农业生态环境-遥感合辑Agro-ecosystem & Environment—Romote sensing
|An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat
ZHAO Yu1, 2*, WANG Jian-wen1, 3*, CHEN Li-ping1, 2, FU Yuan-yuan1, 2, ZHU Hong-chun3, FENG Hai-kuan1, 2, XU Xin-gang1, 2, LI Zhen-hai1, 2
1 Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs/Beijing Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China
2 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China
3 College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, P.R.China
氮素营养指数（NNI）是作物氮素诊断的可靠指标。然而，目前还没有适用于多生育时期NNI反演的光谱指数。为克服传统NNI直接反演方法（NNIT1）和通过反演生物量（AGB）和植株氮浓度（PNC）进行NNI间接反演方法（NNIT2）在多生育期应用的局限性，本文构建了一个新的NNI遥感指数（NNIRS）。本文基于连续四年（2012–2013（Exp.1），2013–2014（Exp.2），2014–2015（Exp.3）和2015–2016（Exp.4））的冬小麦田间试验，采用交叉验证方法利用氮素相关植被指数和生物量相关植被指数构建了遥感关键氮浓度稀释曲线（Nc_RS）和根据NNI构建原理得到的NNIRS进行综合评价。结果表明：（1）由标准叶面积指数决定指数（sLAIDI）和红边叶绿素指数（CIred edge）构建的NNIRS模型表达式为NNIRS=CIred edge/（a×sLAIDIb），在Exp.1/2/4，Exp.1/2/3，Exp.1/3/4和Exp.2/3/4中参数“a”分别等于2.06，2.10，2.08和2.02，参数“b”分别等于0.66，0.73，0.67和0.62；（2）与NNIT1和NNIT2模型相比，NNIRS模型的精度最高（R2的范围为0.50–0.82，RMSE的范围为0.12–0.14）；（3）NNIRS在验证数据集中也达到了较好的精度，RMSE分别为0.09，0.18，0.13和0.10。因此，本文认为NNIRS模型在氮素遥感诊断中具有较大的潜力。
The nitrogen nutrition index (NNI) is a reliable indicator for diagnosing crop nitrogen (N) status. However, there is currently no specific vegetation index for the NNI inversion across multiple growth periods. To overcome the limitations of the traditional direct NNI inversion method (NNIT1) of the vegetation index and traditional indirect NNI inversion method (NNIT2) by inverting intermediate variables including the aboveground dry biomass (AGB) and plant N concentration (PNC), this study proposed a new NNI remote sensing index (NNIRS). A remote-sensing-based critical N dilution curve (Nc_RS) was set up directly from two vegetation indices and then used to calculate NNIRS. Field data including AGB, PNC, and canopy hyperspectral data were collected over four growing seasons (2012–2013 (Exp.1), 2013–2014 (Exp. 2), 2014–2015 (Exp. 3), 2015–2016 (Exp. 4)) in Beijing, China. All experimental datasets were cross-validated to each of the NNI models (NNIT1, NNIT2 and NNIRS). The results showed that: (1) the NNIRS models were represented by the standardized leaf area index determining index (sLAIDI) and the red-edge chlorophyll index (CIred edge) in the form of NNIRS=CIred edge/(a×sLAIDIb), where “a” equals 2.06, 2.10, 2.08 and 2.02 and “b” equals 0.66, 0.73, 0.67 and 0.62 when the modeling set data came from Exp.1/2/4, Exp.1/2/3, Exp.1/3/4, and Exp.2/3/4, respectively; (2) the NNIRS models achieved better performance than the other two NNI revised methods, and the ranges of R2 and RMSE were 0.50–0.82 and 0.12–0.14, respectively; (3) when the remaining data were used for verification, the NNIRS models also showed good stability, with RMSE values of 0.09, 0.18, 0.13 and 0.10, respectively. Therefore, it is concluded that the NNIRS method is promising for the remote assessment of crop N status.
Received: 08 April 2020
|Fund: This research was supported by the earmarked fund for China Agriculture Research System (CARS-03), the National Key Research and Development Program of China (2017YFD0201501 and 2016YFD020060306) and the National Natural Science Foundation of China (41701375 and 61661136003).
Correspondence LI Zhen-hai, Tel: +86-10-51503215, Fax: +86-10-51503750, E-mail: firstname.lastname@example.org
|About author: ZHAO Yu, E-mail: email@example.com; WANG Jian-wen, E-mail: firstname.lastname@example.org; * These authors contributed equally to this study.
Cite this article:
ZHAO Yu, WANG Jian-wen, CHEN Li-ping, FU Yuan-yuan, ZHU Hong-chun, FENG Hai-kuan, XU Xin-gang, LI Zhen-hai.
An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat. Journal of Integrative Agriculture, 20(9): 2535-2551.
| Abdallah F B, Olivier M, Goffart J P, Minet O. 2016. Establishing the nitrogen dilution curve for potato cultivar Bintje in Belgium. Potato Research, 59, 241–258.
