Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

Remote Sensing

James Brinkhoff¹  AndrewJ. Robson¹  RemyL. Dehaan²  TinaS. Dunn³  BrianW. Dunn³ 

[1] Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, Australia;EH Graham Centre for Agricultural Innovation (NSW Department of Primary Industries and Charles Sturt University), Locked Bag 588, Wagga, NSW 2678, Australia;NSW Department of Primary Industries, 2198 Irrigation Way, Yanco, NSW 2703, Australia;

关键词: rice;    nitrogen management;    remote sensing;    multispectral imagery;    reflectance index;    multiple variable linear regression;    Lasso model;   

DOI  :  10.3390/rs11151837

来源: DOAJ

【 摘 要 】

Mid-season nitrogen (N) application in rice crops can maximize yield and profitability. This requires accurate and efficient methods of determining rice N uptake in order to prescribe optimal N amounts for topdressing. This study aims to determine the accuracy of using remotely sensed multispectral data from satellites to predict N uptake of rice at the panicle initiation (PI) growth stage, with a view to providing optimum variable-rate N topdressing prescriptions without needing physical sampling. Field experiments over 4 years, 4−6 N rates, 4 varieties and 2 sites were conducted, with at least 3 replicates of each plot. One WorldView satellite image for each year was acquired, close to the date of PI. Numerous single- and multi-variable models were investigated. Among single-variable models, the square of the NDRE vegetation index was shown to be a good predictor of N uptake (R ${}^2$ = 0.75, RMSE = 22.8 kg/ha for data pooled from all years and experiments). For multi-variable models, Lasso regularization was used to ensure an interpretable and compact model was chosen and to avoid over fitting. Combinations of remotely sensed reflectances and spectral indexes as well as variety, climate and management data as input variables for model training achieved R ${}^2$ < 0.9 and RMSE < 15 kg/ha for the pooled data set. The ability of remotely sensed data to predict N uptake in new seasons where no physical sample data has yet been obtained was tested. A methodology to extract models that generalize well to new seasons was developed, avoiding model overfitting. Lasso regularization selected four or less input variables, and yielded R ${}^2$ of better than 0.67 and RMSE better than 27.4 kg/ha over four test seasons that weren’t used to train the models.

【 授权许可】

Unknown