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

Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level (Sanchez et al., 2008). The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods (Alizade and Kamali, 2007). Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations (Chihda et al., 2010). Examples of such algorithms include SEBAL (Bastiaanssen et al., 1998 Bastiaanssen, 2000;), METRIC (Allen et al., 2007), SEBS (Su, 2002). Among the above mentioned algorithms, energy billing algorithms have been used (Bagheriharooni et al., 2013; Teixeira et al., 2009). Among the factors of superiority of the SEBAL algorithm, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models (Bastiaanssen et al., 2002).

Evapotranspiration is one of the key components of water balance and irrigation planning. Thus, the accurate estimation of this component and the water consumption of plants can improve the management of water use and increase the efficiency of water consumption. Due to the limitation of tools for measuring evaporation-transpiration, remote sensing methods can be used for this purpose. There are several remote sensing algorithms for actual evaporation estimation including SEBAL, SEBS, Metric, etc. In this study we used the triangle method which only was used by Salimifard et al. (2022) in Mashhad Plain. They evaluated the results for the agricultural products, i.e., wheat and maize. The aim of this study is to evaluate the triangle method for a horticultural crop, i.e., pistachio in Kerman Plain. 

The study area is Kerman Plain in which pistachio is one of the most important agricultural products. Due to water scarcity in this plain, determining the water requirement of the crops is crucial for agricultural activities. Accordingly, it is important to have an appropriate estimation of actual evapotranspiration in the plain. In this paper, the triangular algorithm was used to estimate actual evapotranspiration in the Kerman Plain in the growing seasons of 2020 (1399) and 2021 (1400). For this purpose, the Landsat 8 satellite images with less than 10% cloudiness were used. The variables such as NDVI, LST, etc., were calculated by using the JAVA programming language in the Google Earth Engine code (GEE) system environment. The required meteorological data of Kerman station were acquired from IRIMO. The triangular algorithm is based on the two-dimensional spatial plot of normalized LST and normalized NDVI, which were calculated using bands 10, 5, and 4 of the Landsat 8 in the GEE. Estimation of the wet and dry edges was conducted by MATLAB code. the actual evapotranspiration obtained using the triangular method for a pistachio orchard, which was under irrigation management, was compared to the values obtained by the FAO-56 method. The results were evaluated by correlation coefficient (r), Root Mean Square Error (RMSE), and Mean Error (ME).

The results showed that the amount of evapotranspiration for pistachio was estimated with acceptable accuracy (r= 0.73 and RMSE=1.8, nRMSE=0.4, ME=-1.6). However, the NSE less than zero (-1.3) shows that the observed (FAO-56) mean is a better predictor than the Triangle algorithm. The values obtained from the triangular algorithm were lower than the values of FAO 56, which was in line with the results of the previous studies for both Agricultural and horticultural crops. This underestimation could be due to the uncertainty of the algorithm, uncertainty in the measured data, or due to the time difference between the date of the selected images and the date of irrigation. Moreover, inappropriate quality of water and soil in Kerman Plain and the uncertainty of plant coefficients used are among the factors that can underestimate evapotranspiration values by the algorithm. 

In this study, the triangular algorithm was used to estimate actual evapotranspiration in Kerman plain using remote sensing data. Actual evapotranspiration values obtained from the triangular algorithm were lower than FAO 56 values, which might be due to the uncertainty of the algorithm, uncertainty in the measured data, uncertainty of plant coefficients, or due to the time difference between the date of the selected images and the date of irrigation. To have a better evaluation of the remote sensing algorithms, it can be suggested to develop and apply a micro lysimeter in the farms and orchards, or to use the soil water balance of the farms and orchards. These may help to choose the more appropriate algorithm for the given study area, leading to providing the more proper and applicable advices for the farmers for managing the shortage of the water resources. Furthermore, it may help to update the crop coefficients which may lead to better estimation of evapotranspiration.

