메뉴 건너뛰기




Volumn 6, Issue , 2016, Pages

Rapid corn and soybean mapping in US Corn Belt and neighboring areas

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ALGORITHM; CLASSIFIER; CROP; CROPLAND; GROWING SEASON; NONHUMAN; PHENOLOGY; PLANT DEVELOPMENT; RANDOM FOREST; SOYBEAN; VALIDATION PROCESS; AGRICULTURE; ALGORITHM; GOVERNMENT; GROWTH, DEVELOPMENT AND AGING; MAIZE; MAP; PROCEDURES; SPACE FLIGHT; STATISTICS AND NUMERICAL DATA; TIME FACTOR; UNITED STATES;

EID: 84994607536     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep36240     Document Type: Article
Times cited : (46)

References (37)
  • 1
    • 45449111176 scopus 로고    scopus 로고
    • Farming the planet:1. Geographic distribution of global agricultural lands in the year 2000
    • Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem. Cycles 22, GB1003 (2008).
    • (2008) Global Biogeochem. Cycles , vol.22
    • Ramankutty, N.1    Evan, A.T.2    Monfreda, C.3    Foley, J.A.4
  • 2
    • 45449110437 scopus 로고    scopus 로고
    • Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000
    • Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cycles 22, GB1022 (2008).
    • (2008) Global Biogeochem. Cycles , vol.22
    • Monfreda, C.1    Ramankutty, N.2    Foley, J.A.3
  • 3
    • 84872359932 scopus 로고    scopus 로고
    • Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data
    • Gong, P. et al. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 34, 2607-2654 (2013).
    • (2013) Int. J. Remote Sens , vol.34 , pp. 2607-2654
    • Gong, P.1
  • 4
    • 84888134923 scopus 로고    scopus 로고
    • FROM-GC: 30 m global cropland extent derived through multisource data integration
    • Yu, L. et al. FROM-GC: 30 m global cropland extent derived through multisource data integration. International Journal of Digital Earth 6, 521-533 (2013).
    • (2013) International Journal of Digital Earth , vol.6 , pp. 521-533
    • Yu, L.1
  • 5
    • 84896926443 scopus 로고    scopus 로고
    • A production efficiency model-based method for satellite estimates of corn and soybean yields in the midwestern us
    • Xin, Q. et al. A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US. Remote Sensing 5, 5926-5943 (2013).
    • (2013) Remote Sensing , vol.5 , pp. 5926-5943
    • Xin, Q.1
  • 6
    • 34247523027 scopus 로고    scopus 로고
    • Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains
    • Wardlow, B. D., Egbert, S. L. & Kastens, J. H. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sens. Environ. 108, 290-310 (2007).
    • (2007) Remote Sens. Environ , vol.108 , pp. 290-310
    • Wardlow, B.D.1    Egbert, S.L.2    Kastens, J.H.3
  • 7
    • 39749173163 scopus 로고    scopus 로고
    • Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the US Central Great Plains
    • Wardlow, B. D. & Egbert, S. L. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the US Central Great Plains. Remote Sens. Environ. 112, 1096-1116 (2008).
    • (2008) Remote Sens. Environ , vol.112 , pp. 1096-1116
    • Wardlow, B.D.1    Egbert, S.L.2
  • 8
    • 79960831514 scopus 로고    scopus 로고
    • Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program
    • Boryan, C., Yang, Z., Mueller, R. & Craig, M. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto Int. 26, 341-358 (2011).
    • (2011) Geocarto Int , vol.26 , pp. 341-358
    • Boryan, C.1    Yang, Z.2    Mueller, R.3    Craig, M.4
  • 9
    • 84923552740 scopus 로고    scopus 로고
    • Annual crop type classification of the us great plains for 2000 to 2011
    • Howard, D. M. & Wylie, B. K. Annual Crop Type Classification of the US Great Plains for 2000 to 2011. Photogramm. Eng. Remote Sensing 80, 537-549 (2014).
