saeid hamzeh

Soil moisture is one of the key parameters in the management of water, soil and plant resources. Due to problems such as discontinuity in taking samples, lack of access to sufficient information about the characteristics of the regions, as well as spending a lot of time and money to estimate the amount of available soil moisture and its spatial changes, the use of satellite images is proposed as a cost-effective and efficient method. The thermal-visible trapezoidal model is based on the interpretation of pixel distribution in LST-V1 space, which is used to estimate soil surface moisture or real evapotranspiration. The aim of this study was to estimate soil moisture during the season of 2021-22 for three different soil textures in Haft-Tapeh Sugarcane Agro-industry Company. This was done by Landsat 8 and 9 satellite images and using thermal and optical trapezoidal methods. The results indicated similar accuracy of both models in estimating soil moisture in all three soil textures. Based on the fitted regression relationship between both models and the percentage of volumetric soil moisture in the measured points, the highest coefficient of explanation obtained between was 0.96 for the thermal trapezoidal model and 97.00 for the optical trapezoidal model, in loamy soil texture. This indicated the exact fit and distribution of data in LST-V1 and STR-VI space by the desired models. Also, for the efficiency of the obtained maps, the lowest value of RMSE percentage was calculated for the two models in loamy soil texture as 3.74 and 3.77, respectively. In general, it can be concluded that optical and thermal trapezoidal models predict soil moisture with a small difference and with high accuracy for all the three loamy soil textures.

The main objective of this study was to develop machine learning models for predicting soil organic carbon (SOC) content in agricultural soils enriched with calcium-rich materials in the central region of Iran. Soil samples were collected from 336 points in agricultural lands with two land uses, namely cropland and orchard, from Alborz Province. The soil organic carbon was measured using the Walkley-Black oxidation method. Remote sensing data, obtained from various sources such as Landsat 8 imagery and MODIS sensor, were utilized for soil organic carbon analysis. Three machine learning models, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Gene Expression Programming (GEP), were employed in this study to estimate the soil organic carbon content. The results of this research demonstrated positive outcomes, indicating that the simultaneous use of Landsat 8 imagery and MODIS sensor data through the SVR model yielded better performance (R2 = 0.62, RMSE = 0.63, R2/RMSE = 0.98) compared to the use of each image separately. Therefore, this study concluded that the simultaneous synergy of remote sensing data from different sources (referred to as data synergy) can significantly improve the accuracy of predicting soil organic carbon content in calcium-enriched agricultural soils in the central region of Iran. It is recommended to use advanced fusion techniques and deep learning methods for combining features at a higher level.

Soil moisture is one of the most important environmental parameters for water resources management and irrigation planning systems in agricultural areas. In agricultural areas, most soil moisture retrieval models are unstable in terms of their accuracy and performance during crop growth season. As a result, there is no consensus on which model performs optimally during the agricultural season. This is because of the uncertainties associated with model physics, input data, vegetation attenuation and soil characteristics. To deal with these practical concerns, in this research, a simple but effective soil moisture retrieval method for using combination of multiple models based on machine learning has been introduced. Firstly, the WCM with different vegetation descriptors were calibrated and validated in sugarcane fields for Sentinel-1 backscattering coefficients . For this purpose, soil moisture measurements of sugarcane fields (400 samples in total) during the plant growing season in 2020 were used. Then optimization of calculations was done using the GRNN. The results showed that WCMNDWI retrieves soil moisture more accurately than other models in the early stages of sugarcane growth, while WCMVWC and WCMLAI were more accurate in the late sugarcane growth. Time-series soil moisture retrieval accuracy using the combined method based on GRNN was higher than that of single WCM models. According to the results of the in situ validation for sugarcane fields, with the optimal combination of models, the minimum MAE) is less than 0.02 m3m-3, the RMSE is approximately 0.085 and the R it was equal to 0.7 for the growing season.