Accurate estimation of reference evapotranspiration (ET0) is essential in water management in the agricultural sector, especially for arid and semi-arid climates. ET0 plays a vital role in the water and energy cycle and is an essential link between ecological and hydrological processes. Therefore, accurately estimating ET0 is a major issue for understanding the water cycle in continuous soil-plant-atmosphere systems. The traditional ET0 estimation methods are mainly based on physical principles, such as Priestley-Taylor, Hargreaves, and Samani, which have many limitations in accurate ET0 estimation in cases of minimum meteorological parameters (such as radiation solar, wind speed, and air temperature). Numerous studies have focused on ET0 estimation using terrestrial data. However, in the case of a lack of meteorological stations, the conventional methods of estimating ET0 using ground data will be inefficient, so remote sensing (RS) provides the possibility to fill such a gap, in such conditions, satellite images are the most effectivefor evaluating ET0 in large areas. Because satellite images have a suitable spatial and temporal resolution, the time series of satellite images can be used to estimate ET0. The successful estimation of ET0 from satellite images paved the way for its prediction using artificial intelligence models. The primary satellite imagery sources can be obtained from Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Land Surface Satellite (GLASS). Remote sensing data provides the possibility of recording more information through satellite images. Remote sensing methods can be used to extract vegetation information and different types of radiation, which help estimate ET0.

In the current research, two different agro-climatic locations including Ahvaz and Tabriz stations were selected. According to De Martonne classification method, Ahvaz was classified as dry climate and Tabriz as semi-arid climate. In this research, random forest (RF) and multi-layer perceptron (MLP) algorithms have been used to estimate monthly ET0 in Ahvaz and Tabriz stations. The input parameters were selected from Landsat 8 and MODIS satellite images in the time period of 2014 to 2021. The utilized parameters were the monthly average, Landsat Land Surface Temperature (LSTLand), MODIS Land Surface Temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand) and MODIS Normalized Difference Vegetation Index (NDVIMOD). To evaluate the accuracy of the input parameters and models, the estimated monthly ET0 was evaluated with the monthly ET0 of the FAO-Penman-Monteth equation.

The input parameters for implemented models were Landsat land surface temperature (LSTLand), MODIS land surface temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand), and MODIS Normalized Difference Vegetation Index (NDVIMOD). Six possible scenarios were defined to estimate monthly ET0. The first two scenarios were considered as a single parameter (scenarios 1 and 2) and other scenarios were evaluated with two input parameters. Scenarios 3 and 4 were evaluated based on the parameters of the Landsat satellite and MODIS sensor, respectively. In scenarios 5 and 6, monthly ET0 was estimated with Landsat and MODIS NDVI and Landsat and MODIS LST, respectively, to determine the effect of NDVI and LST values on ET0 estimation. According to the obtained results, for the MLP and RF models in Ahvaz station, the value of R2 ranges from 0.440 to 0.972 and 0.271 to 0.983, respectively. In Ahvaz station, the lowest and highest RMSE is 0.279 mm.month-1 (RF-5 model) and 1.396 mm.month-1 (RF-4 model), respectively. Additionally, in this station, the highest and lowest values of NS are 0.962 (RF-5 model) and 0.042 (RF-4 model), respectively. According to the obtained results, in estimating the monthly ET0, the best performance is related to MLP-6 (R2=0.972, RMSE=0.348, and NS=0.940) and RF-4 (R2=0.983, RMSE=0.279, and NS=0.962). The highest and lowest values of R2 in Tabriz station were 0.988 and 0.186, respectively. Moreover, MLP-4 and RF-5 models in this station have the lowest and highest RMSE, respectively. The results showed that in Tabriz station, the best performances were related to MLP-4 (R2=0.988, RMSE=0.299, and NS=0.935) and RF-4 (R2=0.979, RMSE=0.302, and NS=0.933). In addition, in this station, the RF-5 model has the weakest performance among all models with R2=0.186, RMSE=1.169, and NS=0.012.

The results showed that 1) the accuracy of monthly ET0 estimation in Ahvaz (arid climate) and Tabriz stations (semi-arid climate) with scenario 4 including LSTMOD and NDVIMOD was better than other investigated scenarios; 2) in estimating monthly ET0 using a single input parameter including LSTLand (scenario 1) and LSTMOD (scenario 2), in both Ahvaz and Tabriz stations, scenario 2 had better performance with both MLP and RF models; 3) estimation of monthly ET0 in Ahvaz and Tabriz stations has performed best with RF-4 and MLP-4 models, respectively, with LSTMOD and NDVIMOD input parameters (scenario 4); 4) in the comparison of scenario 5 (NDVILand, NDVIMOD) and scenario 6 (LSTLand, LSTMOD) in both RF and MLP models, scenario 6 has the best performance in estimating monthly ET0; and 5) in the comparison of monthly ET0 estimation in both arid and semi-arid climates, the best performance with a high correlation coefficient was obtained with the MLP model in semi-arid climates.

