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

International Journal of Applied Earth Observation and Geoinformation

Volumn 66, Issue , 2018, Pages 14-26

  Roosjen, Peter P J ^a   Brede, Benjamin ^a   Suomalainen, Juha M ^a,b   Bartholomeus, Harm M ^a   Kooistra, Lammert ^a   Clevers, Jan G P W ^a  

^a WAGENINGEN UNIVERSITY   (Netherlands)

^b FINNISH GEOSPATIAL RESEARCH INSTITUTE   (Finland)

Author keywords

Leaf area index; Leaf chlorophyll content; Model inversion; Multi angular reflectance; PROSAIL; Reflectance anisotropy; Unmanned aerial vehicle

Indexed keywords

AIRBORNE SENSING; CHLOROPHYLL; DATA INVERSION; ESTIMATION METHOD; LEAF AREA INDEX; POTATO; SPECTRAL REFLECTANCE; UNMANNED VEHICLE;

SOLANUM TUBEROSUM;

EID: 85040654514     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2017.10.012     Document Type: Article

References (51)

1. Bachmann, C.M., Abelev, A., Montes, M.J., Philpot, W., Gray, D., Doctor, K.Z., Fusina, R.A., Mattis, G., Chen, W., Noble, S.D., et al. Flexible field goniometer system: the goniometer for outdoor portable hyperspectral earth reflectance. J. Appl. Remote Sens., 10, 2016.
2. Baret, F., Buis, S., Estimating canopy characteristics from remote sensing observations: review of methods and associated problems. Advances in Land Remote Sensing: System, Modeling Inversion and Application, 2008, 173–201.
3. 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. Part 1: principles of the algorithm. Remote Sens. Environ. 110 (2007), 275–286.
4. Barnsley, M.J., Strahler, A.H., Morris, K.P., Muller, J.P., Sampling the surface bidirectional reflectance distribution function (brdf): 1: evaluation of current and future satellite sensors. Remote Sens. Rev. 8 (1994), 271–311.
5. Barnsley, M.J., Settle, J.J., Cutter, M.A., Lobb, D.R., Teston, F., The proba/chris mission: a low-cost smallsat for hyperspectral multiangle observations of the earth surface and atmosphere. IEEE Trans. Geosci. Remote Sens. 42 (2004), 1512–1520.
6. Biliouris, D., Verstraeten, W.W., Dutré, P., Van Aardt, J.A.N., Muys, B., Coppin, P., A compact laboratory spectro-goniometer (clabspeg) to assess the brdf of materials Presentation, calibration and implementation on fagus sylvatica l. Leaves. Sensors 7 (2007), 1846–1870.
7. Botha, E.J., Leblon, B., Zebarth, B., Watmough, J., Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted prosail model. Int. J. Appl. Earth Obs. Geoinform. 9 (2007), 360–374.
8. Burkart, A., Aasen, H., Alonso, L., Menz, G., Bareth, G., Rascher, U., Angular dependency of hyperspectral measurements over wheat characterized by a novel uav based goniometer. Remote Sens. 7 (2015), 725–746.
9. Casa, R., Jones, H.G., Retrieval of crop canopy properties: a comparison between model inversion from hyperspectral data and image classification. Int. J. Remote Sens. 25 (2004), 1119–1130.
10. Chen, J.M., Liu, J., Leblanc, S.G., Lacaze, R., Roujean, J.L., Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sens. Environ. 84 (2003), 516–525.
11. Cilia, C., Panigada, C., Rossini, M., Meroni, M., Busetto, L., Amaducci, S., Boschetti, M., Picchi, V., Colombo, R., Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sens. 6 (2014), 6549–6565.
12. Clevers, J., Kooistra, L., van den Brande, M., Using sentinel-2 data for retrieving lai and leaf and canopy chlorophyll content of a potato crop. Remote Sens., 9, 2017, 405.
13. Combal, B., Baret, F., Weiss, M., Trubuil, A., Macé, D., Pragnère, A., Myneni, R., Knyazikhin, Y., Wang, L., Retrieval of canopy biophysical variables from bidirectional reflectance using prior information to solve the ill-posed inverse problem. Remote Sens. Environ. 84 (2003), 1–15.
