农业工程学报2019，Vol.35Issue(19)：89-97,9.DOI:10.11975/j.issn.1002-6819.2019.19.011
覆膜对无人机多光谱遥感反演土壤含盐量精度的影响
Effect of plastic film mulching on soil salinity inversion by using UAV multispectral remote sensing
摘要
Abstract
Estimating soil salinity is imperative for scheduling irrigation and remediating saline soil but difficult at large scales. Remote sensing can bridge this gap because of its advantages in low cost and large-area coverage; it has become an efficient method for assessing soil salinization in field. One issue in use of remote sensing to assess saline soil is the pres-ence of plastic film mulch and bare soil because of their difference in reflecting waves in the spectral bands. In order to in-vestigate the effect of plastic film mulch on soil salinity inversion using UAV multispectral remote sensing, we studied four plots with plastic film mulch at the Shahaoqu Irrigation area in the Hetao Irrigation District, Inner Mongolia of China. From each plot, we took soil samples and measured their salt contents from May to July. We also flew a drone to simultaneously take multispectral images of the sampling sites and extracted the spectral reflectance to calculate the spectral indices. Corre-lation analysis found that the S4,S6, SI1, SI2, SI3 and BI indices can be used to calculate soil salinity. Six-band spectral re-flectances and six spectral indices obtained from different datasets were used as independent variables to calculate the salt content with the support vector machine (SVM), the back propagation neural network (BPNN) and the extreme learning ma-chine (ELM), respectively, before and after the mulch film was removed. We compared the three models based on their de-termination coefficient (R2), root mean squared error (RMSE) and relative error (RE). The results showed that plastic film mulch did impact on soil salinity inversion. Although all three models could adequately estimate the soil salt contents be-fore and after the film removal, they worked better after the film removal than before the film removal. Models based on the spectral indices were more accurate than those based on the spectral reflectances, and the accuracy of the inversely calculat-ed salt content varied with sampling time and treatment. The inversion results based on monthly data differed from those based on by pooling all data. After the film was removed in June, the salt content estimated using the model was most accu-rate, with its associated R2 and RMSE being 0.695 and 0.182 respectively for the spectral reflectance-based method, and 0.663 and 0.191respectively for the spectral indices-based method. The salt content estimated by BPNN was least accurate in May, with its associated R2 and RMSE being 0.766 and 0.161 respectively for the spectral reflectance-based method, and 0.769, 0.162 respectively for the spectral indices-based method. Comparison of the three models revealed that ELM was most accurate, followed by SVM and BPNN, although their errors were within the tolerable range. In summary, this paper provides an effective method to inversely calculate soil salinization at large mulched farmland using UAV multispectral re-mote sensing.关键词
遥感/土壤盐分/光谱反射率/光谱指数/机器学习Key words
remote sensing/soil salt/spectral reflectance/spectral index/machine learning分类
农业科技引用本文复制引用
姚志华,陈俊英,张智韬,谭丞轩,魏广飞,王新涛..覆膜对无人机多光谱遥感反演土壤含盐量精度的影响[J].农业工程学报,2019,35(19):89-97,9.基金项目
国家重点研发计划项目(2017YFC0403302) （2017YFC0403302）
国家自然科学基金资助项目(41502225) （41502225）
杨凌示范区科技计划项目(2018GY-03) （2018GY-03）
