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

中国农业科学 ›› 2020, Vol. 53 ›› Issue (9): 1795-1805.doi: 10.3864/j.issn.0578-1752.2020.09.008

• 耕作栽培·生理生化·农业信息技术 • 上一篇下一篇

LAI无人机多光谱遥感估测及其在盐渍土改良中的应用

史丰智^1,²,王瑞燕^1,²(),李玉环^1,²,闫宏³,张晓鑫¹

¹ 山东农业大学资源与环境学院,山东泰安 271018;
² 土肥资源高效利用国家工程实验室,山东泰安 271018;
³ 商河县农业农村局,山东商河 276200

收稿日期:2019-08-23 接受日期:2020-03-07 出版日期:2020-05-01 发布日期:2020-05-13
通讯作者: 王瑞燕
作者简介:史丰智,E-mail：1204944751@qq.com。
基金资助:
山东省重点研发计划(2017CXGC0306);‘十三五’国家重点研发计划(2017YFD0200702);山东农业大学创新团队项目(SYL2017XTTD02);山东农业大学青年教师成长计划经费和青年创新基金(23694)

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

FengZhi SHI^1,²,RuiYan WANG^1,²(),YuHuan LI^1,²,Hong YAN³,XiaoXin ZHANG¹

¹ College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, Shandong;
² National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Tai’an 271018, Shandong;
³ Agricultural and Rural Bureau of Shanghe County, Shanghe 276200, Shandong

Received:2019-08-23 Accepted:2020-03-07 Online:2020-05-01 Published:2020-05-13
Contact: RuiYan WANG

摘要/Abstract

摘要：

【目的】植被产量能综合直观地反映盐渍土改良效果,冬小麦生长旺盛期的叶面积指数（LAI）是植被产量的常用衡量指标。本研究利用无人机多光谱遥感获取冬小麦生长旺盛期的叶面积指数分布数据,对盐渍土改良效果进行客观准确评价,为人们筛选盐渍土改良技术和产品提供科学指导。【方法】以无棣县渤海粮仓滨海盐渍土改良试验区为研究区,基于无人机多光谱遥感数据,利用线性回归分析、偏最小二乘、随机森林和支持向量机等方法,构建拔节期冬小麦LAI反演模型;并利用因子分析法对盐渍土地区抽样地块进行改良效果评价,建立盐渍土改良效果LAI评价模型,基于该评价模型对整个试验区盐渍土改良效果进行评价。【结果】对冬小麦LAI遥感估测而言,并不是分辨率越高越好,而是5×5均值平滑后的光谱数据与一垄小麦叶面积指数的对应最佳。LAI遥感估测模型中,利用支持向量机建立的模型精度最高。改良效果LAI评价模型的预测结果表明,LAI对盐渍土改良效果的预测精度较高,改良效果最优地块的编号为26、27、28、29、30和31,最优改良方法为引黄淤灌和增施有机肥综合改良措施。【结论】无人机遥感可对盐渍土地区拔节期冬小麦的叶面积指数进行准确反演,基于LAI反演结果的盐渍土改良效果评价能够从众多试验小区中定位出最优的改良效果。与传统方法相比,该方法具有成本低廉、精度高等优势,研究结果有广泛推广前景,可以为盐渍土的改良提供重要技术支持。

关键词: 无人机多光谱, 盐渍土改良, 冬小麦拔节期, 叶面积指数, 遥感估测

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

史丰智,王瑞燕,李玉环,闫宏,张晓鑫. LAI无人机多光谱遥感估测及其在盐渍土改良中的应用[J]. 中国农业科学, 2020, 53(9): 1795-1805.

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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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无人机多光谱遥感估测及其在盐渍土改良中的应用

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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