메뉴 건너뛰기




Volumn 38, Issue , 2015, Pages 78-87

An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data

Author keywords

Ancillary data; Corn yield forecasting; Cropland masks; MODIS; Random forest

Indexed keywords

ZEA MAYS;

EID: 84935029534     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2014.12.017     Document Type: Article
Times cited : (67)

References (37)
  • 1
    • 77949487217 scopus 로고    scopus 로고
    • A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data
    • Becker-Reshef, I., Vermote, E., Lindeman, M., Justice, C., A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens. Environ. 114:6 (2010), 1312–1323.
    • (2010) Remote Sens. Environ. , vol.114 , Issue.6 , pp. 1312-1323
    • Becker-Reshef, I.1    Vermote, E.2    Lindeman, M.3    Justice, C.4
  • 2
    • 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. 26:5 (2011), 341–358.
    • (2011) Geocarto Int. , vol.26 , Issue.5 , pp. 341-358
    • Boryan, C.1    Yang, Z.2    Mueller, R.3    Craig, M.4
  • 3
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L., Random forests. Mach. learn. 45:1 (2001), 5–32.
    • (2001) Mach. learn. , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 4
    • 2942739366 scopus 로고    scopus 로고
    • A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter
    • Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. of Environ. 91:3 (2004), 332–344.
    • (2004) Remote Sens. of Environ. , vol.91 , Issue.3 , pp. 332-344
    • Chen, J.1    Jönsson, P.2    Tamura, M.3    Gu, Z.4    Matsushita, B.5    Eklundh, L.6
  • 7
    • 0002944396 scopus 로고
    • Spring wheat yield assessment using NOAA AVHRR data
    • Doraiswamy, P.C., Cook, P.W., Spring wheat yield assessment using NOAA AVHRR data. Can. J. Remote Sens. 21:1 (1995), 43–51.
    • (1995) Can. J. Remote Sens. , vol.21 , Issue.1 , pp. 43-51
    • Doraiswamy, P.C.1    Cook, P.W.2
  • 8
    • 0027788571 scopus 로고
    • NDVI—crop monitoring and early yield assessment of Burkina Faso
    • Groten, S., NDVI—crop monitoring and early yield assessment of Burkina Faso. Int. J. Remote Sens. 14:8 (1993), 1495–1515.
    • (1993) Int. J. Remote Sens. , vol.14 , Issue.8 , pp. 1495-1515
    • Groten, S.1
  • 9
    • 0030292154 scopus 로고    scopus 로고
    • Using NOAA AVHRR data to estimate maize production in the United States Corn Belt
    • Hayes, M., Decker, W., Using NOAA AVHRR data to estimate maize production in the United States Corn Belt. Int. J. Remote Sens. 17:16 (1996), 3189–3200.
    • (1996) Int. J. Remote Sens. , vol.17 , Issue.16 , pp. 3189-3200
    • Hayes, M.1    Decker, W.2
  • 10
    • 0026359994 scopus 로고
    • Uses of satellite data for famine early warning in sub-Saharan Africa
    • Hutchinson, C., Uses of satellite data for famine early warning in sub-Saharan Africa. Int. J. Remote Sens. 12:6 (1991), 1405–1421.
    • (1991) Int. J. Remote Sens. , vol.12 , Issue.6 , pp. 1405-1421
    • Hutchinson, C.1
  • 11
    • 84867417663 scopus 로고    scopus 로고
    • A 2010 map estimate of annually tilled cropland within the conterminous United States
    • Johnson, D.M., A 2010 map estimate of annually tilled cropland within the conterminous United States. Agric. Sys., 114, 2013.
    • (2013) Agric. Sys. , vol.114
    • Johnson, D.M.1
  • 12
    • 84888050432 scopus 로고    scopus 로고
    • An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States
    • Johnson, D.M., An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 141 (2014), 116–128.
    • (2014) Remote Sens. Environ. , vol.141 , pp. 116-128
    • Johnson, D.M.1
  • 15
    • 56249113343 scopus 로고    scopus 로고
    • Building predictive models in R using the caret package
    • Kuhn, M., Building predictive models in R using the caret package. J. Stat. Software 28:5 (2008), 1–26.
    • (2008) J. Stat. Software , vol.28 , Issue.5 , pp. 1-26
    • Kuhn, M.1
  • 16
    • 0037307653 scopus 로고    scopus 로고
    • Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties
    • Lobell, D.B., Asner, G.P., Ortiz-Monasterio, J.I., Benning, T.L., Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agric. Ecosys. Environ. 94:2 (2003), 205–220.
    • (2003) Agric. Ecosys. Environ. , vol.94 , Issue.2 , pp. 205-220
