Retrieval of winter wheat leaf area index from Chinese GF-1 satellite data using the PROSAIL model

Sensors (Switzerland)

Volumn 18, Issue 4, 2018, Pages

  Li, He ^a   Liu, Gaohuan ^a   Liu, Qingsheng ^a   Chen, Zhongxin ^b   Huang, Chong ^a  

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

^b INSTITUTE OF PLANT PROTECTION   (China)

Author keywords

GF 1; Leaf area index; Look up table; PROSAIL; Winter wheat

Indexed keywords

MEAN SQUARE ERROR; RADIATIVE TRANSFER; TABLE LOOKUP;

GF-1; LEAF AREA INDEX; LOOK UP TABLE; PROSAIL; WINTER WHEAT;

CROPS;

PLANT LEAF; SEASON; SPECTROSCOPY; TELECOMMUNICATION; THEORETICAL MODEL; WHEAT;

MODELS, THEORETICAL; PLANT LEAVES; SATELLITE COMMUNICATIONS; SEASONS; SPECTRUM ANALYSIS; TRITICUM;

EID: 85045106242     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s18041120     Document Type: Article

References (73)

1. Chen, J.M.; Black, T.A. Defining leaf area index for non-flat leaves. Plant Cell Environ. 1992, 15, 421–429. [CrossRef]
2. Campos-Taberner, M.; García-Haro, F.J.; Camps-Valls, G.; Grau-Muedra, G.; Nutini, F.; Crema, A.; Boschetti, M. Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote Sens. Environ. 2016, 187, 102–118. [CrossRef]
3. Soudani, K.; François, C.; le Maire, G.; Le Dantec, V.; Dufrêne, E. Comparative analysis of ikonos, spot, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sens. Environ. 2006, 102, 161–175. [CrossRef]
4. Nguy-Robertson, A.L.; Peng, Y.; Gitelson, A.A.; Arkebauer, T.J.; Pimstein, A.; Herrmann, I.; Karnieli, A.; Rundquist, D.C.; Bonfil, D.J. Estimating green lai in four crops: Potential of determining optimal spectral bands for a universal algorithm. Agric. For. Meteorol. 2014, 192–193, 140–148. [CrossRef]
5. Jay, S.; Maupas, F.; Bendoula, R.; Gorretta, N. Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and prosail inversion for field phenotyping. Field Crop. Res. 2017, 210, 33–46. [CrossRef]
6. Roosjen, P.P.J.; Brede, B.; Suomalainen, J.M.; Bartholomeus, H.M.; Kooistra, L.; Clevers, J.G.P.W. Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data—Potential of unmanned aerial vehicle imagery. Int. J. Appl. Earth Obs. 2018, 66, 14–26. [CrossRef]
7. Kamal, M.; Phinn, S.; Johansen, K. Assessment of multi-resolution image data for mangrove leaf area index mapping. Remote Sens. Environ. 2016, 176, 242–254. [CrossRef]
8. Neinavaz, E.; Skidmore, A.K.; Darvishzadeh, R.; Groen, T.A. Retrieval of leaf area index in different plant species using thermal hyperspectral data. ISPRS J. Photogramm. 2016, 119, 390–401. [CrossRef]
9. Li, Z.; Jin, X.; Wang, J.; Yang, G.; Nie, C.; Xu, X.; Feng, H. Estimating winter wheat (Triticum aestivum) lai and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the prosail model. Int. J. Remote Sens. 2015, 36, 2634–2653. [CrossRef]
10. Duan, S.; Li, Z.; Wu, H.; Tang, B.; Ma, L.; Zhao, E.; Li, C. Inversion of the prosail model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. Int. J. Appl. Earth Obs. 2014, 26, 12–20. [CrossRef]
11. Li, H.; Chen, Z.; Jiang, Z.; Wu, W.; Ren, J.; Liu, B.; Tuya, H. Comparative analysis of GF-1, HJ-1, and landsat-8 data for estimating the leaf area index of winter wheat. J. Integr. Agric. 2017, 16, 266–285. [CrossRef]
12. Hosseini, M.; McNairn, H.; Merzouki, A.; Pacheco, A. Estimation of leaf area index (LAI) in corn and soybeans using multi-polarization C- and L-band radar data. Remote Sens. Environ. 2015, 170, 77–89. [CrossRef]
13. Tang, H.; Brolly, M.; Zhao, F.; Strahler, A.H.; Schaaf, C.L.; Ganguly, S.; Zhang, G.; Dubayah, R. Deriving and validating leaf area index (LAI) at multiple spatial scales through lidar remote sensing: A case study in sierra national forest, ca. Remote Sens. Environ. 2014, 143, 131–141. [CrossRef]
14. Campos-Taberner, M.; García-Haro, F.J.; Confalonieri, R.; Martínez, B.; Moreno, Á. Multitemporal monitoring of plant area index in the valencia rice district with pocketlai. IEEE Geosci. Remote Sens. Mag. 2016, 8, 202. [CrossRef]
