جستجوی مقالات مرتبط با کلیدواژه « landsat » در نشریات گروه « کشاورزی »

Wetlands play an important ecological role in the balance of ecosystems. The excessive increase of aquatic plants causes important changes such as eutrophication, increase of water turbidity, swamping and finally drying of the wetland. Using the time series of satellite data in the Google Earth Engine is an efficient and useful way to investigate the ecological conditions of wetlands. Due to the importance of Anzali Wetland and its complex and strategic conditions, temporal and spatial changes of the vegetation and water body of the wetland were studied using Landsat satellite (TM), (ETM+) and (OLI) sensor data from 1990 to 2020 in Google Earth Engine. Vegetation changes were evaluated using the (NDVI) index and (mNDWI) index was used to study water body changes. The results show the surface of the wetland water body has decreased by 5520 hectares (20%) during the three decades of study, dense vegetation covers have decreased from 7756 to 5350 hectares and semi-dense vegetation has increased from 6967 to 14100 hectares, according to the sampling, these changes were confirmed. By examining the annual precipitation in Guilan province, based on TRMM satellite rain data, it was found that the precipitation factor has a nonsignificant role in reducing the water body of the wetland. And due to human interventions through the introduction of river sediments and the pollution load resulting from the entry of agricultural, urban and industrial effluents into Anzali Wetland, as well as the reduction of the amount of water in the rivers leading to the Wetland due to their excessive use in the upstream of the basin, the water body of the Wetland has decreased and semi-dense vegetation has increased.

The level of air pollution presents a twofold threat to human health and plant life, thereby resulting in economic losses for countries on an annual basis. This investigation specifically focuses on the province of Khuzestan, which has experienced considerable pollution from fine dust in recent years and is noteworthy for its diverse vegetation. By analyzing air quality data acquired from the Environment Agency regarding PM10 and utilizing NDVI (Normalized Difference Vegetation Index) images captured by the MODIS satellite, this study examines the impact of fine dust on vegetation. Differential calculations were performed on the PM10 data and NDVI values across different seasons in order to achieve this objective. Updated maps of Khuzestan were constructed by merging Landsat imagery with the province's land use map, employing supervised classification model to monitor vegetation production and utilization across diverse categories such as agriculture, reeds, pasture, and forest. Subsequently, the datasets were segregated based on vegetation type, and Pearson's correlation coefficient was computed to evaluate the relationship between PM10 concentration and changes in vegetation (dNDVI) within each category. The results emphasize a spatial correlation between PM10 concentration and changes in vegetation index for reed and agriculture land uses during a single season, with the highest correlation observed during the summer season throughout the year.

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.

Given the significance of the Hyrcanian Forests, inscribed by the UNESCO as a world heritage site, it is essential to monitor the changes in and the devastation of the forest cover in this ecoregion for the planning and management of national lands. It is the objective of the present study to monitor changes in both land use and forest cover in Astara region using Landsat TM, OLI 1 & 2 sensors for the years 1995 and 2022. For this purpose, images captured on days with cloud covers of less than 10% were selected over three time intervals and the relevant enhanced Vegetation Index (EVI) values were determined based on Landsat inter-band relations. In the next stage, the index values were combined to derive the land use map using the support vector machine (SVM) algorithm. The results of accuracy evaluation showed that the overall accuracy and Kappa coefficients of the land use map for the year 1995 were equal to 89 and 92% and those for 2022 were 0.86 and 0.75%, respectively, indicating acceptable results. The results of land use changes in Astara city during the period from 1995 to 2022 showed that residential land use had increased by 7% equal to 2954 ha while rangeland and agricultural uses had decreased by 1 to 2% equal to 258 and 997 ha, respectively. However, an important land use along the Caspian coast line – that is, forest cover - stretched over an area of 34283 ha equal to 80% of the study area, which declined in 2022 to 32522 ha equal to 76%, showing a decrease of 4% equal to 1761 ha. It is clear that, owing to its rich archive of satellite images, the GEE system can be used as a strong and useful tool for monitoring and managing forest lands.

Riparian forest ecosystems play a crucial role in maintaining ecological resilience and biodiversity in hot and arid regions. Additionally, these forests serve as vital safeguards against agricultural land erosion and alterations in riverbeds. Long-term monitoring is imperative to preserve the ecological capacity and biodiversity of riparian forests, which are continually impacted by climate change and land use/cover changes (LUCC). These changes have far-reaching implications for the environment, biodiversity, food security, and human health.

