A review of data assimilation of remote sensing and crop models

European Journal of Agronomy

Volumn 92, Issue , 2018, Pages 141-152

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

^a AGRO ENVIRONMENTAL PROTECTION INSTITUTE   (China)

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

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

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

^e UNIVERSITY OF NEW ENGLAND   (Australia)

^f Beijing Research Center for Agrifood Testing and Farmland Monitoring   (China)

Author keywords

Canopy state variables; Crop models; Data assimilation; Remote sensing; Yield

Indexed keywords

ACCURACY ASSESSMENT; AGRICULTURAL MODELING; CANOPY; CROP YIELD; DATA ASSIMILATION; DECISION MAKING; FOOD POLICY; REMOTE SENSING; SATELLITE SENSOR;

EID: 85033228627     PISSN: 11610301     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eja.2017.11.002     Document Type: Review

References (156)

1. Abou-Ismail, O., Rice yield estimation by integrating remote sensing with rice growth simulation model. Pedosphere 14 (2004), 519–526.
2. Alvarez-Mozos, J., Casali, J., Gonzalez-Audicana, M., Verhoest, N.E.C., Correlation between ground measured soil moisture and RADARSAT-1 derived backscattering coefficient over an agricultural catchment of Navarre (North of Spain). Biosyst. Eng. 92:1 (2005), 119–133.
3. Anderson, J.L., Anderson, S.L., A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Weather Rev. 127 (1999), 2741–2758.
4. Atzberger, C., Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models. Remote Sens. Environ. 93:1 (2004), 53–67.
5. Aubert, D., Loumagne, C., Oudin, L., Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model. J. Hydrol. 280 (2003), 145–161.
6. Bach, H., Mauser, W., Methods and examples for remote sensing data assimilation in land surface process modeling. IEEE Trans. Geosci. Remote 41 (2003), 1629–1637.
7. Baghdadi, N., Boyer, N., Todoroff, P., El Hajj, M., Bégué, A., Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island. Remote Sens. Environ. 113 (2009), 1724–1738.
8. Bastiaanssen, W.G., Ali, S., A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric. Ecosyst. Environ. 94 (2003), 321–340.
9. Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M.L., Bareth, G., Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. 39 (2015), 79–87.
10. Berliner, L.M., Hierarchical Bayesian Time Series Models, Maximum Entropy and Bayesian Methods. 1996, Springer, 15–22.
11. Blaes, X., Vanhalle, L., Defourny, P., Efficiency of crop identification based on optical and SAR image time series. Remote Sens. Environ. 96:3 (2005), 352–365.
12. Blaschke, T., Object based image analysis for remote sensing. ISPRS J. Photogramm. 65 (2010), 2–16.
13. Bolten, J.D., Crow, W.T., Zhan, X., Jackson, T.J., Reynolds, C.A., Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. IEEE JSTAR 3 (2010), 57–66.
14. Boogaard, H., Van Diepen, C., Rotter, R., User's Guide for the WOFOST Control Center 1.8 and WOFOST 7.1. 3 Crop Growth Simulation Model. 2011, Alterra, Wagening University & Research Centre 137.
15. Bouman, B., Crop modelling and remote sensing for yield prediction. NJAS Wageningen J. Life Sci. 43 (1995), 143–161.
16. Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., An overview of the crop model STICS. Eur. J Agron. 18 (2003), 309–332.
17. Brown, S.C., Quegan, S., Morrison, K., Bennett, J.C., Cookmartin, G., High-resolution measurements of scattering in wheat canopies-Implications for crop parameter retrieval. IEEE Trans. Geosci. Remote 41:7 (2003), 1602–1610.
