结合植被指数与纹理特征的玉米冠层FAPAR遥感估算研究

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

Maize Canopy FAPAR Remote Sensing Estimation Combining Vegetation Indexes and Texture Characteristics

Wang Siyu, Nie Chenwei, Yu Xun, Shao Mingchao, Wang Zixu, Nuremanguli· Tuohuti, Liu Yadong, Cheng Minghan, Guan Yunlan, Jin Xiuliang

表5 各纹理信息估算玉米FAPAR的最佳回归检验结果

Table 5 The best regression test results of maize FAPAR estimating using each texture

纹理特征 Texture	回归模型 Regression model	建模Modeling			验证Validating
纹理特征 Texture	回归模型 Regression model	R²(×10^-2)	RMSE(×10^-2)	rRMSE(%)	R²(×10^-2)	RMSE(×10^-2)	rRMSE(%)
均值Mean	y=-0.0031x²+0.0493x+0.7169	58.73	8.80	10.38	55.53	9.50	11.23
方差Variance	y=-0.0003x²+0.0192x+0.644	18.44	12.38	14.61	25.00	12.28	14.51
协同性Homogeneity	y=-3.1473x²+1.8839x+0.6007	5.12	13.35	15.75	19.27	13.06	15.43
对比度Contrast	y=-0.0005x²+0.0235x+0.6542	16.42	12.53	14.78	22.56	12.50	14.77
相异性Dissimilarity	y=-0.0199x²+0.1895x+0.5184	11.85	12.87	15.19	19.65	12.79	15.11
信息熵Entropy	y=-2.1224x²+14.464x-23.756	12.53	12.82	15.13	36.23	11.45	13.53
二阶矩Second moment	y=-399x²+38.016x-0.0288	6.75	13.23	15.61	39.64	12.26	14.49
相关性Correlation	y=-14.067x²+19.182x-5.6254	21.68	12.13	14.31	29.39	11.94	14.11