LAI无人机多光谱遥感估测及其在盐渍土改良中的应用

doi:10.3864/j.issn.0578-1752.2020.09.008

Abstract

Abstract:

【Objective】Vegetation yield can comprehensively and intuitively reflect the improvement effect of saline soil. Leaf area index (LAI) of winter wheat during its vigorous growth period is a commonly used measure of vegetation yield. In this study, unmanned aerial vehicle (UAV) multispectral remote sensing was used to obtain the LAI distribution data of winter wheat during its vigorous growth period to objectively and accurately evaluate the improvement effect of saline soil, so as to provide scientific guidance for people to screen saline soil improvement technology and products. 【Method】Taking the experimental area of coastal saline soil improvement in Bohai granary in Wudi county as the research area, based on UAV multispectral remote sensing data, linear regression analysis, partial least squares, random forest, and support vector machine were used to construct LAI inversion model of winter wheat. The factor analysis method was used to evaluate the improvement effect of the sampled land in saline soil area, and the LAI evaluation model of saline soil improvement effect was established to evaluate the improvement effect of saline soil in the entire experimental area. 【Result】The results showed that, for the winter wheat LAI remote sensing estimation, it was not that the higher the resolution, the better, but the smoothed 5×5 mean spectral data corresponded best to the LAI of a ridge of wheat. Among the LAI remote sensing estimation models, the models were built by using SVM with the highest accuracy. The prediction result of the improvement effect LAI evaluation model showed that the prediction accuracy of the improvement effect of saline soil by LAI was higher, and the best improvement land numbers were 26, 27, 28, 29, 30, and 31, and the optimal improvement method was cited. The best improvement method was comprehensive improvement measures of diversion irrigation and adding organic fertilizer. 【Conclusion】UAV remote sensing could accurately invert the LAI of winter wheat at the jointing stage in saline soil area. The evaluation of the improvement effect of saline soil based on the results of LAI inversion could locate the optimal improvement effect from many experimental plots. Compared with the traditional method, this method had the advantages of low cost and high accuracy. The research results had a broad prospect and could provide important technical support for the improvement of saline soil.

Key words: multi-spectral of UAV, improvement effect of saline soil, jointing stage of winter wheat, leaf area index, remote sensing estimation

FengZhi SHI,RuiYan WANG,YuHuan LI,Hong YAN,XiaoXin ZHANG. LAI Estimation Based on Multi-Spectral Remote Sensing of UAV and Its Application in Saline Soil Improvement[J].Scientia Agricultura Sinica, 2020, 53(9): 1795-1805.

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References 31

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不同窗口 Different window	波段名称Band name
不同窗口 Different window	1×1	2×2	3×3	4×4	5×5	6×6	7×7
绿光GRE	-0.53**	-0.54**	-0.54**	-0.58**	-0.57**	-0.58**	-0.58**
红光RED	-0.58**	-0.60**	-0.59**	-0.60**	-0.61**	-0.59**	-0.61**
红边REG	-0.18	-0.19	-0.25*	-0.32**	-0.34**	-0.43**	-0.38**
近红NIR	0.55**	0.55**	0.53**	0.47**	0.49**	0.38**	0.45**

植被指数 Vegetation index	计算公式 Calculation formula
归一化植被指数 NDVI^[25]	NDVI=(ρ_nir-ρ_red)/(ρ_nir+ρ_red)
比值植被指数 RVI^[26]	RVI=ρ_nir/ρ_red
绿度归一化植被指数 GNDVI^[27]	GNDVI=(ρ_nir-ρ_gre)/(ρ_nir+ρ_gre)
绿色比值植被指数 GRVI^[28]	GRVI=ρ_nir/ρ_gre

不同窗口 Different window	植被指数 Vegetation index
不同窗口 Different window	1×1	2×2	3×3	4×4	5×5	6×6	7×7
NDVI	0.62**	0.66**	0.64**	0.66**	0.67**	0.62**	0.67**
RVI	0.78**	0.80**	0.81**	0.81**	0.82**	0.79**	0.79**
GNDVI	0.59**	0.63**	0.62**	0.67**	0.66**	0.63**	0.67**
GRVI	0.75**	0.79**	0.77**	0.79**	0.79**	0.77**	0.79**
(G+R)/NIR	0.55**	0.58**	0.57**	0.60**	0.60**	0.56**	0.61**

	建模集Modeling set (n=44)				验证集Validation set (n=19)
	多元线性回归 Multiple linear regression	偏最小二乘 PLS	支持向量机 SVM	随机森林 RF	多元线性回归 Multiple linear regression	偏最小二乘 PLS	支持向量机 SVM	随机森林 RF
R²	0.84	0.75	0.85	0.94	0.69	0.66	0.62	0.56
RMSE	0.51	0.63	0.48	0.32	0.66	0.67	0.74	0.79
RPD	2.30	1.70	2.40	3.50	1.40	1.50	1.30	1.20

成分Ingredient	初始特征值Initial eigenvalue			提取载荷平方和Extract load sum of squares			旋转载荷平方和Sum of rotation load squares
成分Ingredient	总计 Total	方差百分比 Variance percentage	累积 Accumulation (%)	总计 Total	方差百分比 Variance percentage (%)	累积Accumulation (%)	总计 Total	方差百分比 Variance percentage	累积 Accumulation (%)
1	2.330	58.248	58.248	2.330	58.248	58.248	2.114	52.852	52.852
2	0.959	23.987	82.235	0.959	23.987	82.235	1.175	29.383	82.235
3	0.458	11.445	93.680
4	0.253	6.320	100.000

LAI Estimation Based on Multi-Spectral Remote Sensing of UAV and Its Application in Saline Soil Improvement

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	成分1 Ingredient 1	成分2 Ingredient 2
有机质Organic matter	0.411	-0.023
盐分Salinity	-0.275	-0.260
含氮量Nitrogen content	0.502	-0.263
水分Moisture	-0.171	0.899

地块编号 Parcel number	得分 Score	LAI	排名 Ranking	地块编号 Parcel number	得分 Score	LAI	排名 Ranking	地块编号 Parcel number	得分 Score	LAI	排名 Ranking
6	0.67	4.76	1	9	0.42	2.35	7	12	0.33	1.55	12
1	0.59	3.84	2	15	0.42	2.40	6	13	0.32	1.88	11
8	0.49	2.81	4	10	0.39	2.51	8	16	0.22	1.70	13
5	0.48	3.24	3	18	0.34	2.19	9	17	0.02	1.39	14
11	0.46	2.66	5	14	0.33	1.76	10

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