Maryam Karimian, Omid Beyraghdar Kashkooli, Reza Modarres, Saeid Pourmanafi,
Volume 11, Issue 2 (5-2022)
Abstract
The DINEOF algorithm is a parameter free technique based on iterative EOF analysis that is used to calculate the missing data in a given satellite data set (without requiring any prior information). In this study, the DINEOF technique has been used to fill the gaps in chlorophyll-a data series in the Persian Gulf and Oman Sea. Level 3 data (4 km spatial resolution) of chlorophyll-a concentration obtained from MODIS sensor (2003- 2020) for the study area were used. In some of the images several gaps were found in different months of the year. Images with gap in the Persian Gulf and Oman Sea were reconstructed by rtsa.gapfill R-package and DINEOF algorithm in R software. The linear regression analysis was performed between the missing and reconstructed data, and also parameters such as RMSE, MSE, MAD and SNR were calculated to evaluate the validity and performance of the DINEOF algorithm. The maximum number of the gaps in data series were found in July. Hence, the images of July have been examined and reconstructed as the case study. The original maps of chlorophyll-a concentration showed that the maximum number of the gaps were in July 2009 and 2015. Evaluation of the results showed a high accuracy of DINEOF-reconstruction method (e.g. in July 2014, R2 = 0.83, RSME = 0.34, MAD = 0.14, MSE = 0.10). The results showed that the implementation of the DINEOF algorithm (in R) to reconstruct the gaps in chlorophyll-a concentration images could serve as a rapid and efficient technique.
Volume 22, Issue 5 (7-2020)
Abstract
The effects of abiotic stresses on medicinal plants metabolism are well known, but how plants respond to the interaction of these stressors is little understood. Therefore, the current experiment was aimed to investigate changes in growth and concentration of various primary and secondary metabolites of A. vera grown under water deficit and different light intensity conditions. A split-plot in time research was laid out in a randomized complete block design with four replications in a research greenhouse. The factorial combination of four irrigation regimes (irrigation after depleting 20%, 40%, 60%, and 80% of soil water content) and three light intensities (50%, 75%, and 100% of sunlight) were considered as the main factors. Sampling time was considered as sub factor. The results showed that the highest leaf, gel, and peel fresh weights were observed when the plants were subjected to low light intensity and irrigation was done after depleting 20% soil water moisture. Plants developed under full sunlight produced more pups (4.30, 3, and 3.75 per plant, 90, 180, and 270 days, respectively) and leaves (14.25, 18, and 21.25 per plant, 90, 180 and 270 days, respectively) and showed the higher fresh (165.75 g per plant) and dry root (37.60 g per plant) weight. These traits decreased with increasing water deficit severity during all the sampling times. Glucose (79.30 mg g DW− 1, 270 days), fructose (233.50 mg g DW− 1, 270 days), aloin (27.68%, 90 days), proline (2.07 mg [g FW]-1, 90 days) and phosphoenolpyruvate carboxylase (PEP-Case) (0.463 mmol NADH/g prot*min, 90 days) increased with increasing light intensity and water deficit severity during all the sampling times. Although high light intensity and water deficit led to yield and growth reduction, concentration of various primary and secondary metabolites increased. The results suggest that reduction in light intensity mitigates adverse effects of water deficit by inducing primary and secondary metabolites changes. It can be considered as an acclimation mechanism under water deficit conditions to avoid yield loss in A. vera production.