This study utilized the Landsat satellite series archive for Gotvand County in Iran. The analysis commenced with an image from the MSS sensor (Landsat 1) in 1972, followed by images from the TM, ETM+, and OLI sensors placed in orbit on 7/16/1982, 4/15/1999, and 11/2/2013, respectively. Raw calibrated pixel values underwent conversion to surface reflectance through atmospheric correction. Classification input layers included spectral bands and two indices (NDVI, SAVI for vegetation cover, and MNWDI, NDWI2 for water class), derived from available spectral bands for each year. Object-based classification, employing the SVM algorithm, was implemented to extract forest areas, water bodies, agricultural lands, and other phenomena. Various values for scale, shape, and compression were applied in the object-based classification method to enhance separation. Evaluation metrics such as Overall Accuracy, Producer’s Accuracy, User’s Accuracy, and Kappa Coefficient were employed to assess classification results.

The study observed the lowest difference between Red and NIR bands for the MSS sensor and the highest difference for the ETM+ sensor. Classification accuracy was lower for years when ground sample conditions were validated through satellite images compared to other years. In 2022, with improved spatial and spectral accuracy, the overall accuracy reached 98.9%, the Kappa coefficient was 0.89, and user and producer accuracies for the forest class were 97% and 99%, respectively. Agricultural land changes witnessed a staggering growth of over 4393% from 1972 to 2022. Riparian forest ecosystems, dominant in the area between 1972 and 2000 (ranging from 3670.6 to 2379.2 hectares), experienced a 35% loss. From 2000 to 2022 (covering 1569.6 hectares), an additional 34% of this plant ecosystem's area was lost.

The research findings highlight a 57.23% decrease in riparian forests over the past fifty years, reaching its lowest point of 1279.2 hectares in 2010. Concurrently, agricultural land area expanded by 45 times from 1972 to 2022, indicating a significant shift in land cover from forests to agriculture. The observed changes align with shifting precipitation (-1.5 mm/yr) and temperature (0.04 °C/yr) trends, impacting the studied ecosystems. This study serves as a crucial benchmark for the sustainable management of riparian forests along the Karun River in Gotvand County.

The distribution of organisms and ecosystems is affected by climate change, in fact, climate change has affected the distribution patterns of a large number of species and even led to displacement, extinction of certain species, destruction and loss of habitats. In this regard, remote sensing technology, due to having a spatial and integrated view of the region, as well as geographic information systems (GIS) due to the ability to analyze and analyze, prepare maps and model, can be efficient in the field of monitoring, planning and dynamic and sustainable management of wildlife habitats. The purpose of this research is to investigate the weather-climate changes in Khabar National Park located in the south of Kerman province using remote sensing technology and Landsat satellite thermal bands and geographic information systems (GIS). Therefore, first, Landsat satellite images and sensors (TM-ETM-TIRS) were prepared in the years 1972 to 2022, then pre-processed, processed images and maps of surface temperature changes (LST) in four seasons and for the years 1988-2000-2010-2014- 2020-2022 has been prepared. Landsat satellite and NDVI index have also been used to monitor vegetation changes. The results of the research showed an increase in the temperature of the earth's surface in the last 40 years and in the 4 investigated seasons. Also, the results of analysis and monitoring of vegetation cover (NDVI) showed a decrease in the average vegetation cover of the region in the studied years. Also, in order to investigate the relationship between vegetation percentage and LST changes, linear regression and SPSS software were used, and R=99% and R2=98% and R2=97% were calculated, which is acceptable and shows that with the reduction of vegetation area, the surface temperature has increased. According to the findings, what is evident is that the trend of climate change and the relative increase in the temperature of the earth's surface and the decrease in vegetation in the study area continues and requires strategic planning and systematic management of the Khabar wildlife habitat in order to maintain the indicators of habitat desirability and the balance and parallelism of ecological relations. And protecting the rich fauna and flora diversity of the region.