In recent years, we have witnessed the unsustainable use of water resources, which has led to short-term and long-term water crises. The diversity of water and soil resources, along with climate change, has made the scientific management of agricultural water inevitable. In a such scenario, managing scarce water resources to meet ever-increasing needs is challenging. To make the best use of water resources, it is important to know the amount of water needed for economic production.Determining the water requirement of crops, especially the evapotranspiration potential, indirect ways and in different climates for agricultural and orchard plants is one of the basic strategies of each region. Evapotranspiration (ET) is a process that includes two parts: evaporation (evaporation of water from the surface of soil and vegetation and surface water) and transpiration (evaporation of water from plant organs due to plant physiological activities). The purpose of estimating evapotranspiration is to determine the crop’s water requirement, and irrigation planning, and to evaluate the sensitivity of crops’ performance to water deficiency in different stages of plant growth. Sugar beet is one of agricultural crops that is placed in the cultivation pattern and is cultivated in a wide area of the world due to the need for sugar consumption. Determining the evapotranspiration of this crop and planning its irrigation is of particular importance. Numerous studies showed that the water requirement of sugar beet, based on the variety and climate, differs. Various techniques have been proposed to measure ETc, and each method has advantages and limitations. Some of the widely used methods are lysimetric experiments, eddy covariance, Bowen ratio, energy balance method, and soil water balance method. In recent years, new technologies are also used to estimate evapotranspiration, among them, the Surface Energy Balance Algorithms for Land (SEBAL) can be mentioned. This research aims to compare ET, water requirement, and water productivity of sugar beet in lysimetric data to SEBAL algorithm using Landsat 5 satellite images, from 1996 to 1998.

This experiment was carried out at the Chahar-Takhteh research station (Shahrekord, Iran) at latitude 50 ̊56ʹ and longitude 31̊ 11ʹ, 2066 m above sea level.In the spring of 1996, 1997, and 1998, before planting the crop, the soil inside the lysimeter was irrigated to reach the saturation level. Two days after irrigation and at field capacity, monogram seeds of sugar beet, at the rate of 120,000 crops per hectare, were cropped. The row spacing in the field around the lysimeter was similar. Irrigation was based on the discharge of about 35 to 45 % of the moisture content at field capacity. The required amount of water was calculated by the neutron probe and added to the lysimeter. simultaneously, the surrounding area was also irrigated.Remote sensing data included Landsat 5 satellite images for the years of experiment, path 164, and row 38. The temporal resolution of the satellite was 16 days. Spatial resolution for visible, near, and mid-infrared bands was 30 and 120 m for thermal infrared. The 25 cloud-free images were downloaded (6, 9, and 10 images) for research years. These images were retrieved from the website (https://earthexplorer.usgs.gov) as geometrically and radiometrically corrected and processed in ERDAS Imagine 2022 software. To estimate actual evapotranspiration, the energy balance equation is used, λET=Rn-G0-H. In this equation, Rn is the net incoming radiation flux, H is the sensible heat flux, G0 is the soil heat flux, and λET is the latent heat flux of evaporation (W/m2 ). The statistical indicators include mean absolute error (MAE) which is unsigned, mean bias error (MBE), root mean square error (RMSE), normalized root mean square error (NRMSE), and Coefficient of Determination (R2 ).