Soil moisture is one of the most important parameters in water, soil and plant resources management. Therefore, the present study was conducted to evaluate the efficiency of thermal and optical remote sensing data in order to estimate soil moisture and irrigation planning in sugarcane fields of Khuzestan Province, Iran. For this purpose, soil moisture content for 9 passes of Landsat 8 and Sentinel 2 satellites was calculated using thermal and optical trapezoidal methods from April to October 2020 in Amirkabir Sugarcane Agro-industry fields. To validate the results, the measured soil moisture content data of 337 ground control points located in 18 sugarcane-growing fields measured by TDR350 dehumidifier were used simultaneously with the passage of the satellites. The results showed that TOTRAM model with a determination coefficient of 0.82 and error rate of RMSE and NRMSE as 4.45% and 12.9%, and OPTRAM model with an explanation coefficient of 0.93 and RMSE and NRMSE error of 3.14% and 12.1% were able to properly estimate soil surface moisture in sugarcane fields. Also, the results of evaluation of soil moisture maps for irrigation planning of sugarcane fields showed that these data could be used for irrigation planning with average NRMSE error of 16% and 9% in relation to ground irrigation time data for TOTRAM and OPTRAM models, respectively. In this regard, OPTRAM model data were more efficient compared to thermal data, due to better spatial resolution of optical data and less effect by environmental factors such as temperature and relative humidity of air and also the effect of adjacent pixels.

Leaf Area Index (LAI) plays a critical role in the mass and energy exchanges between the earth and the atmosphere. Like of other plants, LAI of sugarcane is a good indicator of the health status and growth of this crop which is of great economic importance due to its role in the food and energy industries. Launched in 2019, the PRISMA satellite provides one of the most recent hyperspectral data sources which are applicable especially for mapping plant variables. In this study, a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Networkk (BRANN) which applies Bayes' theorem to overcome the overfitting problem of neural networks is used. The model was implemented on a data set consisting of spectrum obtained by PRISMA satellite as an independent variable and sugarcane LAI measurements as a dependent variable. The ground measurements of sugarcane LAI were carried out in 118 elementary sampling units on the fields of Amir Kabir sugarcane cultivation and industry in Khuzestan province and on seven different dates during a sugarcane growth period in 2020. Comparing the performance of BRANN in retrieving sugarcane LAI from PRISMA spectra with that of a conventional ANN trained with the Levenberg-Marquardt algorithm (LMANN) indicates that the retrieval RMSE is reduced from 2.26 m2/m2 applying LMANN to 0.67 m2/m2 applying the BRANN method. In this study, the principle component analysis was also used dimensionality reduction. Retrieving LAI from the first 20  principle components, RMSE was also reduced from 1.41 m2/m2 applying LMANN to 0.71 m2/m2 applying BRANN. Exploiting principal components significantly reduced computational time. By implementing the calibrated BRANN model over the PRISMA image pixel by pixel, the sugarcane LAI map was generated. Evaluating this map showed that this map represents the spatial variations of sugarcane LAI well. The results of this study indicate the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.

Determination of the Crop Water Requirement (CWR) of different crops and the value of crop water consumption is one of the problems at a large scale and in real-time to the soil and water expert. The first step to compute this variable is to determine the reference evapotranspiration (ET0). The standard method to compute this parameter is to utilize the climate data and experimental equations. The problem with classic methods is that the meteorological station isn’t available in the agricultural lands and usually, we have data limitations. The optimized solution is to utilize remote sensing data. So with the combination of different datasets then the reference evapotranspiration and actual evapotranspiration will be estimated. The goal of the study is to an evaluation of open-source WaPOR and ERA5 to compute daily reference evapotranspiration based on the FAO-Penman Monthis equation at the meteorological stations of Sistan and Baluchestan province. The result has shown that the open-source dataset estimated the reference evapotranspiration as more than 80 percent accurate at the place of the meteorological station and in all of the stations RMSE was less than 2 mm per day. The accuracy assessment of results shown at different crop seasons that ET0 in the autumn season is better than in the spring season. So that the ERA5 combined with the GLDAS Wind data has a better correlation with in situ measurement of ET0 than to the WaPOR. All of the results shown that this dataset can be used in each place in the province to estimate ET0. Therefore, the present study is to investigate the possibility of using the products of WaPOR and ERA5 systems to calculate the amount of daily reference evapotranspiration based on the experimental method of Penman-Monteith and to evaluate and validate its outputs in Sistan and Baluchestan Province of Iran.The results showed that remote sensing systems with an accuracy of over 80% at meteorological stations estimated the amount of reference evapotranspiration and an error of less than 2 mm was reported in all stations. Also, studies during the growing season (June 15 to November 6) compared to the growing season (1 November to 15 May) showed that the reference evapotranspiration obtained from satellite data in the first growing season has a higher (R2). Also, the results of NRMSE index evaluation indicate that the reference evapotranspiration obtained from ERA-GLDAS2.1 data is appropriate.Therefore, since the estimated and validated values had acceptable accuracy, in the next step, these systems can be used anywhere in the province.

