Remotely sensed green area index for winter wheat crop monitoring: 10-Year assessment at regional scale over a fragmented landscape

Agricultural and Forest Meteorology

Volumn 166-167, Issue , 2012, Pages 156-168

  Duveiller, Grégory ^a,b   Baret, Frédéric ^c   Defourny, Pierre ^b  

^a JOINT RESEARCH CENTRE   (Italy)

^b UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^c UAPV   (France)

Author keywords

Canopy radiative transfer; Crop growth monitoring; Green area index (GAI); Inter annual variability; MODIS; Regional scale; Winter wheat

Indexed keywords

ADVANCED TECHNOLOGY; ANNUAL VARIATION; ASSESSMENT METHOD; CANOPY EXCHANGE; GROWTH RATE; LANDSCAPE CHANGE; LEAF AREA INDEX; MODIS; MONITORING; RADIATIVE TRANSFER; REGIONAL PATTERN; REMOTE SENSING; SATELLITE IMAGERY; SENESCENCE; SPATIAL RESOLUTION; WHEAT;

TRITICUM AESTIVUM;

EID: 84866143216     PISSN: 01681923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agrformet.2012.07.014     Document Type: Article

References (63)

1. Baret, F., 1986. Contribution au suivi radiométrique de cultures de céréales. Ph.D. thesis, Université de Paris-Sud.
2. Baret F., Buis S. Advances in land remote sensing. System, modeling, inversion and application, chap. Estimating Canopy Characteristics From Remote Sensing Observations. Review of Methods and Associated Problems 2008, Springer, Netherlands, pp. 173-201.
3. Baret F., Hagolle O., Geiger B., Bicheron P., Miras B., Huc M., Berthelot B., Nino F., Weiss M., Samain O., Roujean J.L., Leroy M. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: principles of the algorithm. Remote Sens. Environ. 2007, 110(3):275-286.
4. Baret F., Jacquemoud S., Guyot G., Leprieur C. Modeled analysis of the biophysical nature of spectral shifts and comparison with information-content of broad bands. Remote Sens. Environ. 1992, 41(2-3):133-142.
5. Bastiaanssen W.G.M., Ali S. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric. Ecosyst. Environ. 2003, 94(3):321-340.
6. Boatwright G., Whitehead V. Early warning and crop condition assessment research. IEEE Trans. Geosci. Remote Sens. 1986, GE-24(1):54-64.
7. Boegh E., Thorsen M., Butts M.B., Hansen S., Christiansen J.S., Abrahamsen P., Hasager C.B., Jensen N.O., van der Keur P., Refsgaard J.C. Incorporating remote sensing data in physically based distributed agro-hydrological modelling. J. Hydrol. 2004, 287(1-4):279-299.
8. Bouman B.A.M. Linking physical remote sensing models with crop growth simulation models, applied for sugar beet. Int. J. Remote Sens. 1992, 13(14):2565-2581.
9. Brisson N., Mary B., Ripoche D., Jeuffroy M.H., Ruget F., Nicoullaud B., Gate P., Devienne-Barret F., Antonioletti R., Durr C., Richard G., Beaudoin N., Recous S., Tayot X., Plenet D., Cellier P., Machet J.-M., Meynard J.M., Delécolle R. STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie 1998, 18(5-6):311-346.
10. Casa R., Varella H., Buis S., Guerif M., Solan B.D., Baret F. Forcing a wheat crop model with LAI data to access agronomic variables: Evaluation of the impact of model and LAI uncertainties and comparison with an empirical approach. Eur. J. Agron. 2012, 37(1):1-10.
11. Chen J.M., Black T.A. Defining, leaf area index for non-flat leaves. Plant Cell Environ. 1992, 15(4):421-429.
12. Clevers J., Büker C., van Leeuwen H., Bouman B. A framework for monitoring crop growth by combining directional and spectral remote sensing information. Remote Sens. Environ. 1994, 50(2):161-170.
13. Coleman T.F., Li Y. On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds. Math. Program. 1994, 67:189-224.
14. Coleman T.F., Li Y. An interior trust region approach for nonlinear minimization subject to bounds. SIAM J. Optim. 1996, 6(2):418-445.
15. Cracknell A. Synergy in remote sensing-what's in a pixel?. Int. J. Remote Sens. 1998, 19(11):2025-2047.
16. Curnel Y., de Wit A.J., Duveiller G., Defourny P. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment. Agric. Forest Meteorol. 2011, 151:1843-1855.
17. de Wit A., Duveiller G., Defourny P. Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations. Agric. Forest Meteorol. 2012, 164(0):39-52.