Evapotranspiration of sugar beet in lysimeter and in SEBAL on the days of satellite passage in 1996 to 1998 (25 overpasses without clouds) showed that the difference of evapotranspiration in the two methods was -1.20 % and -0.13 mm d -1 which showed high accuracy. The negative sign means that the SEBAL estimates were lower than the corresponding values in the lysimeter. The statistical indices values of RMSE, NRMSE, MAE, and MBE for 25 pairs of evapotranspiration values were 0.7031 mm d -1 , 0.1102, 0.5552, and -0.1312, respectively. The RMSE, NRMSE, MAE and MBE statistical indices for 18 pairs of monthly evapotranspiration were 54.1155 mm month-1 , 0.3225, 40.9462, and - 28.7955, respectively. The total values of evapotranspiration in lysimeter were equal to 1096.6, 1022.6, and 906.3 mm during the growth period (total mean equal to 1040.6 mm) from 1996 to 1998, respectively. The total values of evapotranspiration in the SEBAL were equal to 1004.6, 831.6, and 666.4 mm during the growth period, total mean of 834.2 mm. The mean difference was around 19.8%. The results of mean water productivity were 5.02 kg m-3 in lysimeter and 6.26 kg m-3 in SEBAL. Because of lower evapotranspiration values in SEBAL compared to the lysimeter, the water productivity values were higher.

Determining the water requirement of crops is the basis of planning for the sustainable use of water resources and irrigation of crops. The sugar beet has a great amount of evapotranspiration due to its large green cover. Accurate quantification of crop evapotranspiration (ETc) at local and regional scales can help water policy and decision-making in water resources and their management. The results indicated that the SEBAL algorithm using Landsat 5 satellite images with a coefficient of determination (R2=0.9889) in the daily time period and a coefficient of determination (R2=0.9318) in the monthly time period had a good correlation with lysimetric data. In general, results showed that SEBAL has a special capability as one of the widely used remote sensing algorithms to estimate crop evapotranspiration.

Considering the importance of water in the agricultural sector, it is necessary to know the usable or effective amount. Therefore, in this research, using remote sensing and implementing the Surface Energy Balance Algorithm (SEBAL) on 28 images from Landsat 8 for the crop years 2014 to 2022 in During the growth period of dry wheat in fields of Khomein city, the rate of evapotranspiration and effective rainfall were estimated. The accuracy of SEBAL has been evaluated with Penman-Monteith and pan evaporation methods, and then the results obtained with experimental methods of effective rainfall estimation have been compared and their relative error (RE) has been estimated. The results showed that the USDA method with a RE of 12.2% had the lowest error and the FAO with a RE of 60% had the highest error compared to the SEBAL.

Using water resources require the determination of components of the hydrological cycle. Understanding of natural systems and physical laws that manage each component of the hydrological cycle is important for the water resources management. One of the crucial components of the hydrological cycle is evapotranspiration. Most methods that have been presented in this study use point measurements to estimate evapotranspiration. Due to dynamic and changing nature of regional evapotranspiration is not generalizable to the basin, the applied methods are only suitable for the local areas. Technology of satellite remote sensing and remote sensing-based methods can be used for the temporal and spatial estimation of actual evapotranspiration in the small and large basins. Remote sensing data derived from satellite imagery calculates the amount of actual evapotranspiration using surface energy balances model. In this study, actual evapotranspiration at the Bakhtegan-Maharloo basin were estimated and evaluated using Landsat 8 satellite images sensor OLI/TIRS and SEBS energy balance models. The values of evapotranspiration derived from energy balance model and FAO-Penman-Monteith method were compared.The results showed that the evapotranspiration values obtained from the energy balance model has a root mean square error (RMSE) and mean absolute difference (MAD) equal to 0.62 and 0.49 mm/d, respectively, indicating its acceptable performance to estimate the evapotranspiration

North Karun Basin is one of the snow-covered regions of Iran and its snowfall has a great role in the water supply situation in the central and southern regions of the country. In this research, an attempt has been made to investigate the changes in the snow-covered surface in the study basin. Access to this information in high and impassable areas is possible only with the help of remote sensing techniques. The MODIS sensor has multiple spectral bands, but the ETM+ sensor has a better spatial resolution. The purpose of this study is to monitor, compare and map snow-covered areas by MODIS and ETM+ data in the northern part of the Karun Basin.

The research has been conducted in the northern part of Karun Basin, as one of the snowy areas in Iran. This area is geographically between 31° 19' 9'' to 31° 39' 17'' northern latitude and 49° 33' 56'' to 51° 54' 23'' eastern longitude. The study basin is over 14802 square kilometers, about 90% of Chaharmahal and Bakhtiari Province and 23% of Karun Basin. The height of the study area is changing between 800 m at Karun No. 4 dam to 4440 m at the Peak of Zard Kooh Mountain. The satellite data included MODIS and ETM+ sensors over a period of 15 years (2000 to 2015) used to calculate NDSI. The satellite pass-times were at Julian day number 360, 333, 365, 355, 360, 363, 331, 350, 336, 355, 342, 345, 363, 334 and 1.