Monitoring seasonal changes of Meighan wetland using SAR, thermal and optical remote sensing imagesAbstractThe aim of this study is to monitor the seasonal changes of Meighan wetland located in Markazi province in Iran. This is a multi-sensor approach; Sentinel-1 and Landsat 8 images were captured from May 2019 to January 2020. Modified Normalized Difference Water Index (MNDWI) and Land surface temperature were computed based on spectral bands of Landsat 8. Backscattering values in VH and VV polarimetric bands of Sentinel 1 images were also considered. Different wetland land cover classes were extracted based on these three measures. The results of each season were further compared with the classification output with support vector machines. The wetland main water body reaches its maximum extent in May 2019 (61.18 square kilometers) and its minimum extent is reported in August 2019 with an extent of 19.25 square kilometers. The outputs of the support vector machine classification were more compatible with MNDWI index. The results of this study show that the multi-sensor approach can efficiently be used in monitoring seasonal changes of wetland.

Wetlands are one of the natural ecosystems that play an important role in plant and animal diversity conservation. Wetlands are very sensitive to environmental changes because they are located in an intermediate zone between land and marine ecosystems. Their constant monitoring is of great importance especially in wetlands with seasonal changes pattern. The Wetland ecosystems are influenced by anthropogenic and natural factors. Drought, reduced rainfall, unsustainable management of water resources, overexploitation, and dam construction threaten wetlands. Field surveying and mapping of natural resources are generally not cost-effective because these methods are expensive and time-consuming. Also, it is not possible to repeat it periodically with a constant interval. Therefore, the use of remote sensing data such as optics and radar data is necessary in the study of natural resources. However, natural landscapes are complex and composed of various land cover types. Optical multispectral images are not always able to classify such a landscape, perfectly. This source of data is also affected by atmospheric conditions; the presence of clouds or fog block capturing these images. SAR sensors unlike optics sensors are capable of capturing images in all weather conditions. In fact, the use of each satellite image has advantages and disadvantages and in many applications they complement each other. Multi-sensor approaches beneficiate from the capabilities of different satellite images. Researches have shown that a multi-sensor approach in natural resources studies, especially wetlands is of great value. The multi-source approach and the seasonal variations discussed in this study have not been followed in any research on Meighan wetland. The benefits of Sentinel-1 characteristics; such as suitable spatial and radiometric resolutions and free access highlight the finding of this research.

Meighan wetland is located in the center of Iran in Markazi province. This wetland has ecological and economical importance in the region. In the last two decades, one road is constructed on it and divided it into two parts; this changes the wetland into a calm environment and subsequently the evaporation has been increased. In this study, the seasonal changes of Meighan wetland were investigated using Landsat 8 and Sentinel-1 images. The images in each season were selected in such a way that the minimum possible difference exist between their acquisition date. The preprocessing steps were done independently on each optic and SAR image. Sentinel-1 SAR images have been calibrated and the digital numbers were converted into the corresponding backscattering values (in decibel) in each polarimetric band. Although, from spectral reflectance values in different Landsat bands, Modified Normalized Difference Water Index (MNDWI) were calculated in each season. Land surface temperatures were also calculated from thermal bands. Five different land cover classes are observed in the wetland and its surroundings; main water body of the wetland, shallow water zone, saline soil, surrounding area and remaining land covers (known as others). These areas were also extracted based on MNDWI index, land surface temperature (LST) and backscattering values in VH and VV sentinel-1 polarimetric bands. Then, the whole area is classified by the support vector machine classifier. In the last step, the extracted regions from different methods were compared with the land cover classification results in each season. The differences and similarities of the extracted areas were discussed further.

