Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor
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- 作者：Qiang Cao ; Yuxin Miao ; Jianning Shen ; Weifeng Yu ; Fei Yuan…
- 关键词：Precision nitrogen management ; Crop Circle sensor ; In ; season nitrogen management ; Active crop canopy sensor ; GreenSeeker sensor ; Response index
- 刊名：Precision Agriculture
- 出版年：2016
- 出版时间：April 2016
- 年：2016
- 卷：17
- 期：2
- 页码：136-154
- 全文大小：1,641 KB
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Yuxin Miao (1)
Jianning Shen (1)
Weifeng Yu (1)
Fei Yuan (3)
Shanshan Cheng (1)
Shanyu Huang (1) (4)
Hongye Wang (1)
Wen Yang (5)
Fengyan Liu (5)
1. International Center for Agro-Informatics and Sustainable Development (ICASD), Center for Resources, Environment and Food Security, China Agricultural University, Beijing, 100193, China
2. National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
3. Department of Geography, Minnesota State University, Mankato, MN, 56001, USA
4. Institute of Geography, University of Cologne, 50923, Cologne, Germany
5. Jiansanjiang Institute of Agricultural Sciences, Jiansanjiang, Heilongjiang, China - 刊物类别：Earth and Environmental Science
- 刊物主题：Environment
Soil Science and Conservation
Agriculture
Meteorology and Climatology
Statistics for Engineering, Physics, Computer Science, Chemistry and Geosciences
Remote Sensing and Photogrammetry - 出版者：Springer Netherlands
- ISSN：1573-1618
文摘
In-season site-specific nitrogen (N) management is a promising strategy to improve crop N use efficiency and reduce risks of environmental contamination. To successfully implement such precision management strategies, it is important to accurately estimate yield potential without additional topdressing N application (YP0) as well as precisely assess the responsiveness to additional N application (RI) during the growing season. Previous research has mainly used normalized difference vegetation index (NDVI) or ratio vegetation index (RVI) obtained from GreenSeeker active crop canopy sensor with two fixed bands in red and near-infrared (NIR) spectrums to estimate these two parameters. The development of three-band Crop Circle active sensor provides a potential to improve in-season estimation of YP0 and RI. The objectives of this study were twofold: (1) identify important vegetation indices obtained from Crop Circle ACS-470 sensor for estimating rice YP0 and RI; and (2) evaluate their potential improvements over GreenSeeker NDVI and RVI. Four site-years of field N rate experiments were conducted in 2012 and 2013 at the Jiansanjiang Experiment Station of China Agricultural University located in Northeast China. The GreenSeeker and Crop Circle ACS-470 active canopy sensor with green, red edge, and NIR bands were used to collect rice canopy reflectance data at different key growth stages. The results indicated that both the GreenSeeker (best R2 = 0.66 and 0.70, respectively) and Crop Circle (best R2 = 0.71 and 0.77, respectively) sensors worked well for estimating YP0 and RI at the stem elongation stage. At the booting stage, Crop Circle red edge optimized soil adjusted vegetation index (REOSAVI, R2 = 0.82) and green ratio vegetation index (R2 = 0.73) explained 26 and 22 % more variability in YP0 and RI, respectively, than GreenSeeker NDVI or RVI. At the heading stage, the GreenSeeker sensor indices became saturated and consequently could not be used for YP0 or RI estimation, while Crop Circle REOSAVI and normalized green index could still explain more than 70 % of YP0 and RI variability. It is concluded that both sensors performed similarly at the stem elongation stage, but significantly better results were obtained by the Crop Circle sensor at the booting and heading stages. Furthermore, the results revealed that Crop Circle green band-based vegetation indices performed well for RI estimation while the red edge-based vegetation indices were the best for estimating YP0 at later growth stages.