    • Lobell, D.B.1    Asner, G.P.2    Ortiz-Monasterio, J.I.3    Benning, T.L.4
  • 18
    • 77958478647 scopus 로고    scopus 로고
    • Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data
    • Lunetta, R.S., Shao, Y., Ediriwickrema, J., Lyon, J.G., Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data. Int. J. App. Earth Obs. Geoinf. 12:2 (2010), 81–88.
    • (2010) Int. J. App. Earth Obs. Geoinf. , vol.12 , Issue.2 , pp. 81-88
    • Lunetta, R.S.1    Shao, Y.2    Ediriwickrema, J.3    Lyon, J.G.4
  • 19
    • 0029749373 scopus 로고    scopus 로고
    • APSIM: a novel software system for model development, model testing and simulation in agricultural systems research
    • McCown, R., Hammer, G., Hargreaves, J., Holzworth, D., Freebairn, D., APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agric. Sys. 50:3 (1996), 255–271.
    • (1996) Agric. Sys. , vol.50 , Issue.3 , pp. 255-271
    • McCown, R.1    Hammer, G.2    Hargreaves, J.3    Holzworth, D.4    Freebairn, D.5
  • 20
    • 0031228124 scopus 로고    scopus 로고
    • Opportunities and limitations for image-based remote sensing in precision crop management
    • Moran, M.S., Inoue, Y., Barnes, E., Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 61:3 (1997), 319–346.
    • (1997) Remote Sens. Environ. , vol.61 , Issue.3 , pp. 319-346
    • Moran, M.S.1    Inoue, Y.2    Barnes, E.3
  • 21
    • 31044453033 scopus 로고    scopus 로고
    • Crop yield estimation model for Iowa using remote sensing and surface parameters
    • Prasad, A.K., Chai, L., Singh, R.P., Kafatos, M., Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Obs. Geoinf. 8:1 (2006), 26–33.
    • (2006) Int. J. Appl. Earth Obs. Geoinf. , vol.8 , Issue.1 , pp. 26-33
    • Prasad, A.K.1    Chai, L.2    Singh, R.P.3    Kafatos, M.4
  • 22
    • 0027334401 scopus 로고
    • The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction
    • Quarmby, N., Milnes, M., Hindle, T., Silleos, N., The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int. J. Remote Sens. 14:2 (1993), 199–210.
    • (1993) Int. J. Remote Sens. , vol.14 , Issue.2 , pp. 199-210
    • Quarmby, N.1    Milnes, M.2    Hindle, T.3    Silleos, N.4
  • 23
    • 0031104588 scopus 로고    scopus 로고
    • Operational yield forecast using AVHRR NDVI data: reduction of environmental and inter-annual variability
    • Rasmussen, M.S., Operational yield forecast using AVHRR NDVI data: reduction of environmental and inter-annual variability. Int. J. Remote Sens. 18:5 (1997), 1059–1077.
    • (1997) Int. J. Remote Sens. , vol.18 , Issue.5 , pp. 1059-1077
    • Rasmussen, M.S.1
  • 24
    • 84903461645 scopus 로고    scopus 로고
    • P. Predicting functional role and occurrence of Whitebark Pine (Pinus albicaulis) at alpine treelines: Model accuracy and variable importance. Annals of the Association of American Geographers, In Press.
    • Resler, L. M., Shao, Y., Tomback, D. F., Malanson, G. P., 2014. Predicting functional role and occurrence of Whitebark Pine (Pinus albicaulis) at alpine treelines: Model accuracy and variable importance. Annals of the Association of American Geographers, In Press.
    • (2014)
    • Resler, L.M.1    Shao, Y.2    Tomback, D.F.3    Malanson, G.4
  • 25
    • 85055574142 scopus 로고    scopus 로고
    • Global food projections to 2020: emerging trends and alternative futures.
    • Rosegrant, M.W., Paisner, M.S., Meijer, S., Witcover, J., 2001. Global food projections to 2020: emerging trends and alternative futures.
    • (2001)
    • Rosegrant, M.W.1    Paisner, M.S.2    Meijer, S.3    Witcover, J.4
  • 26
    • 0026359304 scopus 로고
    • Wheat yield estimation at the farm level using TM Landsat and agrometeorological data
    • Rudorff, B.F.T., Batista, G.T., Wheat yield estimation at the farm level using TM Landsat and agrometeorological data. Int. J. Remote Sens. 12:2 (1991), 2477–2484.
    • (1991) Int. J. Remote Sens. , vol.12 , Issue.2 , pp. 2477-2484
    • Rudorff, B.F.T.1    Batista, G.T.2
  • 27
    • 84872872459 scopus 로고    scopus 로고
    • MODIS-based corn grain yield estimation model incorporating crop phenology information
    • Sakamoto, T., Gitelson, A.A., Arkebauer, T.J., MODIS-based corn grain yield estimation model incorporating crop phenology information. Remote Sens. Environ. 131 (2013), 215–231.