14. D'Urso, G., Dini, L., Vuolo, F., Alonso, L., Guanter, L., Retrieval of Leaf Area Index by Inverting Hyper-spectral, Multi-angular Chris/proba Data from Sparc 2003. 2004, European Space Agency (Special Publication) ESA SP, 58–63.
15. 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. 112 (2008), 2592–2604.
16. Diner, D.J., Beckert, J.C., Reilly, T.H., Bruegge, C.J., Conel, J.E., Kahn, R.A., Martonchik, J.V., Ackerman, T.P., Davies, R., Gerstl, S.A.W., et al. Multi-angle imaging spectroradiometer (misr) instrument description and experiment overview. IEEE Trans. Geosci. Remote Sens. 36 (1998), 1072–1087.
17. Domingues Franceschini, M., Bartholomeus, H., van Apeldoorn, D., Suomalainen, J., Kooistra, L., Intercomparison of unmanned aerial vehicle and ground-based narrow band spectrometers applied to crop trait monitoring in organic potato production. Sensors, 17, 2017, 1428.
18. 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. 5 (2012), 18–29.
19. Duan, S.B., Li, Z.L., Wu, H., Tang, B.H., 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. Geoinform. 26 (2014), 12–20.
20. Goffart, J.P., Olivier, M., Frankinet, M., Potato crop nitrogen status assessment to improve n fertilization management and efficiency: past-present-future. Potato Res. 51 (2008), 355–383.
21. Grenzdörffer, G.J., Niemeyer, F., Uav Based Brdf-measurements of Agricultural Surfaces with Pfiffikus, International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences – ISPRS Archives, 2011, 229–234.
22. Jacquemoud, S., Baret, F., Prospect A model of leaf optical properties spectra. Remote Sens. Environ. 34 (1990), 75–91.
23. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F.M., Jaggard, K., 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 Sens. Environ. 52 (1995), 163–172.
24. Jacquemoud, S., Bacour, C., Poilvé, H., Frangi, J.P., Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. Remote Sens. Environ. 74 (2000), 471–481.
25. 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. 113 (2009), S56–S66.
26. 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 Crops Res. 210 (2017), 33–46.
27. Koetz, B., Baret, F., Poilvé, H., Hill, J., Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sens. Environ. 95 (2005), 115–124.
28. Laurent, V.C.E., Schaepman, M.E., Verhoef, W., Weyermann, J., Chávez, R.O., Bayesian object-based estimation of lai and chlorophyll from a simulated sentinel-2 top-of-atmosphere radiance image. Remote Sens. Environ. 140 (2014), 318–329.
29. Lehnert, L.W., Meyer, H., Bendix, J., Hsdar: Manage, Analyse and Simulate Hyperspectral Data in R. 2016.
30. Milton, E.J., Schaepman, M.E., Anderson, K., Kneubühler, M., Fox, N., Progress in field spectroscopy. Remote Sens. Environ. 113 (2009), S92–S109.
31. Mousivand, A., Menenti, M., Gorte, B., Verhoef, W., Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral-directional radiometric data. Remote Sens. Environ. 158 (2015), 311–330.
32. Qi, J., Cabot, F., Moran, M.S., Dedieu, G., Biophysical parameter estimations using multidirectional spectral measurements. Remote Sens. Environ. 54 (1995), 71–83.
33. Rahman, H., Pinty, B., Verstraete, M.M., Coupled surface-atmosphere reflectance (csar) model 2. Semiempirical surface model usable with noaa advanced very high resolution radiometer data. J. Geophys. Res. 98:20 (1993), 91–720 801.
34. Richter, K., Atzberger, C., Vuolo, F., Weihs, P., D'Urso, G., Experimental assessment of the sentinel-2 band setting for rtm-based lai retrieval of sugar beet and maize. Can. J. Remote Sens. 35 (2009), 230–247.
