Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: The US
Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer
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562309.
Bosecker, R. R. (1988). Sampling methods in agriculture. National Agricultural Statistics Service,
US Department of Agriculture.
Bouman, B. A. M. (1995). Crop modelling and remote sensing for yield prediction. NJAS
Wageningen Journal of Life Sciences, 43, 143–161.
Butler, D. (2014). Many eyes on Earth. Nature, 505, 143–144.
Chamard, P., Courel, M. F., Ducousso, M., et al. (1991). Utilisation des bandes spectrales du vert et
du rouge pour une meilleure évaluation des formations végétales actives. Télédétection et
Cartographie, 203–209.
Chen, C., & Mcnairn, H. (2006). A neural network integrated approach for rice crop monitoring.
International Journal of Remote Sensing, 27, 1367–1393. https://doi.org/10.1080/
01431160500421507.
Chen, J., Jönsson, P., Tamura, M., et al. (2004). A simple method for reconstructing a high-quality
NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment,
91, 332–344. https://doi.org/10.1016/j.rse.2004.03.014.
Chen, J. M., Deng, F., & Chen, M. (2006). Locally adjusted cubic-spline capping for reconstructing
seasonal trajectories of a satellite-derived surface parameter. IEEE Transactions on Geoscience
and Remote Sensing, 44, 2230–2238. https://doi.org/10.1109/TGRS.2006.872089.
Chu, L., Liu, Q., Huang, C., Liu, G. (2016). Monitoring of winter wheat distribution and phenological phases based on MODIS time-series: A case study in the Yellow River Delta, China.
Clevers, J. G. P. (1997). A simplified approach for yield prediction of sugar beet based on optical
remote sensing data. Remote Sensing of Environment, 61, 221–228. https://doi.org/10.1016/
S0034-4257(97)00004-7.
Conrad, C., Fritsch, S., Zeidler, J., et al. (2010). Per-field irrigated crop classification in arid central
Asia using SPOT and ASTER data. Remote Sensing, 2, 1035–1056. https://doi.org/10.3390/
rs2041035.
Dadhwal, V. K., & Ray, S. S. (2000). Crop assessment using remote sensing-Part-II: Crop condition
and yield assessment. Indian Journal of Agricultural Economics, 55, 55.
Danaher, T., Wu, X., & Campbell, N. (2001). Bi-directional reflectance distribution function
approaches to radiometric calibration of Landsat ETM+ imagery. In Geoscience and remote
sensing symposium, 2001. IGARSS’01. IEEE 2001 International. IEEE, pp. 2654–2657.
de Villiers, M. (2017). Predicting tomato crop yield from weather data using statistical learning
techniques. Faculty of Economic and Management Sciences at Stellenbosch University Department of Statistics and Actuarial Sciences, University of Stellenbosch.
Di, L., Yu, G., Kang, L., et al. (2013). A remote-sensing-based flood crop loss assessment cyberservice system for supporting crop statistics and insurance decision making. In Proceedings of
IEEE international conference on systems, man, and cybernetics (IEEE SMC2013) special
session on environmental sensing, networking and decision making, October 13–16, 2013,
Manchester, UK. IEEE, Manchester, UK,
Di, L., Yu, E. G., Yang, Z., et al. (2015). Remote sensing based crop growth stage estimation
model. IEEE, pp. 2739–2742.
Dijk, V. A. N. (1987). Smoothing vegetation index profiles- An alternative method for reducing
radiometric disturbance in NOAA/AVHRR data. Photogrammetric Engineering and Remote
Sensing, 53, 1059–1067.
Doraiswamy, P. (2002). Application of MODIS-derived parameters for regional yield assessment.
In Proceedings of SPIE. Toulouse, France, pp. 1–8.
Doraiswamy, P. C., Moulin, S., Cook, P. W., & Stern, A. (2003). Crop yield assessment from
remote sensing. Photogrammetric Engineering and Remote Sensing, 69, 665–674.
10 Crop Pattern and Status Monitoring
197
Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer
Program. Geocarto International, 26, 341–358. https://doi.org/10.1080/10106049.2011.
