The increasing constellation of satellite sensors with different spatial resolution
shortens the revisit cycles that meet the very requirements of crop monitoring in high
frequency. Further development in machine learning and time series analytics and
massive parallel computing technologies will facilitate the timely monitoring of
crops up to the field level. The application of remotely sensed data and derived
information on crop pattern and status is further expanded into procession
agriculture.
References
Abreu, D. A., McCarthy, J. S., & Colburn, L. A. (2010). Impact of the screening procedures of the
June Area Survey on the number of farms estimates.
Abreu, D. A., Lamas, A. C., Sang, H., et al. (2011). On the feasibility of using NASS’s sampling list
frame to evaluate misclassification errors of the June area survey. United States Department of
Agriculture, National Agricultural Statistics Service.
Allen, R., Hanuschak, G., & Craig, M. (2002). History of remote sensing for crop acreage in
USDA’s National Agricultural Statistics Service.
Anami, B. S., Pujari, J. D., & Yakkundimath, R. (2011). Identification and classification of normal
and affected agriculture/horticulture produce based on combined color and texture feature
extraction. International Journal of Computer Applications in Engineering Sciences, 1,
356–360.
Ashburn, P. (1979). The vegetative index number and crop identification. In Proceeding of the
LACIE symposium. pp. 843–856.
Atkinson, P. M., Jeganathan, C., Dash, J., & Atzberger, C. (2012). Inter-comparison of four models
for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote
Sensing of Environment, 123, 400–417. https://doi.org/10.1016/j.rse.2012.04.001.
Atzberger, C. (2013). Advances in remote sensing of agriculture: Context description, existing
operational monitoring systems and major information needs. Remote Sensing, 5, 949–981.
https://doi.org/10.3390/rs5020949.
Badhwar, G. D., Carnes, J. G., & Austin, W. W. (1982). Use of Landsat-derived temporal profiles
for corn-soybean feature extraction and classification. Remote Sensing of Environment, 12,
57–79.
Bannari, A., Morin, D., Bonn, F., & Huete, A. R. (1995). A review of vegetation indices. Remote
Sensing Reviews, 13, 95–120. https://doi.org/10.1080/02757259509532298.
Baruth, B., Royer, A., Klisch, A., & Genovese, G. (2008). The use of remote sensing within the
MARS crop yield monitoring system of the European Commission. The International Archives
of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 935–940.
Basso, B., Cammarano, D., & Carfagna, E. (2013). Review of crop yield forecasting methods and
early warning systems. In The first Scientific Advisory Committee meeting, Global Strategy.
Food and Agriculture Organization of the United Nations, Rome, Italy.
Beck, P. S. A., Atzberger, C., Høgda, K. A., et al. (2006). Improved monitoring of vegetation
dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of
Environment, 100, 321–334. https://doi.org/10.1016/j.rse.2005.10.021.
Becker-Reshef, I., Justice, C., Sullivan, M., et al. (2010a). Monitoring global croplands with coarse
resolution earth observations: The Global Agriculture Monitoring (GLAM) project. Remote
Sensing, 2, 1589–1609. https://doi.org/10.3390/rs2061589.
Becker-Reshef, I., Vermote, E., Lindeman, M., & Justice, C. (2010b). A generalized regressionbased model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data.
Remote Sensing of Environment, 114, 1312–1323. https://doi.org/10.1016/j.rse.2010.01.010.
196
E. G. Yu and Z. Yang
shortens the revisit cycles that meet the very requirements of crop monitoring in high
frequency. Further development in machine learning and time series analytics and
massive parallel computing technologies will facilitate the timely monitoring of
crops up to the field level. The application of remotely sensed data and derived
information on crop pattern and status is further expanded into procession
agriculture.
References
Abreu, D. A., McCarthy, J. S., & Colburn, L. A. (2010). Impact of the screening procedures of the
June Area Survey on the number of farms estimates.
Abreu, D. A., Lamas, A. C., Sang, H., et al. (2011). On the feasibility of using NASS’s sampling list
frame to evaluate misclassification errors of the June area survey. United States Department of
Agriculture, National Agricultural Statistics Service.
Allen, R., Hanuschak, G., & Craig, M. (2002). History of remote sensing for crop acreage in
USDA’s National Agricultural Statistics Service.
Anami, B. S., Pujari, J. D., & Yakkundimath, R. (2011). Identification and classification of normal
and affected agriculture/horticulture produce based on combined color and texture feature
extraction. International Journal of Computer Applications in Engineering Sciences, 1,
356–360.
Ashburn, P. (1979). The vegetative index number and crop identification. In Proceeding of the
LACIE symposium. pp. 843–856.
Atkinson, P. M., Jeganathan, C., Dash, J., & Atzberger, C. (2012). Inter-comparison of four models
for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote
Sensing of Environment, 123, 400–417. https://doi.org/10.1016/j.rse.2012.04.001.
Atzberger, C. (2013). Advances in remote sensing of agriculture: Context description, existing
operational monitoring systems and major information needs. Remote Sensing, 5, 949–981.
https://doi.org/10.3390/rs5020949.
Badhwar, G. D., Carnes, J. G., & Austin, W. W. (1982). Use of Landsat-derived temporal profiles
for corn-soybean feature extraction and classification. Remote Sensing of Environment, 12,
57–79.
Bannari, A., Morin, D., Bonn, F., & Huete, A. R. (1995). A review of vegetation indices. Remote
Sensing Reviews, 13, 95–120. https://doi.org/10.1080/02757259509532298.
Baruth, B., Royer, A., Klisch, A., & Genovese, G. (2008). The use of remote sensing within the
MARS crop yield monitoring system of the European Commission. The International Archives
of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 935–940.
Basso, B., Cammarano, D., & Carfagna, E. (2013). Review of crop yield forecasting methods and
early warning systems. In The first Scientific Advisory Committee meeting, Global Strategy.
Food and Agriculture Organization of the United Nations, Rome, Italy.
Beck, P. S. A., Atzberger, C., Høgda, K. A., et al. (2006). Improved monitoring of vegetation
dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of
Environment, 100, 321–334. https://doi.org/10.1016/j.rse.2005.10.021.
Becker-Reshef, I., Justice, C., Sullivan, M., et al. (2010a). Monitoring global croplands with coarse
resolution earth observations: The Global Agriculture Monitoring (GLAM) project. Remote
Sensing, 2, 1589–1609. https://doi.org/10.3390/rs2061589.
Becker-Reshef, I., Vermote, E., Lindeman, M., & Justice, C. (2010b). A generalized regressionbased model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data.
Remote Sensing of Environment, 114, 1312–1323. https://doi.org/10.1016/j.rse.2010.01.010.
196
E. G. Yu and Z. Yang