Estimating Rice Nitrogen Status Using Active Canopy Sensor Crop Circle 430 in Northeast China

Jianning Shen, Yuxin Miao*, Qiang Cao, Hongye Wang, Weifeng Yu, Shanshan Hu,Haibing Wu, Junjun Lu, Xiaoyi Hu

International Center for Agro-Informatics and Sustainable Development College of Resources and Environmental Sciences

China Agricultural University Beijing, 100193 China

ymiao@cau.edu.cn; ymiao2014@126.com

Wen Yang, Fengyan Liu Jiansanjiang Institute of Agricultural Sciences

Fujian, Heilongjiang, 156300 China

Abstract—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.

Keywords Crop Circle, rice nitrogen status, red edge, active

canopy sensor, precision nitrogen management.

I. INTRODUCTION

Precision agriculture is listed as one of the top ten agricultural revolution over the past 50 years [1]. Precision nitrogen (N) management is an important part of precision agriculture, with the aim to match N supply and crop demand in space and time[2]. Remote sensing technology can overcome some of the limitations of traditional sampling and laboratory analysis and has a good potential for non-destructive diagnosis of crop N status [3, 4]. Active canopy sensors have been used to develop variable rate N fertilizer management strategies in rice, maize, and wheat [5-7]. A challenge is that traditional vegetation indices (VI) like

NDVI are prone to saturate with high yielding crops [8]. Researchers have found that VIs such as normalized difference red edge (NDRE), red edge ratio vegetation index (RERVI), canopy chlorophyll content index (CCCI), etc. are more closely related to crop N status than NDVI[9-11]. Best indices differed with different crop growth stages and different agronomic parameters [12]. Therefore, active canopy sensors with 3 or more bands are more desirable than two band sensors. Red edge has been found to be very sensitive to crop chlorophyll, LAI and biomass [13]. Preliminary studies indicated that red edge indices like NDRE and CCCI calculated from near-infrared and red edge band have better performance than traditional NDVI in estimation of pine chlorophyll content [14]. Studies have shown that RERVI (R760/R730) was the most effective and stable VI in estimating winter wheat N status [15]. Cao et al. (2013) indicated that several red edge-based indices performed well for estimating N nutrition index (NNI) across rice growth stages (R2=0.76)[16]. Shiratsuchi et al. (2011) found that the indices (R760-R720) / (R760-R670) and (R760-R720) / (R720-R670) were not affected by moisture and had good performance in distinguishing maize N status [17]. Crop Circle ACS-430 (CC-430) is a newly developed active crop canopy reflectance sensor with 3 fixe wavebands covering red (670nm), red-edge (730nm), and near infrared (780nm) regions. It is not affected by measurement height above crop canopy within the range of 0.3 to 2 meter. However, no studies have been reported to evaluate this sensor for estimating N status of high yield rice. Therefore, the main objective of this study is to identify suitable CC-430 VIs for estimating rice N status.

II. MATERIAL AND METHODS

A. Study site description and experment design

The field experiments were carried out in 2012 and 2013 in Jiansanjiang Experiment Station of China Agricultural University, located in Sanjiang Plain, Heilongjiang province, northeast China. The experimental site received five N rates (0, 70, 100, 130, and 160kg N ha-1) and 2 varieties (Kongyu131, 11 leaf variety; Longjing21, 12 leaf variety). N fertilizer was distributed in three splits: 40% as basal N before transplanting, 30% at in the period of tillering stage, and 30% at stem elongation stage. For all treatments, 50kg ha-1 P2O5 as triple

super-phosphate was applied before transplanting and 105kg ha-1K2O as potassium sulfate was applied as two splits: 50% before transplanting and 50% at stem elongation stage. The plot of experiments were replicated three times in a randomized complete block design. The individual plot size was 7 by 9 m. All field management, such as rice seedlings production, irrigation, weeding and pesticide applications, followed the local standard practices.

B. Crop Cirle ACS-430 sensor data collection

In this study, a newly developed active canopy sensor, Crop Circle ACS-430 (Holland Scientific Inc., Lincoln, Nebraska, USA) with 3 fixe wavebands covering red (670nm), red-edge (730nm), and near infrared (780nm) region were used to collect canopy reflectance information across each plot at panicle initiation, stem elongation and heading stage. The CC-430 sensor spectral reflectance data can be easily and quickly recorded to a text file on an SD flash card using the Holland Scientific GeoSCOUTGLS-400 Data logger. The average canopy reflectance values were used to represent each plot. The VIs from CC-430 sensor selected for this study are listed in Table I.