18. Demarez V., Duthoit S., Baret F., Weiss M., Dedieu G. Estimation of leaf area and clumping indexes of crops with hemispherical photographs. Agric. Forest Meteorol. 2008, 148(4):644-655.
19. Dente L., Satalino G., Mattia F., Rinaldi M. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote Sens. Environ. 2008, 112(4):1395-1407.
20. Doraiswamy P.C., Sinclair T.R., Hollinger S., Akhmedov B., Stern A., Prueger J. Application of MODIS derived parameters for regional crop yield assessment. Remote Sens. Environ. 2005, 97(2):192-202.
21. Dorigo W., Zurita-Milla R., de Wit A., Brazile J., Singh R., Schaepman M. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. Geoinf. 2007, 9:165-193.
22. Duveiller G., Baret F., Defourny P. Crop specific green area index retrieval from MODIS data at regional scale by controlling pixel-target adequacy. Remote Sens. Environ. 2011, 115(10):2686-2701.
23. Duveiller G., Defourny P. A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing. Remote Sens. Environ. 2010, 114(11):2637-2650.
24. Duveiller G., Weiss M., Baret F., Defourny P. Retrieving wheat Green Area Index during the growing season from optical time series measurements based on neural network radiative transfer inversion. Remote Sens. Environ. 2011, 115(3):887-896.
25. Fang H.L., Liang S.L., Hoogenboom G., Teasdale J., Cavigelli M. Corn-yield estimation through assimilation of remotely sensed data into the CSM-CERES-Maize model. Int. J. Remote Sens. 2008, 29(10):3011-3032.
26. Genovese G., Bettio M. Methodology of the MARS Crop Yield Forecasting System. Vol. 4. Statistical Data Collection, Data Processing and Analysis 2004, AgriFish unit, Joint Research Centre of the European Commission, Ispra, Italy.
27. Genovese G., Vignolles C., Nègre T., Passera G. A methodology for a combined use of normalised difference vegetation index and CORINE land cover data for crop yield monitoring and forecasting. A case study on Spain. Agronomie 2001, 21:91-111.
28. Gooding M.J., Dimmock J.P.R.E., France J., Jones S.A. Green leaf area decline of wheat flag leaves: the influence of fungicides and relationships with mean grain weight and grain yield. Ann. Appl. Biol. 2000, 136(1):77-84.
29. Hadria R., Duchemin B., Jarlan L., Dedieu G., Baup F., Khabba S., Olioso A., Le Toan T. Potentiality of optical and radar satellite data at high spatio-temporal resolutions for the monitoring of irrigated wheat crops in Morocco. Int. J. Appl. Earth Obs. Geoinf. 2010, 12(Suppl. 1):S32-S37.
30. Holben B. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 1986, 7(11):1417-1434.
31. Jacquemoud S., Baret F. PROSPECT: a model of leaf optical properties spectra. Remote Sens. Environ. 1990, 34(2):75-91.
32. 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(Suppl. 1):S56-S66.
33. Jonckheere I., Fleck S., Nackaerts K., Muys B., Coppin P., Weiss M., Baret F. Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography. Agric. Forest Meteorol. 2004, 121(1-2):19-35.
34. Justice, C.O., Becker-Reshef, I., 2007. Report from the Workshop on Developing a Strategy for Global Agricultural Monitoring in the framework of Group on Earth Observations (GEO), Tech. Rep., Geography Dept., University of Maryland.
35. Klisch A., Royer A., Lazar C., Baruth B., Genovese G. Extraction of Phenological Parameters from Temporally Smoothed Vegetation Indexes 2006, ISPRS International Society of Photogrammetry and Remote Sensing, Stresa, pp. 91-96.
36. Koetz B., Baret F., Poilve H., Hill J. Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sens. Environ. 2005, 95(1):115-124.
37. Kouadio L., Duveiller G., Djaby B., El Jarroudi M., Defourny P., Tychon B. Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data. Int. J. Appl Earth Obs. 2012, 18(0):111-118.
38. Kuusk A. The hot spot effect in plant canopy reflectance. Photon-Vegetation Interactions. Applications in Optical Remote Sensing and Plant Ecology 1991, 139-159. Springer-Verlag, Berlin. R.B. Myneni, J. Ross (Eds.).
39. Launay M., Guerif M. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agric. Ecosyst. Environ. 2005, 111(1-4):321-339.