The minimum and maximum snow-covered areas produced by MODIS in 2010 and 2013 were 107,295 and 1,364,118 ha, respectively, and for ETM+ sensor, were 128,758 and 1,090,580 ha, in 2010 and 2006, respectively. The snow-covered areas estimated by MODIS in 14 dates were more than the corresponding values for ETM+. The exception was 2011 when the snow-covered area in the ETM+ was greater than MODIS (3067 ha or 1.36%). Based on the results in 15 years, the MODIS sensor overestimated the snow-covered area equivalent to 26.95% compared to ETM+. In a small snow-covered area, deviation from the 1:1 line was small but, by increasing snow-covered area, deviation increased. The overall difference in snow pixels in the 15 years between the two sensors was 26.95%. As MODIS pixels are 500 by 500 m, and ETM+ pixels are 30 by 30 m, the size of the pixels in ETM+ resized to 500 m by resampling method in which to use same pixel size for another comparison. After resampling, the slope of the regression line slightly dropped. However, there are no-significant differences. Using data from MODIS and ETM+ sensors had a good performance to monitor spatio-temporal and map snow-covered surfaces in North Karun Basin.

Due to spatio-temporal frequency in obtaining MODIS imageries via internet freely in temporal resolution of eight-days and one-day, and also at very wide area coverage, short-time monitoring is possible for hydrological studies, forecasting and flood warning. On the other hand, the ability and better spatial resolution of ETM+, small snowy fields could be estimated and mapped better than MODIS. Although it may be need to mosaic several ETM+ imageries to cover wide areas. However, paying attention to geographical characteristics and time of the study area, end user decides to choose one or both sensor data.

Land Surface Temperature (LST) is an important parameter in weather and climate systems. Satellite remote sensing is a unique way to estimate this important parameter. However, satellite products have either low spatial resolution or low temporal resolution that limits their potential use in various studies. In recent years, the use of Spatio-temporal fusion techniques to produce high resolution simultaneous spatial and temporal images has been extensively investigated. In this study, a Flexible Spatio-temporal Data Fusion (FSDAF) was used to produce Landsat-like LST images with Landsat spatial resolution and MODIS temporal resolution. The quantitative and qualitative validation of the images was performed by comparing them with the Actual Landsat LST images. The results showed that the FSDAF algorithm has high accuracy in estimating daily LST data both qualitatively and quantitatively. The RMSE and MAE parameters of the images produced compared to the actual Landsat images were 1.18 to 1.71 and 0.88 to 1.29°C, respectively. The correlation coefficient above 0.87 and bias between -0.6 to 1.45°C also confirms the high accuracy of the algorithm in estimating Landsat-like land surface temperature on a daily time scale.

Improper use of land resources due to increased human food needs has led to the destruction and reduction of arable land. One way to increase production per unit area is to land suitability assessment. Land suitability assessment is the fitness of a type of land for defined use. Assessing spatial variability of land suitability class is necessary to increase production and prevent land degradation. Determining the land suitability class requires measuring soil, topography, moisture and climate properties, which are costly and time consuming. One solution to this problem is to use learning machines and auxiliary data. Learning machines are used to relate various properties with auxiliary variables to assess their spatial and temporal variability. Random forest learning machine is one of the most common and widely used learning machines. The aim of this study is to assess land suitability based on FAO land suitability framework and parametric method for three important irrigated crops of the region, including alfalfa, potato and irrigated wheat, and to predict their land suitability classes using random forest learning machine and auxiliary data.

122 soil profiles were dug, described and sampled in the Ghorveh area of Kurdistan Province (covers 6500 ha). Soil texture, acidity, organic carbon, CaCO3, gypsum, ESP, electrical conductivity and gravel were measured in all soil samples. Moreover, topography and climate data were also recorded. Environmental variables in this research were terrain attributes, land unit components map, and data of ETM+ image. To make a relationship between land suitability class and auxiliary data, random forest (RF) learning machine were applied and using cross validation method and statistic indices including overall accuracy and kappa index was validated.