The findings of this study show that the main wetland body reaches its maximum extent in May 2019 based on the SVM classification results. In this month, MNDWI index-based results were closer to the one obtained with the support vector machine classification. The support vector machine classification results and MNDWI index achieved similar results in the delineation of the wetland water zone, the shallow water zone and saline soil. In August 2019, the wetland water area was reduced based on the support vector machine classification. In May 2019 and January 2020, when the wetland water area was larger in comparison to other months, the results of the MNDWI index are close to the results of the support vector machine classification. The extracted area of shallow water class and saline soil class show the highest difference between classification results and MNDWI results. The same results have been obtained by comparison of extracted area based on the backscattering values of VH and VV polarimetric bands and MNDWI index; the maximum differences are observed in shallow water and saline soil classes. This could be related to the sensitivity of SAR backscattering values to moisture content. Over the year, the moisture content varies in response to temperature, rainfall, and evapotranspiration. The changes in moisture content affect the dielectric constant of the material. The dielectric constant governs the magnitude of backscattering values. The moisture changes cause variation in SAR backscattering values over the year.

Long-term wetland change detection is frequently studied with optical remote sensing images. Although, wetlands show the seasonal pattern in response to temperature and rainfall changes over the year, however, wetland seasonal variations are not fully explored. In this study, Sentinel 1 and Landsat8 images covering the study area were captured over the year. The results of the present study showed that the seasonal variation of wetland can be monitored based on a multi-sensor approach. In May 2019, the Meighan main water body reached the highest extent and the smallest area was observed in August 2019. In addition, in January 2020, the wetland water area increased again. Also some differences are observed between the extracted areas based on the MNDWI index, VH and VV polarizations, and the support vector machine classification results in different seasons. These differences are observed more in the spring. The performance of MNDWI index in wetland water area extraction in most seasons is very close to the classification results of the support vector machine. This shows the high capabilities of MNDWI spectral index in monitoring wetlands. In addition, the main water body of the wetland can be well separated by backscattering values of VH and VV Sentinel 1 polarimetric bands.

One of the most important factors that play a major role in reducing soil fertility and ‎agricultural land degradation is soil salinization. Soil salinity problem is more sever in agricultural lands of ‎arid and semi-arid regions. In many cases, human activities and irrigation of agricultural lands with saline ‎water are the cause of salinization. This is a serious problem in different regions of Iran, especially in ‎Khuzestan province. Therefore, the present study has been conducted with the aim of monitoring and ‎evaluating the effect of Gotvand Dam on the salinization of the downstream area and changing its plant ‎ecosystem before and after water intake using remote sensing imagery.‎

The images used in the research, time series of two ETM +, OLI sensors from ‎‎2019-1999 have been collected using plant indices (NDVI, SAVI), biophysical index of leaf cover (LAI) ‎and salinity indices. The soil is classified by salient decision-making method of changes in halophyte and ‎Non-halophyte plants according to the threshold obtained from the indicators used in each year. Then, ‎the final results were evaluated according to the trend of changes obtained from the used indicators and ‎their correlation with changes in the plant ecosystem of the region.‎

The rate of vegetation changes in the four years of 2018, 2013, 2002, and ‎‎1999 was more than other years, were prepared by the method of supervised classification of the area ‎under normal vegetation and saline plants. According to the results obtained in 1999, the total vegetation ‎area of the groves was about 1117 hectares, of which about 134 hectares were related to halophyte ‎vegetation. But in 2018, these values were estimated at 921 hectares, with areas covered by halophyte ‎changing to 445 hectares and halophyte to 476 hectares. ‎

The results of the study indicate the onset of the highest stresses in the plant ecosystem of ‎the region and the simultaneous decline in leaf cover and NDVI with the dewatering of Gotvand Dam ‎since 2011. This coincidence, which is due to the salinity of the water of Gotvand Dam Lake and ‎consequently Karun River, has a significant effect on increasing salinity and changes in soil quality of the ‎region and consequently increasing halophyte plants as well as high vegetation degradation in the region. ‎These conditions can create more serious challenges for the ecosystem of this area and in the long period ‎change the ecosystem and vegetation cover of this region to halophyte plants.‎