18. Canisius, F., Fernandes, R., ALOS PALSAR L-band polarimetric SAR data and in situ measurements for leaf area index assessment. Remote Sens. Lett. 3:3 (2012), 221–229.
19. Capodici, F., D'Urso, G., Maltese, A., Investigating the relationship between X-band SAR data from COSMO-SkyMed satellite and NDVI for LAI detection. Remote Sens. 5:3 (2013), 1389–1404.
20. Chakrabarti, S., Bongiovanni, T., Judge, J., Zotarelli, L., Bayer, C., Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions. IEEE JSTAR 7 (2014), 3867–3879.
21. Chakraborty, M., Manjunath, K.R., Panigrahy, S., Kundu, N., Parihar, J.S., Rice crop parameter retrieval using multi-temporal, multi-incidence angle RADARSAT SAR data. ISPRS J. Photogramm. 59 (2005), 310–322.
22. Claverie, M., Demarez, V., Duchemin, B., Hagolle, O., Keravec, P., Marciel, B., Ceschia, E., Dejoux, J.F., Dedieu, G., Spatialization of crop leaf area index and biomass by combining a simple crop model SAFY and high spatial and temporal resolutions remote sensing data. Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009, IEEE, 2009, III-478–III-481.
23. 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. 50 (1994), 161–170.
24. Clevers, J., Vonder, O., Jongschaap, R., Desprats, J.F., King, C., Prévot, L., Bruguier, N., Using SPOT data for calibrating a wheat growth model under mediterranean conditions. Agronomie 22 (2002), 687–694.
25. Clevers, J.G., Kooistra, L., van den Brande, M.M., Using sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sens., 9(5), 2017, 405, 10.3390/rs9050405.
26. Clevers, J., A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote Sens. Environ. 61 (1997), 221–228.
27. Crow, W.T., Wood, E.F., The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97. Adv. Water Resour. 26 (2003), 137–149.
28. 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. Agr. Forest Meteorol. 151 (2011), 1843–1855.
29. Curran, P., Williamson, H., Sample size for ground and remotely sensed data. Remote Sens. Environ. 20 (1986), 31–41.
30. Díaz-Varela, R.A., de la Rosa, R., León, L., Zarco-Tejada, P.J., High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: application in breeding trials. Remote Sens. 7 (2015), 4213–4232.
31. Daughtry, C.S.T., Walthall, C., Kim, M., De Colstoun, E.B., McMurtrey, J., Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74:2 (2000), 229–239.
32. De Wit, A.D., Van Diepen, C., Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agric. Forest Meteorol. 146 (2007), 38–56.
33. Delécolle, R., Maas, S., Guérif, M., Baret, F., Remote sensing and crop production models: present trends. ISPRS J. Photogramm. 47 (1992), 145–161.
34. 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. 112 (2008), 1395–1407.
35. 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. 58 (2013), 877–885.
36. Dorigo, W., Zurita-Milla, R., de Wit, A.J., Brazile, J., Singh, R., Schaepman, M.E., A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. 9 (2007), 165–193.
37. Duchemin, B., Hadria, R., Rodriguez, J.C., Lahrouni, A., Khabba, S., Boulet, G., Mougenot, B., Maisongrande, P., Watts, C., Spatialisation of a crop model using phenology derived from remote sensing data, Geoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003 IEEE International, IEEE, 2003, 2200–2202.
38. Durbha, S.S., King, R.L., Younan, N.H., Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer. Remote Sens. Environ. 107:1 (2007), 348–361.
39. Ehsani, R., Maja, J.M., The rise of small UAVs in precision agriculture. Resour. Mag. 20 (2013), 18–19.