C. Plant sampling and measurement

Rice plant samples were acquired right after collecting sensor reading at each growth stage (panicle initiation stage, stem elongation stage and heading stage). At the measurement date, destructive plant samples of above ground biomass were taken by random clipping three to four hills from scanned rice plants in each plot. The samples were dried at 105 °C for 30 min and then dried at 70 °C to constant weight and weighed. Aboveground dry production was determined, and total N content was analyzed using the Kjeldahl-N method, and the N uptake was calculated by multiplying plant N concentration (%) and aboveground dry biomass. The NNI was calculated following Lemaire et al. (2008)[29] and the critical N concentration was calculated following Sheehy et al. (1998)[30].

D. Statistical analysis

The descriptive statistics was calculated using Microsoft Excel 2013 (Microsoft Corporation, Redmond, Washington, USA). Analysis of variance and coefficients of determination (R2) for the relationships between spectral VIs and the

agronomic parameters (plant N uptake and NNI) were calculated using SAS 8.0 software (SAS Institute, Cary, NC,

USA). SigmaPlot 12 was also used for plotting.

TABLE I. SPECTRAL INDICES USED IN THIS STUDY.

Index Definition Reference

Normalized difference vegetation index(NDVI) (NIR-R)/(NIR+R) Rouse et al. (1974)[18]

Ratio vegetation index (RVI) NIR/R Jordan. (1969)[19]

Normalized difference red edge (NDRE) (NIR-RE)/(NIR+RE) Barnes et al. (2000)[9]

Red edge ratio vegetation index (RERVI) NIR/RE Jasper et al. (2009)[10]

Soil-adjusted vegetation index (SAVI) 1.5*(NIR-R)/(NIR+R+0.5) Huete (1988)[20]

Optimized SAVI (OSAVI) (1+0.16)* (NIR-R)/(NIR+R+0.16) Rondeaux et al. (1996)[21]

Modified Soil-adjusted vegetation index (MSAVI) 1.5 * [(NIR R)/(NIR+R+0.5)] Qi et al. (1994)[22]

Medium Resolution Imaging Spectrometer (MERIS) (R+NIR)/2 Dash and Curran. (2004)[23]

MERIS terrestrial chlorophyll index (MTCI) (NIR-RE)/(RE-R) Modified from Dash and Curran. (2004)[23]

Canopy chlorophyll content index (CCCI) (NDRE-NDREMIN)/(NDREMAX-NDREMIN) Barnes et al. (2000)[9]

Difference vegetation index (DVI) NIR-R Tucker (1979)[24]

Red edge difference vegetation index (REDVI) NIR-RE Modified from Tucker (1979)[24]

Renormalized difference vegetation index (RDVI) (NIR-R)/ Roujean and Breon (1995)[25]

Transformed normalized vegetation index (TNDVI) SQRT[(NIR-R)/(NIR+R)+0.5] Sandham and Zietsman. (1997)[26]

Normalized difference index (NDI) (NIR-RE)/(NIR-R) Datt (1999)[27]

Simple ratio vegetation index (SR) RE/R Modified from McMurtrey et al. (1994)[28]

III. RESULTS AND DISCUSSION

A. Destrucitve anylysis of plant N uptake and NNI

The plant N uptake and NNI of rice varied greatly across different N rates, varieties, years, and growth stages (Table 2). The standard deviation (SD) and the coefficient of variation (CV) of plant N uptake were larger than NNI in all growth stages. NNI was reported to be a better indicator of crop N status [29]. Across growth stages, plant N uptake ranged from 14-189 kg ha-1, and NNI ranged from 0.3-1.2.

B. Relationship between vegetation indices and rice N status

The 16 vegetation indices performed quite differently for estimating plant N uptake and NNI (Table III). Red edge indices (NDRE, RERVI, REDVI, R2=0.71-0.81) had better performance for estimating plant N uptake and NNI than normalized and simple ratio indices (NDVI, RVI, SAVI,

OSAVI, MSAVI, DVI, TNDVI, NDI, SR, R2= 0.27-0.74) across all growth stages.

Fig. 1 and 2 show relationships between spectral vegetation indices (NDRE, RERVI, NDVI, and RVI) and plant N uptake, NNI across all growth stages, varieties, and years. NDRE and RERVI had better relationships with plant N uptake (R2=0.8-0.81) and NNI (R2=0.71) than NDVI and RVI. NDVI was obviously saturated at values of 0.8. However, red edge indices did not have such problem.

IV. CONCLUSION This study estimated rice N status using active crop

canopy sensor Crop Circle 430 in Northeast China. The results indicated that red edge-based VIs (like NDRE and RERVI) had better performance than NDVI and RVI for estimating rice N uptake and NNI. More studies are needed to further validate these indices at different sites, varieties, growth stages and on farm levels to guide in-season site-specific N management of rice.

Table II. DESCRIPTIVE STATISTICS OF PLANT N UPTAKE AND NNI ACROSS VARIETIES, AND YEARS.