    • (2014) Photogramm. Eng. Remote Sensing , vol.80 , pp. 537-549
    • Howard, D.M.1    Wylie, B.K.2
  • 10
    • 85007485113 scopus 로고    scopus 로고
    • Assessing bioenergy-driven agricultural land use change and biomass quantities in the US Midwest with MODIS time series
    • Wang, C., Zhong, C. & Yang, Z. Assessing bioenergy-driven agricultural land use change and biomass quantities in the US Midwest with MODIS time series. Journal of Applied Remote Sensing 8, 085198 (2014).
    • (2014) Journal of Applied Remote Sensing , vol.8
    • Wang, C.1    Zhong, C.2    Yang, Z.3
  • 11
    • 84884264508 scopus 로고    scopus 로고
    • Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery
    • Zhong, L., Gong, P. & Biging, G. S. Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery. Remote Sens. Environ. 140, 1-13 (2014).
    • (2014) Remote Sens. Environ , vol.140 , pp. 1-13
    • Zhong, L.1    Gong, P.2    Biging, G.S.3
  • 12
    • 0019895232 scopus 로고
    • Automatic corn-soybean classification using Landsat MSS data. II. Early season crop proportion estimation
    • Badhwar, G. B. Automatic corn-soybean classification using Landsat MSS data. II. Early season crop proportion estimation. Remote Sensing of Environment, 14, 31-37 (1984).
    • (1984) Remote Sensing of Environment , vol.14 , pp. 31-37
    • Badhwar, G.B.1
  • 13
    • 84897374226 scopus 로고    scopus 로고
    • Near real-time prediction of U.S. Corn yields based on time-series MODIS data
    • Sakamoto, T., Gitelson, A. A. & Arkebauer, T. J. Near real-time prediction of U.S. corn yields based on time-series MODIS data. Remote Sens. Environ. 147, 219-231 (2014).
    • (2014) Remote Sens. Environ , vol.147 , pp. 219-231
    • Sakamoto, T.1    Gitelson, A.A.2    Arkebauer, T.J.3
  • 15
    • 84994628962 scopus 로고    scopus 로고
    • Identifying main crop classes in an irrigated area using high resolution image time series (Geoscience and Remote Sensing Symposium, 2003
    • Simonneaux, V. & Francois, P. Identifying main crop classes in an irrigated area using high resolution image time series (Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International Ser. 1, 2003).
    • (2003) IGARSS '03. Proceedings. 2003 IEEE International ser , vol.1
    • Simonneaux, V.1    Francois, P.2
  • 16
    • 33646827204 scopus 로고    scopus 로고
    • Regional Scale Land Cover Characterization Using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology-Based Approach
    • Knight, J. F., Lunetta, R. S., Ediriwickrema, J. & Khorram, S. Regional Scale Land Cover Characterization Using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology-Based Approach. GIScience & Remote Sensing 43, 1-23 (2006).
    • (2006) GIScience & Remote Sensing , vol.43 , pp. 1-23
    • Knight, J.F.1    Lunetta, R.S.2    Ediriwickrema, J.3    Khorram, S.4
  • 17
    • 84868019256 scopus 로고    scopus 로고
    • Phenology-based Crop Classification Algorithm and its Implications on Agricultural Water Use Assessments in California's Central Valley
    • Zhong, L., Gong, P. & Biging, G. S. Phenology-based Crop Classification Algorithm and its Implications on Agricultural Water Use Assessments in California's Central Valley. Photogramm. Eng. Remote Sensing 78, 799-813 (2012).
    • (2012) Photogramm. Eng. Remote Sensing , vol.78 , pp. 799-813
    • Zhong, L.1    Gong, P.2    Biging, G.S.3
  • 18
    • 85027922810 scopus 로고    scopus 로고
    • Tracking the dynamics of paddy rice planting area in 1986-2010 through time series Landsat images and phenologybased algorithms
    • Dong, J. et al. Tracking the dynamics of paddy rice planting area in 1986-2010 through time series Landsat images and phenologybased algorithms. Remote Sens. Environ. 160, 99-113 (2015).