40. Eitel, J., Long, D., Gessler, P., Smith, A., Using in-situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status. Int. J. Remote Sens. 28:18 (2007), 4183–4190.
41. Evensen, G., Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. Oceans 99 (1994), 10143–10162.
42. 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. 29 (2008), 3011–3032.
43. 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. 32 (2011), 1039–1065.
44. Ferrazzoli, P., Paloscia, S., Pampaloni, P., Schiavon, G., Sigismondi, S., Solimini, D., The potential of multifrequency polarimetric SAR in assessing agricultural and arboreous biomass. IEEE Trans Geosci. Remote 35:1 (1997), 5–17.
45. Fischer, A., Kergoat, L., Dedieu, G., Coupling satellite data with vegetation functional models: review of different approaches and perspectives suggested by the assimilation strategy. Remote Sens. Rev. 15 (1997), 283–303.
46. Foca, G., Cocchi, M., Vigni, M.L., Caramanico, R., Corbellini, M., Ulrici, A., Different feature selection strategies in the wavelet domain applied to NIR-based quality classification models of bread wheat flours. Chemometr. Intell. Lab. 99:2 (2009), 91–100.
47. Foody, G.M., Mathur, A., A relative evaluation of multiclass image classification by support vector machines. IEEE Trans. Geosci. Remote 42:6 (2004), 1335–1343.
48. Franko, U., Puhlmann, M., Kuka, K., Böhme, F., Merbach, I., Dynamics of Water, Carbon and Nitrogen in an Agricultural Used Chernozem Soil in Central Germany, Modelling Water and Nutrient Dynamics in Soil-Crop Systems. 2007, Springer, 245–258.
49. Gómez-Candón, D., De Castro, A., López-Granados, F., Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precis. Agric. 15 (2014), 44–56.
50. Gao, S., Niu, Z., Huang, N., Hou, X.H., Estimating the leaf area index, height and biomass of maize using HJ-1 and RADARSAT-2. Int. J. Appl. Earth Obs. 24 (2013), 1–8.
51. Gherboudj, I., Magagi, R., Berg, A.A., Toth, B., Soil moisture retrieval overagricultural fields from multi-polarized and multi-angular RADARSAT-2 SARdata. Remote Sens. Environ. 115 (2011), 33–43.
52. Gobron, N., Pinty, B., Verstraete, M.M., Widlowski, J.L., Advanced vegetation indices optimized for up-coming sensors: design, performance, and applications. IEEE Trans. Geosci. Remote 38 (2000), 2489–2505.
53. Gu, H., Li, H., Yan, L., Liu, Z., Blaschke, T., Soergel, U., An object-based semantic classification method for high resolution remote sensing imagery using ontology. Remote Sens., 9(4), 2017, 329, 10.3390/rs9040329.
54. Guérif, M., Duke, C., Calibration of the SUCROS emergence and early growth module for sugar beet using optical remote sensing data assimilation. Eur. J. Agron. 9 (1998), 127–136.
55. Guerif, M., Duke, C., Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation. Agr. Ecosyst. Environ. 81 (2000), 57–69.
56. Guillen-Climent, M., Zarco-Tejada, P.J., Berni, J.A., North, P., Villalobos, F., Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV. Precis. Agric. 13 (2012), 473–500.
57. Guo, X., Huang, X., Zhang, L., Zhang, L., Plaza, A., Benediktsson, J.A., Support tensor machines for classification of hyperspectral remote sensing imagery. IEEE Trans. Geosci. Remote 54:6 (2016), 3248–3264.
58. Guyot, G., Agriculture et statistiques agricoles. Précis de télédétection 2 (1996), 269–316.
59. Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81:2 (2002), 416–426.
