Common Spectral Bands and Optimum Vegetation Indices for Monitoring Leaf Nitrogen Accumulation in Rice andWheat
WANG Wei, YAO Xia, TIAN Yong-chao, LIU Xiao-jun, NI Jun, CAO Wei-xing , ZHU Yan
National Engineering and Technology Center for Information Agriculture, Ministry of Industry and Information Technology/Key Laboratory for Information Agriculture, Science and Technology Department of Jiangsu Province/College of Agriculture, Nanjing Agricultural University, Nanjing 210095, P.R.China
摘要 Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving different cultivars, nitrogen rates, and water regimes, time-course measurements were taken of canopy hyperspectral reflectance between 350-2 500 nm and leaf nitrogen accumulation (LNA) in rice and wheat. A new spectral analysis method through the consideration of characteristics of canopy components and plant growth status varied with phenological growth stages was designed to explore the common central bands in rice and wheat. Comprehensive analyses were made on the quantitative relationships of LNA to soil adjusted vegetation index (SAVI) and ratio vegetation index (RVI) composed of any two bands between 350-2 500 nm in rice and wheat. The results showed that the ranges of indicative spectral reflectance were largely located in 770-913 and 729-742 nm in both rice and wheat. The optimum spectral vegetation index for estimating LNA was SAVI (R822,R738) during the early-mid period (from jointing to booting), and it was RVI (R822,R738) during the mid-late period (from heading to filling) with the common central bands of 822 and 738 nm in rice and wheat. Comparison of the present spectral vegetation indices with previously reported vegetation indices gave a satisfactory performance in estimating LNA. It is concluded that the spectral bands of 822 and 738 nm can be used as common reflectance indicators for monitoring leaf nitrogen accumulation in rice and wheat.
Abstract Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving different cultivars, nitrogen rates, and water regimes, time-course measurements were taken of canopy hyperspectral reflectance between 350-2 500 nm and leaf nitrogen accumulation (LNA) in rice and wheat. A new spectral analysis method through the consideration of characteristics of canopy components and plant growth status varied with phenological growth stages was designed to explore the common central bands in rice and wheat. Comprehensive analyses were made on the quantitative relationships of LNA to soil adjusted vegetation index (SAVI) and ratio vegetation index (RVI) composed of any two bands between 350-2 500 nm in rice and wheat. The results showed that the ranges of indicative spectral reflectance were largely located in 770-913 and 729-742 nm in both rice and wheat. The optimum spectral vegetation index for estimating LNA was SAVI (R822,R738) during the early-mid period (from jointing to booting), and it was RVI (R822,R738) during the mid-late period (from heading to filling) with the common central bands of 822 and 738 nm in rice and wheat. Comparison of the present spectral vegetation indices with previously reported vegetation indices gave a satisfactory performance in estimating LNA. It is concluded that the spectral bands of 822 and 738 nm can be used as common reflectance indicators for monitoring leaf nitrogen accumulation in rice and wheat.
This work was supported by the National High-Tech R&D Program of China (2011AA100703), the National Natural Science Foundation of China (30900868), the Natural Science Foundation of Jiangsu Province, China (BK2010453), the Academic Program Development of Jiangsu Higher Education Institutions, China (PAPD), and the Science and Technology Support Plan of Jiangsu Province, China (BE2011351).
WANG Wei, YAO Xia, TIAN Yong-chao, LIU Xiao-jun, NI Jun, CAO Wei-xing , ZHU Yan.
2012.
Common Spectral Bands and Optimum Vegetation Indices for Monitoring Leaf Nitrogen Accumulation in Rice andWheat. Journal of Integrative Agriculture, 12(12): 2001-2012.
[1]Analytical Spectral Devices Inc. 2002. Field Spec? Pro: UserGuide.Analytical Spectral Devices Inc., Boulder, Co., USA.Asner G P, Martin R E. 2008. Spectral and chemical analysisof tropical forests: scaling from leaf to canopy levels.Remote Sensing of Environment, 112, 3958-3970.
[2]Baret F, Guyot G, Major D J. 1989. TSAVI - a vegetationindex which minimizes soil brightness effects on LAIand APAR estimation. In: Proceedings of the 12thCanadian Symposium on Remote Sensing andIGARSS’89. Vanconver, Canada. pp. 1355-1358.
[3]Barnes E M, Clarke T R, Richards S E, Colaizzi P D,Haberland J, Kostrzewski M, Waller P, Choi C, Riley E,Thompson T. 2000. Coincident detection of crop waterstress, nitrogen status and canopy density usingground based multispectral data. In: Robert P C, Rust RH, Larson W E, eds., Proceedings of the 5thInternational Conference on Precision Agriculture,Bloomington, MN, USA.
[4]Cassman K G, Dobermann A, Walters D T. 2002.Agroecosystems, nitrogen-use efficiency, and nitrogenmanagement. AMBIO: A Journal of the HumanEnvironment, 31, 132-140.
[5]Chen J, Gu S, Shen M G, Tang Y H, Bunkei M. 2009.Estimating aboveground biomass of grassland havinga high canopy cover: an exploratory analysis of in situhyperspectral data. International Journal of RemoteSensing, 30, 6497-6517.
[6]Cho M A, Skidmore A K. 2006. A new technique forextracting the red edge position from hyperspectral data:the linear extrapolation method. Remote Sensing ofEnvironment, 101, 181-193.
[7]Dorigo WA, Zurita-Milla R, de Wit A J W, Brazile J, SinghR, Schaepman M E. 2007. A review on reflective remote sensing and data assimilation techniques for enhancedagroecosystem modeling. International Journal ofApplied Earth Observation and Geoinformation, 9,165-193.
