Evaluating different methods for grass nutrient estimation from canopy hyperspectral reflectance

Remote Sensing

Volumn 7, Issue 5, 2015, Pages 5901-5917

  Wang, Junjie ^a   Wang, Tiejun ^b   Skidmore, Andrew K ^b   Shi, Tiezhu ^a   Wu, Guofeng ^c  

^a WUHAN UNIVERSITY   (China)

^b UNIVERSITY OF TWENTE   (Netherlands)

^c SHENZHEN UNIVERSITY   (China)

Author keywords

Canopy level; Grass nutrients; Hyperspectral reflectance; Statistical modeling

Indexed keywords

ESTIMATION; LEAST SQUARES APPROXIMATIONS; LINEAR REGRESSION; NUTRIENTS; REFLECTION; STATISTICAL METHODS; VEGETATION;

CANOPY LEVEL; HYPERSPECTRAL REFLECTANCE; MULTIVARIATE REGRESSION METHOD; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); STATISTICAL MODELING; STEPWISE MULTIPLE LINEAR REGRESSION; SUCCESSIVE PROJECTIONS ALGORITHM; SUPPORT VECTOR REGRESSION (SVR);

REGRESSION ANALYSIS;

EID: 84930032973     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70505901     Document Type: Article

References (53)

1. Ingestad, T.; Lund, A.B. Theory and techniques for steady state mineral nutrition and growth of plants. Scand. J. For. Res. 1986, 1, 439-453.
2. Van der Graaf, A.; Stahl, J.; Veen, G.; Havinga, R.; Drent, R. Patch choice of avian herbivores along a migration trajectory-From Temperate to Arctic. Basic Appl. Ecol. 2007, 8, 354-363.
3. Van Gils, J.A.; Dekinga, A.; van den Hout, P.J.; Spaans, B.; Piersma, T. Digestive organ size and behavior of red knots (Calidris canutus) indicate the quality of their benthic food stocks. Isr. J. Ecol. Evol. 2007, 53, 329-346.
4. Yoder, B.J.; Pettigrew-Crosby, R.E. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400-2500 nm) at leaf and canopy scales. Remote Sens. Environ. 1995, 53, 199-211.
5. Bogrekci, I.; Lee, W. Spectral phosphorus mapping using diffuse reflectance of soils and grass. Biosyst. Eng. 2005, 91, 305-312.
6. Mutanga, O.; Skidmore, A.; Kumar, L.; Ferwerda, J. Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain. Int. J. Remote Sens. 2005, 26, 1093-1108.
7. Darvishzadeh, R.; Skidmore, A.; Schlerf, M.; Atzberger, C.; Corsi, F.; Cho, M. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J. Photogramm. Remote Sens. 2008, 63, 409-426.
8. Ullah, S.; Si, Y.; Schlerf, M.; Skidmore, A.K.; Shafique, M.; Iqbal, I.A. Estimation of grassland biomass and nitrogen using meris data. Int. J. Appl. Earth Obs. Geoinf. 2012, 19, 196-204.
9. Sanches, I.; Tuohy, M.; Hedley, M.; Mackay, A. Seasonal prediction of in situ pasture macronutrients in New Zealand pastoral systems using hyperspectral data. Int. J. Remote Sens. 2013, 34, 276-302.
10. Curran, P.J. Remote sensing of foliar chemistry. Remote Sens. Environ. 1989, 30, 271-278.
11. Thenkabail, P.S.; Smith, R.B.; De Pauw, E. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 2000, 71, 158-182.
12. Abdel-Rahman, E.M.; Ahmed, F.B.; van den Berg, M. Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy. Int. J. Appl. Earth Obs. Geoinf. 2010, 12, S52-S57.
13. Yu, K.; Li, F.; Gnyp, M.L.; Miao, Y.; Bareth, G.; Chen, X. Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain. ISPRS J. Photogramm. Remote Sens. 2013, 78, 102-115.
14. Zhai, Y.; Cui, L.; Zhou, X.; Gao, Y.; Fei, T.; Gao, W. Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: Comparison of partial least-square regression and support vector machine regression methods. Int. J. Remote Sens. 2013, 34, 2502-2518.