60. Hadria, R., Duchemin, B.I., Lahrouni, A., Khabba, S., Er Raki, S., Dedieu, G., Chehbouni, A., Olioso, A., Monitoring of irrigated wheat in a semi-arid climate using crop modelling and remote sensing data: impact of satellite revisit time frequency. Int. J. Remote Sens. 27 (2006), 1093–1117.
61. Hamill, T.M., Whitaker, J.S., Accounting for the error due to unresolved scales in ensemble data assimilation: a comparison of different approaches. Mon. Weather Rev. 133 (2005), 3132–3147.
62. Hansen, J., Jones, J., Scaling-up crop models for climate variability applications. Agric. Syst. 65 (2000), 43–72.
63. Hansen, S., Jensen, H., Nielsen, N., Swenden, H., DAISY: a Soil Plant System Model. Danish Simulation Model for Transformation and Transport of Energy and Matter in the Soil Plant Atmosphere System. 1990, The National Agency for Environmental Protection pp. 369.
64. Hosseini, M., McNairn, H., Using multi-polarization C-and L-band synthetic aperture radar to estimate biomass and soil moisture of wheat fields. Int. J. Appl. Earth Obs. 58 (2017), 50–64.
65. 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. 170 (2015), 77–89.
66. Hu, S., Mo, X., Lin, Z., Optimizing the photosynthetic parameter V cmax by assimilating MODIS-f PAR and MODIS-NDVI with a process-based ecosystem model. Agric. Forest Meteorol. 198 (2014), 320–334.
67. Huang, Z., Lees, B.G., Combining non-parametric models for multisource predictive forest mapping. Photogramm. Eng. Rem. S 70 (2004), 415–425.
68. Huang, J., Tang, S., Ousama, A.I., Wang, R., Integration of remote sensing data and simulation model to estimate rice yield, info-tech and info-net, 2001. Proceedings, ICII 2001-Beijing, 2001 International Conferences on, IEEE, 2001, 101–107.
69. Huang, Y., Zhu, Y., Li, W., Cao, W., Tian, Y., Assimilating remotely sensed information with the wheatgrow model based on the ensemble square root filter forImproving regional wheat yield forecasts. Plant Prod. Sci. 16 (2013), 352–364.
70. Huang, J., Ma, H., Su, W., Zhang, X., Huang, Y., Fan, J., Wu, W., Jointly assimilating MODIS LAI and ET products into the SWAP model for winter wheat yield estimation. IEEE JSTAR 8 (2015), 4060–4071.
71. Hunt, E.R., Cavigelli, M., Daughtry, C.S., Mcmurtrey, J.E. III, Walthall, C.L., Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis. Agric. 6 (2005), 359–378.
72. Hunt, E.R., Hively, W.D., Daughtry, C.S.T., McCarty, G.W., Fujikawa, S.J., Ng, T.L., Linden, D.S., Yoel, D.W., Remote sensing of crop leaf area index using unmanned airborne vehicles. Proceedings of the Pecora 17 Symposium, Denver, CO, 2008.
73. Hunt, E.R., Hively, W.D., Fujikawa, S.J., Linden, D.S., Daughtry, C.S.T., McCarty, G.W., Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens. 2 (2010), 290–305.
74. Hurtado, E., Artigao, M., Caselles, V., Estimating Maize (Zea mays) cvapotranspiration from NOAA-AVHRR thermal data in the Albacete area, Spain. Int. J. Remote Sens. 15 (1994), 2023–2037.
75. Ines, A.V., Das, N.N., Hansen, J.W., Njoku, E.G., Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sens. Environ. 138 (2013), 149–164.
76. Inoue, Y., Kurosu, T., Maeno, H., Uratsuka, S., Kozu, T., Dabrowska-Zielinska, K., Qi, J., Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables. Remote Sens. Environ. 81 (2002), 194–204.
77. Jégo, G., Pattey, E., Liu, J., Using leaf area index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops. Field Crops Res. 131 (2012), 63–74.
78. Jiang, Z., Chen, Z., Chen, J., Liu, J., Ren, J., Li, Z., Sun, L., Li, H., Application of crop model data assimilation with a particle filter for estimating regional winter wheat yields. IEEE JSTAR 7 (2014), 4422–4431.
