Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data

Remote Sensing of Environment

Volumn 184, Issue , 2016, Pages 668-681

  Battude, Marjorie ^a   Al Bitar, Ahmad ^a   Morin, David ^a   Cros, Jérôme ^a   Huc, Mireille ^a   Marais Sicre, Claire ^a   Le Dantec, Valérie ^a   Demarez, Valérie ^a  

^a UNIVERSITÉ DE TOULOUSE   (France)

Author keywords

Biomass; Crop Modeling; Green Area Index; High spatial and temporal resolution; Maize; Regional scale; Yield

Indexed keywords

BIOMASS; CROPS; ESTIMATION; FORESTRY; PLANTS (BOTANY);

CROP MODEL; GREEN AREAS; MAIZE; REGIONAL SCALE; SPATIAL AND TEMPORAL RESOLUTIONS; YIELD;

REMOTE SENSING;

ACCURACY ASSESSMENT; AGRICULTURAL MODELING; BIOMASS; FORMOSAT; LIGHT USE EFFICIENCY; MAIZE; REMOTE SENSING; SATELLITE DATA; SEASONAL VARIATION; SENSOR; SENTINEL; SPATIAL RESOLUTION; SPOT; TEMPORAL ANALYSIS;

FRANCE;

ZEA MAYS;

EID: 84989829890     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.07.030     Document Type: Article

References (70)

