Assimilation of two variables derived from hyperspectral data into the DSSAT-CERES model for grain yield and quality estimation

Remote Sensing

Volumn 7, Issue 9, 2015, Pages 12400-12418

  Li, Zhenhai ^a,b,c   Wang, Jihua ^a,c   Xu, Xingang ^a,b,d,e   Zhao, Chunjiang ^a,b,d,e   Jin, Xiuliang ^f   Yang, Guijun ^a,b,d,e   Feng, Haikuan ^a,b,d,e  

^a INSTITUTE OF PLANT AND ENVIRONMENT PROTECTION   (China)

^b NATIONAL ENGINEERING RESEARCH CENTER FOR INFORMATION TECHNOLOGY IN AGRICULTURE   (China)

^c ZHEJIANG UNIVERSITY   (China)

^d AGRO ENVIRONMENTAL PROTECTION INSTITUTE   (China)

^e Beijing Engineering Research Center of Agricultural Internet of Things   (China)

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

Author keywords

DSSAT CERES; Grain protein content; Hyperspectral; Particle swarm optimization algorithm; Winter wheat; Yield

Indexed keywords

ALGORITHMS; CROPS; GRAIN GROWTH; OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); PROTEINS; REMOTE SENSING;

DSSAT-CERES; GRAIN PROTEIN CONTENTS; HYPERSPECTRAL; PARTICLE SWARM OPTIMIZATION ALGORITHM; WINTER WHEAT; YIELD;

GRAIN (AGRICULTURAL PRODUCT);

EID: 84942543701     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70912400     Document Type: Article

References (60)

