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Methods, performance and market impacts over 1970–2005.
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methodology.
Haboudane, D. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green
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Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A Web service based application for
exploring and disseminating US conterminous geospatial cropland data products for decision
support. Computers and Electronics in Agriculture, 84, 111–123. https://doi.org/10.1016/j.
compag.2012.03.005.
Hansen, M. C., & Loveland, T. R. (2012). A review of large area monitoring of land cover change
using Landsat data. Remote Sensing of Environment, 122, 66–74. https://doi.org/10.1016/j.rse.
2011.08.024.
Hanuschak, G. A. Sr. (2013). Timely and accurate crop yield forecasting and estimation: History
and initial gap analysis. In The first Scientific Advisory Committee meeting, Global Strategy.
Food and Agriculture Organization of the United Nations, Rome, Italy.
198
E. G. Yu and Z. Yang
monitoring systems in tropical areas. Remote Sensing, 8, 219. https://doi.org/10.3390/
rs8030219.
Engmann, E. T., Schmugge, T. J., & O’Neill, P. E. (1986). Agriculture and resources inventory
surveys through aerospace remote sensing (AgRISTARS).
Fang, H., Liang, S., & Hoogenboom, G. (2011). Integration of MODIS LAI and vegetation index
products with the CSM–CERES–Maize model for corn yield estimation. International Journal
of Remote Sensing, 32, 1039–1065. https://doi.org/10.1080/01431160903505310.
Fermont, A., & Benson, T. (2011). Estimating yield of food crops grown by smallholder farmers
(pp. 1–68). Washington DC: International Food Policy Research Institute.
Fisette, T., Davidson, A., Daneshfar, B., et al. (2014). Annual space-based crop inventory for
Canada: 2009–2014. IEEE, pp. 5095–5098.
Foody, G. M., & Mathur, A. (2004). Toward intelligent training of supervised image classifications:
directing training data acquisition for SVM classification. Remote Sensing of Environment, 93,
107–117. https://doi.org/10.1016/j.rse.2004.06.017.
Fritz, S., Purgathofer, P., Kayali, F., et al. (2012). Landspotting: Social gaming to collect vast
amounts of data for satellite validation. In EGU general assembly conference abstracts. p
13173.
Gallego, F. J. (1999). Crop area estimation in the MARS project. In: Conference on ten years of the
MARS project.
Gallego, F. J. (2004). Remote sensing and land cover area estimation. International Journal of
Remote Sensing, 25, 3019–3047. https://doi.org/10.1080/01431160310001619607.
Gao, B. (1996). NDWI—A normalized difference water index for remote sensing of vegetation
liquid water from space. Remote Sensing of Environment, 58, 257–266. https://doi.org/10.1016/
S0034-4257(96)00067-3.
Gao, F., Anderson, M. C., & Xie, D. (2016). Spatial and temporal information fusion for crop
condition monitoring. IEEE, pp 3579–3582.
Gao, F., Anderson, M. C., Zhang, X., et al. (2017). Toward mapping crop progress at field scales
through fusion of Landsat and MODIS imagery. Remote Sensing of Environment, 188, 9–25.
https://doi.org/10.1016/j.rse.2016.11.004.
GIEWS F. (2013). Global information and early warning system; food price data and analysis tool.
Good, D. L., & Irwin, S. H. (2006). Understanding USDA corn and soybean production forecasts:
Methods, performance and market impacts over 1970–2005.
Good, D., & Irwin, S. (2016). Opening up the black box: More on the USDA corn yield forecasting
methodology.
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, 337–352. https://doi.org/10.1016/j.rse.2003.12.013.
Hale, R. C., Hanuschak, G., & Craig, M. E. (1999). The appropriate role of remote sensing in US
agricultural statistics. FAO Regional Project, Improvement of Agricultural Statistics in Asia and
Pacific Countries.
Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A Web service based application for
exploring and disseminating US conterminous geospatial cropland data products for decision
support. Computers and Electronics in Agriculture, 84, 111–123. https://doi.org/10.1016/j.
compag.2012.03.005.
Hansen, M. C., & Loveland, T. R. (2012). A review of large area monitoring of land cover change
using Landsat data. Remote Sensing of Environment, 122, 66–74. https://doi.org/10.1016/j.rse.
2011.08.024.
Hanuschak, G. A. Sr. (2013). Timely and accurate crop yield forecasting and estimation: History
and initial gap analysis. In The first Scientific Advisory Committee meeting, Global Strategy.
Food and Agriculture Organization of the United Nations, Rome, Italy.
198
E. G. Yu and Z. Yang