The wheat canopy reflectance spectrum is affected by many internal and external factors such as diseases and growth stage.Separating the effects of disease stress on the crop from the observed mixed signals is crucial for increasing the precision of remote sensing monitoring of wheat stripe rust.The canopy spec-trum of winter wheat infected by stripe rust was processed with the difference-in-differences(DID)algo-rithm used in econometrics.The monitoring accuracies of wheat stripe rust before and after processing with the DID algorithm were compared in the presence of various external factors,disease severity,and several simulated satellite sensors.The correlation between the normalized difference vegetation index processed by the DID algorithm(NDVI-DID)and the disease severity level(SL)increased in comparison with the NDVI before processing.The increase in precision in the natural disease area in the field in the presence of large differences in growth stage,growth,planting,and management of the crop was greater than that in the controlled experiment.For low disease levels(SL＜20％),the R2 of the regression of NDVI-DID on SL was 38.8％higher than that of the NDVI and the root mean square error(RMSE)was reduced by 11.1％.The increase in precision was greater than that for the severe level(SL＞40％).According to the measured hyperspectral data,the spectral reflectance of three satellite sensor levels was simulated.The wide-band NDVI was calculated.Compared with the wide-band NDVI and vegetation indexes(VIs)before DID processing,there were increases in the correlation between SL and the various types of VIs-DID,as well as in the correlation between SL and NDVI-DID.It is feasible to apply the DID algo-rithm to multispectral satellite data and diverse types of VIs for monitoring wheat stripe rust.Our results improve the quantification of independent effects of stripe rust infection on canopy reflectance spectrum,increase the precision of remote sensing monitoring of wheat stripe rust,and provide a reference for remote sensing monitoring of other crop diseases.

Xia Jing;Kaiqi Du;Weina Duan;Qin Zou;Tingting Zhao;Bingyu Li;Qixing Ye;Lieshen Yan

College of Geomatics,Xi'an University of Science and Technology,Xi'an 710054,Shaanxi,China;Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China

作物学报（英文版）

[1]Xia Jing;Kaiqi Du;Weina Duan;Qin Zou;Tingting Zhao;Bingyu Li;Qixing Ye;Lieshen Yan-.Quantifying the effects of stripe rust disease on wheat canopy spectrum based on eliminating non-physiological stresses)[J].作物学报（英文版）,2022(05):1284-1291

Quantifying,effects,stripe,rust,wheat,canopy,spectrum,eliminating,physiological,stresses,reflectance,affected,by,many,internal,external,factors,such,diseases,growth,stage,Separating,crop,from,observed,mixed,signals,crucial,increasing,precision,remote,sensing,monitoring,winter,infected,was,processed,differences,DID,used,econometrics,accuracies,before,after,processing,algorithm,were,compared,presence,various,severity,several,simulated,satellite,sensors,correlation,between,normalized,vegetation,NDVI,SL,increased,comparison,natural,area,field,large,planting,management,greater,than,that,controlled,experiment,For,low,levels,regression,higher,root,mean,square,error,RMSE,reduced,severe,According,measured,hyperspectral,data,three,wide,band,calculated,Compared,indexes,VIs,there,increases,types,well,It,feasible,apply,multispectral,diverse,Our,results,improve,quantification,independent,infection,provide,reference,other

0.383245