15. Breda, 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. [CrossRef] [PubMed]
16. Tillack, A.; Clasen, A.; Kleinschmit, B.; Förster, M. Estimation of the seasonal leaf area index in an alluvial forest using high-resolution satellite-based vegetation indices. Remote Sens. Environ. 2014, 141, 52–63. [CrossRef]
17. Atzberger, C.; Richter, K. Spatially constrained inversion of radiative transfer models for improved LAI mapping from future sentinel-2 imagery. Remote Sens. Environ. 2012, 120, 208–218. [CrossRef]
18. Sumnall, M.; Peduzzi, A.; Fox, T.R.; Wynne, R.H.; Thomas, V.A.; Cook, B. Assessing the transferability of statistical predictive models for leaf area index between two airborne discrete return lidar sensor designs within multiple intensely managed loblolly pine forest locations in the south-eastern USA. Remote Sens. Environ. 2016, 176, 308–319. [CrossRef]
19. Tian, J.; Wang, L.; Li, X.; Gong, H.; Shi, C.; Zhong, R.; Liu, X. Comparison of uav and worldview-2 imagery for mapping leaf area index of mangrove forest. Int. J. Appl. Earth Obs. 2017, 61, 22–31. [CrossRef]
20. Fang, H.L.; Liang, S.L. Retrieving leaf area index with a neural network method: Simulation and validation. IEEE Trans. Geosci. Remote Sens. 2003, 41, 2052–2062. [CrossRef]
21. Shang, J.; Liu, J.; Huffman, T.; Qian, B.; Pattey, E.; Wang, J.; Zhao, T.; Geng, X.; Kroetsch, D.; Dong, T.; et al. Estimating plant area index for monitoring crop growth dynamics using landsat-8 and rapideye images. J. Appl. Remote Sens. 2014, 8. [CrossRef]
22. Yao, Y.; Liu, Q.; Liu, Q.; Li, X. Lai retrieval and uncertainty evaluations for typical row-planted crops at different growth stages. Remote Sens. Environ. 2008, 112, 94–106. [CrossRef]
23. González-Sanpedro, M.C.; Le Toan, T.; Moreno, J.; Kergoat, L.; Rubio, E. Seasonal variations of leaf area index of agricultural fields retrieved from landsat data. Remote Sens. Environ. 2008, 112, 810–824. [CrossRef]
24. Korhonen, L.; Hadi; Packalen, P.; Rautiainen, M. Comparison of sentinel-2 and landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sens. Environ. 2017, 195, 259–274. [CrossRef]
25. Vohland, M.; Mader, S.; Dorigo, W. Applying different inversion techniques to retrieve stand variables of summer barley with prospect+sail. Int. J. Appl. Earth Obs. 2010, 12, 71–80. [CrossRef]
26. Jacquemoud, S.; Verhoef, W.; Baret, F.; Bacour, C.; Zarco-Tejada, P.J.; Asner, G.P.; François, C.; Ustin, S.L. Prospect + sail models: A review of use for vegetation characterization. Remote Sens. Environ. 2009, 113, S56–S66. [CrossRef]
27. Fang, H. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sens. Environ. 2003, 85, 257–270. [CrossRef]
28. Verrelst, J.; Camps-Valls, G.; Muñoz-Marí, J.; Rivera, J.P.; Veroustraete, F.; Clevers, J.G.P.W.; Moreno, J. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties—A review. ISPRS J. Photogramm. 2015, 108, 273–290. [CrossRef]
29. Li, X.; Zhang, Y.; Bao, Y.; Luo, J.; Jin, X.; Xu, X.; Song, X.; Yang, G. Exploring the best hyperspectral features for lai estimation using partial least squares regression. Remote Sens. Basel 2014, 6, 6221–6241. [CrossRef]
30. Darvishzadeh, R.; Skidmore, A.; Schlerf, M.; Atzberger, C. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sens. Environ. 2008, 112, 2592–2604. [CrossRef]
31. Baret, F.; Hagolle, O.; Geiger, B.; Bicheron, P.; Miras, B.; Huc, M.; Berthelot, B.; Niño, F.; Weiss, M.; Samain, O.; et al. LAI, fAPAR and fCOVER CYCLOPES global products derived from vegetation. Remote Sens. Environ. 2007, 110, 275–286. [CrossRef]
32. Nigam, R.; Bhattacharya, B.K.; Vyas, S.; Oza, M.P. Retrieval of wheat leaf area index from AWIFS multispectral data using canopy radiative transfer simulation. Int. J. Appl. Earth Obs. 2014, 32, 173–185. [CrossRef]