The results showed that suitability class of the study area has 37, 41 and 57% N2 class, 21, 34 and 27% N1 class and 48, 19 and 16% S3 class for irrigated wheat, alfalfa and potato, respectively. The major limitations of the study area to plant the crops are included high slope, shallow soil depth, high pH and gravel.To predict land suitability class of alfalfa, potato and irrigated wheat, auxiliary variables including MRRTF index, MRVBF index, wetness index, LS factor, elevation and land unit components map were the most important. The results of this study showed that the random forest learning machine for prediction of land suitability class of irrigated wheat with 0.78, and 0.71, alfalfa with 0.75 and 0.70 and potato with 0.79 and 0.72 for overall accuracy and kappa index, respectively, had a suitable accuracy.

Topography is the most important soil forming factor and is effective in distribution of land suitability class. The study area, because of limitation of soil and topography has low to non-suitable suitability to plant these crops and it is suggested proper land improvement operations to increase production and land sustainability management. Random forest learning machine had suitable accuracy for predicting land suitability class. Therefore, it is suggested to map land suitability class learning machine techniques (such as randomized forest) and auxiliary data such as terrain attributes, land unit components map and satellite images were applied.

Runoff estimation is one of the main concerns of hydrologists and plays a key role in various engineering calculations and designs. Many factors such as climate, topography, soil properties, land cover, etc, are involved in producing surface runoff. Land use and land cover changes have a direct impact on the hydrological cycle in the ecosystem. The most common model of surface runoff estimation is the curve number model developed by the US Soil Conservation Service (SCS-CN). Accurate estimation of its important parameters increases its precision and performance. Land use is one of the most important parameters of this model.Remote sensing (RS) and geographic information system (GIS) technologies are used in order to increase its speed and accuracy of estimation. One of the problems that have occurred in the Kashaf-Rood Basin is the extensive land use changes that may cause changes in peak discharge and surface runoff volume. In this study, due to the great importance and impact of land cover change on increasing flood risk, the effects of land use change over 28 years (from 1987 to 2015) on flood hydrograph characteristics were investigated.

The Kashaf-Rood basin is a part of the Ghara-Ghum basin. The total area of the basin is 16779 square kilometers with the highest and lowest elevation of 3235 and 378 meters above sea level, respectively . The length of the Kashaf-Rood River from the highest point to the outlet of the basin is about 374 km and its average and gross river slope are 0.0028 and 0.0043 m/m, respectively. The digital elevation model was used to calculate the topographical properties, hydrological properties and geometrical corrections required on satellite images. In this research, the data of the Global Digital Elevation Model (ASTER) with a spatial accuracy of 30 m was used. Also, the soil hydrologic group map prepared in Ghara-Ghum water resources balance studies was used. Since no land use change occurs in the short term and can be detected at long intervals, a 28-year interval was chosen for satellite imagery. In general, five images of Landsat satellite are needed for full coverage of the Kashaf-Rood Basin. For the oldest data, Landsat 5 images and for the latest data, Landsat 8 images were used. ERDAS IMAGINE 2014 software was used to digitally process satellite images. The images were classified in three

The Minimum distance, Mahalanobis distance and the Maximum Likelihood. In order to select the appropriate method, after applying different classification algorithms for the image of 2015, the accuracy of their classification was evaluated and, the image of 1987 was also classified based on the selected method. By combining soil hydrological group and land use map derived from Landsat satellite imagery using ArcGIS 10.3 software, the curve number maps for 1987 and 2015 were prepared. In the present study, the US soil conservation service standard curve number method (SCS-CN) was used to calculate the amount of rainfall and losses in the HEC-HMS model. For the calibration of the HEC-HMS model, four flood events at the bridge of Khatun Kashaf-Rood hydrometric station with relatively concomitant precipitation were selected. Three flood events were used for calibration and one flood event for validation.