79. Jin, H., Wang, J., Bo, Y., Chen, G., Xue, H., Data assimilation of MODIS and TM observations into CERES-maize model to estimate regional maize yield, SPIE optical engineering + applications. Int. Soc. Opt. Photon., 2010 pp. 780908-780908-10.
80. Jin, M., Liu, X., Wu, L., Liu, M., An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice. Int. J. Appl. Earth Obs. 41 (2015), 118–129.
81. Jin, X.L., Wang, K.R., Xiao, C.H., Diao, W.Y., Wang, F.Y., Chen, B., Li, S.K., Comparison of two methods for estimation of leaf total chlorophyll content using remote sensing in wheat. Field Crops Res. 135 (2012), 24–29.
82. Jin, X.L., Diao, W.Y., Xiao, C.H., Wang, F.Y., Chen, B., Wang, K.R., Li, S.K., Estimation of wheat agronomic parameters using new spectral indices. PLoS One, 8, 2013, e72736.
83. Jin, X.L., Xu, X.G., Song, X.Y., Li, Z.H., Wang, J.H., Guo, W.S., Estimation of leaf water content in winter wheat using grey relational analysis?Partial least squares modeling with hyperspectral data. Agron. J. 105:5 (2013), 1385–1392.
84. Jin, X.L., Yang, G.J., Xu, X.G., Yang, H., Feng, H., Li, Z.H., Shen, J.X., Lan, Y.B., Zhao, C.J., Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using H J and RADARSAR-2 data. Remote Sens. 7 (2015), 13251–13272.
85. Jin, X.L., Li, Z.H., Yang, G.J., Yang, H., Feng, H.K., Xu, X.G., Wang, J.H., Li, X.C., Luo, J.H., Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm. ISPRS J. Photogramm. 126 (2017), 24–37.
86. Jin, X.L., Liu, S.Y., Baret, F., Hemerlé, M., Comar, A., Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens. Environ. 198 (2017), 105–114.
87. Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L., Wilkens, P.W., Singh, U., Gijsman, A.J., Ritchie, J.T., The DSSAT cropping system model. Eur. J. Agron. 18 (2003), 235–265.
88. Jongschaap, R.E., Schouten, L.S., Predicting wheat production at regional scale by integration of remote sensing data with a simulation model. Agron. Sustain. Dev. 25 (2005), 481–489.
89. Jongschaap, R.E., Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen. Eur. J. Agron. 24 (2006), 316–324.
90. Karnieli, A., Natural vegetation phenology assessment by ground spectral measurements in two semi-arid environments. Int. J. Biometeorol. 47 (2003), 179–187.
91. Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N., Meinke, H., Hochman, Z., An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18 (2003), 267–288.
92. Kim, Y., Jackson, T., Bindlish, R., Lee, H., Hong, S., Radar vegetation index for estimating the vegetation water content of rice and soybean. IEEE Geosci. Remote Sens. Lett. 9:4 (2012), 564–568.
93. Kimes, D., Sellers, P., Inferring hemispherical reflectance of the Earth's surface for global energy budgets from remotely sensed nadir or directional radiance values. Remote Sens. Environ. 18 (1985), 205–223.
94. Kross, A., McNairn, H., Lapen, D., Sunohara, M., Champagne, C., Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int. J. Appl. Earth Obs. 34 (2015), 235–248.
95. Kumar, P., Prasad, R., Gupta, D.K., Mishra, V.N., Vishwakarma, A.K., Yadav, V.P., Bala, R., Choudhary, A., Avtar, R., Estimation of winter wheat crop growth parameters using time series Sentinel -1A SAR data. Geocarto Int., 2017, 1–15, 10.1080/10106049.2017.1316781.
96. Launay, M., Guerif, M., Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agric. Ecosyst. Environ. 111 (2005), 321–339.
97. Li, X.W., Zhao, H.R., Zhang, H., Wang, J.D., Global change study and quantitative remote sensing for land surface parameters. Earth Sci. Front. 9:2 (2002), 365–370 In Chinese.