33. Jacquemoud, S.; Baret, F.; Andrieu, B.; Danson, F.M.; Jaggard, K. Extraction of vegetation biophysical parameters by inversion of the prospect plus sail models on sugar-beet canopy reflectance data application to TM and aviris sensors. Remote Sens. Environ. 1995, 52, 163–172. [CrossRef]
34. Jacquemoud, S.; Baret, F. Prospect: A model of leaf optical properties spectra. Remote Sens. Environ. 1990, 34, 75–91. [CrossRef]
35. Verhoef, W. Light scattering by leaf layers with application to canopy reflectance modeling: The sail model. Remote Sens. Environ. 1984, 16, 125–141. [CrossRef]
36. Pasolli, L.; Asam, S.; Castelli, M.; Bruzzone, L.; Wohlfahrt, G.; Zebisch, M.; Notarnicola, C. Retrieval of leaf area index in mountain grasslands in the alps from MODIS satellite imagery. Remote Sens. Environ. 2015, 165, 159–174. [CrossRef]
37. Darvishzadeh, R.; Atzberger, C.; Skidmore, A.; Schlerf, M. Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models. ISPRS J. Photogramm. 2011, 66, 894–906. [CrossRef]
38. Yang, F.; Sun, J.; Fang, H.; Yao, Z.; Zhang, J.; Zhu, Y.; Song, K.; Wang, Z.; Hu, M. Comparison of different methods for corn lai estimation over northeastern china. Int. J. Appl. Earth Obs. 2012, 18, 462–471.
39. Verger, A.; Baret, F.; Camacho, F. Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations. Remote Sens. Environ. 2011, 115, 415–426. [CrossRef]
40. Camps-Valls, G.; Bruzzone, L.; Rojo-Rojo, J.L.; Melgani, F. Robust support vector regression for biophysical variable estimation from remotely sensed images. IEEE Geosci. Remote Sens. 2006, 3, 339–343. [CrossRef]
41. Shawe-Taylor, J.; Cristianini, N. Kernel Methods for Pattern Analysis; Cambridge University Press: Cambridge, UK, 2004.
42. Rasmussen, C.E.; Williams, C.K.I. Gaussian Processes for Machine Learning; The MIT Press: New York, NY, USA, 2006.
43. Camps-Valls, G.; Verrelst, J.; Munoz-Mari, J.; Laparra, V.; Mateo-Jimenez, F.; Gomez-Dans, J. A survey on gaussian processes for earth-observation data analysis: A comprehensive investigation. IEEE Geosci. Remote Sens. Mag. 2016, 4, 58–78. [CrossRef]
44. Lazaro-Gredilla, M.; Titsias, M.K.; Verrelst, J.; Camps-Valls, G. Retrieval of biophysical parameters with heteroscedastic gaussian processes. IEEE Geosci. Remote Sens. 2014, 4, 838–842. [CrossRef]
45. Verrelst, J.; Muñoz, J.; Alonso, L.; Delegido, J.; Rivera, J.P.; Camps-Valls, G.; Moreno, J. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for sentinel-2 and -3. Remote Sens. Environ. 2012, 118, 127–139. [CrossRef]
46. Verrelst, J.; Alonso, L.; Campsvalls, G.; Delegido, J.; Moreno, J. Retrieval of vegetation biophysical parameters using gaussian process techniques. IEEE Trans. Geosci. Remote Sens. 2012, 50, 1832–1843. [CrossRef]
47. Zhang, Q.; Xiao, X.; Braswell, B.; Linder, E.; Baret, F.; Mooreiii, B. Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sens. Environ. 2005, 99, 357–371. [CrossRef]
48. Dorigo, W.A. Improving the robustness of cotton status characterisation by radiative transfer model inversion of multi-angular CHRIS/PROBA data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 18–29. [CrossRef]
49. Li, H.; Chen, Z.; Liu, G.; Jiang, Z.; Huang, C. Improving winter wheat yield estimation from the ceres-wheat model to assimilate leaf area index with different assimilation methods and spatio-temporal scales. Remote Sens. Basel 2017, 9, 190. [CrossRef]
50. Myneni, R.B.; Hoffman, S.; Knyazikhin, Y.; Privette, J.L.; Glassy, J.; Tian, Y.; Wang, Y.; Song, X.; Zhang, Y.; Smith, G.R.; et al. Global products of vegetation leaf area and fraction absorbed par from year one of MODIS data. Remote Sens. Environ. 2002, 83, 214–231. [CrossRef]
51. Deng, F.; Chen, J.M.; Plummer, S.; Chen, M.; Pisek, J. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Trans. Geosci. Remote Sens. 2006, 44, 2219–2229. [CrossRef]
52. Bacour, C.; Baret, F.; Béal, D.; Weiss, M.; Pavageau, K. Neural network estimation of lai, fapar, fcover and LAI × CAB, from top of canopy meris reflectance data: Principles and validation. Remote Sens. Environ. 2006, 105, 313–325.