The images were classified into three methods: The Minimum distance, Mahalanobis distance, and the Maximum Likelihood. Comparing the results of these three methods showed that their overall accuracy in evaluating and identifying land use was 78.5, 83.7 and 87.3, respectively. Thus, the maximum likelihood algorithm was used to classify the images and the image of the year 1987 was classified with this method. Ten land use classes were identified in the study area. The results showed that during the 28 years of study, the area of rocky lands and rangelands did not change. The highest percentage of change was due to water zones, poor rangelands and residential lands, which increased by 189, 143 and 50 percent, respectively. The highest amount of increase in the area occurred in the poor rangelands, which 1514 km2, and the highest decrease occurring in moderate rangelands which is 1278 km2. By combining soil hydrological group maps and land use maps in ArcGIS software and using standard tables, the curve number maps for 1987 and 2015 were prepared. The weighted average of the curve number in the mean moisture conditions for 1987 and 2015 was 77.5 and 78.4 units, respectively. After performing the calibration and validation steps, the HEC-HMS hydrological model was used to investigate the impact of land-use change on the flood hydrograph of the Kashaf-Rood River between 1987 and 2015. According to the results, in all four events which were studied, land-use changes have increased the peak of discharge and the flood volume over the 28 years of study. On average, the peak flood discharge in 2015 was 15.2% higher than the peak flood discharge in 1987, and similarly, the flood volume increased by 13.7% during the study period.

In conclusion, it can be derived that in recent decades, land-use changes which were caused by human interference, affected the flood characteristics and increased the risk of flooding in the Kashaf-Rood river. Therefore, land use must be managed and prevented further destruction of natural resources to prevent flooding in the area.

Achieving satellite images with high simultaneously spatial-temporal resolution has been one of the serious challenges faced by researchers in the field of remote sensing and its applications. In recent years, researchers have made serious efforts to solve the problem. In this study, producing Landsat like land surface temperature images with less than 16 day temporal resolution and over different land covers, using spatio-temporal image fusion algorithm (STI-FM) and MODIS Land surface temperature images, was investigated. The STI-FM technique consist of two main steps. First establishing a linear relationship between two consecutive MODIS LST images acquired at time 1 and time 2; then utilizing the above mentioned relationship as a function of a Landsat-8 LST image acquired at time 1 in order to predict a synthetic Landsat -8 LST image at time 2. The results showed strong linear relationship between the two consecutive MODIS images at times 1 and 2 (R2 in the range 0.85-0.95). The synthetic LST images were evaluated qualitatively and quantitatively and it was found that there is a high visual and strong agreements with the actual Landsat-8 LST images over different land covers. For example R2 and RMSE values were ranged 0.74-0.94 and 1.44-2.52, respectively.

Soil quality is one of the most important soil properties which investigation of it's changes is essential to soil management and degradation. Quantifying soil quality using soil quality index to improve understanding of soil ecosystems is have been wieldy used. The soil quality index is calculated by measuring some soil characteristics which measuring these properties is expensive and time consuming. Therefore, one of the solutions is the use of digital soil mapping technique that can digitally predict soil properties using auxiliary data and data mining models. The purpose of this research is using a random forest model and auxiliary data for mapping the soil quality index.

Based on the geomorphology map, 17 soil profiles and 105 auger samples were taken from a depth of 0-20 cm in the Ghorveh area of Kurdistan Province ( covers 6500 ha) and soil texture, organic carbon, cation exchange capacity, electrical conductivity, pH, carbonate calcium equivalent, total nitrogen, available phosphorus, microbial respiration rate, sodium adsorption ratio (SAR), bulk density, and gravel percentage were measured and calculated then the soil additive weighted index was calculated. Environmental variables in this research were map geomorphology, terrain attributes and data of ETM+ image. Geomorphology map was prepared based on zinc method. Terrain attributes (including 10 parameters), soil adjust vegetative index index (SAVI), and normalized difference vegetative index (NDVI), and brightness index (BI) were computed and extracted using SAGA and Arc GIS software, respectively. To make a relationship between soil quality index and auxiliary data, random forest (RF) model were applied and using cross validation method and statistic criteria including coefficient of determination (R2), mean error (ME) and root mean square error (RMSE) was validated.