40. Lauvernet, C., 2005. Assimilation variationnelle d'observations de télédétection dans les modèles de fonctionnement de la végétation: utilisation du modèle adjoint et prise en compte de contraintes spatiales., Ph.D. thesis, Université Joseph Fourier - Grenoble.
41. Lazar C., Genovese G. Methodology of the Mars Crop Yield Forecasting System. Vol. 2. Agrometeorological Modelling, Processing and Analysis 2004, AgriFish unit, Joint Research Centre of the European Commission, Ispra, Italy.
42. Lobell D., Field B. Global scale climate-crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2007, 2:1-7.
43. MacDonald R., Hall F. Global crop forecasting. Science 1980, 208(4445):670.
44. Micale F., Genovese G. Methodology of the MARS Crop Yield Forecasting System. Vol. 1. Meteorological Data Collection, Processing and Analysis 2004, AgriFish unit, Joint Research Centre of the European Commission, Ispra.
45. Mo X., Liu S., Lin Z., Xu Y., Xiang Y., McVicar T. Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecol. Modell. 2005, 183(2-3):301-322.
46. Myneni R., Hall F., Sellers P., Marshak A. The interpretation of spectral vegetation indexes. IEEE Trans. Geosci. Remote Sens. 1995, 33(2):481-486.
47. Patel N.R., Bhattacharjee B., Mohammed A.J., Tanupriya B., Saha S.K. Remote sensing of regional yield assessment of wheat in Haryana, India. Int. J. Remote Sens. 2006, 27(19):4071-4090.
48. Prévot L., Chauki H., Troufleau D., Weiss M., Baret F., Brisson N. Assimilating optical and radar data into the STICS crop model for wheat. Agronomie 2003, 23:297-303.
49. Ritchie T.J., Otter S. Description and performance of CERES-Wheat: a user-oriented wheat yield model. ARS Wheat Yield Project 1985, Natl. Technol. Info. Serv., Springfield, MI, pp. 159-175.
50. Rouse J., Haas R., Schell J., Deering D. Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium 1973, pp. 309-317.
51. Royer A., Genovese G. Methodology of the MARS Crop Yield Forecasting System. Vol. 3. Remote Sensing Information, Data Processing and Analysis 2004, AgriFish unit, Joint Research Centre of the European Commission, Ispra, Italy.
52. Schowengerdt Remote Sensing: Models and Methods for Image Processing 2007, Academic Press, San Diego. 3rd ed.
53. Sepulcre-Cantó, G., Gellens-Meulenberghs, F., Arboleda, A., Duveiller, G., de Wit, A., Eerens, H., Piccard, I., Djaby, B., Defourny, P. Estimating crop specific evapotranspiration using remote sensing imagery at various spatial resolutions for improving crop growth modeling, Int. J. Remote Sens., submitted for publication.
54. Stöckle C.O., Donatelli M., Nelson R. CropSyst, a cropping systems simulation model. Eur. J. Agron. 2003, 18(3-4):289-307.
55. Tan B., Woodcock C., Hu J., Zhang P., Ozdogan M., Huang D., Yang W., Knyazikhin Y., Myneni R. The impact of gridding artifacts on the local spatial properties of MODIS data: implications for validation, compositing, and band-to-band registration across resolutions. Remote Sens. Environ. 2006, 105(2):98-114.
56. Tucker C.J., Sellers P.J. Satellite remote sensing of primary production. Int. J. Remote Sens. 1986, 7(11):1395-1416.
57. van Diepen C., Wolf J., van Keulen H., Rappoldt C. WOFOST: a simulation model of crop production. Soil Use Management 1989, 5:16-24.
58. Vazifedoust M., van Dam J.C., Bastiaanssen W.G.M., Feddes R.A. Assimilation of satellite data into agrohydrological models to improve crop yield forecasts. Int. J. Remote Sens. 2009, 30(10):2523-2545.
59. 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(2):415-426.
60. Verhoef W. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sens. Environ. 1984, 16(2):125-141.
61. Verhoef W., Bach H. Remote sensing data assimilation using coupled radiative transfer models. Phys. Chem. Earth 2003, 28(1-3):3-13.
62. Weiss M., Baret F., Smith G.J., Jonckheere I., Coppin P. Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling. Agric. Forest Meteorol. 2004, 121(1-2):37-53.
63. Wolfe R.E., Roy D.P., Vermote E. MODIS land data storage, gridding, and compositing methodology: level 2 grid. IEEE Trans. Geosci. Remote Sens. 1998, 36:1324-1338.