Anzali Wetland in Iran as one of the most valuable wetlands registered in the Ramsar Convention is being destroyed by environmental factors and human activities. In the last two decades, among various satellite images, radar images have played a special role in wetland monitoring. Radar is an all-weather sensor and it is sensitive to surface roughness and moisture, they serve as a valuable source for quick and accurate monitoring of wetlands. However, similarities in backscattering coefficients of different wetland classes and relatively difficult processing – in comparison to optical images- are the most important factors that limit their application. In this study, the capabilities of SAR images in the classification of Anzali wetland and the three main land use classes around the wetland (i.e. agricultural lands, reeds, and built-up areas) were evaluated. Two radar images; Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) and Sentinel 1 captured in 2018 were used. The texture parameters of the two images have been extracted. The images and their extracted texture layers have been fused by the feature-level method and further classified by the random forest method. The overall accuracy of feature-level fusion is equal to 75% and the kappa coefficient is equal to 0.62. The evaluation results related to producer and user accuracy are 100% and 83.33%, respectively, show the high capability of radar images in the classification and detection of wetlands. However, some errors have been observed in the separation of agricultural lands, reeds, and built-up areas.

Human activities as well as environmental and climate changes affect the trends of wetlands. Detecting and monitoring aquifers are considered to be very important for evaluation of past, present, and future influential factors, and the findings of such studies are essential for taking measures and making decisions based on the goals of sustainable water and soil resources management. Over the past decade, many researchers around the world have been attracted to remote sensing and especially satellite remote sensing and used this technology to detect such changes over time. The present study has used Landsat (monitoring the area of water body), TRMM (monitoring rainfall), MODIS (monitoring vegetation and evapotranspiration), Grace (monitoring groundwater) satellite images available in Google Earth Engine to study last two decades changes (from 2000 to 2019) in Maharloo wetland, Goshnegan catchment and their surroundings. 

Maharloo wetland is located in Fars province and Goshnegan catchment (426 square kilometers). The present study has used Landsat 7 and 8 images to extract the area of water body, TRMM images to obtain precipitation values, MODIS products to calculate NDVI and evapotranspiration, and data received from Grace to extract changes in groundwater level. These satellite images were available in Google Earth Engine. Mann-Kendall test was also used to assess the overall trend of the aforementioned factors. 

The automated water extraction index was used in the present study to identify and estimate the area covered by ​​water bodies in the study area. The largest area belonged to 2006 (216.76 square kilometers) and the smallest belonged to 2018 (66 square kilometers). In 2000 (the beginning of the reference period), an area of ​​216.52 square kilometers was covered by this wetland which is close to what was observed in 2006. In 2018, this has reduced to 66 square kilometers. Thus, there is about 150.72 square kilometers (69.54 percent) difference between these two years. In 2009, the total area has reduced to 66.67 square kilometers. A numerical comparison between 2000 and 2019 also indicates a reduction of 91.17 square kilometers (42% decrease) in the total area covered by this wetland. Also, a 53.72 square kilometers (29.60%) difference was observed between the average area covered by the water body in the first and second ten years. Since calculated p-value value (< 0.00001) is less than the alpha level (0.05), so a significant trend was observed in the average annual data of the area covered by this wetland. Kendall's tau also indicated declining trend of the collected data. Groundwater level was calculated using data received from Grace Satellite to investigate the role of groundwater level in reducing the area covered by the ​​water body. Results indicated that since 2008, groundwater level ​​have always showed a negative value (a decreasing trend). For an instance, a groundwater level of -10.86 cm in 2019 indicates a decrease in the water level in the study area. As the calculated p-value (< 0.0001) is less than the alpha level (0.05), so a significant decreasing trend was observed in the groundwater level. Results of Mann-Kendall test (-0.6) also indicated that changes in water bodies, vegetation, rainfall and groundwater level had a decreasing, increasing, increasing and decreasing trend, respectively. No significant trend was observed in evapotranspiration. It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. 

