The measurement of precipitation is essential for most environmental studies such as drought monitoring, watershed operations, water hazard management, etc. Development of satellite products has improved their applicability in environmental modelling and could proffer an alternative to gauge-based precipitation data, particularly in areas where there is no sufficient number of gauges or poor gauge distribution but they should be evaluated in different areas using ground-based data as references. In the present study, daily Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM- IMERG- Final (Version 5)) and Global Satellite Mapping of Precipitation-Moving Vector with Kalman filter (GSMaP-MVK (Version 7)) precipitation products were evaluated in comparison with gauges observations in Ardabil province, north-west of Iran, from 1 January 2016 to 21 October 2017. Several statistical indices including linear correlation coefficient, Bias (B), Multiplicative Bias (B_m), Relative Bias (B_r), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI) were used for evaluation. The results showed that the correlation between GSMaP estimates and gauge observations is higher than that of IMERG (0.42 and 0.33, respectively). On the other hand, GSMaP tends to overestimate precipitation substantially, while IMERG is involved in both under and overestimation slightly. Although these products could not show very high accuracy in precipitation estimation, the estimated precipitation values by IMERG were relatively closer to gauge records and can be used as a replacement for gauge observation in the study area where there is lack of weather stations.

The leaf area index (LAI) is an important vegetation biophysical index that provides broad information on the dynamic behavior of an ecosystem’s productivity and related climate, topography, and edaphic impacts. The spatiotemporal changes of LAI were assessed throughout Ardabil Province—a host of relevant plant communities within the critical ecoregion of a semi-arid climate. In a comparative study, novel data from Google Earth Engine (GEE) was tested against traditional ENVI measures to provide LAI estimations. Moreover, it is of important practical significance for institutional networks to quantitatively and accurately estimate LAI, at large areas in a short time, and using appropriate baseline vegetation indices. Therefore, LAI was characterized for ecoregions of Ardabil Province using remote sensing indices extracted from Landsat 8 Operational Land Imager (OLI), including the Enhanced Vegetation Index calculated in GEE (EVI_G) and ENVI5.3 software (EVI_E), as well as the Normalized Difference Vegetation Index estimated in ENVI5.3 software (NDVI_E). Moreover, a new field measurement method, i.e., the LaiPen LP 100 portable device (LP 100), was used to evaluate the accuracy of the derived indices. Accordingly, the LAI was measured in June and July 2020, in 822 ground points distributed in 16 different ecoregions-sub ecoregions having various plant functional types (PFTs) of the shrub, bush, and tree. The analyses revealed heterogeneous spatial and temporal variability in vegetation indices and LAIs within and between ecoregions. The mean (standard deviation) value of EVI_G, EVI_E, and NDVI_E at a province scale yielded 1.1 (0.41), 2.20 (0.78), and 3.00 (1.01), respectively in June, and 0.67 (0.37), 0.80 (0.63), and 1.88 (1.23), respectively, in July. The highest mean values of EVI_G-LAI, EVI_E-LAI, and NDVI_E-LAI in June are found in Meshginshahr (1.40), Meshginshahr (2.80), and Hir (4.33) ecoregions and in July are found in Andabil ecoregion respectively with values of 1.23, 1.5, and 3.64. The lowest mean values of EVI_G-LAI, EVI_E-LAI, and NDVI_E-LAI in June were observed for Kowsar (0.67), Meshginshahr (1.8), and Neur (2.70) ecoregions, and in July, the Bilesavar ecoregion, respectively, with values of 0.31, 0.31, and 0.81. High correlation and determination coefficients (r > 0.83 and R² > 0.68) between LP 100 and remote sensing derived LAI were observed in all three PFTs (except for NDVI_E-LAI in June with r = 0.56 and R² = 0.31). On average, all three examined LAI measures tended to underestimate compared to LP 100-LAI (r > 0.42). The findings of the present study could be promising for effective monitoring and proper management of vegetation and land use in the Ardabil Province and other similar areas.

The main goal of this study was to estimate the geographic distribution of Artemisia aucheri and Bromus tomentellus-Festuca ovina habitat using the maximum entropy modeling technique (MaxEnt) in the Chaharbagh rangeland of Golestan Province in Iran. Vegetation sampling was done using the random- systematic method. A total of 120 plots were placed in the study area. Soil samples were taken 0-30 cm (sampling of the soil due to the mountainous terrain and deep rooted plants, depths were determined at the 0-30 cm layer). Measured soil properties included texture, organic carbon, lime, pH, EC, and N. Topographical data (obtained from a DEM map) was elevation, slope, and aspect. To prepare the data for being enterer into MaxEnt software, first the map of soil factors was obtained through the kriging method in GIS software. Then, for analysis, the elevation, and slope, geographic directions, and soil factors maps and the presence points of plant species were entered. Using the jackknife method and response curve we found the most important environmental predictor variables. Results showed that N, sand, and clay had the greatest impacts on the distribution of A. aucheri and N, sand, silt, clay, and lime in soil had the greatest impacts on the distribution of B. tomentellus-F. ovina in the study area. Correspondence of actual map with the predictive one was assessed at a satisfactory level (Kappa coefficient = 0.05 for A. aucheri but Kappa coefficient = 0.51 for B. tomentellus-F. ovina). So MaxEnt method is the more successful in predicting B. tomentellus-F. ovina habitat than A. aucheri habitat, because the distribution of A. aucheri habitat was vast and outspread in the study area.