53. Knyazikhin, Y.; Martonchik, J.V.; Myneni, R.B.; Diner, D.J.; Running, S.W. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and misr data. J. Geophys. Res.-Atmos. 1998, 103, 32239–32256. [CrossRef]
54. Ganguly, S.; Schull, M.A.; Samanta, A.; Shabanov, N.V.; Milesi, C.; Nemani, R.R.; Knyazikhin, Y.; Myneni, R.B. Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory. Remote Sens. Environ. 2008, 112, 4333–4343. [CrossRef]
55. LI-COR Inc. LAI-2200 Plant Canopy Analyzer, Introduction Manual; LI-COR Inc.: Lincoln, NE, USA, 2010.
56. Matthew, M.W.; Adler-Golden, S.M.; Berk, A.; Richtsmeier, S.C.; Levine, R.Y.; Bernstein, L.S.; Acharya, P.K.; Anderson, G.P.; Felde, G.W.; Hoke, M.P.; et al. Status of atmospheric correction using a modtran4-based algorithm. In Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) 4049; Shen, S.S., Descour, M.R., Eds.; Society of Photo-Optical Instrumentation Engineers: Bellingham, WA, USA, 2000; pp. 199–207.
57. Sims, D.A.; Gamon, J.A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 2002, 81, 337–354. [CrossRef]
58. Datt, B. A new reflectance index for remote sensing of chlorophyll content in higher plants: Tests using eucalyptus leaves. J. Plant Physiol. 1999, 154, 30–36. [CrossRef]
59. Gitelson, A.A.; Gritz, Y.; Merzlyak, M.N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 2003, 160, 271–282. [CrossRef] [PubMed]
60. Gitelson, A.; Merzlyak, M.N. Quantitative estimation of chlorophyll-a using reflectance spectra—Experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. B 1994, 22, 247–252. [CrossRef]
61. Gitelson, A.; Merzlyak, M.N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves—Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 1994, 143, 286–292. [CrossRef]
62. Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289–298. [CrossRef]
63. Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295–309. [CrossRef]
64. Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95–107. [CrossRef]
65. Broge, N.H.; Leblanc, E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 2001, 76, 156–172. [CrossRef]
66. Huete, A.R.; Liu, H.Q.; Batchily, K.; Leeuwen, W.V. A comparison of vegetation indices over a global set of tm images for EOS-MODIS. Remote Sens. Environ. 1997, 59, 440–451. [CrossRef]
67. Huete, A.; Justice, C.; Liu, H. Development of vegetation and soil indices for MODIS-EOS. Remote Sens. Environ. 1994, 49, 224–234. [CrossRef]
68. Darvishzadeh, R.; Matkan, A.A.; Dashti Ahangar, A. Inversion of a radiative transfer model for estimation of rice canopy chlorophyll content using a lookup-table approach. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 1222–1230. [CrossRef]
69. Si, Y.; Schlerf, M.; Zurita-Milla, R.; Skidmore, A.; Wang, T. Mapping spatio-temporal variation of grassland quantity and quality using meris data and the prosail model. Remote Sens. Environ. 2012, 121, 415–425. [CrossRef]
70. Yang, G.; Zhao, C.; Liu, Q.; Huang, W.; Wang, J. Inversion of a radiative transfer model for estimating forest LAI from multisource and multiangular optical remote sensing data. IEEE Trans. Geosci. Remote Sens. 2011, 49, 988–1000. [CrossRef]
71. Richter, K.; Atzberger, C.; Vuolo, F.; D’Urso, G. Evaluation of sentinel-2 spectral sampling for radiative transfer model based lai estimation of wheat, sugar beet, and maize. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4, 458–464. [CrossRef]
72. Richter, K.; Hank, T.B.; Vuolo, F.; Mauser, W.; D’Urso, G. Optimal exploitation of the sentinel-2 spectral capabilities for crop leaf area index mapping. Remote Sens. Basel 2012, 4, 561–582. [CrossRef]
73. Wang, L.; Dong, T.; Zhang, G.; Niu, Z. Lai retrieval using prosail model and optimal angle combination of multi-angular data in wheat. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 1730–1736. [CrossRef]