1. Yu, Z.W. Crop Cultivation; China Agriculture Press: Beijing, China, 2003.
2. Li, C.; Wang, J.; Wang, Q.; Wang, D.; Song, X.; Wang, Y.; Huang, W. Estimating wheat grain protein content using multi-temporal remote sensing data based on partial least squares regression. J. Integr. Agr. 2012, 11, 1445-1452.
3. Reynolds, M.; Foulkes, J.; Furbank, R.; Griffiths, S.; King, J.; Murchie, E.; Parry, M.; Slafer, G. Achieving yield gains in wheat. Plant Cell Environ. 2012, 35, 1799-1823.
4. Liu, L.Y.; Wang, J.J.; Bao, Y.S.; Huang, W.J.; Ma, Z.H.; Zhao, C.J. Predicting winter wheat condition, grain yield and protein content using multi-temporal EnviSat-ASAR and Landsat TM satellite images. Int. J. Remote Sens. 2006, 27, 737-753.
5. Thorp, K.R.; White, J.W.; Porter, C.H.; Hoogenboom, G.; Nearing, G.S.; French, A.N. Methodology to evaluate the performance of simulation models for alternative compiler and operating system configurations. Comput. Electron. Agr. 2012, 81, 62-71.
6. Zhang, Q.; Xiao, X.; Braswell, B.; Linder, E.; Baret, F.; Moore, B., III. 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.
7. Fang, H.; Liang, S.; 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, 3011-3032.
8. Guerif, M.; Duke, C.L. Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation. Agr. Ecosyst. Environ. 2000, 81, 57-69.
9. Maas, S.J. Use of remotely-sensed information in agricultural crop growth models. Ecol. Model. 1988, 41, 247-268.
10. Maas, S.J. Using satellite data to improve model estimates of crop yield. Agron. J. 1988, 80, 655-662.
11. Guerif, M.; Duke, C. Calibration of the SUCROS emergence and early growth module for sugar beet using optical remote sensing data assimilation. Eur. J. Agron. 1998, 9, 127-136.
12. Jongschaap, R.E. Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen. Eur. J. Agron. 2006, 24, 316-324.
13. De Wit, A.; Van Diepen, C.A. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agr. ForestMeteorol. 2007, 146, 38-56.
14. Fang, H.; Liang, S.; Hoogenboom, G. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation, Int. J. Remote Sens. 2011, 32, 1039-1065.
15. Morel, J.; Begue, A.; Todoroff, P.; Martine, J.; Lebourgeois, V.; Petit, M. Coupling a sugarcane crop model with the remotely sensed time series of/IPAR to optimise the yield estimation. Eur. J. Agron. 2014, 61, 60-68.
16. 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. 2014, 291, 15-27.
17. Dong, Y.; Zhao, C.; Yang, G.; Chen, L.; Wang, J.; Feng, H. Integrating a very fast simulated annealing optimization algorithm for crop leaf area index variational assimilation. Math. Comput. Model. 2013, 58, 877-885.
18. Wang, H.; Zhu, Y.; Li, W.; Cao, W.; Tian, Y. Integrating remotely sensed leaf area index and leaf nitrogen accumulation with RiceGrow model based on particle swarm optimization algorithm for rice grain yield assessment. J. Appl. Remote Sens. 2014, 8, 083674.
19. Thorp, K.R.; Wang, G.; West, A.L.; Moran, M.S.; Bronson, K.F.; White, J.W.; Mon, J. Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models. Remote Sens. Environ. 2012, 124, 224-233.
20. Ma, H.Y. Winter Wheat Yield Estimation Based on Assimilating Leaf Area Index and Evapotranspiration into SWAP Crop Model. Ph. D. Thesis, China Agricultural University, Beijing, China, 2013.
21. China Meteorological Data Sharing Service System. Available online: http://cdc.cma.gov.cn (accessed on 12 September 2015).
22. Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements, FAO Irrigation and Drainage Paper No. 56; FAO: Rome, Italy, 1998; 300, p. d05109.
23. Wang, J.H.; Zhao, C.J.; Huang, W.J. Basis and Application of Agriculture Quantitative Remote Sensing; Science Press: Beijing, China, 2008.
24. Zadoks, J.C.; Chang, T.T.; Konzak, C.F. A decimal code for the growth stages of cereals. Weed Res. 1974, 14, 415-421.
25. Breda, N.J. Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. J. Exp. Bot. 2003, 54, 2403-2417.
26. Schepers, J.S.; Francis, D.D.; Thompson, M.T. Simultaneous determination of total C, total N, and 15N on soil and plant material. Commun. Soil Sci. Plan. Anal. 1989, 20, 949-959.
27. Palosuo, T.; Kersebaum, K.C.; Angulo, C.; Hlavinka, P.; Moriondo, M.; Olesen, J.E.; Patil, R.H.; Ruget, F.; Rumbaur, C.; Takac, J. Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models. Eur. J. Agron. 2011, 35, 103-114.
28. Thorp, K.R.; DeJonge, K.C.; Kaleita, A.L.; Batchelor, W.D.; Paz, J.O. Methodology for the use of DSSAT models for precision agriculture decision support. Comput. Electron. Agrc. 2008, 64, 276-285.
29. Jones, J.W.; Hoogenboom, G.; Porter, C.H.; Boote, K.J.; Batchelor, W.D.; Hunt, L.A.; Wilkens, P.W.; Singh, U.; Gijsman, A.J.; Ritchie, J.T. The DSSAT cropping system model. Eur. J. Agron. 2003, 18, 235-265.
30. Ritchie, J.T. Wheat phasic development. In Modeling Plant and Soil Systems; Hanks, J., Ritchie, J.T., Eds.; American Society of Agronomy, Crop Science Society of America, Soil Science Society of America: Madison, WI, USA, 1991; pp. 31-54.