1. Allen, R.G., Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. J. Hydrol. 229:1–2 (2000), 27–41, 10.1016/S0022-1694(99)00194-8.
2. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56. 1998, FAO, Rome (333 pp).
3. Amos, B., Walters, D.T., Maize root biomass and net rhizodeposited carbon. Soil Sci. Soc. Am. J. 70:5 (2006), 1489–1503, 10.2136/sssaj2005.0216.
4. Baillarin, S., Gigord, P., Hagolle, O., Automatic registration of optical images, a stake for future missions: application to ortho-rectification, time series and mosaic products. IEEE International Geoscience and Remote Sensing Symposium, vol. 2, 2008, IEEE, 928–931 Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4779194.
5. 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. 41:2–3 (1992), 133–142, 10.1016/0034-4257(92)90073-S.
6. Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M.,.. Leroy, M., LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: part 1. Principles of the algorithm. Remote Sens. Environ. 110:3 (2007), 275–286, 10.1016/j.rse.2007.02.018.
7. Baret, F., de Solan, B., Lopez-Lozano, R., Ma, K., Weiss, M., GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: theoretical considerations based on 3D architecture models and application to wheat crops. Agric. For. Meteorol. 150:11 (2010), 1393–1401, 10.1016/j.agrformet.2010.04.011.
8. Berjón, A.J., Cachorro, V.E., Zarco-Tejada, P.J., de Frutos, A., Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution remote sensing imagery constrained by a vegetation index. Precis. Agric. 14:5 (2013), 541–557, 10.1007/s11119-013-9315-8.
9. Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D.,.. Sinoquet, H., An overview of the crop model stics. Eur. J. Agron. 18:3–4 (2003), 309–332, 10.1016/S1161-0301(02)00110-7.
10. Bsaibes, A., Courault, D., Baret, F., Weiss, M., Olioso, A., Jacob, F.,.. Kzemipour, F., Albedo and LAI estimates from FORMOSAT-2 data for crop monitoring. Remote Sens. Environ. 113:4 (2009), 716–729, 10.1016/j.rse.2008.11.014.
11. Casadebaig, P., Guilioni, L., Lecoeur, J., Christophe, A., Champolivier, L., Debaeke, P., SUNFLO, a model to simulate genotype-specific performance of the sunflower crop in contrasting environments. Agric. For. Meteorol. 151:2 (2011), 163–178, 10.1016/j.agrformet.2010.09.012.
12. Cavero, J., Farre, I., Debaeke, P., Faci, J.M., Simulation of maize yield under water stress with the EPICphase and CROPWAT models. Agron. J. 92:4 (2000), 679–690, 10.2134/agronj2000.924679x.
13. Chern, J.-S., Wu, A.-M., Lin, S.-F., Lesson learned from FORMOSAT-2 mission operations. Space for Inspiration of Humankind, Selected Proceedings of the 56th International Astronautical Federation Congress, Fukuoka, Japan, 17–21 October 2005 59:1–5 (2006), 344–350, 10.1016/j.actaastro.2006.02.008.
14. Claverie, M., Demarez, V., Duchemin, B., Hagolle, O., Ducrot, D., Marais Sicre, C.,.. Dedieu, G., Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sens. Environ. 124 (2012), 844–857, 10.1016/j.rse.2012.04.005.
15. Claverie, M., Vermote, E.F., Weiss, M., Baret, F., Hagolle, O., Demarez, V., Validation of coarse spatial resolution LAI and FAPAR time series over cropland in southwest France. Remote Sens. Environ. 139 (2013), 216–230, 10.1016/j.rse.2013.07.027.
16. Constantin, J., Willaume, M., Murgue, C., Lacroix, B., Therond, O., The soil-crop models STICS and AqYield predict yield and soil water content for irrigated crops equally well with limited data. Agric. For. Meteorol. 206 (2015), 55–68, 10.1016/j.agrformet.2015.02.011.
17. Courault, D., Bsaibes, A., Kpemlie, E., Hadria, R., Hagolle, O., Marloie, O.,.. Desfonds, V., Assessing the potentialities of FORMOSAT-2 data for water and crop monitoring at small regional scale in South-Eastern France. Sensors 8:5 (2008), 3460–3481, 10.3390/s8053460.
18. Danalatos, N.G., Kosmas, C.S., Driessen, P.M., Yassoglou, N., The change in the specific leaf area of maize grown under Mediterranean conditions. Agron. Sustain. Dev. 14:7 (1994), 433–443, 10.1051/agro:19940702.
19. Demarez, V., Duthoit, S., Baret, F., Weiss, M., Dedieu, G., Estimation of leaf area and clumping indexes of crops with hemispherical photographs. Agric. For. Meteorol. 148:4 (2008), 644–655, 10.1016/j.agrformet.2007.11.015.
20. Dong, J., Kaufmann, R.K., Myneni, R.B., Tucker, C.J., Kauppi, P.E., Liski, J.,.. Hughes, M.K., Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sens. Environ. 84:3 (2003), 393–410, 10.1016/S0034-4257(02)00130-X.
21. Drouet, J.-L., Pagès, L., GRAAL: a model of GRowth, Architecture and carbon ALlocation during the vegetative phase of the whole maize plant: model description and parameterisation. Ecol. Model. 165:2–3 (2003), 147–173, 10.1016/S0304-3800(03)00072-3.
22. 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. Geoinf. 26 (2014), 12–20, 10.1016/j.jag.2013.05.007.
