All Issue

2018 Vol.51, Issue 3 Preview Page

August 2018. pp. 222-238
Abstract


References
1 

Chhikara, R.S., A.G. Houston, and J.C. Lundgren. 1986. Crop acreage estimation using a LANDSAT-based estimator as an auxiliary variable. IEEE Trans. Geosci. Remote Sens. 24:157-168.

10.1109/TGRS.1986.289545
2 

Cihlar, J. 2000. Land cover mapping of large area from satellite: status and research priorities. Int. J. Remote Sens. 21(6):1093-1114.

10.1080/014311600210092
3 

Cochran, W.G. 1977. Sampling Techniques. New York: third ed. John Wiley & Sons.

4 

Cohen, Y. and M. Shoshany. 2002. A national knowledge-based crop recognition in Mediterranean environment. Int. J. Appl. Earth Obs. Geoinfor. 4:75-87.

10.1016/S0303-2434(02)00003-X
5 

Louhaichi, M., M.M. Borman, and D.E. Johnson, 2001. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto Int. 16:65-70.

10.1080/10106040108542184
6 

Mahey, R.K., R. Singh, S.S. Sidhu, R.S. Narang, V.K. Dadhwaal, J.S. Parihar, and A.K. Sharma. 1993. Pre-harvest state level wheat acreage estimation using IRS-IA LISS-Ⅰdata in Punjab(India). Int. J. Remote Sen. 14:1099-1106.

10.1080/01431169308904398
7 

Portmann, F.T., S. Siebert, and P. Dool. 2010. MIRCA2000-global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Global Biogeochem. Cycles 24: GB1011. doi:10.1029/2008GB003435.

10.1029/2008GB003435
8 

Quarmby, N.A. 1992. Towards continental scale crop area estimation. Int. J. Remote Sens. 13:981-989.

10.1080/01431169208904172
9 

Ramankutty, N., A.T. Evan, C. Monfreda, and J.A. Foley. 2008. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem. Cycles. 22: GB1003. doi:10.1029/2007GB002952.

10.1029/2007GB002952
10 

Reynolds, C.A., M. Yitayew, and D.C. Slack. 2000. Estimating crop yields and production by integrating the FAO crop specific water balance model with real-time satellite data and ground-based ancillary data. Int. J. Remote Sens. 21:3487-3508.

10.1080/014311600750037516
11 

Song, X.P., V.P. Peter, K. Alexander, K. LeeAnn, D.B. CarlosM, H. Amy, K. Ahmad, A. Bernard, V.S. Stephen, and C.H. Matthew. 2017. National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey. Remote Sens. Environ. 190:383-395.

10.1016/j.rse.2017.01.008
12 

Tao, F.L., Y. Masayuki, and Z. Zhan. 2005. Remote sensing of crop production in China by production efficiency models: models comparisons estimates and uncertainties. Ecol. Model. 183:385-396.

10.1016/j.ecolmodel.2004.08.023
13 

Thenkabail, P.S., C.M. Biradar, P. Noojipady, V. Dheeravath, Y. Li, M. Velpuri, M. Gumma, O.R.P. Gangalakunta, H. Turral, X. Cai, J. Vithanage, M.A. Schull, and R. Dutta. 2009. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. Int. J. Remote Sens. 30:3679-3733.

10.1080/01431160802698919
14 

You, L., S. Wood, U. Wood-Sichra, and W. Wu. 2014. Generating global crop distribution maps: from census to grid. Agric. Sys. 127:53-60.

10.1016/j.agsy.2014.01.002
Information
  • Publisher :Korean Society of Soil Science and Fertilizer
  • Publisher(Ko) :한국토양비료학회
  • Journal Title :Korean Journal of Soil Science and Fertilizer
  • Journal Title(Ko) :한국토양비료학회 학회지
  • Volume : 51
  • No :3
  • Pages :222-238
  • Received Date :2018. 06. 11
  • Revised Date :2018. 06. 30
  • Accepted Date : 2018. 08. 31