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Yao Y.,China Agricultural University | Miao Y.,University of Cologne | Cao Q.,Colorado State University | Wang H.,Jiansanjiang Institute of Agricultural science | And 6 more authors.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

Timely nondestructive estimation of crop nitrogen (N) status is crucial for in-season site-specific N management. Active crop canopy sensors are the promising tools to obtain the needed information without being affected by environmental light conditions. The objective of this study was to evaluate the potential for the GreenSeeker active crop canopy sensor to estimate rice (Oryza sativa L.) N status. Nine N rate experiments were conducted from 2008 to 2012 in Jiansanjiang, Heilongjiang Province in Northeast China. The results indicated that across site-years and growth stages, normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) obtained with the GreenSeeker sensor could explain 73%-76% and 70%-73% of rice aboveground biomass and plant N uptake variability in this study, respectively. The NDVI index became saturated when biomass reached about {\bf 4\nbsp\hbox{t}\nbsp\hbox{ha}^{-1}} or when plant N uptake reached about {\bf 100\nbsp\hbox{kg}\nbsp\hbox{ha}^{-1}}, whereas RVI did not show obvious saturation effect. The validation results, however, indicated that both indices performed similarly, and their relative errors (RE) were still large ( {\bf \gt 40\% }). Although the two indices only explained less than 40% of plant N concentration or N nutrition index (NNI) variability, the RE values were acceptable ({\bf \lt 26\%} ). The results indicated some potentials of using the GreenSeeker sensor to estimate rice N status nondestructively, but more studies are needed to further evaluate and improve its performance for practical applications. © 2008-2012 IEEE. Source


Cao Q.,China Agricultural University | Cao Q.,Nanjing Agricultural University | Miao Y.,China Agricultural University | Shen J.,China Agricultural University | And 8 more authors.
Precision Agriculture | Year: 2015

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. © 2015 Springer Science+Business Media New York Source


Shi W.,China Agricultural University | Lu J.,China Agricultural University | Miao Y.,China Agricultural University | Cao Q.,Nanjing Agricultural University | And 6 more authors.
2015 4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015 | Year: 2015

It has been widely reported that active crop canopy sensor-based precision nitrogen (N) management strategy can improve N use efficiency and reduce possibilities of environmental pollution simultaneously. The two-band GreenSeeker sensor is commonly used in such strategies. However, GreenSeeker-based normalized difference vegetation index (NDVI) can become saturated at moderate to high biomass conditions. The Crop Circle ACS-470 active canopy sensor is a three-band user-configurable sensor with a choice of six spectral bands. Little research has been carried out to compare the results of precision nitrogen management strategies based on these two active canopy sensors for rice. The objective of this research was to evaluate Crop Circle ACS-470 sensor-based precision N management strategy for rice yield and N use efficiency in Northeast China. Two field experiments were conducted in 2014 in Jiansanjiang, Heilongjiang Province, China, using a randomized complete block design with four treatments and three replications. Each experiment had the same three treatments: The Regional Optimum N Management (RONM), GreenSeeker-based Precision N Management (GS-PNM) and Crop Circle-based Precision N Management (CC-PNM). The difference was that Experiment 1 used an 11 leave variety (Longjing31) and Experiment 2 used a 12-leaf variety (Longjing21). The results indicated that CC-PNM increased grain yield, N agronomic efficiency (AEN), N partial factor productivity (PFPN) in Experiment 2 by 18%, 46% and 20% over GreenSeeker-based precision N management (GS-PNM), respectively, which was similar to RONM. However, in Experiment 1, there was no significant difference among the three N management strategies. More studies are needed to further improve and more systematically evaluate these precision N management strategies under different on-farm conditions. © 2015 IEEE. Source


Shen J.,China Agricultural University | Miao Y.,China Agricultural University | Cao Q.,China Agricultural University | Wang H.,China Agricultural University | And 7 more authors.
2014 The 3rd International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2014 | Year: 2014

Active crop canopy sensors are commonly used to estimate crop nitrogen (N) status in precision N management to improve N use efficiency and reduce negative environmental impacts caused by over-application of N. However, traditional vegetation indices (VI) like normalized difference vegetation index (NDVI) obtained from GreenSeeker sensor can become saturated under medium to high crop biomass conditions, making it unsuitable for application in high yield crop management systems. Crop Circle ACS-430 (CC-430) is a newly developed active crop canopy sensor with 3 fixe wavebands covering red (670nm), red-edge (730nm), and near infrared (780nm) regions. The objective of this study is to identify optimum VIs obtained with the CC-430 sensor for estimating rice N status in Northeast China. A total of four field experiments involving five N rates (0, 70, 100, 130 and 160 kg ha-1) and two rice varieties (Kongyu 131 and Longjing 21) were conducted in 2012 and 2013 in Jiansanjiang Experimental Station of China Agriculture University in Heilongjiang Province, Northeast China. The preliminary results indicated that among 16 different VIs evaluated, red edge-based indices, normalized difference red edge (NDRE) and red edge ratio vegetation index (RERVI) had consistent better correlations with rice plant N uptake (R2=0.8-0.81) and NNI (R2=0.71) across different growth stages, varieties, and years, which have better performance than red light based VI (NDVI, RVI) (R2=0.57-0.66). These results indicated that red edge-based VIs have better potential for estimating rice N status than red radiation-based VIs. More studies are needed to further evaluate this sensor and develop corresponding precision N management strategies to achieve high crop yield and high N use efficiency using this new red edge-based active crop canopy sensor. © 2014 IEEE. Source

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