    • (2013) Remote Sens. Environ. , vol.131 , pp. 215-231
    • Sakamoto, T.1    Gitelson, A.A.2    Arkebauer, T.J.3
  • 28
    • 80052340128 scopus 로고    scopus 로고
    • Sub-pixel mapping of tree canopy, impervious surfaces, and cropland in the Laurentian Great Lakes Basin using MODIS time-series data
    • Shao, Y., Lunetta, R.S., Sub-pixel mapping of tree canopy, impervious surfaces, and cropland in the Laurentian Great Lakes Basin using MODIS time-series data. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 4:2 (2011), 336–347.
    • (2011) IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. , vol.4 , Issue.2 , pp. 336-347
    • Shao, Y.1    Lunetta, R.S.2
  • 29
    • 84860601047 scopus 로고    scopus 로고
    • Comparison of support vector machine neural network, and CART algorithms for the land-cover classification using limited training data points
    • Shao, Y., Lunetta, R.S., Comparison of support vector machine neural network, and CART algorithms for the land-cover classification using limited training data points. Isprs J. Photogr. Remote Sens. 70 (2012), 78–87.
    • (2012) Isprs J. Photogr. Remote Sens. , vol.70 , pp. 78-87
    • Shao, Y.1    Lunetta, R.S.2
  • 30
    • 76249131406 scopus 로고    scopus 로고
    • Mapping cropland and major crop types across the Great Lakes Basin using MODIS-NDVI data
    • Shao, Y., Lunetta, R.S., Ediriwickrema, J., Liames, J., Mapping cropland and major crop types across the Great Lakes Basin using MODIS-NDVI data. Photogr. Eng. Remote Sens. 76:1 (2010), 73–84.
    • (2010) Photogr. Eng. Remote Sens. , vol.76 , Issue.1 , pp. 73-84
    • Shao, Y.1    Lunetta, R.S.2    Ediriwickrema, J.3    Liames, J.4
  • 31
    • 84871732476 scopus 로고    scopus 로고
    • Assessing sediment yield for selected watersheds in the Laurentian great lakes basin under future agricultural scenarios
    • Shao, Y., Lunetta, R.S., Macpherson, A.J., Luo, J., Chen, G., Assessing sediment yield for selected watersheds in the Laurentian great lakes basin under future agricultural scenarios. Environ. Manage. 51:1 (2013), 59–69.
    • (2013) Environ. Manage. , vol.51 , Issue.1 , pp. 59-69
    • Shao, Y.1    Lunetta, R.S.2    Macpherson, A.J.3    Luo, J.4    Chen, G.5
  • 32
    • 0037232380 scopus 로고    scopus 로고
    • CropSyst, a cropping systems simulation model
    • Stöckle, C.O., Donatelli, M., Nelson, R., CropSyst, a cropping systems simulation model. Eur. J. Agron. 18:3 (2003), 289–307.
    • (2003) Eur. J. Agron. , vol.18 , Issue.3 , pp. 289-307
    • Stöckle, C.O.1    Donatelli, M.2    Nelson, R.3
  • 33
    • 85055502477 scopus 로고    scopus 로고
    • USDA The yield forecasting program of NASS. SMB staff 12–01 (Retrieved June 12 from).
    • USDA The yield forecasting program of NASS. SMB staff report number SMB 12–01 (Retrieved June 12, 2013, from http://www.nass.usda.gov/Publications/Methodology_and_Data_Quality/Advanced_Topics/Yield%20Forecasting%20Program%20of%20NASS.pdf).
    • (2013)
  • 34
    • 85055469681 scopus 로고    scopus 로고
    • Feed Grains: Yearbook Tables. United States Department of Agriculture, Economic Research Service, downloaded March 13.
    • USDA, Feed Grains: Yearbook Tables. United States Department of Agriculture, Economic Research Service http://www.ers.usda.gov/data-products/feed-grains-database/feed-grains-yearbook-tables.aspx#26766, downloaded March 13, 2013.
    • (2013)
    • USDA1
  • 35
    • 55349114714 scopus 로고    scopus 로고
    • Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression
    • Walton, J.T., Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression. Photogr. Eng. Remote Sens. 74:10 (2008), 1213–1222.
    • (2008) Photogr. Eng. Remote Sens. , vol.74 , Issue.10 , pp. 1213-1222
    • Walton, J.T.1
  • 36
    • 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. 112:3 (2008), 1096–1116.
    • (2008) Remote Sens. Environ. , vol.112 , Issue.3 , pp. 1096-1116
    • Wardlow, B.D.1    Egbert, S.L.2
  • 37
    • 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. 108:3 (2007), 290–310.
    • (2007) Remote Sens. Environ. , vol.108 , Issue.3 , pp. 290-310
    • Wardlow, B.D.1    Egbert, S.L.2    Kastens, J.H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.