Wetlands provide many ecological services including water treatment, natural hazard prevention, soil and water protection, and coastline management (Amani et al., 2019). Therefore, understanding the importance of wetlands and their management need to be seriously considered by relevant organizations in different countries of the world, and Iran is no exception. Satellite data and remote sensing methods and techniques are considered to be one of the most important and cost-effective methods of monitoring wetlands. The present study used satellite data collected by Landsat, MODIS, Grace, and TRMM to monitor water bodies, vegetation, groundwater level, and rainfall in Goshnegan catchment in which Maharloo wetland is located. The results of Mann-Kendall test showed a decreasing annual trend for changes in the average area of ​​this wetland. This decreasing trend is considered to be a serious threat to human settlements around the wetland which can intensify over time. It will also affect the thermal islands of Shiraz and Sarvestan in near future. Obviously, management of agricultural and forest land uses with the aim of stopping their increasing trend can improve water balance in catchment areas. A 132.2 ha (approximately 36.16%) difference was observed between the average vegetation cover in this catchment area over the first and second ten years (233.4 vs. 365.6 ha). It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. Due to the proximity of this wetland to the city of Shiraz and its importance as an ecological and tourist attraction, it is suggested that related authorities (Department of Environment and Water Organization) demarcate lake bed and riparian zone with the help of remote sensing researchers to improve the management of this wetland and prevent it from drying up. Also, it is suggested that the Organization of Agriculture Jihad review and improve water consumption methods and cultivation patterns in the areas surrounding this wetland.

In the present study, long-term (during 1999 to 2019) and seasonal changes (during 2018) of the area of Gorgan Bay and its relationship with the changes in precipitation and Caspian Sea water level have been studied. The long-term water body was calculated by using the time series of Landsat8, Landsat5 and Sentinel-2 satellites imagery, and employ the MNDWI2 index. Also, data from Topix and Json satellites were used to study the changes of Caspian Sea water level and TRMM satellite data was used for precipitation changes. The results showed that the water body of Gorgan Bay has decreased significantly during the study period and this trend continues. The long term changes in the water area of Gorgan Bay have a high correlation (0.92) with the amount of fluctuations in the water level of the Caspian Sea. During this 20-year period, the changes in the water level of the Caspian Sea have been about 120 cm. But the correlation between water area and precipitation in the long period is low (0.1). This trend is quite the opposite in a short period of one year and since the changes in the water level of the Caspian Sea in a period of one year is very small (5 cm), so the water area of Gorgan Bay has a very low correlation (0.09) with it. But in the same period, there is a relatively moderate correlation between rainfall changes and the area of Gorgan Bay water body with a delay of one month.

The presence of dry and green vegetation in pixels containing spectral information is essential in geological and mineralogical studies. Thus, retrieving sub-pixel information, including estimation of a mineral’s quantity in a single hyperspectral RS image pixel is very important. In this study, the vegetation corrected continuum depth (VCCD) method was trained and its results were validated using spectrometry, laboratory mineralogy, and Hyperion image to reduce the effect of vegetation on the estimation of minerals. The study was conducted in Oghlansar region located in northwestern Iran. SAVI and absorption depth (2102 μm) were used for the estimation of the green and dry vegetation, respectively. Meanwhile, the trained models do not have a high sensitivity to the presence of noise in the spectrum and vegetation type changes. The correction of continuum removed band depth (CRBD) analysis was possible up to 60% for maximum green vegetation cover threshold, 56-60% for dry vegetation, and 72-76% for both dry and green vegetation. Effect of noise and different vegetation types on model capability was examined and the result shows that VCCD is not highly sensitive to random noise and changes in vegetation types. After correction of the coefficients and confirmation of its efficiency, the model was used to correct CRBD and reduce the effect of vegetation on Hyperion image. In the estimation of kaolinite and muscovite, the presence of green and dry vegetation led to the underestimation of the minerals present in the study area. The results showed that VCCD was able to increase the prediction accuracy (R2) by 0.25 and 0.13 and reduce RMSE by 0.0108 and 0.125 for kaolinite and muscovite, respectively.

Leaf area index (LAI) plays an important role in hydrological, agricultural, and land irrigation management studies. In order to adopt an appropriate, accurate, and robust algorithm to estimate the spatial-temporal distribution of LAI using Sentinel-2 images, the Support Vector Regression (SVR), Kernel Ridge Regression (KRR), Relevance Vector Machines (RVM), and Gaussian Process Regression (GPR) were calibrated and investigated. The research data were collected from silage maize farms in Ghaleh-Now county in Tehran province during the whole growing season in summer 2018 through destructive measurement and hemispherical photography. Our results were compared with the conventional algorithms in this field, i.e. random forest (RF) and artificial neural network (ANN). The results revealed that the GPR algorithm not only has higher accuracy (in 20-m band group, R2=0.913 and RMSE=0.641), speed, and robustness to estimate the LAI, but also it has the unique ability to generate uncertainty pixel-based map (uncertainty and relative uncertainty were less than 0.7 and 30% by 96% and 74% of the total area, respectively). Based on R2 and RMSE, SVR is the second accurate technique for LAI estimation followed by RVM, KRR, RF and ANN, respectively. Comparison of the estimated and field LAI at sampling times with RMSE=0.276 and bias=0.099 and other superiorities indicated the efficiency of GPR algorithm to estimate the spatial-temporal distribution of LAI.

