LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status

Field Crops Research

Volumn 159, Issue , 2014, Pages 21-32

  Eitel, Jan U H ^a   Magney, Troy S ^a   Vierling, Lee A ^a   Brown, Tabitha T ^b,c   Huggins, David R ^c  

^a UNIVERSITY OF IDAHO   (United States)

^b WASHINGTON STATE UNIVERSITY   (United States)

^c AGRICULTURAL RESEARCH SERVICE   (United States)

Author keywords

Crop biochemistry; Crop biomass; Fertilizer decisions; LiDAR; Nitrogen nutrition index; Precision agriculture

Indexed keywords

EID: 84894065180     PISSN: 03784290     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fcr.2014.01.008     Document Type: Article

References (55)

1. Bréda N.J.J. Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. J. Exp. Bot. 2003, 54:2403-2417.
2. Cassman K., Dobermann A., Walters D. Agroecosystems, nitrogen-use efficiency, and nitrogen management. AMBIO 2002, 31:132-140.
3. Cassman K.G. Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture. Proc. Natl. Acad. Sci. U. S. A. 1999, 96:5952-5959.
4. Cerovic Z.G., Masdoumier G., Ghozlen N., Ben Latouche G. A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids. Physiol. Plant. 2012, 146:251-260.
5. Clawges R., Vierling L., Calhoon M., Toomey M. Use of a ground-based scanning lidar for estimation of biophysical properties of western larch (Larix occidentalis). Int. J. Remote Sens. 2007, 28:4331-4344.
6. Diacono M., Rubino P., Montemurro F. Precision nitrogen management of wheat. A review. Agron. Sustain. Dev. 2012, 33:219-241.
7. Ehlert D., Adamek R., Horn H.-J. Laser rangefinder-based measuring of crop biomass under field conditions. Precis. Agric. 2009, 10:395-408.
8. Eitel J., Vierling L., Magney T. A lightweight, low cost autonomously operating terrestrial laser scanner for quantifying and monitoring ecosystem structural dynamics. Agric. For. Meteorol. 2013, 180:86-96.
9. Eitel J.U.H., Long D.S., Gessler P.E., Hunt E.R., Brown D.J. Sensitivity of ground-based remote sensing estimates of wheat chlorophyll content to variation in soil reflectance. Soil Sci. Soc. Am. J. 2009, 73:1715-1723.
10. Eitel J.U.H., Vierling L.A., Long D.S. Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner. Remote Sens. Environ. 2010, 114:2229-2237.
11. Eitel J.U.H., Vierling L.A., Long D.S., Hunt E.R. Early season remote sensing of wheat nitrogen status using a green scanning laser. Agric. For. Meteorol. 2011, 151:1338-1345.
12. Erdle K., Mistele B., Schmidhalter U. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crop. Res. 2011, 124:74-84.
13. Evans J.R. Nitrogen and Photosynthesis in the Flag Leaf of Wheat (Triticum aestivum L.). Plant Physiol. 1983, 72:297-302.
14. Evans J.S., Hudak A.T. A multiscale curvature algorithm for classifying discrete return lidar in forested environments. IEEE Trans. Geosci. Remote Sens. 2007, 45:1029-1038.
15. Gastal F., Lemaire G. N uptake and distribution in crops: an agronomical and ecophysiological perspective. J. Exp. Bot. 2002, 53:789-799.
16. Gaulton R., Danson F. The potential of dual-wavelength laser scanning for estimating vegetation moisture content. Remote Sens. 2013, 132:32-39.
17. Hakala T., Suomalainen J., Kaasalainen S., Chen Y. Full waveform hyperspectral LiDAR for terrestrial laser scanning. Opt. Express 2012, 20:7119-7127.
18. Hebert M., Krotkov E. 3D measurements from imaging laser radars: how good are they?. Image Vis. Comput. 1992, 10:170-178.
19. Hosoi F., Omasa K. Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging. ISPRS J. Photogramm. Remote Sens. 2009, 64:151-158.
20. Hosoi F., Omasa K. Estimation of vertical plant area density profiles in a rice canopy at different growth stages by high-resolution portable scanning lidar with a lightweight mirror. ISPRS J. Photogramm. Remote Sens. 2012, 74:11-19.
21. Houlès V., Guérif M., Mary B. Elaboration of a nitrogen nutrition indicator for winter wheat based on leaf area index and chlorophyll content for making nitrogen recommendations. Eur. J. Agron. 2007, 27:1-11.
22. Huete A.R., Jackson R.D., Post D.F. Spectral response of a plant canopy with different soil backgrounds. Remote Sens. Environ. 1985, 17:37-53.
23. Jackson R., Huete A. Interpreting vegetation indices. Prev. Vet. Med. 1991, 11:185-200.
24. Jacquemoud S., Baret F. PROSPECT: a model of leaf optical properties spectra. Remote Sens. Environ. 1990, 34:75-91.
25. Justes E., Mary B., Meynard J. Determination of a critical nitrogen dilution curve for winter wheat crops. Ann. Bot. 1994, 74:397-407.
26. Karele I. Chlorophyll distribution in leaves, stems, and ears in winter wheat. Developments in plant and soil sciences 2001, vol 92:720-721. Springer. Horst (Ed.).
