
|
||
results[algo_name] = np.nan
|
||
|
||
# 保存结果
|
||
if output_file:
|
||
results.to_csv(output_file, index=False)
|
||
print(f"\n结果已保存到: {output_file}")
|
||
|
||
return results
|
||
|
||
|
||
def main():
|
||
"""主函数"""
|
||
calculator = WaterQualityIndexCalculator()
|
||
|
||
print("=" * 80)
|
||
print("水质光谱指数计算器")
|
||
print("=" * 80)
|
||
|
||
# 计算指数
|
||
input_file = r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\training_spectra.csv" # 修改为您的输入文件路径
|
||
output_file = r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\water_quality_results.csv"
|
||
|
||
try:
|
||
# 设置为 None 时默认计算所有已实现的算法；也可以设置为算法名称列表，例如 ['Al10SABI', 'TurbBe16RedOverViolet']
|
||
selected_algorithms = None
|
||
results = calculator.calculate_all_indices(input_file, output_file, selected_algorithms)
|
||
|
||
# 显示结果统计
|
||
print("\n" + "=" * 80)
|
||
print("计算结果统计:")
|
||
print("=" * 80)
|
||
|
||
# 只显示计算出的指数列的统计信息
|
||
original_columns = pd.read_csv(input_file).columns
|
||
calculated_columns = [col for col in results.columns if col not in original_columns]
|
||
|
||
if calculated_columns:
|
||
stats = results[calculated_columns].describe()
|
||
print(stats)
|
||
|
||
# 按类别显示统计
|
||
categories = {
|
||
'叶绿素算法': [col for col in calculated_columns if not col.startswith('Turb') and not any(x in col for x in ['BDA', 'FLH', 'ND', 'sub', 'CI', 'SLH', 'MPI', 'Phy']) or col in ['Al10SABI', 'Am092Bsub', 'Be16FLHblue', 'Be16FLHviolet', 'Be16NDTIblue', 'Be16NDTIviolet', 'De933BDA', 'Gi033BDA', 'Kn07KIVU', 'MM12NDCI', 'Zh10FLH']],
|
||
'蓝藻/藻蓝蛋白算法': [col for col in calculated_columns if col not in ['Al10SABI', 'Am092Bsub', 'Be16FLHblue', 'Be16FLHviolet', 'Be16NDTIblue', 'Be16NDTIviolet', 'De933BDA', 'Gi033BDA', 'Kn07KIVU', 'MM12NDCI', 'Zh10FLH'] and not col.startswith('Turb')],
|
||
'浊度算法': [col for col in calculated_columns if col.startswith('Turb')]
|
||
}
|
||
|
||
for category, algo_list in categories.items():
|
||
if algo_list:
|
||
print(f"\n{category}统计:")
|
||
print(results[algo_list].describe())
|
||
else:
|
||
print("没有成功计算任何指数")
|
||
|
||
except FileNotFoundError:
|
||
print(f"\n错误: 找不到输入文件 {input_file}")
|
||
print("请确保文件存在，或修改 input_file 变量为正确的文件路径")
|
||
except Exception as e:
|
||
print(f"\n计算过程中发生错误: {str(e)}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main() |