Monitoring wetlands and especially their coastlines due to recording their periodic changes gives us a better understanding of the management of these areas. Remote sensing data and satellite images are one of the reliable sources for studying and interpreting changes in wetlands. In this study, Landsat images (TM-OLI) 2007 and 2018 were examined in order to examine the changes. After performing the required processing and corrections, the NDWI index was used to separate the water and land border. Accordingly, the maximum coastline changes were in the east, west and center of the wetland with distances of 10885.82, 5781.22 and 3370.830 meters, respectively, compared to 2007. The results of the study of the wetland water area showed that the area of the wetland had a decreasing trend 2007 to 2018 (100.06 and 29.5 km2, respectively) and the most changes were related to the eastern and central parts. Overall the results showed fluctuations of coastlines and water area in the studied years have been affected by changes in the water level of the Caspian Sea. Also, this study showed the efficiency remote sensing performance and NDWI index in identifying critical area in Anzali Wetland and better knowledge of the dynamic conditions of the wetland.

Vegetation as a natural component plays a significant role in increasing permeability, improving soil, reducing evaporation, and reducing the runoff and thus reducing the possibility of flooding. The use of new technologies such as remote sensing and geographic information system to study plant ecosystems and prepare land cover maps is necessary to know the effectiveness of these tools and to identify the best methods of their use. The purpose of this research is to investigate the vegetation cover using the NDVI and compare the performance of three supervised classification methods, the maximum likelihood method, the minimum distance from the mean, and the parallelepiped method in a part of the Great Karun watershed. To this end, TM and ETM images of Landsat satellite were used in one interval and NDVI in a 10-year interval (May 2008 to May 2018) with the help of supervised classification and maximum likelihood algorithm. The above data were prepared and analyzed using ENVI4.2 software, and the effectiveness of each method was evaluated with the overall accuracy index and Kappa coefficient. Based on the results in the maximum likelihood method, the overall accuracy rate is 90.35% and the Kappa coefficient is 0.878, in the minimum distance method, the distance from the mean is 74.32% and its Kappa coefficient is 0.675, and in the parallelepiped method, the overall accuracy is 67.09% and the Kappa coefficient was calculated as 0.593. Based on the results, the maximum likelihood method has the highest level of accuracy in satellite data group classification. Moreover, the results showed that in the 10-year period in Dez, Karun, and Karkheh watersheds, the spectral reflectance related to vegetation has decreased by 7.4%, 10.64%, and 13.83%, respectively. The results of this research can be effective for the practical use of the analysis that was done in relation to the studies of runoff and flood. According to the process of vegetation changes due to natural or human factors, the need for proper management in this area seems necessary.

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.

Salehiyeh Wetland, located in Alborz and Qazvin provinces, is one of the wetlands that has dried up in recent years and has become a center for dust production. The aim of this study is to evaluate the trend of changes in moisture in Salehiyeh Wetland and investigate the role of drainage channels in the drying of the wetland bed. To achieve this goal, the MNDWI index was calculated using Landsat 5, 7, 8, and 9 satellite images from 1990 to 2023. The construction of drainage channels has caused some changes in the wetland catchment area. Therefore, the non-parametric Mann-Kendall test was performed before and after the construction of drainage channels in two previous and current catchment areas. Also, the wind data from synoptic stations around the wetland were analyzed by drawing the storm rose diagram. The results of the study show that the overall trend of changes in moisture in Salehiyeh Wetland during the study period has been decreasing, and the MNDWI index has been negative in most years due to the drought prevailing in the region. Due to this, the phenomenon of dust and the problems resulting from it have spread to the surrounding cities. Therefore, measures need to be taken to preserve and manage the water resources of Salehiyeh Wetland and prevent its drying, such as optimizing water consumption, preventing the construction of inappropriate drainage channels, and using various solutions such as implementation of mechanical windbreak to reduce the sand and dust storm.

Land use changes are one of the important factors in changing the hydrological status of the watershed and soil erosion, so the type of land use is one of the important and determining factors in the production of surface runoff. By knowing the process of land use changes, it is possible to manage the effects of land use changes in the hydrology of the affected areas and led the natural ecosystems towards balance. The following research was carried out with the aim of revealing land use changes using satellite data and analyzing its role in changing the hydrological situation.

The study area is the upstream part of the Siahroud river basin located in the south of Qaemshahr city. In this research, to extract land use maps of different time periods, integrated pixel-based and object-oriented methods, unsupervised and supervised classification of TM sensor images of Landsat 5 satellite related to 1998 and 2009 and OLI sensor images of Landsat 8 satellite related to 2015 and 2019 were used. After preparing the land use maps for each time period, the CN values for each land use were determined based on the standard instructions of the SCS method and the weighted average CN was estimated for the entire study area. At the end, the amounts of runoff production and depth of runoff resulting from rainfall are calculated.