23. Duchemin, B., Maisongrande, P., Boulet, G., Benhadj, I., A simple algorithm for yield estimates: evaluation for semi-arid irrigated winter wheat monitored with green leaf area index. Environ. Model. Softw. 23:7 (2008), 876–892, 10.1016/j.envsoft.2007.10.003.
24. Duchemin, B., Fieuzal, R., Rivera, M.A., Ezzahar, J., Jarlan, L., Rodriguez, J.C.,.. Watts, C., Impact of sowing date on yield and water use efficiency of wheat analyzed through spatial modeling and Formosat-2 images. Remote Sens. 7:5 (2015), 5951–5979, 10.3390/rs70505951.
25. Durand, Y., Brun, E., Mérindol, L., Guyomarc'h, G., Lesaffre, B., Martin, E., A meteorological estimation of relevant parameters for snow models. Ann. Glaciol. 18 (1993), 65–71.
26. Dyke, P.T., Kiniry, J.R., Jones, C.A., CERES-Maize: A Simulation Model of Maize Growth and Development. 1986, Texas A&M University Press, College Station Retrieved from http://catalog.hathitrust.org/Record/000810137.
27. Faivre, R., Leenhardt, D., Voltz, M., Benoît, M., Papy, F., Dedieu, G., Wallach, D., Spatialising crop models. Agron. Sustain. Dev. 24:4 (2004), 205–217, 10.1051/agro:2004016.
28. Ferrant, S., Gascoin, S., Veloso, A., Salmon-Monviola, J., Claverie, M., Rivalland, V.,.. Bustillo, V., Agro-hydrology and multi-temporal high-resolution remote sensing: toward an explicit spatial processes calibration. Hydrol. Earth Syst. Sci. 18:12 (2014), 5219–5237, 10.5194/hess-18-5219-2014.
29. Fieuzal, R., Duchemin, B., Jarlan, L., Zribi, M., Baup, F., Merlin, O.,.. Garatuza-Payan, J., Combined use of optical and radar satellite data for the monitoring of irrigation and soil moisture of wheat crops. Hydrol. Earth Syst. Sci. 15:4 (2011), 1117–1129, 10.5194/hess-15-1117-2011.
30. Hadria, R., Duchemin, B., Baup, F., Le Toan, T., Bouvet, A., Dedieu, G., Le Page, M., Combined use of optical and radar satellite data for the detection of tillage and irrigation operations: case study in Central Morocco. Agric. Water Manag. 96:7 (2009), 1120–1127, 10.1016/j.agwat.2009.02.010.
31. 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. 12:1 (2010), S32–S37, 10.1016/j.jag.2009.09.003.
32. Hagolle, O., Dedieu, G., Mougenot, B., Debaecker, V., Duchemin, B., Meygret, A., Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: application to Formosat-2 images. Remote Sensing of Environment: Data Assimilation Special Issue 112:4 (2008), 1689–1701, 10.1016/j.rse.2007.08.016.
33. Hagolle, O., Huc, M., Pascual, D.V., Dedieu, G., A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images. Remote Sens. Environ. 114:8 (2010), 1747–1755, 10.1016/j.rse.2010.03.002.
34. Hagolle, O., Huc, M., Villa Pascual, D., Dedieu, G., A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sens. 7:3 (2015), 2668–2691, 10.3390/rs70302668.
35. Hsiao, T.C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D., Fereres, E., AquaCrop - the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agron. J. 101:3 (2009), 448–459, 10.2134/agronj2008.0218s.
36. Jamieson, P.D., Semenov, M.A., Brooking, I.R., Francis, G.S., Sirius: a mechanistic model of wheat response to environmental variation. Eur. J. Agron. 8:3–4 (1998), 161–179, 10.1016/S1161-0301(98)00020-3.
37. Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M., Baret, F., Review of methods for in situ leaf area index (LAI) determination: part I. Theories, sensors and hemispherical photography. Agric. For. Meteorol. 121:1–2 (2004), 19–35, 10.1016/j.agrformet.2003.08.027.
38. Katerji, N., Campi, P., Mastrorilli, M., Productivity, evapotranspiration, and water use efficiency of corn and tomato crops simulated by AquaCrop under contrasting water stress conditions in the Mediterranean region. Agric. Water Manag. 130 (2013), 14–26, 10.1016/j.agwat.2013.08.005.
39. van Keulen, H., Seligman, N.G., Simulation of Water Use, Nitrogen Nutrition and Growth of a Spring Wheat Crop. 1987, Pudoc.
40. Kiniry, J.R., Bean, B., Xie, Y., Chen, P., Maize yield potential: critical processes and simulation modeling in a high-yielding environment. Agric. Syst. 82:1 (2004), 45–56, 10.1016/j.agsy.2003.11.006.
41. van Laar, H.H., Goudriaan, J., van Keulen, H., Simulation of Crop Growth for Potential and Water - Limited Production Situations: As Applied to Spring Wheat. (p. 72 pp), 1992, Wageningen, CABO-DLO Retrieved from http://edepot.wur.nl/359573.
42. Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E., Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J. Optim. 9:1 (1998), 112–147, 10.1137/S1052623496303470.
43. Li, Y., Zhou, Q., Zhou, J., Zhang, G., Chen, C., Wang, J., Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions. Ecol. Model. 291 (2014), 15–27, 10.1016/j.ecolmodel.2014.07.013.
44. Liu, J., Pattey, E., Miller, J.R., McNairn, H., Smith, A., Hu, B., Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model. Remote Sens. Environ. 114:6 (2010), 1167–1177, 10.1016/j.rse.2010.01.004.
45. Liu, J., Pattey, E., Jégo, G., Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sens. Environ. 123 (2012), 347–358, 10.1016/j.rse.2012.04.002.
