Abstract
Leaf nitrogen content (LNC) is a good indicator of the nutritional status of winter wheat, and remote sensing monitoring of nitrogen level in winter wheat growth period can not only grasp the crop nutrient and growth conditions, but also help to improve the yield and quality. In this study, field data of canopy reflectance and LNC of winter wheat of three critical growth stages were collected for different treatments during 2014/2015 and 2015/2016. The correlation between LNC of winter wheat and 16 spectral indices was compared and analyzed, and then 4 spectral indices of NDSI (R594, R506), RSI (R592, R506), mSR705 and mNDVI705 were selected. On the basis of this, linear regression (LR) model, multiple stepwise regression (MSR) model and random forest regression (RFR) model were constructed and validated with independent data sets in 2014/2015. To further compare the accuracy, stability and applicability of three inversion models, the robustness tests were conducted based on the independent data sets under three different conditions in 2015/2016. The result showed that the RFR model had the best estimation accuracy among the three models, and the value of R2 and RMSE in modeling set respectively were 0.962 and 0.276, and the value of R2 and RMSE in validation set were 0.898 and 0.401. In addition, the RFR model had a higher R2 and lower RMSE than the other two models under each condition. It indicated that the RFR model combined with multiple spectral indices and random forest algorithm had higher precision and applicability, so it can effectively and rapidly retrieve the LNC of winter wheat.
Highlights
As a new learning method developed on the basis of statistical learning theory, is a very powerful tool for data analysis and mining, and it can solve the defects of linear regression model very well
The spectral indices used in this study were strongly related to Leaf nitrogen content (LNC), and |r| was above 0.70, and all of them have reached a very significant level of 0.01
The estimation accuracy of three models was more than 0.8, and the root mean square error (RMSE) was between 0.276 and 0.288. These results showed that the three models can be used for rapid, nondestructive and accurate monitoring of LNC of winter wheat, and among them, the effect of random forest regression (RFR) model was the best
Summary
Nitrogen has the most significant effect on photosynthesis, growth and development, yield and quality formation, and is a mineral element with high crop demand and application [1].When the crop is lack of nitrogen, it will seriously affect the yield and quality of the crop, otherwise, it will cause certain pollution to the environment. Winter wheat is one of the main grain crops in China, and its nitrogen nutrition assessment is beneficial to growth diagnosis and field technical management. The traditional method of crop nitrogen diagnosis was mainly through laboratory chemical analysis, which usually required destructive sampling, resulting in poor timeliness and strong subjectivity. Hyperspectral remote sensing technology has gained extensive attention in the field of crop nitrogen nutrition diagnosis because of its large amount of information, high spectral resolution and continuous wave band [3]. Many scholars have done a lot of researches on crop nitrogen content estimation. The estimation accuracy of crop nitrogen content was improved by screening sensitive bands and constructing vegetation index [1, 5, 6]. Predecessors have done a great deal of work and achieved fruitful results in monitoring crop nitrogen content, different inversion methods had their own characteristics. As a new learning method developed on the basis of statistical learning theory, is a very powerful tool for data analysis and mining, and it can solve the defects of linear regression model very well
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