The land use changes of the studied area in the period of 22 years showed that 281%, equivalent to 657 hectares, was added to the area of citrus orchards. About 5% of the area of deciduous forest has decreased and the area of residential areas has increased by 57%. Due to the changes in land use during this 22-year period, the weighted average value of CN has decreased from 63.01 to 62.73. Also, the estimation of the amount of runoff for rainfall equal to 59.8 mm (average maximum rainfall of 24 hours during the period of 22 years) showed that the depth of runoff has decreased from 4.83 mm in the 1988 situation to 4.73 mm in the condition of 2019.

Considering the changes in land use and its effect on watershed runoff production, it seems that these effects have been moderated and land use changes have played a role in reducing runoff production over a period of 22 years. Therefore, it is expected that with the continuation of this process of land use changes, the production of runoff in the upstream of the Siahroud River watershed will decrease.

The limitations of satellite sensors make it impossible to access thermal bands with high spatial and temporal resolution simultaneously. Therefore, downscaling methods are essential because they provide simultaneous access to thermal data with high spatio-temporal resolution. LST parameter is critical in agriculture, it is one of the most important in estimating the amount of evapotranspiration and significantly impacts crop growth. LST images of the MODIS with spatial resolution of 1000 meters are available daily. Still, the low spatial resolution in these images is a limitation that makes it impossible to use for agricultural management. On the other hand, high humidity changes in irrigated regions cause errors in LST downscaling process. this research was carried out with the aim of downscaling the LST of the MODIS sensor from 1000 meters to OLI resolution from Landsat 8 satellite (30 meters) in irrigated regions. In the first stage, the DisTRAD model was implemented for the downscaling of the LST of MODIS in Amirkabir and Mirzakoochak Khan Farms. The results show the poor performance of the DisTRAD model in the downscaling of LST from 1000 meters to 30 meters. Next, to check the results of LST downscaling of the MODIS by TOTRAM and OPTRAM soil moisture estimation models, the root mean square error (RMSE) statistic was used. The results indicate that the average value of RMSE in the downscaled images by the OPTRAM-TOTRAM model shows a decrease of about 2.53°C compared to the DisTRAD model. Also, the average value of RMSE in the first six months of the year when irrigation has been done, compared to the DisTRAD model, shows a decrease of about 4.11 °C. As a result, the use of the OPTRAM-TOTRAM model offers much better performance than the DisTRAD model in downscaling the LST of the MODIS in irrigated regions.

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.

Soil is one of the most important natural resources of any country, which plays a key role in food security, self-sufficiency in food production, national economy, and sustainable agriculture. Soil erosion is one of the most obvious factors of soil loss, and rain erosion is one of the most important forms of erosion. Therefore, the knowledge of the processes governing soil erosion and sediment transport is very important in water and soil resources management, as well as, the development of soil erosion models to achieve sustainable development is of great importance. Previous research has shown that rainfall patterns are one of the factors influencing rain erosion. Vegetation also reduces soil erosion by protecting the soil against the effects of raindrops and runoff. Rain erosion is especially important in arid and semi-arid areas due to the lack of vegetation and low initial soil moisture. This research was conducted, regarding the effect of rainfall patterns on rain erosion, by investigating the rainfall pattern and vegetation changes over 25 years in Ebrahim Abad and Royan watersheds situated in Semnan City. 

First, the physical characteristics of the watersheds were obtained; using ArcGIS software, and the precipitation information was extracted from the rain gauge sheets with an accuracy of 10 minutes. To compare the rainfall for different amounts of precipitation, the dimensionless cumulative rainfall curve of each event was obtained. The time of each rainfall was divided into 10 parts and the percentage of rainfall was determined for each part. The rainfall curve was divided into 4 quartiles (1st, 2nd, 3rd, and 4th quartiles) depending on the occurrence of the maximum precipitation. According to the information on the sediment layers in check dams located at the outlet of each watershed and the precipitation data, the storm-related to each sediment layer was determined and the effect of the storm pattern on the sediment pattern was investigated. To check the similarity of precipitation and sedimentation patterns in check dams, the average difference in precipitation and sedimentation in each time step and standard deviation changes were used. Considering the dynamic changes of vegetation compared to other characteristics of the watershed, remote sensing data were used to investigate the changes in vegetation and its area. Due to the effective performance and high accuracy of NDVI index and landsat satellite images in dry areas, Google Earth Engine system was used to estimate vegetation cover, manage and recall the satellite images. Then, the influence of watershed characteristics such as slope, area, soil type, shape factor, and vegetation cover on watershed sedimentation was investigated. 

