Song, C., Woodcock, C. E., Seto, K. C., et al. (2001). Classification and change detection using
landsat TM data: When and how to correct atmospheric effects? Remote Sensing of Environment, 75, 230–244. https://doi.org/10.1016/S0034-4257(00)00169-3.
Song, X.-P., Potapov, P. V., Krylov, A., et al. (2017). National-scale soybean mapping and area
estimation in the United States using medium resolution satellite imagery and field survey.
Remote Sensing of Environment, 190, 383–395. https://doi.org/10.1016/j.rse.2017.01.008.
Supit, I., van Diepen, C. A., de Wit, A. J. W., et al. (2012). Assessing climate change effects on
European crop yields using the crop growth monitoring system and a weather generator.
Agricultural and Forest Meteorology, 164, 96–111. https://doi.org/10.1016/j.agrformet.2012.
05.005.
Toutin, T. (2004). Review article: Geometric processing of remote sensing images: models,
algorithms and methods. International Journal of Remote Sensing, 25, 1893–1924. https://
doi.org/10.1080/0143116031000101611.
Tucker, C. J. (1980). A critical review of remote sensing and other methods for non-destructive
estimation of standing crop biomass. Grass and Forage Science, 35, 177–182. https://doi.org/
10.1111/j.1365-2494.1980.tb01509.x.
Tucker, C. J., & Sellers, P. J. (1986). Satellite remote sensing of primary production. International
Journal of Remote Sensing, 7, 1395–1416. https://doi.org/10.1080/01431168608948944.
Ul Qayyum, Z., Akhtar, A., Sarwar, S., Ramzan, M. (2013). Optimal feature extraction technique
for crop classification using aerial imagery. IEEE, pp 1–5.
Velleman, P. F. (1977). Robust nonlinear data smoothers: Definitions and recommendations. PNAS,
74, 434–436.
Velleman, P. F. (1980). Definition and comparison of Robust Nonlinear data smoothing algorithms.
Journal of the American Statistical Association, 75, 609–615. https://doi.org/10.2307/2287657.
Vermote, E. F., Tanré, D., Deuze, J. L., et al. (1997). Second simulation of the satellite signal in the
solar spectrum, 6S: An overview. IEEE Transactions on Geoscience and Remote Sensing, 35,
675–686.
Vermote, E. F., El Saleous, N. Z., & Justice, C. O. (2002). Atmospheric correction of MODIS data
in the visible to middle infrared: first results. Remote Sensing of Environment, 83, 97–111.
https://doi.org/10.1016/S0034-4257(02)00089-5.
Vicenteserrano, S., Perezcabello, F., & Lasanta, T. (2008). Assessment of radiometric correction
techniques in analyzing vegetation variability and change using time series of Landsat images.
Remote Sensing of Environment, 112, 3916–3934. https://doi.org/10.1016/j.rse.2008.06.011.
Viovy, N., Arino, O., & Belward, A. S. (1992). The Best Index Slope Extraction ( BISE): A method
for reducing noise in NDVI time-series. International Journal of Remote Sensing, 13,
1585–1590. https://doi.org/10.1080/01431169208904212.
Wall, L., Larocque, D., & Léger, P.-M. (2008). The early explanatory power of NDVI in crop yield
modelling. International Journal of Remote Sensing, 29, 2211–2225.
Wardlow, B. D., & Egbert, S. L. (2008). Large-area crop mapping using time-series MODIS 250 m
NDVI data: An assessment for the U.S. Central Great Plains. Remote Sensing of Environment,
112, 1096–1116. https://doi.org/10.1016/j.rse.2007.07.019.
Whitcraft, A. K., Vermote, E. F., Becker-Reshef, I., & Justice, C. O. (2015). Cloud cover
throughout the agricultural growing season: Impacts on passive optical earth observations.
Remote Sensing of Environment, 156, 438–447. https://doi.org/10.1016/j.rse.2014.10.009.
Wu, B., & Li, Q. (2004). China crop watch system with remote sensing. Journal of Remote Sensing,
8, 482–496.
Wu, B., & Li, Q. (2012). Crop planting and type proportion method for crop acreage estimation of
complex agricultural landscapes. International Journal of Applied Earth Observation and
Geoinformation, 16, 101–112. https://doi.org/10.1016/j.jag.2011.12.006.
Wu, B., Meng, J., Li, Q., et al. (2010). Latest development of “CropWatch”—An global crop
monitoring system with remote sensing. Advances in Earth Science. CNKI:SUN:DXJZ.0.201010-004.
