Estimating rice nitrogen status with the Crop Circle multispectral active canopy sensor

Q. Cao; Y. Miao; S. Huang; H. Wang; R. Khosla; R. Jiang

Estimating rice nitrogen status with the Crop Circle multispectral active canopy sensor

In: Precision agriculture '13

Authors:

Q. Cao

Q. Cao ¹International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environmental Sciences, China Agricultural University, Beijing, China; ymiao@cau.edu.cn; ymiao2007@gmail.com

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Y. Miao

Y. Miao ¹International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environmental Sciences, China Agricultural University, Beijing, China; ymiao@cau.edu.cn; ymiao2007@gmail.com

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S. Huang

S. Huang ¹International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environmental Sciences, China Agricultural University, Beijing, China; ymiao@cau.edu.cn; ymiao2007@gmail.com

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H. Wang

H. Wang ¹International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environmental Sciences, China Agricultural University, Beijing, China; ymiao@cau.edu.cn; ymiao2007@gmail.com

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R. Khosla

R. Khosla ²Department of Soil and Crop Sciences, Colorado State University, Ft. Collins, Colorado, USA

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, and

R. Jiang

R. Jiang ¹International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environmental Sciences, China Agricultural University, Beijing, China; ymiao@cau.edu.cn; ymiao2007@gmail.com

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Type:: Chapter

Pages:: 95–101

DOI:: https://doi.org/10.3920/9789086867783_012

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The objective of this study was to determine which vegetation indices calculated from the Crop Circle active sensor bands will perform best for estimating rice nitrogen (N) status. Six field experiments were conducted in Sanjiang Plain in Heilongjiang Province, China during 2011 and 2012. The results of the study indicated that six vegetation indices were significantly related to N uptake and nitrogen nutrition index (NNI) across different years, varieties and growth stages. Subsequently, six farm fields in two different villages were selected as datasets to validate the models developed in this study. The results indicated that using Normalized Difference Red Edge (NDRE) to predict plant N uptake had the highest coefficient of determination (R², 0.76), the lowest root mean square error (RMSE, 17.00 kg N/ha), and relative error (RE, 23.61%) across different years, varieties and locations. The NDRE also gave the best prediction for NNI, with R² being 0.76, RMSE being 0.09 and RE being 11.63%. The second best performing vegetation index was Red Edge Chlorophyll Index (CI_RE), which performed similarly to NDRE.

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Precision agriculture '13

E-Book ISBN:: 9789086867783

Publisher:: Wageningen Academic

Print Publication Date:: 01 Nov 2013

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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Estimating rice nitrogen status with the Crop Circle multispectral active canopy sensor

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Precision agriculture '13

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