Chin J Plant Ecol ›› 2017, Vol. 41 ›› Issue (12): 1273-1288.DOI: 10.17521/cjpe.2017.0231
Special Issue: 生态遥感及应用
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GAO Lin1,2,*, WANG Xiao-Fei1,2,3, GU Xing-Fa4, TIAN Qing-Jiu1,2,3,**(), JIAO Jun-Nan1,2, WANG Pei-Yan1,2, LI Dan4
Online:
2017-12-10
Published:
2018-02-23
Contact:
TIAN Qing-Jiu
GAO Lin, WANG Xiao-Fei, GU Xing-Fa, TIAN Qing-Jiu, JIAO Jun-Nan, WANG Pei-Yan, LI Dan. Exploring the influence of soil types underneath the canopy in winter wheat leaf area index remote estimating[J]. Chin J Plant Ecol, 2017, 41(12): 1273-1288.
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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2017.0231
土壤类型 Soil type | LAI | p | ||||
---|---|---|---|---|---|---|
最小值 Min | 最大值 Max | 平均值 Mean | 标准差 SD | 变异系数 CV (%) | ||
潮土 Fluvo-aquic soil | 1.970 | 5.870 | 3.650 | 1.031 | 28.24 | 0.105 |
水稻土 Paddy soil | 2.240 | 5.880 | 4.114 | 0.962 | 23.39 | 0.593 |
棕壤 Brown soil | 1.100 | 6.710 | 3.482 | 1.557 | 44.71 | 0.610 |
砂姜黑土 Lime concretion black soil | 1.700 | 5.500 | 3.463 | 1.018 | 29.39 | 0.536 |
褐土 Cinnamon soil | 1.320 | 5.860 | 4.483 | 1.235 | 27.54 | < 0.005 |
5种土壤 Five type soils | 1.100 | 6.710 | 3.838 | 1.232 | 32.09 | 0.242 |
Table 1 Descriptive statistics of winter wheat leaf area index (LAI) values (five soil types)
土壤类型 Soil type | LAI | p | ||||
---|---|---|---|---|---|---|
最小值 Min | 最大值 Max | 平均值 Mean | 标准差 SD | 变异系数 CV (%) | ||
潮土 Fluvo-aquic soil | 1.970 | 5.870 | 3.650 | 1.031 | 28.24 | 0.105 |
水稻土 Paddy soil | 2.240 | 5.880 | 4.114 | 0.962 | 23.39 | 0.593 |
棕壤 Brown soil | 1.100 | 6.710 | 3.482 | 1.557 | 44.71 | 0.610 |
砂姜黑土 Lime concretion black soil | 1.700 | 5.500 | 3.463 | 1.018 | 29.39 | 0.536 |
褐土 Cinnamon soil | 1.320 | 5.860 | 4.483 | 1.235 | 27.54 | < 0.005 |
5种土壤 Five type soils | 1.100 | 6.710 | 3.838 | 1.232 | 32.09 | 0.242 |
光谱指数 Spectral indices | 公式 Equation | 来源 Reference |
---|---|---|
归一化差异植被指数 Normalized difference vegetation index (NDVI) | $NDVI=\frac{{{R}_{800}}-{{R}_{680}}}{{{R}_{800}}+{{R}_{680}}}$ | Rouse et al., 1974 |
修正型土壤调节植被指数 Modified soil-adjusted vegetation index (MSAVI) | $MSAVI=\frac{1}{2}\times \left[ 2\times {{R}_{800}}+1-\sqrt{{{\left( 2\times {{R}_{800}}+1 \right)}^{2}}-8\times \left( {{R}_{800}}-{{R}_{670}} \right)} \right]$ | Qi et al., 1994 |
修正的叶绿素吸收比率指数 Modified chlorophyll absorption ratio index 2 (MCARI2) | $MCAR{{I}_{2}}=\frac{1.5\times \left[ 2.5\times \left( {{R}_{800}}-{{R}_{670}} \right)-1.3\times \left( {{R}_{800}}-{{R}_{550}} \right) \right]}{\sqrt{{{\left( 2\times {{R}_{800}}+1 \right)}^{2}}-\left( 6\times {{R}_{800}}-5\times \sqrt{{{R}_{670}}} \right)-0.5}}$ | Haboudane et al., 2004 |
