Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor

Precision Agriculture

Volumn 17, Issue 2, 2016, Pages 136-154

  Cao, Qiang ^a,b   Miao, Yuxin ^a   Shen, Jianning ^a   Yu, Weifeng ^a   Yuan, Fei ^c   Cheng, Shanshan ^a   Huang, Shanyu ^a,d   Wang, Hongye ^a   Yang, Wen ^e   Liu, Fengyan ^e  

^a CHINA AGRICULTURAL UNIVERSITY   (China)

^b NANJING AGRICULTURAL UNIVERSITY   (China)

^c MINNESOTA STATE UNIVERSITY   (United States)

^d UNIVERSITY OF COLOGNE   (Germany)

^e Jiansanjiang Institute of Agricultural Research   (China)

Author keywords

Active crop canopy sensor; Crop Circle sensor; GreenSeeker sensor; In season nitrogen management; Precision nitrogen management; Response index

Indexed keywords

ACCURACY ASSESSMENT; CROP YIELD; DATA ACQUISITION; ESTIMATION METHOD; FERTILIZER APPLICATION; GROWING SEASON; NDVI; NITROGEN COMPOUND; PRECISION AGRICULTURE; SEASONALITY; SENSOR; VEGETATION INDEX;

EID: 84939183435     PISSN: 13852256     EISSN: 15731618     Source Type: Journal    
DOI: 10.1007/s11119-015-9412-y     Document Type: Article

References (59)

1. Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., & Kostrzewski, M., et al. (2000) Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In P. C. Robert, R. H. Rust, & W. E. Larson (Eds.), Proceedings of the 5th International Conference on Precision Agriculture, 16–19 July 2000. Bloomington, USA.
2. Bijay-Singh, Sharma, R. K., Jaspreet-Kaur, Jat, M. L., Martin, K. L., Yadvinder-Singh, et al. (2011). Assessment of the nitrogen management strategy using an optical sensor for irrigated wheat. Agronomy for Sustainable Development, 31(3), 589–603.
3. Broge, N. H., & Leblanc, E. (2000). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2), 156–172.
4. Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14(4), 711–722.
5. Cao, Q., Cui, Z., Chen, X., Khosla, R., Dao, T. H., & Miao, Y. (2012). Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming. Precision Agriculture, 13(1), 45–61.
6. Cao, Q., Miao, Y., Feng, G., Gao, X., Li, F., Liu, B., et al. (2015). Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems. Computers and Electronics in Agriculture, 112, 54–67.
7. Cao, Q., Miao, Y., Wang, H., Huang, S., Cheng, S., Khosla, R., et al. (2013). Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research, 154, 133–144.
8. Cassman, K. G., Dobermann, A., Walters, D. T., & Yang, H. (2003). Meeting cereal demand while protecting natural resources and improving environmental quality. Annual Review of Environment and Resources, 28(1), 315–358.
9. Chen, J. M. (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(3), 229–242.
10. Chen, X., Cui, Z., Fan, M., Vitousek, P., Zhao, M., Ma, W., et al. (2014). Producing more grain with lower environmental costs. Nature, 514(7523), 486–489.
11. Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing, 20(14), 2741–2759.
12. Daughtry, C., Walthall, C., Kim, M., De Colstoun, E. B., & McMurtrey, J. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229–239.
13. Dobermann, A., Witt, C., Dawe, D., Abdulrachman, S., Gines, H., Nagarajan, R., et al. (2002). Site-specific nutrient management for intensive rice cropping systems in Asia. Field Crops Research, 74(1), 37–66.
14. Eitel, J., Long, D., Gessler, P., & Smith, A. (2007). Using in situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status. International Journal of Remote Sensing, 28(18), 4183–4190.
15. FAOSTAT (2013). FAO statistical databases. http://www.fao.org. Accessed 20 Dec 2014.
16. Gitelson, A. A. (2004). Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161(2), 165–173.
17. Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298.
18. Gitelson, A. A., Viña, A. S., Ciganda, V. N., & Rundquist, D. C. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32, L08403. doi:10.01029/02005GL022688.
19. Gnyp, M. L., Miao, Y., Yuan, F., Ustin, S. L., Yu, K., Yao, Y., et al. (2014). Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crops Research, 155, 42–55.
20. Good, A. G., & Beatty, P. H. (2011). Fertilizing nature: a tragedy of excess in the commons. PLoS Biology, 9(8), e1001124.
21. Grassini, P., Eskridge, K. M., & Cassman, K. G. (2013). Distinguishing between yield advances and yield plateaus in historical crop production trends. Nature Communications, 4, 2918–2928.
22. Guo, J. H., Liu, X. J., Zhang, Y., Shen, J. L., Han, W. X., Zhang, W. F., et al. (2010). Significant acidification in major Chinese croplands. Science, 327(5968), 1008–1010.
23. Haboudane, D. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337–352.
24. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2), 416–426.
25. Harrell, D. L., Tubaña, B. S., Walker, T. W., & Phillips, S. B. (2011). Estimating rice grain yield potential using normalized difference vegetation index. Agronomy Journal, 103(6), 1717–1723.
26. Heffer, P. (2008). Assessment of fertilizer use by crop at the global level. Paris, France: International Fertilizer Industry Association (IFA).
27. Jasper, J., Reusch, S., & Link, A. Active sensing of the N status of wheat using optimized wave-length combination: Impact of seed rate, variety and growth stage. In E. J. van Henten, D. Goense, & C. Lokhorst (Eds.), 9th Precision agriculture, Wageningen, The Netherlands, 2009 (pp. 23–30). Wageningen: Wageningen Academic Publishers.
28. Johnson, G., & Raun, W. (2003). Nitrogen response index as a guide to fertilizer management. Journal of Plant Nutrition, 26(2), 249–262.
29. Justice, C. O., Vermote, E., Townshend, J. R. G., DeFries, R., Roy, D. P., Hall, D. K., et al. (1998). The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1228–1249.
30. Le Maire, G., Francois, C., & Dufrene, E. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89(1), 1–28.
31. Li, F., Miao, Y., Zhang, F., Cui, Z., Li, R., Chen, X., et al. (2009). In-season optical sensing improves nitrogen use efficiency for winter wheat. Soil Science Society of America Journal, 73(5), 1566–1574.
32. Miao, Y., Stewart, B. A., & Zhang, F. (2011). Long-term experiments for sustainable nutrient management in China. A review. Agronomy for Sustainable Development, 31(2), 397–414.
33. Mullen, R. W., Freeman, K. W., Raun, W. R., Johnson, G. V., Stone, M. L., & Solie, J. B. (2003). Identifying an in-season response index and the potential to increase wheat yield with nitrogen. Agronomy Journal, 95(2), 347–351.
34. Nosengo, N. (2003). Fertilized to death. Nature, 425, 894–895.
35. Peng, S., Buresh, R. J., Huang, J., Zhong, X., Zou, Y., Yang, J., et al. (2010). Improving nitrogen fertilization in rice by sitespecific N management. A review. Agronomy for Sustainable Development, 30(3), 649–656.
36. Qi, J., Chehbouni, A., Huete, A., Kerr, Y., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119–126.
37. Raper, T. B., Varco, J. J., & Hubbard, K. J. (2013). Canopy-based normalized difference vegetation index sensors for monitoring cotton nitrogen status. Agronomy Journal, 105(5), 1345–1354.
38. Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Mullen, R. W., Freeman, K. W., et al. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agronomy Journal, 94(4), 815–820.
39. Raun, W. R., Solie, J. B., & Stone, M. L. (2011). Independence of yield potential and crop nitrogen response. Precision Agriculture, 12(4), 508–518.
40. Raun, W. R., Solie, J. B., Stone, M. L., Martin, K. L., Freeman, K. W., Mullen, R. W., et al. (2005). Optical sensor-based algorithm for crop nitrogen fertilization. Communications in Soil Science and Plant Analysis, 36(19–20), 2759–2781.
41. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107.
42. Roujean, J.-L., & Breon, F.-M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3), 375–384.
43. Seck, P. A., Diagne, A., Mohanty, S., & Wopereis, M. C. S. (2012). Crops that feed the world 7: Rice. Food Security, 4(1), 7–24.
44. Sripada, R. P., Heiniger, R. W., White, J. G., & Meijer, A. D. (2006). Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal, 98(4), 968–977.
45. Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260–20264.
46. Tubaña, B., Harrell, D., Walker, T., Teboh, J., Lofton, J., & Kanke, Y. (2012). In-season canopy reflectance-based estimation of rice yield response to nitrogen. Agronomy Journal, 104, 1604–1611.
47. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.
48. Wang, Y., & Yang, Y. (2001). Effects of agriculture reclamation on the hydrologic characteristics in the Sanjiang Plain, China. Chinese Geographical Science, 11(2), 163–167.
49. Wang, W., Yao, X., Yao, X., Tian, Y., Liu, X., Ni, J., et al. (2012). Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crops Research, 129, 90–98.
50. Wang, Z., Zhang, B., Zhang, S., Li, X., Liu, D., Song, K., et al. (2006). Changes of land use and of ecosystem service values in Sanjiang Plain, Northeast China. Environmental Monitoring and Assessment, 112(1–3), 69–91.
51. Xue, L., Li, G., Qin, X., Yang, L., & Zhang, H. (2014). Topdressing nitrogen recommendation for early rice with an active sensor in south China. Precision Agriculture, 15(1), 95–110.
52. Xue, L., & Yang, L. (2008). Recommendations for nitrogen fertiliser topdressing rates in rice using canopy reflectance spectra. Biosystems Engineering, 100(4), 524–534.
53. Yan, M., Deng, W., & Chen, P. (2002). Climate change in the Sanjiang Plain disturbed by large-scale reclamation. Journal of Geographical Sciences, 12(4), 405–412.
54. Yao, Y., Miao, Y., Cao, Q., Wang, H., Gnyp, M. L., Bareth, G., et al. (2014). In-season estimation of rice nitrogen status with an active crop canopy sensor. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11), 4403–4413.
55. Yao, Y., Miao, Y., Huang, S., Gao, L., Ma, X., Zhao, G., et al. (2012). Active canopy sensor-based precision N management strategy for rice. Agronomy for Sustainable Development, 32(4), 925–933.
56. Zarco-Tejada, P. J., Miller, J., Morales, A., Berjón, A., & Agüera, J. (2004). Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing of Environment, 90(4), 463–476.
57. Zhang, F., Chen, X., & Vitousek, P. (2013). Chinese agriculture: An experiment for the world. Nature, 497(7447), 33–35.
58. Zhang, F., Cui, Z., Chen, X., Ju, X., Shen, J., Chen, Q., et al. (2012). Integrated nutrient management for food security and environmental quality in China. Advances in Agronomy, 116, 1–40.
59. Zhao, G., Miao, Y., Wang, H., Su, M., Fan, M., Zhang, F., et al. (2013). A preliminary precision rice management system for increasing both grain yield and nitrogen use efficiency. Field Crops Research, 154, 23–30.