Unmanned Aerial Vehicle Multispectral Remote Sensing for Monitoring of Nitrogen Nutritional Indicators in High-Yielding Spring Maize in Northeast China
To investigate the accuracy of the random forest algorithm for nitrogen nutrition prediction in spring maize,this study conducted a nitrogen fertilization gradient trial in northeast China in 2021-2022.10 nitrogen appli-cation levels were set with Deka 159 as the test material,and remote sensing data were acquired at the small trum-pet stage(V9),large trumpet stage(V12)and male tapping stage(VT)using a UAV with multispectral.Using 19 vege-tation indices,four models were constructed,including aboveground biomass(AGB),nitrogen uptake by plants(PNU),leaf area index(LAI)and specific leaf nitrogen(SLN).The results showed that the random forest algorithm had high accuracy in predicting AGB and LAI,and R2 is 0.83 and 0.9.The correlation analysis of LAI,AGB,PNU and SLN had the highest correlation with the structurally insensitive pigment index(SIPI),with correlation coeffi-cients of-0.75,-0.7,-0.84 and 0.63,respectively;Structurally SIPIis the highest importance among the four mod-els.The results of the study indicate that the random forest algorithm has a certain development potential in spring maize nitrogen monitoring,and theSIPI has an important role in nitrogen monitoring,and the results of the study can provide a reference basis for monitoring nitrogen nutrients in spring maize.
Spring maizeMultispectral remote sensingRandom forestNitrogen nutrient index
NETL
NSTL
万方数据