31. Nearing, G.S.; Crow, W.T.; Thorp, K.R.; Moran, M.S.; Reichle, R.H.; Gupta, H.V. Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment. Water Resour. Res. 2012, 48, W05525.
32. Ritchie, J.T.; Godwin, D. CERES Wheat 2.0. Available online: http://nowlin.css.msu.edu/indexritchie.html (accessed on 12 September 2015).
33. Thorp, K.R.; Hunsaker, D.J.; French, A.N.; White, J.W.; Clarke, T.R.; Pinter, P.J., Jr. Evaluation of the CSM-CROPSIM-CERES-Wheat model as a tool for crop water management. Trans. ASABE 2010, 53, 87-102.
34. DeJonge, K.C.; Ascough, J.C.; Ahmadi, M.; Andales, A.A.; Arabi, M. Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments. Ecol. Model. 2012, 231, 113-125.
35. Liu, H.L.; Yang, J.Y.; Drury, C.A.; Reynolds, W.D.; Tan, C.S.; Bai, Y.L.; He, P.; Jin, J.; Hoogenboom, G. Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production. Nutr. Cycl. Agroecosys. 2011, 89, 313-328.
36. Asseng, S.; Bar-Tal, A.; Bowden, J.W.; Keating, B.A.; Van Herwaarden, A.; Palta, J.A.; Huth, N.I.; Probert, M.E. Simulation of grain protein content with APSIM-Nwheat. Eur. J. Agron. 2002, 16, 25-42.
37. Pearson, R. L.; Miller, L. D. Remote mapping of standing crop biomass for estimation of the productivity of the short grass prairie. In Proceedings of 8th International Symposium on Remote Sensing of the Environment, Michigan, USA, 2-6 October 1972; pp. 1355-1379.
38. Chen, J. M. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens. 1996, 22, 229-242.
39. Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95-107.
40. Gitelson, A.A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 2004, 161, 165-173.
41. Gitelson, A.A.; Vina, A.; Ciganda, V.; Rundquist, D.C.; Arkebauer, T.J. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 2005, 32, L08403.
42. Zarco-Tejada, P.J.; Berjón, A.; López-Lozano, R.; Miller, J.R.; Martín, P.; Cachorro, V.; González, M.R.; De Frutos, A. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 2005, 99, 271-287.
43. Reyniers, M.; Walvoort, D.J.; De Baardemaaker, J. A linear model to predict with a multi-spectral radiometer the amount of nitrogen in winter wheat. Int. J. Remote Sens. 2006, 27, 4159-4179.
44. Eitel, J.U.H.; Long, D.S.; Gessler, P.E.; Smith, A.M.S. Using in-situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status. Int. J. Remote Sens. 2007, 28, 4183-4190.
45. Dash, J.; Curran, P.J. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Adv. Space Res. 2007, 39, 100-104.
46. Delalieux, S.; Somers, B.; Hereijgers, S.; Verstraeten, W.W.; Keulemans, W.; Coppin, P. A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards. Remote Sens. Environ. 2008, 112, 3762-3772.
47. Jiang, Z.; Huete, A.R.; Didan, K.; Miura, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 2008, 112, 3833-3845.
48. Fitzgerald, G.; Rodriguez, D.; O'Leary, G. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index-The canopy chlorophyll content index (CCCI). Field Crop. Res. 2010, 116, 318-324.
49. Mishra, S.; Mishra, D.R. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sens. Environ. 2012, 117, 394-406.
50. Jin, X.; Xu, X.; Song, X.; Li, Z.; Wang, J.; Guo, W. Estimation of leaf water content in winter wheat using grey relational analysis-partial least squares modeling with hyperspectral data. Agron. J. 2013, 105, 1385-1392.
51. Jin, X.; Li, Z.; Feng, H.; Xu, X.; Yang, G. Newly combined spectral indices to improve estimation of total leaf chlorophyll content in cotton. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 2014, 7, 4589-4600.
52. Eberhart, R.C.; Kennedy, J. A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 4-6 October, 1995; pp. 39-43.
53. Eberhart, R.C.; Shi, Y.H. Particle swarm optimization: Developments, applications and resources. In Proceedings of the 2001 IEEE Congress on Evolutionary Computation, Seoul, Korea, 27-30 May 2001.
54. Li, Z.H.; Jin, X.L.; Liu, H.L.; Xu, X.G.; Wang, J.H.; Li, C.J. Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method. Field Crop. Res. 2015, under review.
55. Vincini M.; Frazzi E.; D'Alessio P. Angular dependence of maize and sugar beet VIs from directional Compact High Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/Proba) data. In Proceedings of 4th ESA CHRIS PROBA Workshop, Frascati, Italy, 19-21 September 2006.
56. 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.
57. Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004, 90, 337-352.
58. Jin, X.; Diao, W.; Xiao, C.; Wang, F.; Chen, B.; Wang, K.; Li, S. Estimation of Wheat Agronomic Parameters using New Spectral Indices. PLoS ONE 2013, 8, e72736.
59. Feng, W.; Yao, X.; Zhu, Y.; Tian, Y.C.; Cao, W.X. Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. Eur. J. Agron. 2008, 28, 394-404.
60. Wang, J.; Li, X.; Lu, L.; Fang, F. Parameter sensitivity analysis of crop growth models based on the extended Fourier Amplitude Sensitivity Test method. Environ. Modell. Softw. 2013, 48, 171-182.