红边拐点 Red edge inflection point (REIP) | $REIP=700+40\times \frac{\left( {{R}_{670}}+{{R}_{780}} \right)\text{/2}-{{R}_{700}}}{{{R}_{\text{740}}}-{{R}_{\text{700}}}}$ | Danson & Plummer, 1995 |
红边振幅 Red edge amplitude (Dr) | $d{{\lambda }_{\text{red-edge}}}$ | Feng et al., 2009 |
红边面积 Red edge area (SDr) | $\sum{d{{\lambda }_{\left( 680\text{-}750 \right)}}}$ | Filella & Penuelas, 1994 |
红边对称指数 Red edge symmetry (RES) | $RES=\frac{{{R}_{718}}-{{R}_{675}}}{{{R}_{755}}-{{R}_{675}}}$ | Ju et al., 2010 |
归一化差异光谱指数 Normalized difference spectral index (NDSI) | $NDS{{I}_{(i,j)}}=\frac{{{R}_{i}}-{{R}_{j}}}{{{R}_{\text{i}}}+{{R}_{j}}}$ | Li et al., 2013 |
Table 2 Spectral indices for winter wheat leaf area index estimating in this paper
光谱指数 Spectral indices | 公式 Equation | 来源 Reference |
---|---|---|
归一化差异植被指数 Normalized difference vegetation index (NDVI) | $NDVI=\frac{{{R}_{800}}-{{R}_{680}}}{{{R}_{800}}+{{R}_{680}}}$ | Rouse et al., 1974 |
修正型土壤调节植被指数 Modified soil-adjusted vegetation index (MSAVI) | $MSAVI=\frac{1}{2}\times \left[ 2\times {{R}_{800}}+1-\sqrt{{{\left( 2\times {{R}_{800}}+1 \right)}^{2}}-8\times \left( {{R}_{800}}-{{R}_{670}} \right)} \right]$ | Qi et al., 1994 |
修正的叶绿素吸收比率指数 Modified chlorophyll absorption ratio index 2 (MCARI2) | $MCAR{{I}_{2}}=\frac{1.5\times \left[ 2.5\times \left( {{R}_{800}}-{{R}_{670}} \right)-1.3\times \left( {{R}_{800}}-{{R}_{550}} \right) \right]}{\sqrt{{{\left( 2\times {{R}_{800}}+1 \right)}^{2}}-\left( 6\times {{R}_{800}}-5\times \sqrt{{{R}_{670}}} \right)-0.5}}$ | Haboudane et al., 2004 |
红边拐点 Red edge inflection point (REIP) | $REIP=700+40\times \frac{\left( {{R}_{670}}+{{R}_{780}} \right)\text{/2}-{{R}_{700}}}{{{R}_{\text{740}}}-{{R}_{\text{700}}}}$ | Danson & Plummer, 1995 |
红边振幅 Red edge amplitude (Dr) | $d{{\lambda }_{\text{red-edge}}}$ | Feng et al., 2009 |
红边面积 Red edge area (SDr) | $\sum{d{{\lambda }_{\left( 680\text{-}750 \right)}}}$ | Filella & Penuelas, 1994 |
红边对称指数 Red edge symmetry (RES) | $RES=\frac{{{R}_{718}}-{{R}_{675}}}{{{R}_{755}}-{{R}_{675}}}$ | Ju et al., 2010 |
归一化差异光谱指数 Normalized difference spectral index (NDSI) | $NDS{{I}_{(i,j)}}=\frac{{{R}_{i}}-{{R}_{j}}}{{{R}_{\text{i}}}+{{R}_{j}}}$ | Li et al., 2013 |
Fig. 3 Different soil spectral reflectance curves and soil lines. ρL(λNIR) and ρL(λRed) are spectral reflectance of lime concretion back soil in near infrared band and red band, respectively; ρF(λNIR) and ρF(λRed) are spectral reflectance of fluvo-aquic soil in near infrared band and red band, respectively; ρB(λNIR) and ρB(λRed) are spectral reflectance of brown soil in near infrared band and red band, respectively; ρP(λNIR) and ρP(λRed) are spectral reflectance of paddy soil in near infrared band and red band, respectively; ρC(λNIR) and ρC(λRed) are spectral reflectance of cinnamon soil in near infrared band and red band, respectively.
