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Showing 3 results for Satellite Imagery
Estimation of Reference Evapotranspiration at Regional Scale using Remote Sensing Data in Shahrekord Plain - Comparing the Results of SEBAL and METRIC Models to Lysimetric Measurements
A. Morshedi, M. Naderi, S. H. Tabatabaei, J. Mohammadi,
Volume 21, Issue 2 (8-2017)
Abstract

Conventional methods for estimating evapotranspiration are based on point measurement and suitable for local areas, therefore, cannot be generalized for larger areas or watershed basins. The remote sensing technology is capable of using satellite images and meteorological data to estimate evapotranspiration in a wider area. In this study, estimates of evapotranspiration (ET) by SEBAL and METRIC models based on Landsat 7 ETM+ sensor were compared against ET measured by lysimeter on seven satellites passing time over Shahrekord plain located in Karun basin. The results showed that the lowest indices of NRMSE, MAE and MBE (respectively, 0.317, 1.503 and -0.973 mm per day) and the maximum of d index (0.768) belonged to SEBAL. These indices were 0.420, 2.120, 2.023 and 0.646 for METRIC, respectively. The results showed that the SEBAL was more accurate than METRIC model for estimating ET under Shahrekord plain conditions. As long as the possibility of getting complete hourly meteorological data be provided, or some modifications on METRIC model were done, SEBAL show closer results to reality, and therefore is recommended.
 


Evaluation of Precipitation Data using CHIRPS and PERSIANN Models (Case Study: Bandar Abbas)
H. Siasar, A. Salari,
Volume 27, Issue 1 (5-2023)
Abstract
Access to large precipitation data with appropriate accuracy can play an effective role in irrigation planning and water resources management. Satellite images generate high, wide, cheap, and up-to-date data is a good way to estimate precipitation. In this research, the Google Earth engine system and precipitation products from satellite images of PERSIANN and CHIRPS models in daily, monthly, and annual time intervals were used to evaluate and validate the amount of precipitation in Bandar Abbas station during the statistical period of 1983-2020. The results showed that the precipitation estimation by PERSIANN and CHIRPS satellites on a monthly and annual scale is more accurate than the daily scale. The highest correlation coefficient and the least RMSE belonged to the PERSIANN algorithm on monthly and annual scales. The value of the correlation coefficient in the PERSIANN algorithm on daily, monthly, and annual scales is equal to 0.32, 0.83, and 0.94, respectively. The correlation coefficient in the CHIRPS algorithm in daily, monthly, and annual scales is equal to 0.24, 0.71, and 0.90, respectively. The coefficient of determination (R2) of PERSIANN and Chrips algorithms on a monthly scale were 0.89 and 0.70, respectively, and for an annual scale were 0.88 and 0.80, respectively. The general conclusion of this study indicated that the accuracy of the two algorithms in determining the spatial pattern of rainfall on a monthly and annual scale is appropriate, and the PERSIANN algorithm had a higher accuracy on a monthly time scale.

Assessment of Shoreline Changes in Mazandaran Province Using Remote Sensing Data
J. Karami, M. Habibi Nokhandan, M. Azadi, A. Rashidi Ebrahim Hesari,
Volume 29, Issue 3 (10-2025)
Abstract
The present study investigates shoreline changes along the southern Caspian Sea coast in Mazandaran Province over 24 years (2000-2023) using Landsat 8 and Sentinel-2 satellite imagery. The images were obtained from the USGS and Google Earth Engine platforms, and after geometric and radiometric corrections were processed using near-infrared and shortwave Infrared bands to accurately detect the boundary between land and water. Shorelines were visually extracted from the imagery and digitized for each time interval. Spatial variations in the shoreline were analyzed using the Digital Shoreline Analysis System (DSAS) within the ArcGIS environment, applying statistical methods including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR), and Linear Regression Rate (LRR). The results indicate a significant shoreline retreat in many areas of the study region, alongside a continuous decline in the Caspian Sea water level during the last decade. The integration of remote sensing analyses with atmospheric and hydrological data (temperature, precipitation, and river discharge) improved the accuracy of the results and suggests that the southern coastlines—particularly in Mazandaran—may experience more severe retreat by 2050, if current trends continue. These findings underscore the need for intelligent water resource management and the adoption of climate-adaptive policies in the region.

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