Comparison of different methods for corn LAI estimation over northeastern China

International Journal of Applied Earth Observation and Geoinformation

Volumn 18, Issue 1, 2012, Pages 462-471

  Yang, Fei ^a,b   Sun, Jiulin ^a   Fang, Hongliang ^a   Yao, Zuofang ^b,c   Zhang, Jiahua ^d   Zhu, Yunqiang ^a   Song, Kaishan ^b   Wang, Zongming ^b   Hu, Maogui ^a  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b NORTHEAST INSTITUTE OF GEOGRAPHY AND AGRICULTURAL ECOLOGY   (China)

^c UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^d STATE KEY LABORATORY OF SEVERE WEATHER   (China)

Author keywords

Comparison; Corn; Estimation; Lai; Pca

Indexed keywords

ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; ESTIMATION METHOD; LEAF AREA INDEX; MAIZE; NDVI; PRINCIPAL COMPONENT ANALYSIS;

CHINA;

ZEA MAYS;

EID: 84864513781     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2011.09.004     Document Type: Article

References (76)

1. Amdev Ren, H., Dem, R.N., Kanik, A., Kesk, N.S., 2005. Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs. Ecological Modelling 181, 581-589.
2. Atzberger, C., Guérif, M., Baret, F., Werner, W., 2010. Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat. Computers and Electronics in Agriculture 73, 165-173.
3. Bacour, C., Baret, F., Beal, D., Weiss, M., Pavageau, K., 2006. Neural network estimation of LAI, fAPAR, fCover and LAI Cab, from top of canopy MERIS reflectance data: principles and validation. Remote Sensing of Environment 105, 313-325.
4. Bacour, C., Jacquemoud, S., Leroy, M., Hautecoeur, O., Weiss, M., Prevot, L., Bruguier, N., Chauki, H., 2002. Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne POLDER data. Agronomie 22, 555-565.
5. Barnes, W.L., Pagano, T.S., Salomonson, V.V., Fellow, I., 1998. Prelaunch characteristics of the moderate resolution imaging spectroradiometer (MODIS) on EOS-AM1. IEEE Transactions on Geoscience and Remote Sensing 36, 1088-1100.
6. Ben-Dor, E., Levin, N., Singer, A., Karnieli, A., Braun, O., Kidron, G., 2006. Quantitative mapping of the soil rubification process on sand dunes using an airborne hyperspectral sensor. Geoderma 131, 1-21.
7. Breunig, F.M., Galvão, L.S., Formaggio, A.R., Epiphanio, J.C.N., 2011. Directional effects on NDVI and LAI retrievals from MODIS: a case study in Brazil with soybean. International Journal of Applied Earth Observation and Geoinformation 13, 34-42.
8. Chaurasia, S., Dadhwal, V., 2004. Comparison of principal component inversion with VI-empirical approach for LAI estimation using simulated reflectance data. International Journal of Remote Sensing 25, 2881-2887.
9. Chen, J., Cihlar, J., 1996. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment 55, 153-162.
10. Chen, J., Leblanc, S., 1997. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing 35, 1316-1337.
11. Chen, J., Pavlic, G., Brown, L., Cihlar, J., Leblanc, S., White, H., Hall, R., Peddle, D., King, D., Trofymow, J., 2002. Derivation and validation of Canada-wide coarseresolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of Environment 80, 165-184.
12. Chen, J.M., Rich, P.M., Gower, S.T., Norman, J.M., Plummer, S., 1997. Leaf area index of boreal forests: theory, techniques, and measurements. Journal of Geophysical Research 102, 29429-29444.
13. Combal, B., Baret, F., Weiss, M., Trubuil, A., Mace, D., Pragnere, A., Myneni, R., Knyazikhin, Y., Wang, L., 2003. Retrieval of canopy biophysical variables from bidirectional reflectance: using prior information to solve the ill-posed inverse problem. Remote Sensing of Environment 84, 1-15.
14. Cramer, W., Kicklighter, D., Bondeau, A., Iii, B., Churkina, G., Nemry, B., Ruimy, A., Schloss, A., 1999. Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Global Change Biology 5, 1-15.
15. Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., 2008. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sensing of Environment 112, 2592-2604.
16. Elmasry, G., Wang, N., Vigneault, C., 2009. Detecting chilling injury in red delicious apple using hyperspectral imaging and neural networks. Postharvest Biology and Technology 52, 1-8.
17. Fang, H., Liang, S., 2005. A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies. Remote Sensing of Environment 94, 405-424.
18. Fang, H., Liang, S., Kuusk, A., 2003a. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing of Environment 85, 257-270.
19. Fang, H.L., Liang, S.L., Senior Member, I., 2003b. Retrieving leaf area index with a neural network method: simulation and validation. IEEE Trancsactions on Geoscience and Remote Sensing 41, 2052-2062.
20. Gao, X., Huete, A.R., Ni, W., Miura, T., 2000. Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment 74, 609-620.
21. Goel, P.K., Prasher, S.O., Patel, R.M., Landry, J.A., Bonnell, R.B., Viau, A.A., 2003. Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Computers and Electronics in Agriculture 39, 67-93.