35. Roosjen, P.P.J., Clevers, J.G.P.W., Bartholomeus, H.M., Schaepman, M.E., Schaepman-Strub, G., Jalink, H., van der Schoor, R., de Jong, A., A laboratory goniometer system for measuring reflectance and emittance anisotropy. Sensors 12 (2012), 17358–17371.
36. Roosjen, P.P.J., Bartholomeus, H.M., Clevers, J.G.P.W., Effects of soil moisture content on reflectance anisotropy – laboratory goniometer measurements and rpv model inversions. Remote Sens. Environ. 170 (2015), 229–238.
37. Roosjen, P.P.J., Suomalainen, J.M., Bartholomeus, H.M., Clevers, J.G.P.W., Hyperspectral reflectance anisotropy measurements using a pushbroom spectrometer on an unmanned aerial vehicle-results for barley, winter wheat, and potato. Remote Sens., 8, 2016, 909.
38. Roosjen, P.P.J., Suomalainen, J.S., Bartholomeus, H.M., Kooistra, L.K., Clevers, J.G.P.W., Mapping reflectance anisotropy of a potato canopy using aerial images acquired with an unmanned aerial vehicle. Remote Sens., 9, 2017, 417.
39. Sandmeier, S., Müller, C., Hosgood, B., Andreoli, G., Sensitivity analysis and quality assessment of laboratory brdf data. Remote Sens. Environ. 64 (1998), 176–191.
40. Sandmeier, S.R., Acquisition of bidirectional reflectance factor data with field goniometers. Remote Sens. Environ. 73 (2000), 257–269.
41. Schaepman, M.E., Koetz, B., Schaepman-Strub, G., Itten, K.I., Spectrodirectional remote sensing for the improved estimation of biophysical and – chemical variables: two case studies. Int. J. Appl. Earth Obs. Geoinform. 6 (2005), 271–282.
42. Schopfer, J., Dangel, S., Kneubühler, M., Itten, K.I., The improved dual-view field goniometer system figos. Sensors 8 (2008), 5120–5140.
43. Suomalainen, J., Hakala, T., Peltoniemi, J., Puttonen, E., Polarised multiangular reflectance measurements using the finnish geodetic institute field goniospectrometer. Sensors 9 (2009), 3891–3907.
44. Verhoef, W., Light scattering by leaf layers with application to canopy reflectance modeling: the sail model. Remote Sens. Environ. 16 (1984), 125–141.
45. Verrelst, J., Rivera, J.P., Leonenko, G., Alonso, L., Moreno, J., Optimizing lut-based rtm inversion for semiautomatic mapping of crop biophysical parameters from sentinel-2 and −3 data: role of cost functions. IEEE Trans. Geosci. Remote Sens. 52 (2014), 257–269.
46. Vuolo, F., Dini, L., D'Urso, G., Retrieval of leaf area index from chris/proba data: an analysis of the directional and spectral information content. Int. J. Remote Sens. 29 (2008), 5063–5072.
47. Weiss, M., Baret, F., Evaluation of canopy biophysical variable retrieval performances from the accumulation of large swath satellite data. Remote Sens. Environ. 70 (1999), 293–306.
48. Weiss, M., Baret, F., Myneni, R.B., Pragnère, A., Knyazikhin, Y., Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data. Agronomie 20 (2000), 3–22.
49. Widlowski, J.L., Pinty, B., Gobron, N., Verstraete, M.M., Diner, D.J., Davis, A.B., Canopy structure parameters derived from multi-angular remote sensing data for terrestrial carbon studies. Clim. Change 67 (2004), 403–415.
50. Zhang, C., Kovacs, J.M., The application of small unmanned aerial systems for precision agriculture: a review. Precis. Agric. 13 (2012), 693–712.
51. van Evert, F.K., Booij, R., Jukema, J.N., ten Berge, H.F.M., Uenk, D., Meurs, E.J.J.B., van Geel, W.C.A., Wijnholds, K.H., Slabbekoorn, J.J.H., Using crop reflectance to determine sidedress n rate in potato saves n and maintains yield. Eur. J. Agron. 43 (2012), 58–67.