Brinkhoff, James; Dunn, Brian W; Robson, Andrew J; Dunn, Tina S; Dehaan, Remy L

Author(s)

Brinkhoff, James

Dunn, Brian W

Robson, Andrew J

Dunn, Tina S

Dehaan, Remy L

Publication Date

2019-08-06

Abstract

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 (R² = 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 R² < 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 R² 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.

Citation

Remote Sensing, 11(15), p. 1-22

ISSN

2072-4292

Link

https://hdl.handle.net/1959.11/28431

Publisher

MDPI AG

Rights

Attribution 4.0 International

Title

Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

Type of document

Journal Article

Entity Type

Publication

Author(s)	Brinkhoff, James Dunn, Brian W Robson, Andrew J Dunn, Tina S Dehaan, Remy L
Publication Date	2019-08-06
Abstract	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 (R² = 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 R² < 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 R² 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.
Citation	Remote Sensing, 11(15), p. 1-22
ISSN	2072-4292
Link	https://hdl.handle.net/1959.11/28431
Publisher	MDPI AG
Rights	Attribution 4.0 International
Title	Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data
Type of document	Journal Article
Entity Type	Publication

Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

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