Ata-Ul-Karim S T, Liu X, Lu Z, Lu Z, Yuan Z, Zhu Y. 2016. In-season estimation of rice grain yield using critical nitrogen dilution curve. Feild Crops Research, 195, 1–8.
Ata-Ul-Karim S T, Yao X, Liu X, Cao W, Zhu Y. 2014a. Determination of critical nitrogen dilution curve based on stem dry matter in rice. PLoS ONE, 9, e104540.
Ata-Ul-Karim S T, Zhu Y Yao X, Cao W. 2014b. Determination of critical nitrogen dilution curve based on leaf area index in rice. Field Crops Research, 167, 76–85.
Blackburn G A. 1998. Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing, 19, 657–675.
Chen J M. 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22, 229–242.
Chen P. 2015. A comparison of two approaches for estimating the wheat nitrogen nutrition index using remote sensing. Remote Sensing, 7, 4527–4548.
Chen P, Wang J, Huang W, Tremblay N, Qu Y, Zhang Q. 2013. Critical nitrogen curve and remote detection of nitrogen nutrition index for corn in the northwestern plain of Shandong Province, China. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 6, 682–689.
Chen P F, Haboudane D, Tremblay N, Wang J, Vigneault P, Li B. 2010a. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment, 114, 1987–1997.
Chen P F, Tremblay N, Wang J H, Vigneault P, Huang W, Li B. 2010b. New index for crop canopy fresh biomass estimation. Spectroscopy & Spectral Analysis, 30, 512–517. (in Chinese)
Cilia C, Panigada C, Rossini M, Meroni M, Busetto L, Amaducci S, Boschetti M, Picchi V, Colombo R. 2014. Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sensing, 6, 6549–6565.
Dash J, Curran P J. 2004. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing, 25, 5403–5413.
Delalieux S, Somers B, Hereijgers S, Verstraeten W W, Keulemans W, Coppin P. 2008. A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards. Remote Sensing of Environment, 112, 3762–3772.
Evans J R. 1983. Nitrogen and photosynthesis in the flag leaf of wheat (Triticum aestivum L.). Plant Physiology, 72, 297–302.
Gitelson A A, Gritz Y, Merzlyak M N. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160, 271–282.
Gitelson A A, Kaufman Y J, Merzlyak M N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298.
Gitelson A A, Merzlyak M N. 1994. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143, 286–292.
Gitelson A A, Merzlyak M N. 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22, 689–692.
Goloboff P A, Arias J S. 2019. Likelihood approximations of implied weights parsimony can be selected over the Mk model by the Akaike information criterion. Cladistics, 35, 1–22.
Haboudane D, Miller J R, Pattey E, Zarco-Tejada P J, Stracha I B. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90, 337–352.
Haboudane D, Miller J R, Tremblay N, Zarco-Tejada P J, Dextraze L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, 416–426.
He Z, Qiu X, Ataulkarim S T, Li Y, Liu X, Cao Q, Zhu Y, Cao W, Tang L. 2017. Development of a critical nitrogen dilution curve of double cropping rice in south China. Frontiers in Plant Science, 8, 638.
Hu Q, Yang Y, Han S, Wang J. 2019. Degradation of agricultural drainage water quantity and quality due to farmland expansion and water-saving operations in arid basins. Agricultural Water Management, 213, 185–192.
Huang S, Miao Y, Zhao G, Yuan F, Ma X, Tan C, Yu W, Gnyp M L, Lenz-Widemann V I S, Rascher U, Bareth G. 2015. Satellite Remote sensing-based in-season diagnosis of rice nitrogen status in Northeast China. Remote Sensing, 7, 10646–10667.
Huete A, Justice C, Liu H. 1994. Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment, 49, 224–234.
Huete A R. 1989. Soil influences in remotely sensed vegetation-canopy spectra. In: Asrar G, ed., Theory & Applications of Optical Remote Sensing. John Wiley & Sons, New York, USA. pp. 107–141.
Hurcom S J, Harrison A R. 1998. The NDVI and spectral decomposition for semiarid vegetation abundance estimation. International Journal of Remote Sensing, 19, 3109–3125.
Justes E, Mary B, Meynard J M, Merrien A. 1994. Determination of a critical nitrogen dilution curve for winter wheat crops. Annals of Botany, 74, 397–407.
Large E C. 1954. Growth stages in cereals illustration of the feeks scale. Plant Pathology, 3, 128–129.
Lemaire G, Francois C, Soudani K, Berveiller D, Pontailler J Y, Breda N, Genet H, Davi H, Dufrene E. 2008a. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment, 112, 3846–3864.
Lemaire G, Gastal F. 1997. Nitrogen uptake and distribution in plant canopies. In: Lemaire G, ed., Diagnosis of the Nitrogen Status in Crops. Springer, Berlin, Heidelberg. pp. 3–43.
Lemaire G, Jeuffroy M H, Gastal F. 2008b. Diagnosis tool for plant and crop N status in vegetative stage: Theory and practices for crop N management. European Journal of Agronomy, 28, 614–624.