22. Govaerts, Y., Verstraete, M., Pinty, B., Gobron, N., 1999. Designing optimal spectral indices: a feasibility and proof of concept study. International Journal of Remote Sensing 20, 1853-1873.
23. Gurung, R.B., Breidt, F.J., Dutin, A., Ogle, S.M., 2009. Predicting enhanced vegetation index (EVI) curves for ecosystem modeling applications. Remote Sensing of Environment 113, 2186-2193.
24. Haboudane, D., Miller, J., Pattey, E., Zarco-Tejada, P., Strachan, I., 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.
25. Houborg, R., Soegaard, H., Boegh, E., 2007. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using terra and aqua MODIS reflectance data. Remote Sensing of Environment 106, 39-58.
26. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195-213.
27. Huete, A., Justice, C., Liu, H., 1994. Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment 49, 224-234.
28. Ifarraguerri, A., Chang, C.I., 2000. Unsupervised hyperspectral image analysis with projection pursuit. IEEE Transactions on Geoscience and Remote Sensing 38, 2529-2538.
29. Jacquemoud, S., Bacour, C., Poilve, H., Frangi, J., 2000. Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. Remote Sensing of Environment 74, 471-481.
30. Jacquemoud, S., Baret, F., 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment 34, 75-91.
31. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F., Jaggard, K., 1995. Extraction of vegetation biophysical parameters by inversion of the PROSPECT+ SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors, Remote Sensing of Environment 52, 163-172.
32. Jacquemoud, S., Ustin, S., Verdebout, J., Schmuck, G., Andreoli, G., Hosgood, B., 1996. Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing of Environment 56, 194-202.
33. Kimes, D., Nelson, R., Manry, M., Fung, A., 1998. Review article: attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. International Journal of Remote Sensing 19, 2639-2663.
34. Knyazikhin, Y., Martonchik, J., Diner, D., Myneni, R., Verstraete, M., Pinty, B., Gobron, N., 1998. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere-corrected MISR data. Journal of geophysical research 103, 32239-32256.
35. Krasnopolsky, V., Chevallier, F., 2003. Some neural network applications in environmental sciences. Part II: advancing computational efficiency of environmental numerical models. Neural Networks 16, 335-348.
36. Kuusk, A., 1991. The hot-spot effect in plant canopy reflectance. In: Photon-Vegetation Interactions, p. 21.
37. Kuusk, A., 1995. A Markov chain model of canopy reflectance. Agricultural and Forest Meteorology 76, 221-236.
38. López-Lozano, R., Baret, F., García de Cortázar-Atauri, I., Bertrand, N., Casterad, M.A., 2009. Optimal geometric configuration and algorithms for LAI indirect estimates under row canopies: the case of vineyards. Agricultural and Forest Meteorology 149, 1307-1316.
39. le Maire, G., Franç ois, C., Soudani, K., Berveiller, D., Pontailler, J.-Y., Bréda, N., Genet, H., Davi, H., Dufrêne, E., 2008. 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.
40. Li, L.K, Jiang, J.J., Mao, R.Z., Ni, S.X., 2005. The modeling of vegetation through leaf area index by means of remote sensing. Acta Ecologica Sinica 25, 1491-1496.
41. Li, X.W., Strahler, A.H., 1986. Geometrical-optical modeling of a conifer forest canopy. IEEE Transactions on Geoscience and Remote Sensing 24, 906-919.
42. Liu, Z.Y., Wu, H.F., Huang, J.F., 2010. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Computers and Electronics in Agriculture 72, 99-106.
43. Lucas, N., Curran, P., Plummer, S., Danson, F., 2000. Estimating the stem carbon production of a coniferous forest using an ecosystem simulation model driven by the remotely sensed red edge. International Journal of Remote Sensing 21, 619-631.
44. Martinez-Beltran, C., Jochum, M.A., Osann, Calera, A., Melia, J., 2009. Multisensor comparison of NDVI for a semi-arid environment in Spain. International Journal of Remote Sensing 30, 1355-1384.
45. Maselli, F., Chiesi, M., 2006. Integration of multi-source NDVI data for the estimation of Mediterranean forest productivity. International Journal of Remote Sensing 27, 55-72.
46. Meroni, M., Colombo, R., Panigada, C., 2004. Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations. Remote Sensing of Environment 92, 195-206.
47. Mouazen, A.M., Kuang, B., De Baerdemaeker, J., Ramon, H., 2010. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma 158, 23-31.
48. Myneni, R., Hoffman, S., Knyazikhin, Y., Privette, J., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G., 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment 83, 214-231.
49. Nakaji, T., Ide, R., Takagi, K., Kosugi, Y., Ohkubo, S., Nasahara, K.N., Saigusa, N., Oguma, H., 2008. Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Japan. Agricultural and Forest Meteorology 148, 776-787.
