Crop Circle is an active multispectral canopy sensor developed to support precision crop management. The Crop Circle ACS-470 model is user configurable, with a choice of six wavebands covering blue, green, red, red edge and near infrared spectral regions. The objectives of this study were to determine how well nitrogen (N) status of rice (Oryza sativa L.) can be estimated with the Crop Circle ACS-470 active sensor using green, red edge and near infrared (NIR) bands at key growth stages and identify important vegetation indices for estimating rice N status indicators. Six field experiments involving different N rates and two varieties were conducted in Sanjiang Plain in Heilongjiang Province, China during 2011 and 2012. Crop sensor data and plant samples were also collected from five farmers' fields to further evaluate the sensor and selected vegetation indices. The results of the study indicated that among 43 different vegetation indices evaluated, modified chlorophyll absorption reflectance index 1 (MCARI1) had consistent correlations with rice aboveground biomass (R2=0.79) and plant N uptake (R2=0.83) across growth stages. Four red edge-based indices, red edge soil adjusted vegetation index (RESAVI), modified RESAVI (MRESAVI), red edge difference vegetation index (REDVI) and red edge re-normalized difference vegetation index (RERDVI), performed equally well for estimating N nutrition index (NNI) across growth stages (R2=0.76). For rice plant N concentration, the highest R2 was 0.33, and none of the indices performed satisfactorily with validation using farmers' field data. We conclude that the Crop Circle ACS-470 active canopy sensor allows users the flexibility to select suitable bands and calculate different vegetation indices and has a great potential for in-season non-destructive estimation of rice biomass, plant N uptake and NNI.
|Original language||English (US)|
|Number of pages||12|
|Journal||Field Crops Research|
|State||Published - Dec 2013|
Bibliographical noteFunding Information:
This study was financially supported by Natural Science Foundation of China ( 31071859 ), National Basic Research Program ( 973-2009CB118606 ), Chinese Universities Scientific Fund ( 2012YJ043 ; 2013QJ061 ) and The Innovative Group Grant of Natural Science Foundation of China ( 31121062 ). The kind assistance and supports provided by Yuan Gao, Wen Yang, Yongxing Wang, Huamin Zhu, Shu Dang and Xiaolong Wang at Jiansanjiang Institute of Agricultural Research and Jiansanjiang Branch Bureau of Agricultural Reclamation for this research are highly appreciated.
Copyright 2013 Elsevier B.V., All rights reserved.
- Active crop sensor
- Nitrogen nutrition index
- Nitrogen status
- Nitrogen status diagnosis
- Precision agriculture
- Precision nitrogen management
- Red edge vegetation index