Li F, Miao Y, Hennig S D, Gnyp M L, Chen X, Jia L, Bareth G. 2010. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precision Agriculture, 11, 335–357.
Li Z, Jin X, Yang G, Drummond J, Yang H, Clark B, Li Z, Zhao C. 2018. Remote sensing of leaf and canopy nitrogen status in winter wheat (Triticum aestivum L.) based on N-PROSAIL model. Remote Sensing, 10, 1463.
Liu H, Zhu H, Li Z, Yang G. 2019. Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat. International Journal of Remote Sensing, 41, 858–881.
Liu X, Chen G, Wang L, Liu X, Li X. 2018. Monitoring leaf relative water content of winter wheat based on hyperspectral index at different growth stages. Journal of Triticeae Crops, 38, 854–862. (in Chinese)
Padilla F M, Peña-Fleitas M T, Gallardo M, Thompson R B. 2015. Threshold values of canopy reflectance indices and chlorophyll meter readings for optimal nitrogen nutrition of tomato. Annals of Applied Biology, 166, 271–285.
Pearson R L, Miller L D. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado. Proceedings of the Eighth International Symposium on Remote Sensing of Environment. Ann Arbor, Michigan, pp. 7–12.
Penuelas J, Baret F, Filella I. 1995. Semiempirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica, 31, 221–230.
Peñuelas J, Filella I, Biel C S, Serrano L, Save R. 1993. The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 14, 1887–1905.
Qi J, Chehbouni A, Huete A R, Kerr Y H, Sorooshian S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119–126.
Read J J, Tarpley L, Mckinion J M, Reddy K R. 2002. Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton. Journal of Environmental Quality, 31, 1442–1452.
Richardson A J, Wiegand C L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43, 1541–1552.
Rodriguez D, Fitzgerald G J, Belford R, Christensen L K. 2006. Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts. Australian Journal of Agricultural Research, 57, 781–789.
Rondeaux G, Steven M, Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107.
Roujean J L, Breon F M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51, 375–384.
Sims D A, Gamon J A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 337–354.
Steddom K, Heidel G, Jones D, Rush C M. 2003. Remote detection of rhizomania in sugar beets. Phytopathology, 93, 720–726.
Vincini M, Frazzi E, Alessio P D. 2006. Angular dependence of maize and sugar beet VIs from directional CHRIS/Proba data. In: Proceedings of the 4th ESA CHRIS PROBA Workshop. ESRIN, Frascati, Italy. pp. 19–21.
Wang L, Qu J J, Hao X, Hunt E R. 2011. Estimating dry matter content from spectral reflectance for green leaves of different species. International Journal of Remote Sensing, 32, 7097–7109.
Wang X, Ye T, Ataulkarim S T, Zhu Y, Liu L, Cao W, Tang L. 2017. Development of a critical nitrogen dilution curve based on leaf area duration in wheat. Frontiers in Plant Science, 8, 1517.
Wu W, Ma B L, Fan J J, Sun M, Yi Y, Guo W S, Voldeng H D. 2019. Management of nitrogen fertilization to balance reducing lodging risk and increasing yield and protein content in spring wheat. Field Crops Research, 241, 107584.
Xia T, Miao Y, Wu D, Shao H, Khosla R, Mi G. 2016. Active optical sensing of spring maize for in-season diagnosis of ntrogen status based on nitrogen nutrition index. Remote Sensing, 8, 605.
Xue L, Yang L. 2008. Recommendations for nitrogen fertilizer topdressing rates in rice using canopy reflectance spectra, Biosystems Engineering, 100, 524–534.
Yao X, Ata-Ul-Karim S T, Zhu Y, Tian Y, Liu X, Cao W. 2014. Development of critical nitrogen dilution curve in rice based on leaf dry matter. European Journal of Agronomy, 55, 20–28.
Yue S, Meng Q, Zhao R, Li F, Chen X, Zhang F, Cui Z. 2012. Critical nitrogen dilution curve for optimizing nitrogen management of winter wheat production in the North China Plain. Agronomy Journal, 104, 523.
Ziadi N, Bélanger G, Claessens A, Lefebvre L, Cambouris A N, Tremblay N, Nolin M C, Parent L E. 2010. Determination of a critical nitrogen dilution curve for spring wheat. Agronomy Journal, 102, 241–250.
Zhao B, Duan A, Ata-Ul-Karim S T, Liu Z, Chen Z, Gong Z, Zhang J, Xiao J, Liu Z, Qin A, Ning D. 2018. Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. European Journal of Agronomy, 93, 113–125.
Zhao J, Huang W J, Zhang Y H, Jing Y. 2013. Inversion of leaf area index during different growth stages in winter wheat. Spectroscopy and Spectral Analysis, 9, 2546–2552. (in Chinese)
Zhao Z, Wang E, Wang Z, Zang H, Liu Y, Angus J F. 2014. A reappraisal of the critical nitrogen concentration of wheat and its implications on crop modeling, Field Crops Research, 164, 65–73.
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