结合植被指数与纹理特征的玉米冠层FAPAR遥感估算研究
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王思宇, 聂臣巍, 余汛, 邵明超, 王梓旭, 努热曼古丽·托乎提, 刘亚东, 程明瀚, 官云兰, 金秀良
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Maize Canopy FAPAR Remote Sensing Estimation Combining Vegetation Indexes and Texture Characteristics
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Wang Siyu, Nie Chenwei, Yu Xun, Shao Mingchao, Wang Zixu, Nuremanguli· Tuohuti, Liu Yadong, Cheng Minghan, Guan Yunlan, Jin Xiuliang
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表4 各植被指数估算玉米FAPAR的最佳回归检验结果
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Table 4 The best regression test results of maize FAPAR estimating using each vegetation indice
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植被指数
Vegetation indice | 回归模型
Regression model | 建模Modeling | | 验证Validating | | R2(×10-2) | RMSE(×10-2) | rRMSE(%) | R2(×10-2) | RMSE(×10-2) | rRMSE(%) | | DVI | y=-8.8039x2+6.3709x-0.2476 | 66.13 | 7.98 | 9.42 | | 66.35 | 9.10 | 10.75 | | GNDVI | y=-1.5792x2+2.4437x-0.0391 | 78.52 | 6.35 | 7.492 | | 78.00 | 7.33 | 8.66 | | MTCI | y=-0.1751x2+0.4515x+0.6248 | 61.13 | 8.54 | 10.08 | | 70.63 | 7.90 | 9.34 | | TVI | y=0.5125ln(x)-0.6934 | 56.48 | 9.04 | 10.67 | | 57.39 | 10.05 | 11.88 | | MTVI2 | y=-1.2913x2+2.3547x-0.1493 | 71.74 | 7.28 | 8.59 | | 66.93 | 9.17 | 10.84 | | OSAVI | y=1.169x1.0069 | 72.18 | 7.24 | 8.54 | | 72.18 | 7.80 | 9.22 | | NDVI | y=0.9303x1.2449 | 73.74 | 7.03 | 8.30 | | 74.97 | 7.59 | 8.97 | | RVI1 | y=0.0497ln(x)+0.636 | 33.95 | 11.14 | 13.14 | | 37.53 | 11.14 | 13.16 | | RVI2 | y=-0.0024x2+0.062x+0.5651 | 44.55 | 10.20 | 12.04 | | 52.06 | 9.80 | 11.58 | | MNLI | y=-5.4349x2+2.9606x+0.5071 | 77.83 | 6.45 | 7.61 | | 75.59 | 8.59 | 10.15 | | SAVI | y=1.3688x0.8208 | 66.58 | 7.93 | 9.36 | | 67.87 | 8.21 | 9.70 | | MSR | y=0.0908ln(x)+0.672 | 40.98 | 10.53 | 12.42 | | 46.45 | 10.31 | 12.18 | | NLI | y=0.3779x+0.5515 | 74.54 | 6.91 | 8.15 | | 74.59 | 7.42 | 8.77 | | RDVI | y=1.487x0.9052 | 68.16 | 7.74 | 9.13 | | 69.25 | 8.08 | 9.55 |
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