98. Li, R., Li, C.J., Dong, Y.Y., Feng, L., Wang, J.H., Yang, X.D., Pan, Y.C., Assimilation of remote sensing and crop model for LAI estimation based on ensemble Kaiman Filter. Agric. Sci. China 10 (2011), 1595–1602.
99. Li, Y., Zhou, G.Q., Zhou, J., Zhang, G.F., Chen, C., Wang, J., Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions. Ecolog. Model. 291 (2014), 15–27.
100. Li, Z.H., Jin, X.L., Wang, J.H., Yang, G.J., Nie, C.W., Xu, X.G., Feng, H.K., 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. 36:10 (2015), 2634–2653.
101. Li, H., Chen, Z.X., Jiang, Z.W., Wu, W.B., Ren, J.Q., 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. 16:2 (2017), 266–285.
102. Liang, S., Li, X., Xie, X., 2013. Land Surface Observation, Modeling and Data Assimilation World Scientific.
103. Liu, Y., Gupta, H.V., Uncertainty in hydrologic modeling: toward an integrated data assimilation framework. Water Resour. Res., 2007, 43 W07401.
104. Liu, C., Gao, W., Liu, P., Sun, Z., Assimilation of remote sensing data into crop growth model to improve the estimation of regional winter wheat yield, SPIE optical engineering + applications. Int. Soc. Opt. Photon., 2014 pp. 922104-922104-9.
105. Liu, F., Liu, X., Zhao, L., Ding, C., Jiang, J., Wu, L., The dynamic assessment model for monitoring cadmium stress levels in rice based on the assimilation of remote sensing and the WOFOST model. IEEE JSTAR 8 (2015), 1330–1338.
106. Lorenc, A.C., Ballard, S.P., Bell, R.S., Ingleby, N.B., Andrews, P.L.F., Barker, D.M., Bray, J.R., Clayton, A.M., Dalby, T., Li, D., Payne, T.J., Saunders, F.W., The Met. Office global three-dimensional variational data assimilation scheme. Q. J. Roy. Meteor. Soc. 126 (2000), 2991–3012.
107. Luscier, J.D., Thompson, W.L., Wilson, J.M., Gorham, B.E., Dragut, L.D., Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots. Front. Ecol. Environ. 4:8 (2006), 408–413.
108. Ma, Y.P., Wang, S.L., Zhang, L., Hou, Y.Y., Zhuang, L.W., He, Y.B., Wang, F.T., Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. Int. J. Appl. Earth Obs. 10 (2008), 426–437.
109. Ma, G.N., Huang, J.X., Wu, W.B., Fan, J.L., Zou, J.Q., Wu, S.J., Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield. Math. Comput. Model. 58 (2013), 634–643.
110. Maas, S.J., Use of remotely-sensed information in agricultural crop growth models. Ecol. Model. 41 (1988), 247–268.
111. Maghsoudi, Y., Collins, M., Leckie, D.G., Polarimetric classification of Boreal forest using nonparametric feature selection and multiple classifiers. Int. J. Appl. Earth Obs. 19 (2012), 139–150.
112. McCown, R., Hammer, G., Hargreaves, J., Holzworth, D., Huth, N., APSIM: an agricultural production system simulation model for operational research. Math. Comput. Simul. 39 (1995), 225–231.
113. Merzouki, A., McNairn, H., A hybrid (multi-angle and multipolarization) approach to soil moisture retrieval using the integral equation model: preparing for the RADARSAT constellation mission. Can. J. Remote Sens. 41:5 (2015), 349–362.
114. Moradkhani, H., Hsu, K.L., Gupta, H., Sorooshian, S., Uncertainty assessment of hydrologic model states and parameters: sequential data assimilation using the particle filter. Water Resour. Res. 41 (2005), 1–17.
115. Morel, J., Martiné, J.-F., Bégué, A., Todoroff, P., Petit, M., A comparison of two coupling methods for improving a sugarcane model yield estimation with a NDVI-derived variable, SPIE Remote Sensing. Int. Soc. Opt. Photon., 2012 pp. 85310E-85310E-10.
