基于Sentinel卫星及无人机多光谱的滨海冬小麦种植区土壤盐分反演研究—

基于Sentinel卫星及无人机多光谱的滨海冬小麦种植区土壤盐分反演研究——以黄三角垦利区为例

奚雪,赵庚星,高鹏,崔昆,李涛

Inversion of Soil Salinity in Coastal Winter Wheat Growing Area Based on Sentinel Satellite and Unmanned Aerial Vehicle Multi-Spectrum— A Case Study in Kenli District of the Yellow River Delta

XI Xue,ZHAO GengXing,GAO Peng,CUI Kun,LI Tao

表5 基于无人机多光谱的土壤盐分估测模型

Table 5 Soil salinity estimation model based on multi-spectral of UAV

建模方法 Modeling approach	光谱参量 Spectral parameter	估测模型 Estimating model	建模精度 Modeling accuracy		验证精度 Verification accuracy
建模方法 Modeling approach	光谱参量 Spectral parameter	估测模型 Estimating model	R²	RMSE	R²	RMSE
逐步回归 Stepwise regression	b_G	Y=18.609×b_G+0.169	0.458	1.267	0.401	0.882
	b_G,b_R	Y=12.546×b_G+19.044×b_R-1.120	0.599	1.089	0.600	0.729
	b_G,b_R,b_NIR	Y=8.360×b_G+15.775×b_R-9.912×b_NIR +3.116	0.673	0.983	0.650	0.847
	b_G,b_R,b_NIR,b_REG	Y=7.988×b_G+12.282×b_R-8.525×b_NIR+6.979×b_REG+2.101	0.694	0.951	0.648	0.771
	NDVI	Y=-7.507×NDVI+6.308	0.620	1.061	0.668	0.685
	NDVI,RVI	Y=-13.261×NDVI+0.69×RVI+6.734	0.715	0.918	0.692	0.897
	NDVI,RVI,SI	Y=-10.287×NDVI+0.651×RVI+13.486×SI+3.843	0.756	0.850	0.710	0.907
偏最小二乘法 Partial least squares	b_G,b_R,b_NIR,b_REG	Y=6.021×b_G+6.5986×b_R+6.2650×b_NIR-4.1737×b_REG+1.3260	0.689	1.114	0.719	1.177
偏最小二乘法 Partial least squares	NDVI,RVI,SI	Y=-9.4774×NDVI+0.4794×RVI+3.0747×SI+5.0604	0.734	0.954	0.784	0.769
BP神经网络 The BP neural network	b_G,b_R,b_NIR,b_REG	—	—	—	0.714	0.893
BP神经网络 The BP neural network	NDVI,RVI,SI	—	—	—	0.753	0.993
支持向量机 Support vector machine	b_G,b_R,b_NIR,b_REG	—	0.804	0.590	0.467	0.473
支持向量机 Support vector machine	NDVI,RVI,SI	—	0.835	0.353	0.640	0.512