光谱指数 Spectral indices | LAI | ||||||||
---|---|---|---|---|---|---|---|---|---|
1-1.5 | 1.5-2 | 2-2.5 | 2.5-3 | 3-3.5 | 3.5-4 | 4-4.5 | 4.5-5 | >5 | |
NDVI | 6.32 | 10.91 | 7.18 | 7.94 | 4.37 | 1.95 | 1.9 | 2.01 | 1.44 |
MSAVI | 13.86 | 24.48 | 18.57 | 24.42 | 12.77 | 8.72 | 10.01 | 8.02 | 4.25 |
MCARI2 | 15.72 | 25.98 | 20.51 | 26.03 | 13.95 | 8.94 | 9.97 | 8.28 | 4.84 |
RES | 20.54 | 25.8 | 24.06 | 18.15 | 16.26 | 8.36 | 8.49 | 11.69 | 7.8 |
REIP | 0.44 | 0.45 | 0.53 | 0.39 | 0.41 | 0.23 | 0.25 | 0.29 | 0.24 |
NDSI | 100.97 | 206.38 | 187.41 | 139.56 | 154.61 | 155.92 | 114.89 | 158.33 | 150.22 |
Dr | 19.51 | 28.93 | 20.25 | 31.48 | 15.98 | 15.68 | 12.38 | 13.61 | 7.38 |
SDr | 17.01 | 23.61 | 14.44 | 27.18 | 12.2 | 16.32 | 11.07 | 11.35 | 6.68 |
Table 3 Coefficient of variation (%) of spectral indices in different leaf area index (LAI) intervals
光谱指数 Spectral indices | LAI | ||||||||
---|---|---|---|---|---|---|---|---|---|
1-1.5 | 1.5-2 | 2-2.5 | 2.5-3 | 3-3.5 | 3.5-4 | 4-4.5 | 4.5-5 | >5 | |
NDVI | 6.32 | 10.91 | 7.18 | 7.94 | 4.37 | 1.95 | 1.9 | 2.01 | 1.44 |
MSAVI | 13.86 | 24.48 | 18.57 | 24.42 | 12.77 | 8.72 | 10.01 | 8.02 | 4.25 |
MCARI2 | 15.72 | 25.98 | 20.51 | 26.03 | 13.95 | 8.94 | 9.97 | 8.28 | 4.84 |
RES | 20.54 | 25.8 | 24.06 | 18.15 | 16.26 | 8.36 | 8.49 | 11.69 | 7.8 |
REIP | 0.44 | 0.45 | 0.53 | 0.39 | 0.41 | 0.23 | 0.25 | 0.29 | 0.24 |
NDSI | 100.97 | 206.38 | 187.41 | 139.56 | 154.61 | 155.92 | 114.89 | 158.33 | 150.22 |
Dr | 19.51 | 28.93 | 20.25 | 31.48 | 15.98 | 15.68 | 12.38 | 13.61 | 7.38 |
SDr | 17.01 | 23.61 | 14.44 | 27.18 | 12.2 | 16.32 | 11.07 | 11.35 | 6.68 |
光谱指数 Spectral indices | 不同土壤类型区的冬小麦LAI/相关系数 Winter wheat LAI in different soil types/Correlation coefficient (r) | |||||
---|---|---|---|---|---|---|
水稻土 Paddy soil (n = 30) | 潮土 Fluvo-aquic soil (n = 30) | 棕壤 Brown soil (n = 30) | 砂姜黑土 Lime concretion black soil (n = 30) | 褐土 Cinnamon soil (n = 30) | 5种土壤 Five type soils (n = 150) | |
NDVI | 0.789 | 0.655 | 0.079 | 0.145 | 0.630 | 0.379 |
MSAVI | 0.753 | 0.724 | 0.037 | 0.283 | 0.313 | 0.383 |
MCARI2 | 0.769 | 0.722 | -0.008 | 0.258 | 0.401 | 0.386 |
NDSI(i, j) | 0.854/NDSI(727, 932) | 0.852/NDSI(416, 429) | 0.788/NDSI(781, 783) | 0.701/NDSI(669, 676) | 0.679/NDSI(629, 636) | 0.561/NDSI(628, 636) |
REIP | 0.836 | 0.735 | 0.500 | 0.208 | 0.426 | 0.408 |
Dr | 0.714 | 0.732 | 0.016 | 0.375 | 0.186 | 0.340 |
SDr | 0.624 | 0.704 | -0.086 | 0.371 | 0.008 | 0.265 |
RES | -0.834 | -0.675 | -0.368 | -0.176 | -0.477 | -0.404 |
Table 4 Correlations between spectral indices and winter wheat leaf area index (LAI) in different soil types
光谱指数 Spectral indices | 不同土壤类型区的冬小麦LAI/相关系数 Winter wheat LAI in different soil types/Correlation coefficient (r) | |||||
---|---|---|---|---|---|---|
水稻土 Paddy soil (n = 30) | 潮土 Fluvo-aquic soil (n = 30) | 棕壤 Brown soil (n = 30) | 砂姜黑土 Lime concretion black soil (n = 30) | 褐土 Cinnamon soil (n = 30) | 5种土壤 Five type soils (n = 150) | |