15. Pacheco-Labrador, J.; González-Cascón, R.; Martín, M.P.; Riaño, D. Understanding the optical responses of leaf nitrogen in Mediterranean holm oak (Quercus ilex) using field spectroscopy. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 105-118.
16. Wu, C.; Niu, Z.; Tang, Q.; Huang, W. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agric. For. Meteorol. 2008, 148, 1230-1241.
17. Pearson, R.L.; Miller, L.D. Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Shortgrass Prairie; Eighth International Symposium on Remote Sensing of Environment; Willow Run Laboratories: Ann Arbor, MI, USA, 1972; p. 1355.
18. Jackson, R.D.; Huete, A.R. Interpreting vegetation indices. Prev. Vet. Med. 1991, 11, 185-200.
19. Vogelmann, J.; Rock, B.; Moss, D. Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sens. 1993, 14, 1563-1575.
20. Mutanga, O.; Skidmore, A.K. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 2004, 25, 3999-4014.
21. Cho, M.A.; Skidmore, A.K. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sens. Environ. 2006, 101, 181-193.
22. Corp, L.A.; Middleton, E.M.; Campbell, P.E.; Daughtry, C.S.; Russ, A.; Cheng, Y.-B.; Huemmrich, K.F. Spectral indices to monitor nitrogen-driven carbon uptake in field corn. J. Appl. Remote Sens. 2010, 4, 043555.
23. Yao, X.; Zhu, Y.; Tian, Y.; Feng, W.; Cao, W. Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. Int. J. Appl. Earth Obs. Geoinf. 2010, 12, 89-100.
24. Tian, Y.; Yao, X.; Yang, J.; Cao, W.; Hannaway, D.; Zhu, Y. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground-and space-based hyperspectral reflectance. Field Crop. Res. 2011, 120, 299-310.
25. Wang, W.; Yao, X.; Yao, X.; Tian, Y.; Liu, X.; Ni, J.; Cao, W.; Zhu, Y. Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crop. Res. 2012, 129, 90-98.
26. Thomas, J.; Oerther, G. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agron. J. 1972, 64, 11-13.
27. Ramoelo, A.; Skidmore, A.K.; Schlerf, M.; Mathieu, R.; Heitkönig, I. Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations. ISPRS J. Photogramm. Remote Sens. 2011, 66, 408-417.
28. Karimi, Y.; Prasher, S.; Madani, A.; Kim, S. Application of support vector machine technology for the estimation of crop biophysical parameters using aerial hyperspectral observations. Can. Biosyst. Eng. 2008, 50, 13-20.
29. Wise, B.; Gallagher, N.; Bro, R.; Shaver, J.; Windig, W.; Koch, R. Chemometrics Tutorial for PLS_Toolbox and Solo; Eigenvector Research: Wenatchee, WA, USA, 2006.
30. Arkan, E.; Shahlaei, M.; Pourhossein, A.; Fakhri, K.; Fassihi, A. Validated QSAR analysis of some diaryl substituted pyrazoles as CCR2 inhibitors by various linear and nonlinear multivariate chemometrics methods. Eur. J. Med. Chem. 2010, 45, 3394-3406.
31. Cui, L.; Fei, T.; Qi, Q.; Liu, Y.; Wu, G. Estimating carex quality with laboratory-based hyperspectral measurements. Int. J. Remote Sens. 2013, 34, 1866-1878.
32. Araújo, M.C.U.; Saldanha, T.C.B.; Galvão, R.K.H.; Yoneyama, T.; Chame, H.C.; Visani, V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom. Intell. Lab. Syst. 2001, 57, 65-73.
33. Di Nezio, M.S.; Pistonesi, M.F.; Fragoso, W.D.; Pontes, M.J.; Goicoechea, H.C.; Araujo, M.C. Successive projections algorithm improving the multivariate simultaneous direct spectrophotometric determination of five phenolic compounds in sea water. Microchem. J. 2007, 85, 194-200.