The average similarities in precipitation and sediment pattern in Ebrahim Abad and Royan watersheds were 48.2 and 46.1%, respectively. Also, the percentage of coarse-grained sediments augments by increasing the precipitation quarter number, during each storm event, which shows the important role of the rainfall pattern on the sedimentation pattern in each watershed. The average monthly vegetation cover (obtained from Landsat images) in Ebrahim Abad and Royan watersheds during the mentioned period was 5.15 and 4.99%, respectively, which is less estimated than reported by previous research. In this research, a threshold limit of 0.1 has been used for the NDVI index, in which very weak vegetation has been omitted.

In both watersheds, in more than 51% of cases, by increasing vegetation cover in each storm event, the thickness of the corresponding sediment layer augments, which shows the effect of vegetation cover on the erosion and sedimentation of the watersheds.

land degradation and desertification in arid areas are the most important environmental challenges in the world. This process due to the lack of precipitation and the occurrence of drought, while the unreasonable exploitation of natural and agricultural areas with increasing demand to provide human food needs, affects various environmental and socio-economic dimensions. So, the continuation of this condition during recent years with the destruction of vegetation and soil, wind and water erosion, soil salinity, soil compaction, and declining groundwater aquifers have significant consequences for the production of agricultural products and biodiversity in an arid region. Since the pattern and dimensions of vegetation changes are the most important factors in detecting land degradation, monitoring the vegetation changes is the best approach to analyzing land degrading and desertification trends in an arid region. Therefore, according to the capabilities of remote sensing data due to the wide coverage and multi-timed,  the use of satellite imagery to monitor vegetation changes by using vegetation index is one of the best methods that developed in recent years. Moreover, concurrent access to high spatial and temporal resolution imageries is one of the important factors that affect the monitoring of vegetation changes. To achieve this goal, It needs to incorporate different satellites with high spatial (e.g., Landsat satellite) and temporal (e.g., MODIS satellite) images. The purpose of this study is the monitoring vegetation changes using daily Landsat simulated images at 30 m Spatial Resolution in three years of wet, normal, and drought in the Nimroze area.

 The study area is located in the north of the Sistan and Baluchistan provinces. Low precipitation (50 mm), high temperature (48 oC), high evaporation (5 m), and 120-day winds are among the specific climatic conditions that characterize this region. In this study, at first, the hydrological drought status of the Hirmand River was investigated. Using the Hydrostats package in R software, the amount of threshold of flood by running the related codes (by running codes such: daily.cv, ann.cv, high. spell, and low. spell) during the statistical period of study (29 years) was calculated. To determine wet, normal, and drought years calculated the length of periods that flood is higher (high. spell. lengths) and lower (low. spell. lengths) than the threshold. To increase the accuracy of monitoring vegetation changes, it needs to incorporate different satellites with high spatial (e.g., Landsat) and temporal (e.g., MODIS) images. To achieve this purpose, in this study, the Enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was evaluated with actual satellite data (OLI, ETM+, TM image). For this purpose at first, pre-processing (geometric, radiometric, and atmospheric correction) was performed on satellite images, and by using the ESTRFM model, simulated daily Landsat images at 30 m spatial resolution for wet, normal, and drought years. In-field operations from different plant communities by GPS were sampled. Comparing filed data with the Normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI), the vegetation index that had the highest correlation with field data was selected. To investigate vegetation changes, using the vegetation index (the vegetation index with high correlation), the map of vegetation for each year was prepared (wet, normal, and drought years). After the classification maps of vegetation, by comparison, approach (cross tab), the map of vegetation changes was extracted.

