Page 385 - Special Topic Session (STS) - Volume 4
P. 385
STS2319 Lakshman N. R. et al.
7. Gao, F., Hilker, T., Zhu, X., Anderson, M., Masek, J., Wang, P. & Yang, Y.
(2015). Fusing Landsat and MODIS Data for Vegetation Monitoring. IEEE
Geoscience and Remote Sensing Magazine, 3(3), 47–60.
8. Gao, F., Masek, J., Schwaller, M., & Hall, F. (2006). On the Blending of the
Landsat and MODIS Surface Reflectance: Predicting Daily Landsat Surface
Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8).
2207– 2218.
9. Hansen, M. C., Defries, R.S., Townshend, J. R. G., & Sohlberg, R. (2000).
Global Land Cover Classification at 1 km Spatial Resolution Using a
Classification Tree Approach. International Journal of Remote Sensing,
21(6). 1331–1364.
10. Kuenzer, C. & Knauer, K. (2013). Remote Sensing of Rice Crop Areas.
International Journal of Remote Sensing, 34(6). 2101–2139.
11. Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Little, B.B. (2015). A Scalable
Satellite-Based Crop Yield Mapper. Remote Sensing of Environment.
12. Mosleh, M.K., Hassan, Q.K., & Chowdhury, E.H. (2015). Application of
Remote Sensors in Mapping Rice Area and Forecasting Its Production: A
Review. Sensors, 15(1). 769–791.
13. Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E., &
Wulder, M.A. (2014). Good Practices for Estimating Area and Assessing
Accuracy of Land Change. Remote Sensing of Environmen,. 148, 42–57.
14. Pal, M. (2005). Random Forest Classifier for Remote Sensing
Classification. International Journal of Remote Sensing, 26(1), 217–222.
15. Sibley, A.M., Grassini, P., Thomas, N.E., Cassman, K.G., & Lobell, D.B.
(2014). Testing Remote Sensing Approaches for Assessing Yield
Variability among Maize Fields. Agronomy Journal, 106(1), 24.
16. Zhu, X., Liu, D., & Chen, J. (2012). A New Geostatistical Approach for
Filling Gaps in Landsat ETM+ SLC-off Images. Remote Sensing of
Environment, 124, 49–60.
17. Zhu, Z., Fu, Y., Woodcock, C.E., Olofsson, P., Vogelmann, J.E., Holden, C., &
Yu, Y. (2016). Including Land Cover Change in Analysis of Greenness
Trends Using All Available Landsat 5, 7, and 8 Images: A Case Study from
Guangzhou, China (2000-2014). Remote Sensing of Environment,
185,243–257.
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