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August 12–16, 2013, Fairfax, VA USA. IEEE, Fairfax, VA, USA,
Shrestha, R., Di, L., Yu, E. G., et al. (2016). Regression based corn yield assessment using MODIS
based daily NDVI in Iowa state. IEEE, pp. 1–5.
Shrestha, R., Di, L., Yu, E. G., et al. (2017). Regression model to estimate flood impact on corn
yield using MODIS NDVI and USDA cropland data layer. Journal of Integrative Agriculture,
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10 Crop Pattern and Status Monitoring
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Accessed 7 Nov 2013.
Ok, A. O., Akar, O., & Gungor, O. (2012). Evaluation of random forest method for agricultural crop
classification. European Journal of Remote Sensing, 45, 421–432.
Parihar, J. S., Oza, M. P. (2006). FASAL: An integrated approach for crop assessment and
production forecasting. In Proceedings of the Asia-Pacific remote sensing symposium. International Society for Optics and Photonics, pp 641101–641113.
Pearson, R. L., & Miller, L. D. (1972). Remote mapping of standing crop biomass for estimation of
the productivity of the shortgrass prairie. In Remote Sensing of Environment, VIII. p 1355.
Peddle, D. R., & Ferguson, D. T. (2002). Optimisation of multisource data analysis: An example
using evidential reasoning for GIS data classification. Computers & Geosciences, 28, 45–52.
https://doi.org/10.1016/S0098-3004(01)00012-7.
Peña-Barragán, J. M., Ngugi, M. K., Plant, R. E., & Six, J. (2011). Object-based crop identification
using multiple vegetation indices, textural features and crop phenology. Remote Sensing of
Environment, 115, 1301–1316. 16/j.rse.2011.01.009.
Pradhan, S. (2001). Crop area estimation using GIS, remote sensing and area frame sampling.
International Journal of Applied Earth Observation and Geoinformation, 3, 86–92.
Pupin Mello, M., Rudorff, B. F. T., Adami, M., et al. (2010). A simplified Bayesian network to map
soybean plantations. IEEE, pp. 351–354.
Purdy, L. (2016). Farming from space. Engineering & Technology, 11, 40–44.
Rembold, F., Atzberger, C., Savin, I., & Rojas, O. (2013). Using low resolution satellite imagery for
yield prediction and yield anomaly detection. Remote Sensing, 5, 1704–1733.
Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites
using Fourier analysis of time series. International Journal of Remote Sensing, 21, 1911–1917.
https://doi.org/10.1080/014311600209814.
Rouse, J. W. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of
natural vegetation.
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in
the great plains with ERTS. In NASA. Goddard Space Flight Center 3d ERTS-1 Symp.
pp. 309–317.
Roy, D. P., Wulder, M. A., Loveland, T. R., et al. (2014). Landsat-8: Science and product vision for
terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.
org/10.1016/j.rse.2014.02.001.
Roy, D. P., Zhang, H. K., Ju, J., et al. (2016). A general method to normalize Landsat reflectance
data to nadir BRDF adjusted reflectance. Remote Sensing of Environment, 176, 255–271. https://
doi.org/10.1016/j.rse.2016.01.023.
Ruban, T., Bhargava, R., & Sitzmann, V. Planet labels-how do we use our planet?
Schaaf, C. B., Gao, F., Strahler, A. H., et al. (2002). First operational BRDF, albedo nadir
reflectance products from MODIS. Remote sensing of Environment, 83, 135–148.
Shelestov, A., Lavreniuk, M., & Kussul, N., et al. (2017). Exploring Google Earth engine platform
for big data processing: Classification of multi-temporal satellite imagery for crop mapping.
Frontiers in Earth Science. https://doi.org/10.3389/feart.2017.00017
Shrestha, R., Di, L., Yu, G., et al. (2013). Detection of flood and its impact on crops using NDVI –
Corn case. In Proceedings of the second international conference on agro-geoinformatics,
August 12–16, 2013, Fairfax, VA USA. IEEE, Fairfax, VA, USA,
Shrestha, R., Di, L., Yu, E. G., et al. (2016). Regression based corn yield assessment using MODIS
based daily NDVI in Iowa state. IEEE, pp. 1–5.
Shrestha, R., Di, L., Yu, E. G., et al. (2017). Regression model to estimate flood impact on corn
yield using MODIS NDVI and USDA cropland data layer. Journal of Integrative Agriculture,
16, 398–407. https://doi.org/10.1016/S2095-3119(16)61502-2.
Silleos, N. G., Alexandridis, T. K., Gitas, I. Z., & Perakis, K. (2006). Vegetation indices: Advances
made in biomass estimation and vegetation monitoring in the last 30 years. Geocarto International, 21, 21–28. https://doi.org/10.1080/10106040608542399.
10 Crop Pattern and Status Monitoring
201