Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). Crop evapotranspiration. Guidelines for computing crop water requirements. Irrigation and Drainage Paper No. 56. FAO, Rome.
Aher, P. D., Adinarayana, J., and Gorantiwar, S. D. (2011). Remote Sensing and Artificial Neural Network in Spatial Assessment of Air Temperature in a Semi-arid Watershed. International Journal of Earth Sciences and Engineering, 4(6): 351-354.
Atkinson, P. M. and Tatnall, A. R. L. (1997). Introduction neural networks in remote sensing. International Journal of Remote Sensing, 18:699–709.
Bhattacharya, B. and Solomatine, D. P. (2005). Neural networks and M5 model trees in modeling water level–discharge relationship. Neurocomputing, 63: 381-396.
Cresswell, M. P., Morse, A. P., Thomson, M. C., Connor, S. J. (1999). Estimating surface air temperatures, from Meteosat land surface temperatures, using an empirical solar zenith angle model. International Journal of Remote Sensing, 20(6): 1125-1132.
Emamifar, S., Rahimikhoob, A., and Noroozi, A. A. (2013). Daily mean air temperature estimation from MODIS land surface temperature products based on M5 model tree. International Journal of Climatology. 33(15): 3174–3181.
Hagan, M. T. and Menhaj, M. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5: 989–993.
Jang, J. D., Viau, A. A., and Anctil, F. (2004). Neural Network Estimation of Air Temperatures from AVHRR data. International Journal of Remote Sensing, 25(21): 4541-4554.
Mitchell, T. M. (1997). Machine learning. The McGraw-Hill Comp. Press.
Pal, M. and Deswal, S. (2009). M5 model tree based modelling of reference evapotranspiration. Hydrological Processes, 23: 1437–1443.
Parviz, L., Kholghi, M., and Valizadeh, K. (2011). Estimation of Air Temperature Using Temperature-Vegetation Index (TVX) Method. Journal of Science and Technology of Agriculture and Natural Resources,Water and Soil Science, 15 (56) :21-34. (In Farsi)
Prechelt, L. (1998). Automatic early stopping using cross validation: quantifying the criteria. Neural Networks, 11: 761–767.
Prihodko, L. and Goward, S. N. (1997). Estimation of air temperature from remotely sensed surface observations. Remote Sensing of Environment, 60(3): 335–346.
Quinlan, J. R. (1992). Learning with continuous classes. In Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence, Hobart, Australia, 16-18 November, World Scientific, Singapore: 343–348.
Quinlan, J. R. (1986). Introduction of decision trees. Machine learning, 1: 81-106.
Rahimikhoob, A., Behbahani, M. R., and Nazarifar, M. H. (2008). Estimating Maximum Air Temperature in Khoozestan Province Using NOAA Satellite Images Data and Artificial Neural Network. Journal of Science and Technology of Agriculture and Natural Resources,Water and Soil Science, 11(42): 357-364. (In Farsi)
Solomatine, D. P. and Xue, Y. (2004). M5 model trees compared to neural networks: application to flood forecasting in the upper reach of the Huai River in China. Journal of Hydrologic Engineering, 9(6): 491–501.
Solomatine, D. P. and Dulal, K. N. (2003). Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrological Sciences Journal, 48(3): 399–411.
Vancutsem, C., Ceccato, P., Dinku, T., and Connor, S. J. (2010). Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114: 449–465.
Witten, I. H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Technique. Morgan Kaufmann Publishers, San Francisco.
Yao, Y. and Zhang, B. (2012). MODIS-based air temperature estimation in the southeastern Tibetan Plateau and neighboring areas. Journal of Geographical Sciences
, 22(1): 152-166.
Yan, H., Zhang, J., Hou, Y., and He, Y. (2012). Estimation of air temperature from MODIS data in east China. International Journal of Remote Sensing, 30(23): 6261-6275.