 As a type of mass movement involving slow or rapid movement of soil, rock material or both on the lower hillsides, landslide is under the effect of gravity.Landslide is recognized as one of the most common geological disasters causing worldwide damages and casualties.Landslide susceptibility maps provide important and valuable information,including time scale of possible future landslides, which are usedfor predicting landslide hazards. Since predicting the time of landslide occurrence is beyond the capability of science and knowledge, identifying areas susceptible to landslide and ranking them can extensively restrict the damages caused by landslide. Therefore, it is essential to zone landslide risk and identify factors affecting it. Analytic Network Process(ANP) is aGIS-based Multi-Criteria Decision Analysis(GIS MCDA) method successfully applied to many decision-making systems. The present study seeks to evaluate landslide risk and achieve a zoning map for the sub-basin under study using ANP and Weighted Overlaymethods.  

Based on the literature and using different experts’ viewpoint, criteria affecting landslide risk were identified and five major criteria including topography, land use and land cover, geology, hydrometry and infrastructure were selected. The selected criteria include the following sub-criteria: slope, slope direction, curvature, elevation, lithology, soil type, land use, vegetation density, distance from roads, distance from habitat, river and drainage density and precipitation. The effective factor layers were standardized and a specific scale was defined for their units.Then, each layer was assigned a weight based on its role and importanceusing Analytic Network Process.Proposed to modify Analytic Hierarchical Process(AHP), this method (ANP) relies on the analyses of the human brain for complex and fuzzy problems.Network Analysis Process generally includes the following steps: determining indicators, criteria and options;classifying identified criteria into clusters and elements; determining the relationship between clusters, elements and options; performing pairwise comparisons between clusters, elements and options, and finally calculating the final weight of elements and options. UsingWeightedOverlaymethod, these elements were then integrated with their related coefficients and the final landslide risk map was obtained.

Each criteria and sub-criteria were weighted using Analytic Network Processmethod.Topographic and land cover criteria had the most and hydrographic criteria had the least impact on the landslide occurrence. According to the final map, most landslides have occurred in eastern and southern slopes at an altitude of 500 to 2,200 meters. Moreover, 17/31% of the study area was located in the very high-risk class and 33% in the high risk class (about half of the area has high potential of landslide). Previous landslide data were used to assess the landslide zoning map results. Results indicate that most landslides have occurred in the high risk class (about 35% of landslides) and only about 4% of landslides have occurred in the very low risk class. 

Landslide is one of the natural hazards causing serious harms and problems for human life. Identifying the factors affecting landslide and zoning its hazard is especially important for the identification of risky and susceptible areas.So, landslides were selected as one of the main topics of the study with the aim of controlling and managing its hazards.The ANP network analysis method was used to model and predict landslide risk in this research.Each criteria and sub-criteria were weighted and overlapped to producethe map of relative landsliderisk.The lowest risk was observed in the northern parts of the region, and the highest landslide risk was observed in the northern hillsides with higher humidity.WeightedOverlaymethod and network analysis model were effective in predicting landslide susceptibility and producing landslide zoning map.

