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Space Applications Center.
Flood, N. (2014). Continuity of reflectance data between Landsat-7 ETM+ and Landsat-8 OLI, for
both top-of-atmosphere and surface reflectance: A study in the Australian landscape. Remote
Sensing, 6, 7952–7970. https://doi.org/10.3390/rs6097952.
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country. Food and Agriculture Organization of the United Nations, FAO Aquastat Reports.
Ghannam, S., Awadallah, M., Abbott, A. L., & Wynne R. H.. (2014). Multisensor multitemporal
data fusion using the wavelet transform. In The international archives of the photogrammetry,
remote sensing and spatial information sciences, ISPRS technical commission I symposium,
17–20 November 2014, Volume XL-1, Denver, Colorado, USA.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press, ISBN:
9780262035613.
Herndl, M. (2008). Use of modeling to characterize phenology and associated traits among wheat
cultivars. Dissertation zur Erlangung des Grades eines Doktors der Agrarwissenschaften (PhD
Thesis), Universität Hohenheim.
Jin, B., Kim, G., & Cho, N. I. (2014 May 21). Wavelet-domain satellite image fusion based on a
generalized fusion equation. Journal of Applied Remote Sensing, 8(1), 080599. https://doi.org/
10.1117/1.JRS.8.080599.
Kaiming, H., Zhang, X., Ren, S., Sun, J. (2015). Deep residual learning for image recognition,
arXiv:1512.03385
Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Corrigendum to ‘Multisensor data
fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44. https://doi.org/10.
1016/j.inffus.2011.08.001.
Kulaglic, A., & Üstündağ, B. (2014). Estimation of soil moisture profile using wavelet neural
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1109/Agro-Geoinformatics.2014.6910632.
Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A. (2017). Deep learning classification of land
cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters,
pp. 1–5. https://doi.org/10.1109/LGRS.2017.2681128.
Li, T., Li, Q., Zhu, S., & Ogihara, M. (2002). A survey on wavelet applications in data mining. ACM
SIGKDD Explorations Newsletter, 4(2), 49–68. https://doi.org/10.1145/772862.772870.
Manos, B., Paparizzos, K., Matsatsinis, K., Papathanasiou, J. (2010). Decision support systems in
agriculture, food and the environment. ISI Global, ISBN-13: 978-1615208814.
Odoh, M., Chinedum, I. (2014). Estimation theory. IOSR Journal of Computer Engineering (IOSRJCE), e-ISSN: 2278-0661, p-ISSN: 2278-8727, 16(6), Ver. II (Nov – Dec. 2014), pp 30–35
Partal, T., & Cigizoglu, H. K. (2009). Prediction of daily precipitation using wavelet—neural
networks. Hydrological Sciences Journal, 54(2), 234–246. https://doi.org/10.1623/hysj.54.2.
234.
Patterson, D. W. (1998). Artificial neural networks: Theory and applications”, Prentice Hall,
ISBN:978-0-13-295353-5.
Percival, D. B., & Walden, A. T. (2000). Wavelet methods for time series analysis. Cambridge
Series in Statistical and Probabilistic Mathematics.
Rajput, R. P. (1980). Response of soybean crop to climate and soil environments, Doctoral
dissertation, Doctoral Thesis, IARI, New Delhi, India
Rao, G. S. L. H. V. P. (2003). Agricultural meteorology (pp. 95–112). Thrissur: Director of
Extension, Kerala Agricultural University.
Rouse J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in
the Great Plains with ERTS. In Third ERTS symposium, NASA SP-351 I, 309–317, USA, 1973.
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B. Üstündağ
production estimates for Patiala and Ludhiana districts based on Landsat – MSS and Agro
meteorological data (Scientific Note. IRS-UP/SAC/CPF/SN/08/87) (pp. 1–34). Ahmadabad:
Space Applications Center.
Flood, N. (2014). Continuity of reflectance data between Landsat-7 ETM+ and Landsat-8 OLI, for
both top-of-atmosphere and surface reflectance: A study in the Australian landscape. Remote
Sensing, 6, 7952–7970. https://doi.org/10.3390/rs6097952.
Frenken, K., & Gillet, V. (2012, November). Irrigation water requirement and water withdrawal by
country. Food and Agriculture Organization of the United Nations, FAO Aquastat Reports.
Ghannam, S., Awadallah, M., Abbott, A. L., & Wynne R. H.. (2014). Multisensor multitemporal
data fusion using the wavelet transform. In The international archives of the photogrammetry,
remote sensing and spatial information sciences, ISPRS technical commission I symposium,
17–20 November 2014, Volume XL-1, Denver, Colorado, USA.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press, ISBN:
9780262035613.
Herndl, M. (2008). Use of modeling to characterize phenology and associated traits among wheat
cultivars. Dissertation zur Erlangung des Grades eines Doktors der Agrarwissenschaften (PhD
Thesis), Universität Hohenheim.
Jin, B., Kim, G., & Cho, N. I. (2014 May 21). Wavelet-domain satellite image fusion based on a
generalized fusion equation. Journal of Applied Remote Sensing, 8(1), 080599. https://doi.org/
10.1117/1.JRS.8.080599.
Kaiming, H., Zhang, X., Ren, S., Sun, J. (2015). Deep residual learning for image recognition,
arXiv:1512.03385
Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Corrigendum to ‘Multisensor data
fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44. https://doi.org/10.
1016/j.inffus.2011.08.001.
Kulaglic, A., & Üstündağ, B. (2014). Estimation of soil moisture profile using wavelet neural
networks. In The third international conference on agro-geoinformatics, https://doi.org/10.
1109/Agro-Geoinformatics.2014.6910632.
Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A. (2017). Deep learning classification of land
cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters,
pp. 1–5. https://doi.org/10.1109/LGRS.2017.2681128.
Li, T., Li, Q., Zhu, S., & Ogihara, M. (2002). A survey on wavelet applications in data mining. ACM
SIGKDD Explorations Newsletter, 4(2), 49–68. https://doi.org/10.1145/772862.772870.
Manos, B., Paparizzos, K., Matsatsinis, K., Papathanasiou, J. (2010). Decision support systems in
agriculture, food and the environment. ISI Global, ISBN-13: 978-1615208814.
Odoh, M., Chinedum, I. (2014). Estimation theory. IOSR Journal of Computer Engineering (IOSRJCE), e-ISSN: 2278-0661, p-ISSN: 2278-8727, 16(6), Ver. II (Nov – Dec. 2014), pp 30–35
Partal, T., & Cigizoglu, H. K. (2009). Prediction of daily precipitation using wavelet—neural
networks. Hydrological Sciences Journal, 54(2), 234–246. https://doi.org/10.1623/hysj.54.2.
234.
Patterson, D. W. (1998). Artificial neural networks: Theory and applications”, Prentice Hall,
ISBN:978-0-13-295353-5.
Percival, D. B., & Walden, A. T. (2000). Wavelet methods for time series analysis. Cambridge
Series in Statistical and Probabilistic Mathematics.
Rajput, R. P. (1980). Response of soybean crop to climate and soil environments, Doctoral
dissertation, Doctoral Thesis, IARI, New Delhi, India
Rao, G. S. L. H. V. P. (2003). Agricultural meteorology (pp. 95–112). Thrissur: Director of
Extension, Kerala Agricultural University.
Rouse J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in
the Great Plains with ERTS. In Third ERTS symposium, NASA SP-351 I, 309–317, USA, 1973.
140
B. Üstündağ