116. Morel, J., Todoroff, P., Bégué, A., Bury, A., Martiné, J.F., Petit, M., Toward a satellite-based system of sugarcane yield estimation and forecasting in smallholder farming conditions: a case study on Reunion Island. Remote Sens. 6 (2014), 6620–6635.
117. Morel, J., Bégué, A., Todoroff, P., Martiné, J.F., Lebourgeois, V., Petit, M., Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation. Eur. J. Agron. 61 (2014), 60–68.
118. Moulin, S., Bondeau, A., Delecolle, R., Combining agricultural crop models and satellite observations: from field to regional scales. Int. J. Remote Sens. 19 (1998), 1021–1036.
119. 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.
120. Nendel, C., Berg, M., Kersebaum, K., Mirschel, W., Specka, X., Wegehenkel, M., Wenkel, K., Wieland, R., The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics. Ecol. Model. 222 (2011), 1614–1625.
121. Niu, X., Ban, Y., Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. Int. J. Remote Sens. 34:1 (2013), 1–26.
122. Nouvellon, Y., Moran, M.S., Seen, D.L., Bryant, R., Rambal, S., Ni, W., Bégué, A., Chehbouni, A., Emmerich, W.E., Heilman, P., Coupling a grassland ecosystem model with Landsat imagery for a 10-year simulation of carbon and water budgets. Remote Sens. Environ 78 (2001), 131–149.
123. Pellenq, J., Boulet, G., A methodology to test the pertinence of remote-sensing data assimilation into vegetation models for water and energy exchange at the land surface. Agronomie 24 (2004), 197–204.
124. Plant, N.G., Holland, K.T., Prediction and assimilation of surf-zone processes using a Bayesian network: part I: forward models. Coast. Eng. 58 (2011), 119–130.
125. 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 23 (2003), 297–303.
126. Qi, Z., Yeh, A.G.-O., Li, X., Lin, Z., A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data. Remote Sens. Environ. 118 (2012), 21–39.
127. Ren, J., Yu, F., Du, Y., Qin, J., Chen, Z., Assimilation of field measured LAI into crop growth model based on SCE-UA optimization algorithm. Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009, IEEE, 2009, III-573–III-576.
128. Ren, J., Yu, F., Qin, J., Chen, Z., Tang, H., Integrating remotely sensed LAI with EPIC model based on global optimization algorithm for regional crop yield assessment. Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, IEEE, 2010, 2147–2150.
129. Rowlandson, T., Berg, A.A., Bullock, P.R., Ojo, E., McNairn, H., Wiseman, G., Cosh, M.H., Calibration procedures for surface soil moisture measurementsduring soil moisture active passive experiment 2012 (SMAPVEX-12). J. Hydrol. 498 (2013), 335–344.
130. Sahu, S.K., Yip, S., Holland, D.M., Improved space–time forecasting of next day ozone concentrations in the eastern US. Atmos. Environ. 43 (2009), 494–501.
131. Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H., A crop phenology detection method using time-series MODIS data. Remote Sens. Environ. 96 (2005), 366–374.
132. Schläpfer, D., Richter, R., Geo-atmospheric processing of airborne imaging spectrometry data: part 1: parametric orthorectification. Int. J. Remote Sens. 23 (2002), 2609–2630.
133. Schneider, K., Assimilating remote sensing data into a land-surface process model. Int. J. Remote Sens. 24 (2003), 2959–2980.
134. Shao, Y., Fan, X., Liu, H., Xiao, J., Ross, S., Brisco, B., Brown, R., Staples, G., Rice monitoring and production estimation using multitemporal RADARSAT. Remote Sens. Environ. 76:3 (2001), 310–325.
135. Shen, S., Yang, S., Li, B., Tan, B., Li, Z., Le Toan, T., A scheme for regional rice yield estimation using ENVISAT ASAR data. Sci. China Ser. D 52 (2009), 1183–1194.