Growth stages N Range Mean SD CV (%)

Plant N uptake(kg/ha)

Panicle initiation stage 66 14.1-68 34.8 12.5 35.9

Stem elongation stage 66 24.7-105.9 64.5 20.8 32.2

Heading stage 60 48.7-188.8 119.7 35.5 29.7

All stages combined 192 14.1-88.8 71.5 42.4 59.3

NNI

Panicle initiation stage 66 0.3-0.82 0.54 0.12 22.2

Stem elongation stage 66 0.4-1.04 0.71 0.16 22.5

Heading stage 60 0.49-1.2 0.87 0.19 21.8

All stages combined 192 0.3-1.2 0.7 0.21 30.0

NDVI0.2 0.4 0.6 0.8

Plan

t N u

ptak

e (k

g N

ha-

1 )

0

50

100

150

200

250y=373.44x2-162.87x+46.359R2=0.66P<0.0001

RVI2 4 6 8

Pla

nt N

upt

ake

(kg

N h

a-1 )

0

20

40

60

80

100

120

140

160

180

200y=-1.1043x2+30.114x-21.389R2=0.65P<0.0001

(a) (b)

NDRE0.1 0.2 0.3 0.4

Pla

nt N

upt

ake

(kg

N h

a-1 )

0

20

40

60

80

100

120

140

160

180

200y=1793.5x2-288.15x+37.183R2=0.81P<0.0001

RERVI1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

Pla

nt N

upt

ake

(kg

N h

a-1 )

020406080

100120140160180200

y=90.505x2-140.79+59.763R2=0.8P<0.0001

(c) (d)

Figure 1. Relationship between rice plant N uptake and NDVI (a), RVI (b), NDRE (c), and RERVI (d) across different growth stages, varieties, and years.

NDVI0.2 0.4 0.6 0.8

NN

I

0.2

0.4

0.6

0.8

1.0

1.2

1.4y=0.6263x2+0.4053x+0.3024R2=0.58P<0.0001

RVI2 4 6 8

NN

I

0.2

0.4

0.6

0.8

1.0

1.2

1.4y=-0.0124x2+0.2019x+0.159R2=0.57P<0.0001

(a) (b)

NDRE0.1 0.2 0.3 0.4

NN

I

0.2

0.4

0.6

0.8

1.0

1.2

1.4

RERVI1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

NN

I

0.2

0.4

0.6

0.8

1.0

1.2

1.4y=2.6987x2+1.2924x+0.2587R2=0.71P<0.0001

y=-0.0621x2+0.9426x-0.6481R2=0.71P<0.0001

(c) (d)

Figure 3. Relationship between rice NNI and NDVI (a), RVI (b), NDRE (c), and RERVI (d) across different growth stages, varieties, and years.

TABLE3. COEFFICIENT OF DETERMINATION (R2) FOR RELATIONSHIPS BETWEEN SPECTRAL INDICES (CALCULATED FROM CC-430) AND

PLANT N UPTAKE AND NNI ACROSS CULTIVARS, GROWTH STAGES, AND YEARS.

Agronomic

parameters

Spectral

indices

Panicle

initiation stage

Stem

elongation stage

Heading

stage

All stages

combined

Plant N uptake NDVI 0.5 0.52 0.32 0.66

RVI 0.53 0.48 0.32 0.65

NDRE 0.58 0.58 0.64 0.81

RERVI 0.58 0.58 0.64 0.8

SAVI 0.51 0.52 0.32 0.66

OSAVI 0.5 0.52 0.32 0.65

MSAVI 0.49 0.51 0.31 0.65

MERIS 0.21 0.22 0.58 0.13

MTCI 0.05 0.19 0.73 0.37

CCCI 0.21 0.20 0.31 0.32

DVI 0.53 0.56 0.47 0.74

RDVI 0.52 0.54 0.4 0.7

TNDVI 0.5 0.52 0.32 0.66

REDVI 0.58 0.57 0.64 0.81

NDI 0.1 0.21 0.75 0.31

SR 0.48 0.36 0.17 0.56

NNI NDVI 0.5 0.53 0.35 0.58

RVI 0.51 0.51 0.36 0.57

NDRE 0.59 0.59 0.68 0.71

RERVI 0.59 0.58 0.69 0.71

SAVI 0.5 0.53 0.36 0.58

OSAVI 0.5 0.53 0.36 0.58

MSAVI 0.49 0.54 0.35 0.58

MERIS 0.21 0.04 0.58 0.1

MTCI 0.06 0.26 0.72 0.34

CCCI 0.20 0.22 0.29 0.35

DVI 0.54 0.57 0.52 0.65

RDVI 0.52 0.55 0.44 0.62

TNDVI 0.5 0.54 0.35 0.58

REDVI 0.59 0.59 0.68 0.71

NDI 0.12 0.21 0.74 0.27

SR 0.46 0.4 0.2 0.49

ACKNOWLEDGEMENT

This study was financially supported by Natural Science Foundation of China (31071859), Chinese Universities Scientific Fund (2014JD066), and the National Science and Technology Support Project (2012BAD04B01-06-03).

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