    • (2015) Remote Sens. Environ , vol.160 , pp. 99-113
    • Dong, J.1
  • 19
    • 33750796985 scopus 로고    scopus 로고
    • Using USDA crop progress data for the evaluation of greenup onset date calculated from MODIS 250-meter data
    • Wardlow, B. D., Kastens, J. H. & Egbert, S. L. Using USDA crop progress data for the evaluation of greenup onset date calculated from MODIS 250-meter data. Photogramm. Eng. Remote Sensing 72, 1225-1234 (2006).
    • (2006) Photogramm. Eng. Remote Sensing , vol.72 , pp. 1225-1234
    • Wardlow, B.D.1    Kastens, J.H.2    Egbert, S.L.3
  • 20
    • 77955282289 scopus 로고    scopus 로고
    • A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data
    • Sakamoto, T. et al. A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sens. Environ. 114, 2146-2159 (2010).
    • (2010) Remote Sens. Environ , vol.114 , pp. 2146-2159
    • Sakamoto, T.1
  • 21
    • 84933557278 scopus 로고    scopus 로고
    • Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps
    • Waldner, F., Fritz, S., Di Gregorio, A. & Defourny, P. Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps. Remote Sensing 7 (2015).
    • (2015) Remote Sensing , vol.7
    • Waldner, F.1    Fritz, S.2    Di Gregorio, A.3    Defourny, P.4
  • 23
    • 82055192022 scopus 로고    scopus 로고
    • A phenology-based approach to map crop types in the San Joaquin Valley, California
    • Zhong, L., Hawkins, T., Biging, G. & Gong, P. A phenology-based approach to map crop types in the San Joaquin Valley, California. International Journal of Remote Sensing 32, 7777-7804 (2011).
    • (2011) International Journal of Remote Sensing , vol.32 , pp. 7777-7804
    • Zhong, L.1    Hawkins, T.2    Biging, G.3    Gong, P.4
  • 24
    • 0030621388 scopus 로고    scopus 로고
    • Using thematic mapper data to identify contrasting soil plains and tillage practices
    • Van Deventer, A., Ward, A., Gowda, P. & Lyon, J. Using thematic mapper data to identify contrasting soil plains and tillage practices. Photogramm. Eng. Remote Sensing 63, 87-93 (1997).
    • (1997) Photogramm. Eng. Remote Sensing , vol.63 , pp. 87-93
    • Van Deventer, A.1    Ward, A.2    Gowda, P.3    Lyon, J.4
  • 25
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L. Random forests. Mach. Learning 45, 5-32 (2001).
    • (2001) Mach. Learning , vol.45 , pp. 5-32
    • Breiman, L.1
  • 26
    • 31344453556 scopus 로고    scopus 로고
    • Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest)
    • Lawrence, R. L., Wood, S. D. & Sheley, R. L. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sens. Environ. 100, 356-362 (2006).
    • (2006) Remote Sens. Environ , vol.100 , pp. 356-362
    • Lawrence, R.L.1    Wood, S.D.2    Sheley, R.L.3
  • 27
    • 43949125818 scopus 로고    scopus 로고
    • Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
    • Chan, J. C. & Paelinckx, D. Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens. Environ. 112, 2999-3011 (2008).
    • (2008) Remote Sens. Environ , vol.112 , pp. 2999-3011
    • Chan, J.C.1    Paelinckx, D.2
  • 28
    • 79959669722 scopus 로고    scopus 로고
    • Improved Land Cover Mapping using Random Forests Combined with Landsat Thematic Mapper Imagery and Ancillary Geographic Data
    • Na, X., Zhang, S., Li, X., Yu, H. & Liu, C. Improved Land Cover Mapping using Random Forests Combined with Landsat Thematic Mapper Imagery and Ancillary Geographic Data. Photogramm. Eng. Remote Sensing 76, 833-840 (2010).