562309.
Bosecker, R. R. (1988). Sampling methods in agriculture. National Agricultural Statistics Service,
US Department of Agriculture.
Bouman, B. A. M. (1995). Crop modelling and remote sensing for yield prediction. NJAS
Wageningen Journal of Life Sciences, 43, 143–161.
Butler, D. (2014). Many eyes on Earth. Nature, 505, 143–144.
Chamard, P., Courel, M. F., Ducousso, M., et al. (1991). Utilisation des bandes spectrales du vert et
du rouge pour une meilleure évaluation des formations végétales actives. Télédétection et
Cartographie, 203–209.
Chen, C., & Mcnairn, H. (2006). A neural network integrated approach for rice crop monitoring.
International Journal of Remote Sensing, 27, 1367–1393. https://doi.org/10.1080/
01431160500421507.
Chen, J., Jönsson, P., Tamura, M., et al. (2004). A simple method for reconstructing a high-quality
NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment,
91, 332–344. https://doi.org/10.1016/j.rse.2004.03.014.
Chen, J. M., Deng, F., & Chen, M. (2006). Locally adjusted cubic-spline capping for reconstructing
seasonal trajectories of a satellite-derived surface parameter. IEEE Transactions on Geoscience
and Remote Sensing, 44, 2230–2238. https://doi.org/10.1109/TGRS.2006.872089.
Chu, L., Liu, Q., Huang, C., Liu, G. (2016). Monitoring of winter wheat distribution and phenological phases based on MODIS time-series: A case study in the Yellow River Delta, China.
Clevers, J. G. P. (1997). A simplified approach for yield prediction of sugar beet based on optical
remote sensing data. Remote Sensing of Environment, 61, 221–228. https://doi.org/10.1016/
S0034-4257(97)00004-7.
Conrad, C., Fritsch, S., Zeidler, J., et al. (2010). Per-field irrigated crop classification in arid central
Asia using SPOT and ASTER data. Remote Sensing, 2, 1035–1056. https://doi.org/10.3390/
rs2041035.
Dadhwal, V. K., & Ray, S. S. (2000). Crop assessment using remote sensing-Part-II: Crop condition
and yield assessment. Indian Journal of Agricultural Economics, 55, 55.
Danaher, T., Wu, X., & Campbell, N. (2001). Bi-directional reflectance distribution function
approaches to radiometric calibration of Landsat ETM+ imagery. In Geoscience and remote
sensing symposium, 2001. IGARSS’01. IEEE 2001 International. IEEE, pp. 2654–2657.
de Villiers, M. (2017). Predicting tomato crop yield from weather data using statistical learning
techniques. Faculty of Economic and Management Sciences at Stellenbosch University Department of Statistics and Actuarial Sciences, University of Stellenbosch.
Di, L., Yu, G., Kang, L., et al. (2013). A remote-sensing-based flood crop loss assessment cyberservice system for supporting crop statistics and insurance decision making. In Proceedings of
IEEE international conference on systems, man, and cybernetics (IEEE SMC2013) special
session on environmental sensing, networking and decision making, October 13–16, 2013,
Manchester, UK. IEEE, Manchester, UK,
Di, L., Yu, E. G., Yang, Z., et al. (2015). Remote sensing based crop growth stage estimation
model. IEEE, pp. 2739–2742.
Dijk, V. A. N. (1987). Smoothing vegetation index profiles- An alternative method for reducing
radiometric disturbance in NOAA/AVHRR data. Photogrammetric Engineering and Remote
Sensing, 53, 1059–1067.
Doraiswamy, P. (2002). Application of MODIS-derived parameters for regional yield assessment.
In Proceedings of SPIE. Toulouse, France, pp. 1–8.
Doraiswamy, P. C., Moulin, S., Cook, P. W., & Stern, A. (2003). Crop yield assessment from
remote sensing. Photogrammetric Engineering and Remote Sensing, 69, 665–674.
10 Crop Pattern and Status Monitoring
197