50. Natraj, V., Shia, R.L., Yung, Y.L., 2010. On the use of principal component analysis to speed up radiative transfer calculations. Journal of Quantitative Spectroscopy and Radiative Transfer 111, 810-816.
51. Noori, R., Karbassi, A., Salman Sabahi, M., 2010a. Evaluation of PCA and gamma test techniques on ANN operation for weekly solid waste prediction. Journal of Environmental Management 91, 767-771.
52. Noori, R., Khakpour, A., Omidvar, B., Farokhnia, A., 2010b. Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems with Applications 37, 5856-5862.
53. Propastin, P., Erasmi, S., 2010. A physically based approach to model LAI from MODIS 250 m data in a tropical region. International Journal of Applied Earth Observation and Geoinformation 12, 47-59.
54. Pu, R., Gong, P., 2004. Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping. Remote Sensing of Environment 91, 212-224.
55. Pu, R.L., Gong, P., 2000. Hyperspectral Remote Sensing and Its Application. High Education Press, Beijing, p. 70.
56. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings of Third Earth Resources Technology Satellite-1 Symposium, Greenbelt, p. 8.
57. Running, S., Nemani, R., Heinsch, F., Zhao, M., Reeves, M., Hashimoto, H., 2004. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547-560.
58. Schlerf, M., Atzberger, C., 2006. Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sensing of Environment 100, 281-294.
59. Sellers, P., Dickinson, R., Randall, D., Betts, A., Hall, F., Berry, J., Collatz, G., Denning, A., Mooney, H., Nobre, C., 1997. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 275, 502.
60. Sjöström, M., Ardö, J., Arneth, A., Boulain, N., Cappelaere, B., Eklundh, L., de Grandcourt, A., Kutsch, W.L., Merbold, L., Nouvellon, Y., Scholes, R.J., Schubert, P., Seaquist, J., Veenendaal, E.M., 2011. Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems. Remote Sensing of Environment 115, 1081-1089.
61. Smith, J., 1993. LAI inversion using a back-propagation neural network trained with a multiple scattering model. IEEE Transactions on Geoscience and Remote Sensing 31, 1102-1106.
62. Song, K.S., Zhang, B., Wang, Z.M., Zhang, Y., Liu, H.J., 2006. Soybean LAI estimation with in-situ collected hyperspectral data based on BP-neural networks. Scientia Agricultura Sinica 39, 1138-1145.
63. Stagakis, S., Markos, N., Sykioti, O., Kyparissis, A., 2010. Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: an application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sensing of Environment 114, 977-994.
64. Stephan, K., Hibbitts, C.A., Hoffmann, H., Jaumann, R., 2008. Reduction of instrumentdependent noise in hyperspectral image data using the principal component analysis: Applications to Galileo NIMS data. Planetary and Space Science 56, 406-419.
65. Tan, B., Hu, J., Zhang, P., Huang, D., Shabanov, N., Weiss, M., Knyazikhin, Y., Myneni, R., 2005. Validation of moderate resolution imaging spectroradiometer leaf area index product in croplands of Alpilles, France. Journal of Geophysical Research 110, D01107.
66. Tang, S., Chen, J., Zhu, Q., Li, X., Chen, M., Sun, R., Zhou, Y., Deng, F., Xie, D., 2007. LAI inversion algorithm based on directional reflectance kernels. Journal of Environmental Management 85, 638-648.
67. Turner, D., Cohen, W., Kennedy, R., Fassnacht, K., Briggs, J., 1999. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment 70, 52-68.
68. Verhoef, W., 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment 16, 125-141.
69. Verhoef, W., Bach, H., 2007. Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sensing of Environment 109, 166-182.
70. Walthall, C., Dulaney, W., Anderson, M., Norman, J., Fang, H., Liang, S., 2004. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery. Remote Sensing of Environment 92, 465-474.
71. Wang, X.Z., Huang, J.F., Li, Y.M., Wang, R.C., 2004. The study on hyperspectral remote sensing estimation models about LAI of rice. Journal of Remote Sensing 8, 81-88.
72. Weiss, M., Baret, F., Leroy, M., Hautecoeur, O., Bacour, C., Privot, L., Bruguier, N., 2002. Validation of neural net techniques to estimate canopy biophysical variables from remote sensing data. Agronomie 22, 547-553.
73. Weiss, M., Baret, F., Myneni, R., Pragnre, A., Knyazikhin, Y., 2000. Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data. Agronomie 20, 3-22.
74. White, J., Running, S., Nemani, R., Keane, R., Ryan, K., 1997. Measurement and remote sensing of LAI in Rocky Mountain montane ecosystems. Canadian Journal of Forest Research 27, 1714-1727.
75. Williams, P., Geladi, P., Fox, G., Manley, M., 2009. Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Analytica Chimica Acta 653, 121-130.
76. Zarco-Tejada, P., Berj, N., Lpez-Lozano, R., Miller, J., Mart, N., Cachorro, V., Gonzlez, M., De Frutos, A., 2005. Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment 99, 271-287.