136. Skakun, S., Kussul, N., Shelestov, A.Y., Lavreniuk, M., Kussul, O., Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine. IEEE J-STRAR 9:8 (2016), 3712–3719.
137. Stöckle, C.O., Donatelli, M., Nelson, R., CropSyst, a cropping systems simulation model. Eur. J. Agron. 18 (2003), 289–307.
138. 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 (2009), 426–437.
139. Steele, B.M., Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping. Remote Sens. Environ. 74 (2000), 545–556.
140. Thorp, K.R., Hunsaker, D.J., French, A.N., Assimilating leaf area index estimates from remote sensing into the simulations of a cropping systems model. Trans. ASABE 53 (2010), 251–262.
141. Tilman, D., Global environmental impacts of agricultural expansion: the need for sustainable and efficient practices. Proc. Natl. Acad. Sci. U. S. A. 96 (1999), 5995–6000.
142. Trémolet, Y., Model-error estimation in 4D-Var. Q. J. R. Meteorol. Soci. 133 (2007), 1267–1280.
143. Tripathy, R., Chaudhari, K.N., Mukherjee, J., Ray, S.S., Patel, N., Panigrahy, S., Parihar, J.S., Forecasting wheat yield in Punjab state of India by combining crop simulation model WOFOST and remotely sensed inputs. Remote Sens. Let. 4 (2013), 19–28.
144. Verger, A., Vigneau, N., Chéron, C., Gilliot, J.M., Baret, F., Green area index from unmanned aerial system over wheat and rapeseed crops. Remote Sens. Environ. 152 (2014), 654–664.
145. Walker, J.P., Willgoose, G.R., Kalma, J.D., One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: a comparison of retrieval algorithms. Adv. Water Resour. 24 (2001), 631–650.
146. Wang, J., Li, X., Lu, L., Fang, F., Estimating near future regional corn yields by integrating multi-source observations into a crop growth model. Eur. J. Agron. 49 (2013), 126–140.
147. 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., 8, 2014, 083674.
148. Wei, C., Huang, J., Mansaray, L.R., Li, Z., Liu, W., Han, J., Estimation and mapping of winter oilseed rape LAI from high spatial resolution satellite data based on a hybrid method. Remote Sens., 9(5), 2017, 488, 10.3390/rs9050488.
149. Wiseman, G., McNairn, H., Homayouni, S., Shang, J., RADARSAT-2 polarimetric SAR response to crop biomass for agricultural production monitoring. IEEE J-STARS 7:11 (2014), 4461–4471.
150. Wu, S., Huang, J., Liu, X., Fan, J., Ma, G., Zou, J., Assimilating MODIS-LAI into Crop Growth Model with EnKF to Predict Regional Crop Yield, Computer and Computing Technologies in Agriculture V. 2011, Springer, 410–418.
151. Xin, J., Yu, Z., van Leeuwen, L., Driessen, P.M., Mapping crop key phenological stages in the North China Plain using NOAA time series images. Int. J. Appl. Earth Obs. 4 (2002), 109–117.
152. Yao, F., Tang, Y., Wang, P., Zhang, J., Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain. Phys. Chem. Earth Parts A/B/C 87 (2015), 142–152.
153. Yuzugullu, O., Erten, E., Hajnsek, I., Estimation of rice crop height from X-and C-band PolSAR by metamodel-based optimization. IEEE J-STARS 10:1 (2017), 194–204.
154. Zarco-Tejada, P.J., Guillén-Climent, M.L., Hernández-Clemente, R., Catalinac, A., Gonzálezc, M.R., Martínc, P., Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric. For. Meteorol. 171 (2013), 281–294.
155. Zhang, F., Snyder, C., Sun, J., Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Weather Rev. 132 (2004), 1238–1253.
156. Zhao, Y., Chen, S., Shen, S., Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation. Ecol. Model. 270 (2013), 30–42.