202
E. G. Yu and Z. Yang
landsat TM data: When and how to correct atmospheric effects? Remote Sensing of Environment, 75, 230–244. https://doi.org/10.1016/S0034-4257(00)00169-3.
Song, X.-P., Potapov, P. V., Krylov, A., et al. (2017). National-scale soybean mapping and area
estimation in the United States using medium resolution satellite imagery and field survey.
Remote Sensing of Environment, 190, 383–395. https://doi.org/10.1016/j.rse.2017.01.008.
Supit, I., van Diepen, C. A., de Wit, A. J. W., et al. (2012). Assessing climate change effects on
European crop yields using the crop growth monitoring system and a weather generator.
Agricultural and Forest Meteorology, 164, 96–111. https://doi.org/10.1016/j.agrformet.2012.
05.005.
Toutin, T. (2004). Review article: Geometric processing of remote sensing images: models,
algorithms and methods. International Journal of Remote Sensing, 25, 1893–1924. https://
doi.org/10.1080/0143116031000101611.
Tucker, C. J. (1980). A critical review of remote sensing and other methods for non-destructive
estimation of standing crop biomass. Grass and Forage Science, 35, 177–182. https://doi.org/
10.1111/j.1365-2494.1980.tb01509.x.
Tucker, C. J., & Sellers, P. J. (1986). Satellite remote sensing of primary production. International
Journal of Remote Sensing, 7, 1395–1416. https://doi.org/10.1080/01431168608948944.
Ul Qayyum, Z., Akhtar, A., Sarwar, S., Ramzan, M. (2013). Optimal feature extraction technique
for crop classification using aerial imagery. IEEE, pp 1–5.
Velleman, P. F. (1977). Robust nonlinear data smoothers: Definitions and recommendations. PNAS,
74, 434–436.
Velleman, P. F. (1980). Definition and comparison of Robust Nonlinear data smoothing algorithms.
Journal of the American Statistical Association, 75, 609–615. https://doi.org/10.2307/2287657.
Vermote, E. F., Tanré, D., Deuze, J. L., et al. (1997). Second simulation of the satellite signal in the
solar spectrum, 6S: An overview. IEEE Transactions on Geoscience and Remote Sensing, 35,
675–686.
Vermote, E. F., El Saleous, N. Z., & Justice, C. O. (2002). Atmospheric correction of MODIS data
in the visible to middle infrared: first results. Remote Sensing of Environment, 83, 97–111.
https://doi.org/10.1016/S0034-4257(02)00089-5.
Vicenteserrano, S., Perezcabello, F., & Lasanta, T. (2008). Assessment of radiometric correction
techniques in analyzing vegetation variability and change using time series of Landsat images.
Remote Sensing of Environment, 112, 3916–3934. https://doi.org/10.1016/j.rse.2008.06.011.
Viovy, N., Arino, O., & Belward, A. S. (1992). The Best Index Slope Extraction ( BISE): A method
for reducing noise in NDVI time-series. International Journal of Remote Sensing, 13,
1585–1590. https://doi.org/10.1080/01431169208904212.
Wall, L., Larocque, D., & Léger, P.-M. (2008). The early explanatory power of NDVI in crop yield
modelling. International Journal of Remote Sensing, 29, 2211–2225.
Wardlow, B. D., & Egbert, S. L. (2008). Large-area crop mapping using time-series MODIS 250 m
NDVI data: An assessment for the U.S. Central Great Plains. Remote Sensing of Environment,
112, 1096–1116. https://doi.org/10.1016/j.rse.2007.07.019.
Whitcraft, A. K., Vermote, E. F., Becker-Reshef, I., & Justice, C. O. (2015). Cloud cover
throughout the agricultural growing season: Impacts on passive optical earth observations.
Remote Sensing of Environment, 156, 438–447. https://doi.org/10.1016/j.rse.2014.10.009.
Wu, B., & Li, Q. (2004). China crop watch system with remote sensing. Journal of Remote Sensing,
8, 482–496.
Wu, B., & Li, Q. (2012). Crop planting and type proportion method for crop acreage estimation of
complex agricultural landscapes. International Journal of Applied Earth Observation and
Geoinformation, 16, 101–112. https://doi.org/10.1016/j.jag.2011.12.006.
Wu, B., Meng, J., Li, Q., et al. (2010). Latest development of “CropWatch”—An global crop
monitoring system with remote sensing. Advances in Earth Science. CNKI:SUN:DXJZ.0.201010-004.
202
E. G. Yu and Z. Yang