无人机多光谱遥感监测冬小麦拔节期根域土壤含水率
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摘要
快速精确地获取冬小麦根域土壤含水率对实现精准灌溉具有重要意义。以拔节期不同水分处理的冬小麦为对象,利用低空无人机搭载六波段多光谱相机获取其冠层光谱反射率,并同时采集5个不同深度(10、20、30、40、60 cm)土壤含水率数据,通过逐步回归法、偏最小二乘法、岭回归法建立光谱数据与5个深度的多元回归模型。结果表明,三种回归模型对10、20 cm深度土壤含水率都有较高的监测精度,可以较好地对作物根域土壤含水率进行定量预测,其中逐步回归模型效果最好,其模型的决定系数R~2达到0.815、0.747,预测模型的R~2为0.774、0.717,相对分析误差R_(PD)为2.007、1.862,但三种回归模型对深度为30、40、60 cm根域土壤含水率的监测精度都较低。该研究结果对指导精准灌溉具有一定的参考价值。
Rapid and accurate acquisition of soil moisture content in the root region of winter wheat is of great significance for the realization of precise irrigation. In this study, winter wheat at jointing stage with different moisture treatment was taken as the study object, the canopy spectral reflectance was obtained by using a unmanned aerial vehicle(UAV) equipped with a six-band multispectral camera, and soil moisture content at 5 different depths(10, 20, 30, 40 and 60 cm) were simultaneously collected. The multiple regression models between soil moisture and the reflectance of different bands were established by stepwise regression, partial least squares and ridge regression. The results showed that the three regression models had high monitoring accuracy for soil moisture content at 10 and 20 cm depth, which could better predict the soil moisture content of crop roots; the stepwise regression model had the best prediction ability(modeling R~2 were 0.815 and 0.747, validation R~2 were 0.774 and 0.717, RPD were 2.007 and 1.862); however, the three regression models of 30, 40 and 60 cm had low monitoring accuracy for the soil moisture content. The results of this study have certain reference value for guiding precision irrigation.
Rapid and accurate acquisition of soil moisture content in the root region of winter wheat is of great significance for the realization of precise irrigation. In this study, winter wheat at jointing stage with different moisture treatment was taken as the study object, the canopy spectral reflectance was obtained by using a unmanned aerial vehicle(UAV) equipped with a six-band multispectral camera, and soil moisture content at 5 different depths(10, 20, 30, 40 and 60 cm) were simultaneously collected. The multiple regression models between soil moisture and the reflectance of different bands were established by stepwise regression, partial least squares and ridge regression. The results showed that the three regression models had high monitoring accuracy for soil moisture content at 10 and 20 cm depth, which could better predict the soil moisture content of crop roots; the stepwise regression model had the best prediction ability(modeling R~2 were 0.815 and 0.747, validation R~2 were 0.774 and 0.717, RPD were 2.007 and 1.862); however, the three regression models of 30, 40 and 60 cm had low monitoring accuracy for the soil moisture content. The results of this study have certain reference value for guiding precision irrigation.
引文
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[22] 王亚萍,胡正华,张雪松,等.土壤水分胁迫对冬小麦根域分布规律的影响[J].江苏农业科学,2016,44(11):67-71.
[23] 杨荣光,于春霞,张兴强,等.不同土壤水分条件下冬小麦根域分布规律研究[J].耕作与栽培,2014(1):4-5,8.
[2] 蒋静,翟登攀,张超波.灌溉施肥水平对盐渍化农田水盐分布及玉米产量的影响[J].应用生态学报,2019(4):1-13.
[3] 潘宁,王帅,刘焱序,等.土壤水分遥感反演进展[J].生态学报,2019(13):1-11.
[4] 付青,郭晨,龙满生.高分辨率光学卫星遥感影像无控区域网平差[J].激光与光电子学进展,2019(4):1-12.
[5] 杨曦光,于颖.基于试验反射光谱数据的土壤含水率遥感反演[J].农业工程学报,2017,33(22):195-199.
[6] 陈雯,武威,孙成明,等.无人机遥感在作物监测中的应用与展望[J].上海农业学报,2016,32(2):138-143.
[7] 田国珍,武永利.土壤水分遥感监测及关键技术[J].山西农业科学,2013,41(9):1 021-1 026.
[8] 纪景纯,赵原,邹晓娟,等.无人机遥感在农田信息监测中的应用进展[J].土壤学报,2019(4):1-13.
[9] 高燕,梁泽毓,王彪,等.基于无人机和卫星遥感影像的升金湖草滩植被地上生物量反演[J].湖泊科学,31(2).
[10] 滕连泽,罗勇,张洪吉,等.无人机遥感在农业监测中的应用研究综述[J].科技资讯,2018,16(23):122-124.
[11] Li H,R J Lascano,E M Barnes,et al.Multispectral Reflectance of Cotton Related to Plant Growth,Soil Water and Texture,and Site Elevation.Agron.J.2001,93:1 327-1 337.
[12] Hassan-Esfahani L,Torres-Rua A,Jensen A,et al.Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks[J].Remote Sensing,2015,7(3):2 627-2 646.
[13] 王海峰,张智韬,付秋萍,等.低空无人机多光谱遥感数据的土壤含水率反演[J].节水灌溉,2018(1):90-94,102.
[14] Oliver Wigmore,Bryan Mark,Jeffrey McKenzie,et al.Sub-metre mapping of surface soil moisture in proglacial valleys of the tropical Andes using a multispectral unmanned aerial vehicle,Remote Sensing of Environment,Volume,2019,222:104-118.
[15] 陈硕博,陈俊英,张智韬,等.无人机多光谱遥感反演抽穗期冬小麦土壤含水率研究[J].节水灌溉,2018(5):39-43.
[16] 张秋霞,张合兵,张会娟,等.粮食主产区耕地土壤重金属高光谱综合反演模型[J].农业机械学报,2017,48(3):148-155.
[17] 张智韬,王海峰,韩文霆,等.基于无人机多光谱遥感的土壤含水率反演研究[J].农业机械学报,2018.
[18] 王锐.岭回归分析在解决经济数据共线性问题中的应用[J].经济研究导刊,2018(22):144-147.
[19] 周涌.基于谱分解的广义岭回归方法及其优良性探讨[J].统计与信息论坛,2018,33(10):22-26.
[20] 胡良平.岭回归分析[J].四川精神卫生,2018,31(3):193-196.
[21] 农淦,路培,王林华,等.黄土高原坡耕地小麦根域分布特征研究[J].水土保持研究,2013,20(5):92-98.
[22] 王亚萍,胡正华,张雪松,等.土壤水分胁迫对冬小麦根域分布规律的影响[J].江苏农业科学,2016,44(11):67-71.
[23] 杨荣光,于春霞,张兴强,等.不同土壤水分条件下冬小麦根域分布规律研究[J].耕作与栽培,2014(1):4-5,8.
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