Comprehensive evaluation of a spatio-temporal gap filling algorithm: Using remotely sensed precipitation, LST and ET data.

Temporal and spatial continuity of remote sensing data is flawed due to cloudiness, sensor malfunction or atmospheric pollution. Different methods have been presented to estimate missing values in remote sensing data. In this study, we evaluate the performance of a spatio-temporal gap filling algorithm developed by Weiss et al. (2014). This algorithm is interesting and worthy for further evaluation because it achieves high accuracy while maintaining the computational complexity considerably low. To conduct a comprehensive evaluation, we applied the algorithm to MODIS (Land Surface Temperature (LST) and evapotranspiration (ET)) and TRMM (precipitation) time series and investigate the effects of several factors including seasonality, variable type, gap size and surface characteristics through simulation scenarios. The performances were discussed using qualitative and quantitative assessments based on different simulation scenarios. A crucial finding of this study is a subtle structural deficiency of the algorithm. In particular, the algorithm outputs highly erroneous estimations when dealing with pixels with values mostly between zero and one. Such unexpected errors were observed in the seasonal assessment of land surface temperature estimations. In addition, according to the results of this study, the algorithm was sensitive to the variable type; however there was no correlation between the studied gap sizes and the error values. Among the three studied variables, LST and ET missing values were restored very accurately while estimations of precipitation missing values were more erroneous. The results also exhibit that in heterogeneous areas with complex topography, the errors of estimations were higher than homogeneous regions and areas with less complex topography. Based on the results, the algorithm should be used with caution in the discrete parameters like precipitation and area with abrupt variations. Furthermore, the design of the method may be refined for such datasets which include values with range between zero and one.

Role of bulk and Nanosized SiO2 to overcome salt stress during Fenugreek germination (Trigonella foenum- graceum L.).

The effects of bulk and Nanosized SiO2 on seed germination and seedling growth indices of fenugreek under salinity stress were studied in the College of Agriculture, Ferdowsi University of Mashhad, Iran, in 2013. The experimental treatments included 4 levels of salinity stress (0, 50, 100 and 150 mM), 2 concentrations of bulk (50 and 100 ppm), 2 concentrations of nanosized SiO2 (50 and 100 ppm), and control (without any SiO2 types). Seedling growth attributes significantly improved when bulk and nanosized SiO2 concentrations applied singly or with different levels of salt stress. However, they significantly declined with salt application. The adverse effects of salt on shoot, root and seedling lengths were alleviated by application of 50 ppm nanosized SiO2 treatment. Under salt stress condition, addition of 50 and 100 ppm nanosized SiO2 to fenugreek seeds increased shoot, root and seedling dry weights as compared to bulk SiO2 concentrations and control treatments, though 50 ppm nanosized SiO2 was more effective than 100 ppm nanosized SiO2 application. It was concluded that nanosized SiO2 improves growth attributes of fenugreek and mitigate adverse effects of salt stress.