34. Liu, F.; He, Y. Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar. Food Chem. 2009, 115, 1430-1436.
35. Kemper, T.; Sommer, S. Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ. Sci. Technol. 2002, 36, 2742-2747.
36. Saeys, W.; Mouazen, A.M.; Ramon, H. Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosyst. Eng. 2005, 91, 393-402.
37. Myung, I.J.; Pitt, M.A. Applying occam's razor in modeling cognition: A Bayesian approach. Psychon. Bull. Rev. 1997, 4, 79-95.
38. Zeaiter, M.; Roger, J.-M.; Bellon-Maurel, V.; Rutledge, D. Robustness of models developed by multivariate calibration. Part I: The assessment of robustness. TrAC Trends Anal. Chem. 2004, 23, 157-170.
39. Zhang, J.; Lu, J. Feeding ecology of two wintering geese species at Poyang Lake, China. J. Freshw. Ecol. 1999, 14, 439-445.
40. Bradstreet, R. Kjeldahl method for organic nitrogen. Anal. Chem. 1954, 26, 185-187.
41. Yuan, G.; Lavkulich, L. Colorimetric determination of phosphorus in citrate-bicarbonate-dithionite extracts of soils. Commun. Soil Sci. Plant Anal. 1995, 26, 1979-1988.
42. Schmidt, K.; Skidmore, A. Smoothing vegetation spectra with wavelets. Int. J. Remote Sens. 2004, 25, 1167-1184.
43. Mirik, M.; Norland, J.E.; Crabtree, R.L.; Biondini, M.E. Hyperspectral one-meter-resolution remote sensing in Yellowstone National Park, Wyoming: I. Forage nutritional values. Rangel. Ecol. Manag. 2005, 58, 452-458.
44. Pimstein, A.; Karnieli, A.; Bansal, S.K.; Bonfil, D.J. Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. Field Crop. Res. 2011, 121, 125-135.
45. Neter, J.; Kutner, M.H; Wasserman, W.; Nachtsheim, C. Applied Linear Regression Models, 4th ed.; McGraw-Hill/Irwin: New York, NY, USA, 1996.
46. Geladi, P.; Kowalski, B.R. Partial least-squares regression: A tutorial. Anal. Chim. Acta 1986, 185, 1-17.
47. Kooistra, L.; Salas, E.; Clevers, J.; Wehrens, R.; Leuven, R.; Nienhuis, P.; Buydens, L. Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains. Environ. Pollut. 2004, 127, 281-290.
48. Smola, A.J.; Schölkopf, B. A tutorial on support vector regression. Stat. Comput. 2004, 14, 199-222.
49. Basak, D.; Pal, S.; Patranabis, D.C. Support vector regression. Neural Inf. Process.-Lett. Rev. 2007, 11, 203-224.
50. Ye, S.; Wang, D.; Min, S. Successive projections algorithm combined with uninformative variable elimination for spectral variable selection. Chemom. Intell. Lab. Syst. 2008, 91, 194-199.
51. Williams, P. Near-Infrared Technology: Getting the Best Out of Light: A Short Course in the Practical Implementation of Near-Infrared Spectroscopy for the User, 2nd ed.; Value Added Wheat CRC: Sydney, NSW, Australia, 2004.
52. Bian, M.; Skidmore, A.K.; Schlerf, M.; Wang, T.; Liu, Y.; Zeng, R.; Fei, T. Predicting foliar biochemistry of tea (Camellia sinensis) using reflectance spectra measured at powder, leaf and canopy levels. ISPRS J. Photogramm. Remote Sens. 2013, 78, 148-156.
53. Kawamura, K.; Mackay, A.; Tuohy, M.; Betteridge, K.; Sanches, I.; Inoue, Y. Potential for spectral indices to remotely sense phosphorus and potassium content of legume-based pasture as a means of assessing soil phosphorus and potassium fertility status. Int. J. Remote Sens. 2011, 32, 103-124.