 The results of analyzing wet and dry periods showed that, flood volume in dry years compare to normal and wet years decreased 31 and 82 percentages, respectively. To incorporation MODIS and Landsat (OLI, ETM+, TM) Images, using enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), finding indicate that this model improves the accuracy of predicted fine-resolution reflectance and preserves spatial details for heterogeneous landscapes too. So that the mean coefficient of determination (R2) of blue, green, red and near-infrared estimation bands with actual satellite images data is 0.91, 0.89, 0.92 and 0.91 respectively. Also the average Root-Mean-Square Error (RMSE) in four bands obtained 0.01, 0.027, 0.028 and 0.031 successively. Comparing the obtained field data with the Normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI), indicate that SAVI index has the highest correlation (R2= 87) with vegetation of study region. By calculate the regression model (using SAVI and field data) and classify the vegetation maps of wet, normal and drought years, 6 class obtained (class1=0-10%, class2=20-10%, class3=20-30%, class4=40-50%, class5=60-80% and class6=>80%). The results of investigation vegetation changes indicate that during the drought period 70% of study area has less than 10% vegetation (equal to 138176.3 hectares) and during normal and wet years by increasing vegetation, this area decreased by 30 and 48% respectively (equal to 66269.98 and 50559.7 hectares, respectively). According to the results during the study period, the most vegetation changes is relate to conversion of class 1 to class 2 (equivalent to 48.5%). moreover 18 and 27% of vegetation changes relate to class 1 and 2 to class 4 and 5 respectively (equal to 16284.26 and 11471.88 hectares, respectively). Also the finding indicates that the most vegetation changes occurrence in wetland-forest (28%), forest-rangeland (21%) and poor rangeland (19%) land uses respectively. Field study also showed that, the most important plant species that grows in this land-use such as the results of analyzing wet and dry periods showed that flood volume in dry years compare to normal and wet years decreased by 31 and 82 percent, respectively. To incorporation MODIS and Landsat (OLI, ETM+, TM) Images, using enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the finding indicates that this model improves the accuracy of predicted fine-resolution reflectance and preserves spatial details for heterogeneous landscapes too. So that the mean coefficient of determination (R2) of blue, green, red, and near-infrared estimation bands with actual satellite images data is 0.91, 0.89, 0.92, and 0.91 respectively. Also, the average Root-Mean-Square Error (RMSE) in four bands obtained 0.01, 0.027, 0.028, and 0.031 successively. Comparing the obtained field data with the Normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI), indicate that the SAVI index has the highest correlation (R2=87) with the vegetation of the study region. By calculating the regression model (using SAVI and field data) and classifying the vegetation maps of wet, normal, and drought years, 6 classes obtained (class1=0-10%, class2=20-10%, class3=20-30%, class 4=40-50%, class5=60-80% and class6=>80%). The results of the investigation of vegetation changes indicate that during the drought period, 70% of the study area has less than 10% vegetation (equal to 138176.3 hectares) and during normal and wet years by increasing vegetation, this area decreased by 30 and 48% respectively (equal to 66269.98 and 50559.7 hectares, respectively). According to the results during the study period, most vegetation changes are related to the conversion of class 1 to class 2 (equivalent to 48.5%). moreover, 18 and 27% of vegetation changes relate to class 1 and 2 to class 4 and 5 respectively (equal to 16284.26 and 11471.88 hectares, respectively). Also, the finding indicates that the most vegetation changes occur in wetland-forest (28%), forest-rangeland (21%), and poor rangeland (19%) land use respectively. The field study also showed that the most important plant species that grow in this land use such as Aeluropus littoralis, Chenopodiace sp, Tamarix aphylla, Haloxylon aphylum are adaptive to climatic regime in study area.

 In this research for the first time in the Nimroz region of Sistan Vegetation changes were studied using Landsat simulated images during periods of low water, normal, and high water years. Due to low rainfall and harsh climate in the study area, floods in the Helmand River are the only source of water supply required in the study area. The results of analyzing wet and dry periods showed that flood volume in dry years compared to normal and wet years has decreased by 31 and 82, respectively. According to the reduction of flood volume during a drought year, 70% of the study area has poor vegetation and during normal and wet years, providing plants with water needs and increasing vegetation, this area had decreased by 30% and 48%, respectively. According to the results of this study, change in hydrological conditions of the Hirmand River has a significant role in vegetation changes in the study area by using simulated images with high spatial and temporal resolution can improve the accuracy of monitoring vegetation changes to control and management the desertification in Sistan area.