    • (2010) Photogramm. Eng. Remote Sensing , vol.76 , pp. 833-840
    • Na, X.1    Zhang, S.2    Li, X.3    Yu, H.4    Liu, C.5
  • 30
    • 84879047611 scopus 로고    scopus 로고
    • Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: A segmentation-based approach
    • Yu, L., Wang, J. & Gong, P. Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach. Int. J. Remote Sens. 34, 5851-5867 (2013).
    • (2013) Int. J. Remote Sens , vol.34 , pp. 5851-5867
    • Yu, L.1    Wang, J.2    Gong, P.3
  • 31
    • 84904971625 scopus 로고    scopus 로고
    • Meta-discoveries from a synthesis of satellite-based land-cover mapping research
    • Yu, L. et al. Meta-discoveries from a synthesis of satellite-based land-cover mapping research. Int. J. Remote Sens. 35, 4573-4588 (2014).
    • (2014) Int. J. Remote Sens , vol.35 , pp. 4573-4588
    • Yu, L.1
  • 32
    • 84894607481 scopus 로고    scopus 로고
    • Comparison of classification algorithms and training sample sizes in urban land classification with landsat thematic mapper imagery
    • Li, C., Wang, J., Wang, L., Hu, L. & Gong, P. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery. Remote Sensing 6 (2014).
    • (2014) Remote Sensing , vol.6
    • Li, C.1    Wang, J.2    Wang, L.3    Hu, L.4    Gong, P.5
  • 33
    • 79957630538 scopus 로고    scopus 로고
    • Detecting spatiotemporal changes of corn developmental stages in the U.S. Corn belt using modis wdrvi data
    • Sakamoto, T., Wardlow, B. D. & Gitelson, A. A. Detecting Spatiotemporal Changes of Corn Developmental Stages in the U.S. Corn Belt Using MODIS WDRVI Data. IEEE Transactions on Geoscience and Remote Sensing 49, 1926-1936 (2011).
    • (2011) IEEE Transactions on Geoscience and Remote Sensing , vol.49 , pp. 1926-1936
    • Sakamoto, T.1    Wardlow, B.D.2    Gitelson, A.A.3
  • 35
    • 84994623572 scopus 로고    scopus 로고
    • Volume 3: Remote Sensing, data processing and analysis (Office for Official Publications of the European Communities, Luxemburg, EU-Monograph EU 21291/EN3
    • Eerens, H., Piccard, I., Royer, A. & Orlandi, S. In Methodology of the MARS Crop Yield Forecasting System. Volume 3: Remote Sensing, data processing and analysis (Office for Official Publications of the European Communities, Luxemburg, EU-Monograph EU 21291/EN3 2004).
    • (2004) Methodology of the MARS Crop Yield Forecasting System
    • Eerens, H.1    Piccard, I.2    Royer, A.3    Orlandi, S.4
  • 36
    • 84890908097 scopus 로고    scopus 로고
    • Image time series processing for agriculture monitoring
    • Eerens, H. et al. Image time series processing for agriculture monitoring. Environmental Modelling & Software 53, 154-162 (2014).
    • (2014) Environmental Modelling & Software , vol.53 , pp. 154-162
    • Eerens, H.1
  • 37
    • 84875729390 scopus 로고    scopus 로고
    • Using thermal time and pixel purity for enhancing biophysical variable time series: An interproduct comparison
    • Duveiller, G., Baret, F. & Defourny, P. Using Thermal Time and Pixel Purity for Enhancing Biophysical Variable Time Series: An Interproduct Comparison. IEEE Transactions on Geoscience and Remote Sensing 51, 2119-2127 (2013).
    • (2013) IEEE Transactions on Geoscience and Remote Sensing , vol.51 , pp. 2119-2127
    • Duveiller, G.1    Baret, F.2    Defourny, P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.