NDVI | 0.789 | 0.655 | 0.079 | 0.145 | 0.630 | 0.379 |
MSAVI | 0.753 | 0.724 | 0.037 | 0.283 | 0.313 | 0.383 |
MCARI2 | 0.769 | 0.722 | -0.008 | 0.258 | 0.401 | 0.386 |
NDSI(i, j) | 0.854/NDSI(727, 932) | 0.852/NDSI(416, 429) | 0.788/NDSI(781, 783) | 0.701/NDSI(669, 676) | 0.679/NDSI(629, 636) | 0.561/NDSI(628, 636) |
REIP | 0.836 | 0.735 | 0.500 | 0.208 | 0.426 | 0.408 |
Dr | 0.714 | 0.732 | 0.016 | 0.375 | 0.186 | 0.340 |
SDr | 0.624 | 0.704 | -0.086 | 0.371 | 0.008 | 0.265 |
RES | -0.834 | -0.675 | -0.368 | -0.176 | -0.477 | -0.404 |
土壤类型 Soil type | PLSR模型 PLSR models | 潜变量个数 Number of latent variables | 建模 Calibration | 交叉验证 Cross validation | |||
---|---|---|---|---|---|---|---|
R2 | RMSEC | R2 | RMSECV | RPDCV | |||
水稻土 Paddy soil (n = 30) | RES + NDSI | 2 | 0.733 | 0.489 | 0.684 | 0.533 | 1.805 |
潮土 Fluvo-aquic soil (n = 30) | RES + REIP + NDSI | 3 | 0.803 | 0.450 | 0.743 | 0.518 | 1.990 |
棕壤 Brown soil (n = 30) | REIP + Dr + SDr + NDSI | 4 | 0.837 | 0.617 | 0.771 | 0.735 | 2.118 |
砂姜黑土 Lime concretion black soil (n = 30) | REIP + NDSI | 2 | 0.579 | 0.649 | 0.490 | 0.721 | 1.412 |
褐土 Cinnamon soil (n = 30) | NDVI + NDSI | 1 | 0.466 | 0.887 | 0.385 | 0.954 | 1.295 |
5种土壤 Five type soils (n = 150) | RES + REIP + Dr + NDSI | 4 | 0.378 | 0.968 | 0.341 | 0.997 | 1.236 |
Table 5 Results of estimating winter wheat leaf area index (LAI) by optimal partial least squares regression (PLSR) models
土壤类型 Soil type | PLSR模型 PLSR models | 潜变量个数 Number of latent variables | 建模 Calibration | 交叉验证 Cross validation | |||
---|---|---|---|---|---|---|---|
R2 | RMSEC | R2 | RMSECV | RPDCV | |||
水稻土 Paddy soil (n = 30) | RES + NDSI | 2 | 0.733 | 0.489 | 0.684 | 0.533 | 1.805 |
潮土 Fluvo-aquic soil (n = 30) | RES + REIP + NDSI | 3 | 0.803 | 0.450 | 0.743 | 0.518 | 1.990 |
棕壤 Brown soil (n = 30) | REIP + Dr + SDr + NDSI | 4 | 0.837 | 0.617 | 0.771 | 0.735 | 2.118 |
砂姜黑土 Lime concretion black soil (n = 30) | REIP + NDSI | 2 | 0.579 | 0.649 | 0.490 | 0.721 | 1.412 |
褐土 Cinnamon soil (n = 30) | NDVI + NDSI | 1 | 0.466 | 0.887 | 0.385 | 0.954 | 1.295 |
5种土壤 Five type soils (n = 150) | RES + REIP + Dr + NDSI | 4 | 0.378 | 0.968 | 0.341 | 0.997 | 1.236 |
土壤类型 Soil type | 随机森林回归模型的优化参数 The optimized parameters in RFR | 模型精度 Model precision | ||||
---|---|---|---|---|---|---|
ntree | mtry | 变量解释 Variable explained (%) | R2 | RMSE | RPD | |
水稻土 Paddy soil (n = 30) | 1 000 | 2 | 60.08 | 0.604 | 0.598 | 1.609 |
潮土 Fluvo-aquic soil (n = 30) | 1 000 | 8 | 72.86 | 0.733 | 0.528 | 1.953 |
棕壤 Brown soil (n = 30) | 2 000 | 6 | 43.07 | 0.441 | 1.155 | 1.348 |
砂姜黑土 Lime concretion black soil (n = 30) | 1 000 | 6 | 48.65 | 0.488 | 0.717 | 1.420 |
褐土 Cinnamon soil (n = 30) | 1 000 | 3 | 24.58 | 0.258 | 1.054 | 1.172 |
5种土壤 Five type soils (n = 150) | 2 000 | 4 | 42.61 | 0.427 | 0.930 | 1.325 |
Table 6 Results of estimating winter wheat leaf area index (LAI) by optimal random forest regression (RFR) models