According to the communality (share of each soil indicator), bulk density, sand, cation exchange capacity and clay had the highest weight (≥ 0.1) and gravel and SAR had the lowest weight (≤0.05) among the soil quality properties. To predict soil quality index, auxiliary variables including slope, SAVI index, wetness index, MrVBF index, LS factor, elevation, NDVI index and geomorphology map were the most important. The results of this study showed that the random forest model with 0.65, 0.042 and 0.062 for determination of coefficient (R2), mean error (ME), and root mean square root (RMSE) had a fairly suitable accuracy for prediction of soil quality index. The soil quality index was ranged between 0.3-0.65 and its mean values in geomorphologic units with low gradient and low soil depth (Mo131, Mo141 and Hi231) were the lowest and in geomorphologic units with low slope and high soil depth (Pi111, Pi311, Pi322, Pi211 and Pi312) were the highest which these differences were statistically significant.

In this research, a randomized forest model was used to study the spatial variation of soil quality index in Ghorveh area of Kurdistan province. The geomorphologic conditions of the study area have affected many soil characteristics and subsequently the soil quality index in the region. The soil quality index content was the lowest in highlands of north, northwest and northeast with high slope and low soil depth. The slope was the most important auxiliary variables to predict soil quality index in the region. Based on the results of statistical indices, random forest model also had relatively accurate estimation of the soil quality index. Therefore, it is suggested to map soil properties podometric techniques (such as randomized forest) and auxiliary data such as geomorphologic map, terrain attributes, and satellite images were applied.

The aim of this study is to determine the growth pattern and to investigate the vegetation indices power for sugarcane yield modelling at field scale in Imam Khomeini Agro-industry. For this purpose, the vegetation indices extracted from Landsat7 satellite images were investigated using time series analysis. Overall, 306 Landsat7 satellite images from March 2004 to February 2017 were used. All of the images were converted to surface reflectance via FLAASH algorithm. The average values of 13 vegetation indices related to the study region extracted from satellite images and converted to seven days' time-series via interpolation. In order to eliminate the noise, all series were reconstructed using the Savitzky-Golay algorithm. Thus, 13 different time series of vegetation indices were made for 523 sugarcane fields. Then the growth pattern was drawn via averaging NDVI time series and it was divided into three growth periods. Then the accumulative values of vegetation indices related to the first and second periods of growth stage were extracted since 2004 to 2017. Therefore, 3286 samples were prepared overall, of which 2628 samples were used for modelling and 658 samples for evaluation. The samples extracted from time series were evaluated by simple linear regression model against the average observed yields. The result showed that the accumulative vegetation index of GNDVI for the first growth period with R2=0.47, RMSE=11.70 ton/ha and the accumulative vegetation index of NDI for the second growth period with R2=0.56, RMSE=10.62 ton/ha are a better indeces for sugarcane yield estimation as compared to the other vegetation indices. Also, the sum of GNDVI and NDI indeces for summation of first and second growth periods had a better result (R2=0.65, RMSE=9.47 ton/ha) than that's where one index at one period was used. Finally, the sugarcane yield of 658 samples was estimated for evaluation and the R2 and RMSE of the best model was obtained to be 0.58 and 10.99 ton/ha, respectively. The results of this study confirm the suitability of the GNDVI and NDI indeces for monitoring sugarcane growth during the first and second growth stages.

The determination of maize water requirement and crop coefficient in Babol agrigultural research station in Mazandaran province using Landsat images and SEBAL algorithm was considered in the current study. Therefore, 11 satellite images of Landsat8 and Landsat7 during maize growth period in 2014 and 2015 were prepared. For using SEBAL, each band data was calibrated according to their corresponding coefficients. Then, net radiation flux on the ground surface and soil heat flux was calculated based on input and output radiation fluxes and computation of albedo, surface emissivity, ground surface temperature, and crop indices such as NDVI, SAVI and LAI. Sensible heat flux is also calculated by computation of friction velocity, aerodynamic resistance to heat transport and consideration of cold and hot pixels and atmospheric stability conditions. Finally, maize water requirement maps during growth period were prepared which had RMSE equals to 0.99, 1.09 and 0.65 mm/day compared to Reference Book, National Water Document and FAO56. Then, by computing reference evapotranspiration, maize crop coefficient in different growth stages was determined. This coefficient was 1.24 for mid stage which had 28, 8 and 3 percent difference with Reference Book, National Water Document and FAO56.