IntroductionGroundwater is among the most precious natural resources for human health, economic development and environmental diversity. Since the measurement of groundwater parameters and water quality is difficult, costly and far from being available, interpolation techniques are an easy solution. At the same time, there is a strong correlation between groundwater quality and land use in areas with sensitive aquifers. Changes in land use caused by factors such as rapid growth and expansion of urban centers, rapid population growth, and the lack of land, the need for increased production and the evolution of technologies are important concerns. The literature review shows that the quantitative and qualitative decline in groundwater is a global crisis. As a result, the factors affecting the quantitative and qualitative decline in groundwater range from climate factors to socio-economic factors.In the current research, find an answer to the poor condition of the Isin Plain aquifer by looking at the relationship between some hydrological factors and changes in cultivation pattern of the region is the main goal. For this purpose, the water table and EC of groundwater were interpolated using geostatistical methods. Using satellite imagery, the trend of culture pattern changes over time was obtained. Finally, the relation between the factors on the Isin plain was established. Material and MethodsFor this purpose, the quantity and quality of groundwater in eastern and western Isin plains were interpolated using the Kriging and IDW methods, during the four statistical years of 2004, 2011, 2018, and 2021 and the time series of 2004-2021. The RMSE statistic was used to evaluate the performance of the methods.Then, satellite images and ground truth data was used for land use change classes to investigate the land use changes during the cropping season, along with the determination of changes in the quantity and quality of groundwater in the eastern and western Isin plains for the mentioned years. Satellite data including Landsat 5 multi-temporal satellite images in 2004, 2011, and 2018 and Landsat 8 and Sentinel 2 images for February 2021 were obtained from the USGS.Following preparation of the related images using the flash module, atmospheric and radiometric corrections were performed. Then, the corrections information was extracted into the text file appended to each image. With field survey, the coordinates of the representative pixels were determined and seven land use classes of gardens, vegetables, bare lands, residential and industrial areas, saline lands, and Prosopis Cineraria and Juliflora species were determined. The maximum likelihood classification method was used to separate seven main land use classes based on 127 training samples. For the purpose of assessing accuracy, an error matrix was created for the producer's accuracy, the user's accuracy, the overall accuracy, and the kappa coefficient calculation. Finally, to examine the relationship, the land use map and the groundwater and EC interpolation maps were overlapped into the Arc Map software environment. Results and DiscussionBy comparing the interpolation methods of IDW and Kriging with the RMSE validation technique, it was found that the best interpolation method for estimating water table and EC is Kriging, followed by the IDW method. A review of the land use maps of the Eastern and Western Plains of Isin showed the increase and decrease of different land use categories over the years under study. The overall accuracy and Kappa coefficient were over 82% and 0.79, indicating the acceptable accuracy of the classification and maps obtained. The results of overlapping land use maps and spatial changes in ground water indicate that the location of agricultural land, especially gardens in the eastern Isin plain and vegetables in the western Isin plain, is compatible with the areas of having low water table. The results of overlapping the land use map obtained from Landsat 8 data and EC spatial changes showed the highest amount of EC in can be observed in Prosopis Cineraria and Juliflora species and residential and industrial uses in eastern and western Isin plain. The results obtained from Sentinel2 indicate that the value of EC was significant in the bare lands of eastern Isin and in the saline lands of western Isin. However, the increase in agricultural use, especially for gardens and vegetables, and the pairing with areas with the lowest water table indicates an over-extraction of groundwater for agricultural purposes. On the other hand, the significant extent of bare lands and the upward trend of saline lands, residential and industrial areas, and matching with areas with high EC and the adaptation of maximum EC with Prosopis Cineraria and Juliflora species uses may be a warning for poor condition of the Isin plain aquifer.

Dust is one of the most important atmospheric phenomena, which has attracted the attention of many scientists as an emergent phenomenon. Current research has studied trend of the number of dust days and its relation to climatic parameters and vegetation index from 2005 to 2017 in the Yazd-Ardakan plain. The DSI was computed for the two meteorological stations at Yazd and Meybod. By classifying the two Landsat 7 and 8 satellite images, the land form/cover maps, including agricultural lands, urban, mantled pediments, covered pediment and erg have been prepared. The NDVI layer was then computed in two classes for the Landsat 7 and 8 satellites. The class area was calculated for all levels of the region and also within two buffers of 10 and 20 km at the center of stations Yazd and Meybod. The relationship between the climatic parameters, including annual average temperature, annual average precipitation and annual average relative humidity, was then calculated. Finally, the relationship between dust index and land form types, NDVI layer and climatic parameters has been investigated. The results showed that the relationship between the DSI and the erg type is direct, but there is an inverse relation with the mantled pediments, covered pediment and agricultural land. Based on the results obtained, urban lands have little effect on the dust storm index. By comparing the DSI with the NDVI, with the increase in the region's dust storm index, the NDVI values for all levels decreased. By comparing the DSI with the climatic parameters, with the decrease in annual average precipitation and relative humidity and the increase in annual average temperature, the dust Storm index increased.