جستجوی مقالات مرتبط با کلیدواژه « satellite imagery » در نشریات گروه « آب و خاک »

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.

The presence of salts in irrigation water and high evapotranspiration rate in Sistan and Baluchestan cause the accumulation of salt in the soil and as a result increases the osmotic force in soil. One method for crop irrigation planning is the use of Idso crop water stress index (CWSI) and Moran water deficit index (WDI). In order to investigate the effect of irrigation water salinity on CWSI and WDI and also wheat irrigation planning, an experiment was performed with three different irrigation water quality in Iranshahr, through the 1398-99 crop year. The experiment was performed as a randomized complete block design with 4 replications and 3 treatments including (1) irrigation water with salinity of 0.7, (2) irrigation water with salinity of 2.5 and (3) irrigation water with salinity of 5.2 dSm-1. The results showed that irrigation water with salinity of 0.7 and 2.5 dSm-1 have no significant difference in terms of yield and water use efficiency. However, the use of irrigation water with a salinity of 5.2 dSm-1 caused a significant reduction in yield and water use efficiency at a 1% level. Moreover, the salinity of irrigation water increased the upper baseline in the Idso diagram and the upper side of the proposed trapezoid of Moran, but it had no effect on the baseline (plant transpiration limit under standard conditions). As a result, according to the definition of CWSI and WDI indices, these two indices decreased by 19% and 22%, respectively compared to the control treatment. The average optimal CWSI and WDI in the first (non-saline) treatment were 0.39 and 0.38, respectively, and they were 0.33 and 0.32 in the very saline treatment, respectively. This showed a decreasing trend of irrigation frequency with increasing salinity of irrigation water.

Soil is one of the main non-renewable natural resources that today its destruction is considered one of the most severe problems all over the world. In recent decades, rapid and unsustainable changes and land use due to the urban development activities and increasing population created a great deal of modifications in land cover and land use and has been increasing the environmental degradation including soil degradation. Therefore, reviewing these changes through satellite images, evaluating and forecasting their potentialities via modeling can help managers and planners to make more effective decisions. The aim of this study was to assess the changes in land use during the period 1992 to 2015 via using satellite images to calculate the rate of land use changes to each other and predict possible changes in land use in the years 2020, 2025, 2030 and 2035, using cellular automata - Markov model (CA-Markov) in Joupar plain, Kerman province.
In order to prepare land use plans the three periods of Landsat satellite images including Landsat 5 satellite TM (1992), Landsat 7 ETM (2000) and Landsat 8 satellite OLI (2015) were used in this study. To prepare land use maps through satellite images, initially the mentioned images were exposed to primary pre-processing such as geometric and atmospheric corrections. In addition, via providing training samples the satellite images were classified and their accuracy were evaluated using Idrisi imagery software through maximum probability algorithm. The developed land use maps of different periods were transited to CA-Markov model in order to produce transition probability matrix. Ultimately, the transition probability matrix was produced that shows the likelihood of transition of one land use to others. Then the chain analysis of cellular automata – Markov on the basis of land use plans and transition probability matrix in CA-Markov model with an emphasis on land use changes were expected in 2020, 2025, 2030 and 2035 were implemented in Idrisi software with various numbers of repetitions and steps. Based on the survey results, changes in land use and the level of current land use changes calculated, compared and evaluated and the future land use changes were predicted.
Results and The results of the detection of changes in the first period (1992-2000) revealed the highest increase in land area which was attributed to the use of pasture, grassland, irrigated agriculture and orchard and the highest decrease in land area was related to bed stream. In the second period (2000-2015) the greatest increase in land area was associated to the use of irrigated agriculture, orchards and bed stream and the greatest reduction was in pasture and grassland use. The results obtained from the prediction of future user changes of the region based on CA-Markov showed decreasing levels of land use attributed to orchard and irrigated agriculture and increasing levels of land use associated to pasture, grassland and bed stream comparing to 2015. Also the results obtained from the prediction of the findings regarding the years 2020, 2025, 2030 and 2035 revealed a reduction in land use related to bed stream, pasture and grassland due to the lack of rainfall and temperature rise and this will lead in the destruction of vegetation cover as wellas the more soil degradation. Also, due to the lack of rainfall, the recent droughts and previous studies we can conclude that the approach of CA-Markov model is more compatible with the conditions of the region.

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.