46. Lobell, D.B., Asner, G.P., Ortiz-Monasterio, J.I., Benning, T.L., Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agric. Ecosyst. Environ. 94:2 (2003), 205–220, 10.1016/S0167-8809(02)00021-X.
47. Maas, S.J., Parameterized model of gramineous crop growth: I. Leaf area and dry mass simulation. Agron. J. 85:2 (1993), 348–353, 10.2134/agronj1993.00021962008500020034x.
48. Mailhol, J.C., Olufayo, A.A., Ruelle, P., Sorghum and sunflower evapotranspiration and yield from simulated leaf area index. Agric. Water Manag. 35:1–2 (1997), 167–182, 10.1016/S0378-3774(97)00029-2.
49. Monsi, M., Saeki, T., On the factor light in plant communities and its importance for matter production. Ann. Bot. 95:3 (2005), 549–567, 10.1093/aob/mci052.
50. Monteith, J.L., Solar radiation and productivity in tropical ecosystems. The Journal of Applied Ecology 9:3 (1972), 747–766, 10.2307/2401901.
51. Moulin, S., Bondeau, A., Delecolle, R., Combining agricultural crop models and satellite observations: from field to regional scales. Int. J. Remote Sens. 19:6 (1998), 1021–1036, 10.1080/014311698215586.
52. Nana, E., Corbari, C., Bocchiola, D., A model for crop yield and water footprint assessment: study of maize in the Po valley. Agric. Syst. 127 (2014), 139–149, 10.1016/j.agsy.2014.03.006.
53. Padilla, F.L.M., Maas, S.J., González-Dugo, M.P., Mansilla, F., Rajan, N., Gavilán, P., Domínguez, J., Monitoring regional wheat yield in Southern Spain using the GRAMI model and satellite imagery. Field Crop Res. 130 (2012), 145–154, 10.1016/j.fcr.2012.02.025.
54. Paredes, P., de Melo-Abreu, J.P., Alves, I., Pereira, L.S., Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focus on model parameterization. Agric. Water Manag. 144 (2014), 81–97, 10.1016/j.agwat.2014.06.002.
55. Paredes, P., Rodrigues, G.C., Alves, I., Pereira, L.S., Partitioning evapotranspiration, yield prediction and economic returns of maize under various irrigation management strategies. Agric. Water Manag. 135 (2014), 27–39, 10.1016/j.agwat.2013.12.010.
56. Pinter, P.J., Hatfield, J.L., Schepers, J.S., Barnes, E.M., Moran, M.S., Daughtry, C.S.T., Upchurch, D., Remote sensing for crop management. Photogramm. Eng. Remote. Sens. 69:6 (2003), 647–664.
57. Porter, J.R., AFRCWHEAT2: a model of the growth and development of wheat incorporating responses to water and nitrogen. Eur. J. Agron. 2:2 (1993), 69–82, 10.1016/S1161-0301(14)80136-6.
58. Sibley, A.M., Grassini, P., Thomas, N.E., Cassman, K.G., Lobell, D.B., Testing remote sensing approaches for assessing yield variability among maize fields. Agron. J., 106(1), 2014, 24, 10.2134/agronj2013.0314.
59. Steduto, P., Hsiao, T.C., Raes, D., Fereres, E., AquaCrop - the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J. 101:3 (2009), 426–437, 10.2134/agronj2008.0139s.
60. Steduto, P., Hsiao, T.C., Fereres, E., Raes, D., Crop Yield Response to Water - FAO Irrigation and Drainage Paper 66. 2012, FAO, Rome (505 pp).
61. Supit, I., Hooijer, A.A., van Diepen, C.A., System Description of the Wofost 6.0 Crop Simulation Model Implemented in CGMS. 1994, Joint Research Centre; European Commission.
62. Tollenaar, M., Lee, E.A., Yield potential, yield stability and stress tolerance in maize. Field Crop Res. 75:2–3 (2002), 161–169, 10.1016/S0378-4290(02)00024-2.
63. Tucker, C.J., Sellers, P.J., Satellite remote sensing of primary production. Int. J. Remote Sens. 7:11 (1986), 1395–1416, 10.1080/01431168608948944.
64. Tucker, C.J., Vanpraet, C., Boerwinkel, E., Gaston, A., Satellite remote sensing of total dry matter production in the Senegalese Sahel. Remote Sens. Environ. 13:6 (1983), 461–474, 10.1016/0034-4257(83)90053-6.
65. Ugray, Z., Lasdon, L., Plummer, J., Glover, F., Kelly, J., Martí, R., Scatter search and local NLP solvers: a multistart framework for global optimization. INFORMS J. Comput. 19:3 (2007), 328–340.
66. Varlet-Grancher, C., Bonhomme, R., Chartier, M., Artis, P., Efficience de la conversion de l'energie solaire par un couvert vegetal. Acta Oecologica Oecologia Plantarum 3:1 (1982), 3–26.
67. Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., Soubeyroux, J.-M., A 50-year high-resolution atmospheric reanalysis over France with the Safran system. Int. J. Climatol. 30:11 (2010), 1627–1644, 10.1002/joc.2003.
68. 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. For. Meteorol. 121:1 (2004), 37–53.
69. 2 Advanced Very High Resolution Radiometer (AVHRR) NDVI in Kruger National Park, South Africa. Int. J. Remote Sens. 27:5 (2006), 951–973, 10.1080/01431160500169098.
70. Wit, C.T.D., Brouwer, R., Vries, F.W.T.P.D., The simulation of photosynthetic systems, Presented at the Prediction and measurement of photosynthetic productivity. Proceedings of the IBP/PP Technical Meeting, Tfebon, [Czechoslovakia], 14–21 September, 1969, Wageningen, Netherlands, 1970, PUDO, 47–70.