27. Lemaire G., Gastal F. N uptake and distribution in plant canopies. Diagnosis of the Nitrogen Status in Crops 1997, 3-41. Springer. G. Lemaire (Ed.).
28. Lemaire G., Gastal F. Quantifying crop responses to nitrogen deficiency and avenues to improve nitrogen use efficiency. Crop Physiology: Applications for Genetic Improvement 2009, 171-211. Academic Press, San Diego, CA. V.O. Sadras, D.F. Calderini (Eds.).
29. Lemaire G., Jeuffroy M., Gastal F. Diagnosis tool for plant and crop N status in vegetative stage: theory and practices for crop N management. Eur. J. Agron. 2008, 28:614-624.
30. Li F., Miao Y., Chen X., Zhang H., Jia L., Bareth G. Estimating winter wheat biomass and nitrogen status using an active crop sensor. Intell. Autom. Soft Comput. 2010, 16:1219-1228.
31. Li F., Miao Y., Hennig S.D., Gnyp M.L., Chen X., Jia L., Bareth G. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precis. Agric. 2010, 11:335-357.
32. Long D.S., McCallum J.D. Mapping straw yield using on-combine light detection and ranging (lidar). Int. J. Remote Sens. 2013, 34. 10.1080/01431161.2013.793869.
33. Loudermilk E.L., Hiers J.K., O'Brien J.J., Mitchell R.J., Singhania A., Fernandez J.C., Cropper W.P., Slatton K.C. Ground-based LIDAR: a novel approach to quantify fine-scale fuelbed characteristics. Int. J. Wildl. Fire 2009, 18:676.
34. Lovell J.L., Jupp D.L.B., Culvenor D.S., Coops N.C., Les R. Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. Can. J. Remote Sens. 2003, 29:607-622.
35. Magney T.S., Eusden S.A., Eitel J.U.H., Logan B.A., Jiang J., Vierling L.A. Assessing leaf photoprotective mechanisms using terrestrial LiDAR: towards mapping canopy photosynthetic performance in three dimensions. New Phytol. 2014, 201:344-356.
36. Mistele B., Schmidhalter U. Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. Eur. J. Agron. 2008, 29:184-190.
37. Morsdorf F., Mårell A., Koetz B., Cassagne N., Pimont F., Rigolot E., Allgöwer B. Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning. Remote Sens. Environ. 2010, 114:1403-1415.
38. Mueller N.D., Gerber J.S., Johnston M., Ray D.K., Ramankutty N., Foley J.a. Closing yield gaps through nutrient and water management. Nature 2012, 490:254-257.
39. Peddle D.R., Smith a.M. Spectral mixture analysis of agricultural crops: endmember validation and biophysical estimation in potato plots. Int. J. Remote Sens. 2005, 26:4959-4979.
40. Pierce F., Nowak P. Aspects of precision agriculture. Adv. Agron. 1999, 67:1-85.
41. Piñeiro G., Perelman S., Guerschman J.P., Paruelo J.M. How to evaluate models: observed vs. predicted or predicted vs. observed?. Ecol. Modell. 2008, 216:316-322.
42. Raun W.R., Solie J.B., Johnson G.V., Stone M.L., Mullen R.W., Freeman K.W., Thomason W.E., Lukina E.V. Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron. J. 2002, 94:815-820.
43. R Development Core Team, 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL . http://www.r-project.org/.
44. Soil Survey Staff, 2013. Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at http://websoilsurvey.nrcs.usda.gov/. Accessed [07/08/2013].
45. Tilman D., Cassman K.G., Matson P.a., Naylor R., Polasky S. Agricultural sustainability and intensive production practices. Nature 2002, 418:671-677.
46. Tremblay N., Fallon E., Ziadi N. Sensing of crop nitrogen status: opportunities, tools, limitations and support system requirements. Horttechnology 2011, 21:274-281.
47. Tuley J., Vandapel N., Hebert M. Analysis and Removal of Artifacts in 3-D LADAR Data 2005, 15213. Robotic Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania.
48. Ulrich A. Physiological bases for assessing the nutritional requirements of plants. Annu. Rev. Plant Physiol. 1952, 207-228.
49. Verhoef W. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sens. Environ. 1984, 16:125-141.
50. Vigneau N., Ecarnot M., Rabatel G., Roumet P. Potential of field hyperspectral imaging as a non-destructive method to assess leaf nitrogen content in wheat. Field Crop. Res. 2011, 122:25-31.
51. Vosselman G. Slope based filtering of laser altimetry data. IAPRS, Amsterdam 2000, XXXIII:1-8.
52. Woodhouse I.H., Nichol C., Sinclair P., Jack J., Morsdorf F., Malthus T.J., Patenaude G. A multispectral canopy LiDAR demonstrator project. IEEE Geosci. Remote Sens. Lett. 2011, 8:839-843.
53. Zambrano-Bigiarini M. HydroGOF: goodness-of-fit functions for comparison of simulated and observed hydrological time series. R package version 0.3-6 2013, http://CRAN.R-project.org/package=hydroGOF.
54. Zarco-Tejada P.J., Miller J.R., Noland T.L., Mohammed G.H., Sampson P.H. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2001, 39:1491-1507.
55. Zheng G., Moskal L.M., Kim S.-H. Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning. IEEE Trans. Geosci. Remote Sens. 2013, 51:777-786.