TY - GEN
T1 - Estimating rice nitrogen status with the Crop Circle multispectral active canopy sensor
AU - Cao, Q.
AU - Miao, Y.
AU - Huang, S.
AU - Wang, H.
AU - Khosla, R.
AU - Jiang, R.
PY - 2013/12/1
Y1 - 2013/12/1
N2 - 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 (CIRE), which performed similarly to NDRE.
AB - 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 (CIRE), which performed similarly to NDRE.
KW - Active crop sensor
KW - Nitrogen nutrition index (NNI)
KW - Nitrogen status
KW - Precision nitrogen management
KW - Red edge vegetation index
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UR - http://www.scopus.com/inward/citedby.url?scp=84887823063&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84887823063
SN - 9789086862245
T3 - Precision Agriculture 2013 - Papers Presented at the 9th European Conference on Precision Agriculture, ECPA 2013
SP - 95
EP - 101
BT - Precision Agriculture 2013 - Papers Presented at the 9th European Conference on Precision Agriculture, ECPA 2013
T2 - 9th European Conference on Precision Agriculture, ECPA 2013
Y2 - 7 July 2013 through 11 July 2013
ER -