土壤类型 Soil type | 随机森林回归模型的优化参数 The optimized parameters in RFR | 模型精度 Model precision | ||||
---|---|---|---|---|---|---|
ntree | mtry | 变量解释 Variable explained (%) | R2 | RMSE | RPD | |
水稻土 Paddy soil (n = 30) | 1 000 | 2 | 60.08 | 0.604 | 0.598 | 1.609 |
潮土 Fluvo-aquic soil (n = 30) | 1 000 | 8 | 72.86 | 0.733 | 0.528 | 1.953 |
棕壤 Brown soil (n = 30) | 2 000 | 6 | 43.07 | 0.441 | 1.155 | 1.348 |
砂姜黑土 Lime concretion black soil (n = 30) | 1 000 | 6 | 48.65 | 0.488 | 0.717 | 1.420 |
褐土 Cinnamon soil (n = 30) | 1 000 | 3 | 24.58 | 0.258 | 1.054 | 1.172 |
5种土壤 Five type soils (n = 150) | 2 000 | 4 | 42.61 | 0.427 | 0.930 | 1.325 |
Fig. 5 Results of different groups of partial least squares regression model for estimating leaf area index (LAI). NDVI, MSAVI, MCARI2, RES, REIP, Dr, SDr, NDSI see Table 2. RMSE, root mean square error.
Fig. 7 Measured vs. predicted leaf area index (LAI). A, Paddy soil. B, Fluvo-aquic soil. C, Brown soil. D, Lime concretion black soil. E, Cinnamon soil. F, Five soil types. Dash lines indicate the confidence intervals of prediction. PLSR, partial least squares regression; RFR, random forest regression; RMSE, root mean square error; RPD, relative percent deviation.
土壤类型 Soil type | 考虑土壤类型因素 Considering the soil background | 不考虑土壤类型因素 Neglecting the soil background | ||||||
---|---|---|---|---|---|---|---|---|
PLSR | RFR | PLSR | RFR | |||||
R2 | RMSECV | R2 | RMSE | R2 | RMSECV | R2 | RMSE | |
水稻土 Paddy soil | 0.684 | 0.533 | 0.604 | 0.598 | 0.529 | 0.689 | 0.722 | 0.553 |
潮土 Fluvo-aquic soil | 0.743 | 0.518 | 0.733 | 0.528 | 0.559 | 0.682 | 0.635 | 0.632 |
棕壤 Brown soil | 0.771 | 0.735 | 0.441 | 1.155 | 0.098 | 1.467 | 0.244 | 1.387 |
砂姜黑土 Lime concretion black soil | 0.490 | 0.721 | 0.488 | 0.717 | 0.173 | 0.953 | 0.413 | 0.768 |
褐土 Cinnamon soil | 0.385 | 0.954 | 0.258 | 1.054 | 0.349 | 0.986 | 0.287 | 1.052 |
Table 7 Leaf area index (LAI) predicted accuracy
土壤类型 Soil type | 考虑土壤类型因素 Considering the soil background | 不考虑土壤类型因素 Neglecting the soil background | ||||||
---|---|---|---|---|---|---|---|---|
PLSR | RFR | PLSR | RFR | |||||
R2 | RMSECV | R2 | RMSE | R2 | RMSECV | R2 | RMSE | |
水稻土 Paddy soil | 0.684 | 0.533 | 0.604 | 0.598 | 0.529 | 0.689 | 0.722 | 0.553 |
潮土 Fluvo-aquic soil | 0.743 | 0.518 | 0.733 | 0.528 | 0.559 | 0.682 | 0.635 | 0.632 |
棕壤 Brown soil | 0.771 | 0.735 | 0.441 | 1.155 | 0.098 | 1.467 | 0.244 | 1.387 |
砂姜黑土 Lime concretion black soil | 0.490 | 0.721 | 0.488 | 0.717 | 0.173 | 0.953 | 0.413 | 0.768 |
褐土 Cinnamon soil | 0.385 | 0.954 | 0.258 | 1.054 | 0.349 | 0.986 | 0.287 | 1.052 |
Fig. 8 Comparison of measured leaf area index (LAI) and predicted LAI in box plots. PLSR, partial least squares regression; RFR, random forest regression. RMSE, root mean square error. From top to bottom, the box plot represents the maximum, upper quartile, median, lower quartile, and minimum of a set data, respectively.
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