spatial regression

Plant transpiration is a process through which part of the water in the plant is transfered out of the pores. The amount of plant transpiration data is useful in developing strategies for water sustainability. Field measurement of plant transpiration is pointwise and discontinious and has some difficulties. The purpose of this study is to provide a plant transpiration map using satellite images and spatial modeling to identify the impact of environmental variables on transpiration in western Iran. First, by using the algorithm written in Google Earth cloud system, the plant vegetation translocation map was extracted as a dependent variable, and then the layers of solar radiation, water vapor pressure, wind speed and maximum temperature, vegetation index were selected as independent variables for modeling. The results showed that the prevalence of plant transpiration in the studied area range from 0 to 2.6 mm per day. Field data collected from 16 typical farms of Agricultural Research Center of Kermanshah and Kurdistan Provinces were used for validation and by comparing the map pixels and the ground data, the root mean square error and the Nash Sutcliffe coefficient were obtained to be 0.71 and 0.63, respectively. After implementing general regression and spatial regression models based on evaluation criteria, the spatial regression showed better explanatory and estimation power than the general regression. Based on this model, the coefficients of each variable were estimated spatially, making it possible to determine the spatial variation of the relationships between the variables. Also, the results of both models showed that the vegetation indices and water vapor pressure deficiency in western of Iran have the most positive effect on vegetation transpiration. Using the results of this study, areas prone to severe plant transpiration can be identified for improving the management of irrigation systems and providing intelligent agricultural services.