[8]FengW, Yao X, Zhu Y, Tian Y C, CaoWX. 2008.Monitoringleaf nitrogen accumulation in wheat with hyper-spectralremote sensing. Acta Ecologica Sinica, 28, 23-32.
[9]Hansen P M, Schjoerring J K. 2003. Reflectancemeasurement of canopy biomass and nitrogen statusin wheat crops using normalized difference vegetationindices and partical least squares regression. RemoteSensing of Environment, 86, 542-553.
[10]Huang Z, Turner B J, Dury S J, Wallis I R, FoleyW J. 2004.Estimating foliage nitrogen concentration from HYMAPdata using continuum removal analysis. Remote Sensingof Environment, 93, 18-29.
[11]Huete A R. 1988. A soil-adjusted vegetation index (SAVI).Remote Sensing of Environment, 25, 295-309.
[13]Jacobsen S E P H, Jensen C R. 1998. Reflectancemeasurements,a quick and nondestructive technique for use inagricultural research. In: Janeiro R D, ed., InternationalConference on Sustainable Agriculture in Tropicaland Subtropical Highlands with Special Reference toLatin America (SATHLA). Condensan, Lima. pp. 1-5.
[14]Jensen A, Lorenzen B, Stergaard H S, Hvelplund E K. 1990.Radiometric estimation of biomass and nitrogen contentof barley grown at different nitrogen levels. InternationalJournal of Remote Sensing, 11, 1809-1820.
[15]Ju X T, Xing G X, Chen X P, Zhang S L, Zhang L J, Liu X J,Cui Z L, Yin B, Christie P, Zhu Z L, et al. 2009. Reducingenvironmental risk by improving N management inintensive Chinese agricultural systems. Proceedingsof the National Academy of Sciences of the UnitedStates of America, 106, 3041-3046.
[16]Knox N M, Skidmore A K, Schlerf M, de Boer W F, vanWieren S E, van der Waal C, Prins H H T, Slotow R.2010. Nitrogen prediction in grasses: effect of bandwidthand plant material state on absorption feature selection.International Journal of Remote Sensing, 31, 691-704.
[17]LeeY J, Yang CM, ChangKW, Shen Y. 2008.A simple spectralindex using reflectance of 735 nm to assess nitrogen statusof rice canopy. Agronomy Journal, 100, 205-212.
[18]Li Y X, Zhu Y, Tian Y C, Yao X, Qin X, Cao W X. 2006.Quantitative relationship between leaf nitrogenaccumulation and canopy reflectance spectra in wheat.Acta Agronomica Sinica, 32, 203-209.
[19]Liang S L. 2004. Quantitative Remote Sensing of LandSurfaces. Wiley-Interscience, USA.Pearson R L, Miller L D. 1972. Remote mapping of standingcrop biomass for estimation of the productivity of theshort-grass prairie. In: Asrar G, ed., Proceedings of the8th International Symposium on Remote Sensing ofEnvironment. Pawnee National Grasslands, Colorado.pp. 1357-1381.
[20]Ray S S, Jain N, Miglani A, Singh J P, Singh A K, PanigrahyS, Parihar J S. 2010. Defining optimum spectral narrowbands and bandwidths for agricultural applications.Current Science, 98, 1365-1369.
[21]Richardson A J, Wiegand C L. 1977. Distinguishingvegetation from soil background information.Photogrammetric Engineering and Remote Sensing,43, 1541-1552.
[22]Rouse JW,Haas RH, Schell JA,DeeringDW, Harlan J C. 1974.Monitoring the vernal advancement and retrogradation(green wave effect) of natural vegetation. NASA/GSFC,Type III, Final Report. Remote Sensing Center, TexasA&MUniversity-College Station, USA.Shanahan J F, Kitchen N R, Raun W R, Schepers J S. 2008.Responsive in-season nitrogen management for cereals.Computers and Electronics in Agriculture, 61, 51-62.
[23]Sims D A, Gamon J A. 2002. Relationships between leafpigment content and spectral reflectance across a widerange of species, leaf structures and developmentalstages. Remote Sensing of Environment, 81, 337-354.
[24]Vogelmann J E, Rock B N,Moss DM. 1993. Red edge spectralmeasurements from sugar maple leaves. InternationalJournal of Remote Sensing, 14, 1563-1573.
[25]Yao X, Zhu Y, FengW, Tian Y C, CaoWX. 2009a. Exploringnovel hyperspectral band and key index for leaf nitrogenaccumulation in wheat. Spectroscopy and SpectralAnalysis, 29, 2191-2195.
[26](in Chinese)Yao X, Zhu Y, Tian Y C, FengW, CaoWX. 2009b. Researchof the optimum hyperspectral vegetation indices onmonitoring the nitrogen content in wheat leaves.Scientia Agricultura Sinica, 42, 2716-2725. (in Chinese)
[27]Yoder B J, Pettigrew-Crosby R E. 1995. Predicting nitrogenand chlorophyll content and concentrations fromreflectance spectra (400-2 500 nm) at leaf and canopyscales. Remote Sensing of Environment, 53, 199-211.
[28]Zhao D, Huang L, Li J, Qi J. 2007. A comparative analysis ofbroadband and narrowband derived vegetation indices inpredicting LAI and CCD of a cotton canopy. ISPRS Journalof Photogrammetry and Remote Sensing, 62, 25-33.
[29]Zhao Y S. 2003. Remote Sensing application Analysis ofPrinciple and Methods. Science Press, Beijing. (in Chinese)
[30]Zhu Y, Yao X, Tian Y C, Liu X J, CaoWX. 2008.Analysis ofcommon canopy vegetation indices for indicating leafnitrogen accumulations in wheat and rice. InternationalJournal of Applied Earth Observation and Geoinformation,10, 1-10.