The present study aimed to evaluate the efficiency of digital hemispherical photography (DHP) in deriving LAI in silage maize farms in the south of Tehran. For this purpose, the DHP as well as destructive measurements for comparison were used to estimate LAI in silage maize farms in Qaleh-Now County in the south of Tehran in 2018 considering the nature of spatio-temporal variability in agricultural fields during a growing season. The results showed LAI obtained through DHP at different periods of plant growth has a strong linear correlation with the values measured by the destructive method (R2 = 0.92, RMSE = 0.45 and Bias = 0.31). However, the intermediate LAI range (LAI: 2 -

The expansion of cities and urbanization and the gradual increase in the number of major cities in the world, especially in developing countries on the one hand, and the growth of cities, concentration and population accumulation on the other, cause problems such as the emergence of heat islands. Urban heat islands are the obvious negative impacts of urbanization that largely depends on land use and land cover type, most aspects of urbanization become apparent when humans interfere with the natural structure of the earth and alter the natural landscape of the earth, causing many environmental problems such as urban heat islands in general, urban heat island is the result of the complex impacts of urban processes on its climate. These processes cause cities to be surrounded by a hot air mass that is about 120 meters in height during the day and more than twice as much at night. In this phenomenon, the city center has the highest temperature in comparison to the surrounding rural areas, and with the departure from the city center this temperature and height of the hot air mass decreases. This is called the urban heat island. In most studies, it is concluded that urban vegetation reduces surface temperature, as opposed to the inconsistency between land surface temperature and land use in land use. The objectives of this study are to determine the distribution of land surface temperature in land use types, as well as to investigate the difference between land surface temperature with land use types and vegetation composition, and finally to analyze the impact of human factors on land surface temperature. This study is expected to provide a better understanding of urban heat islands by analyzing land use types and land cover and social and economic interactions. the results of this study can guide managers in planning to regulate urban socio-economic activities to reduce urban heat islands.

District 6 of Tehran is one of the relatively old districts of Tehran which is located in the central area of Tehran. District 6, as one of the busiest areas in Tehran, has a residential density of 75 percent, with 30 percent allocated to transportation networks.This study uses the Landsat 8 satellite image on August 7, 2016. The images are available free of charge at the US Geological Survey. In order to complete the input parameters for mapping the surface temperature using satellite images, the Modis water vapor product with a spatial resolution of 5000 m was used. For visual interpretation, 1: 10000 maps were used, and the type of files of the target area extracted from 1: 2000 maps for 2015 extracted from the Tehran Information Technology Organization, In this study we consider only the vegetation case with respect to available data. There are 5 types of land uses in this area and the user map has been prepared using Arc GIS software.1- Old residential area (over 25 years old) 2- New residential area (between 5 and 15 years) 3. Wasteland (landfills vacant) 4- Industrial areas (including various industrial activities such as factory and warehouse space) 5. Organizational Areas (Infrastructure Related to Schools, Colleges, Universities and Research Institutions)

Approximately 49% of the total area under study was considered for all types of land uses, including 6.4% new residential, 21% old residential, 2.5% waste, 0.6% industrial and 18.3% Percentage of organizational usage. Data provided by the municipality's ICT and satellite imagery used to match the time of preparation. In this study, based on the findings, the highest surface temperature is related to industrial and organizational use, and the surface temperature of the wasteland is ranked third after organizational use, The average value of land surface temperature varies with different types of land use, indicating that the factors affecting the land surface temperature vary by land use. Linear correlation analysis showed that the mean land surface temperature significantly depended on both land cover composition and land use. The results show that there is a slight correlation between vegetation indices in each land use type. The land cover composition has a direct relationship with the land cover metrics, but the correlation coefficient of the vegetation cover composition and measures varies among different land use types. The findings show that the composition of the green space in small plots is more sensitive to the surface temperature of the earth. The results also suggest that there is a complex mechanism in urban heat islands, which may be caused not only by the biophysical process but also by human resources. The variations in land surface temperature between different types of land use indicate that energy consumption and human heat emission have important effects on the land surface temperature. In human sources of heat dissipation, such as human metabolism, the presence of buildings and traffic significantly contribute to the increase of urban heat and the creation of urban islands. The intensity of heat varies according to the type of climate, population density and intensity of industrial and commercial activities.

The findings of this study show that land use has the potential to explain the effects of complex human activities in urban areas compared to land cover. these findings highlight the contradictory effects of land use composition and land cover on urban heat islands, which not only help to better understand the mechanism of urban heat islands but also provide practical solutions for urban planning and management. Surface temperatures can be reduced by optimizing vegetation patterns (based on the relationship between land surface temperature and land cover) for any human-assisted use. to reduce the effect of urban heat islands, increasing vegetation density and scattering, it is also advisable to create green roofs or roof gardens and maintain plants in buildings.

The climate change over the past few decades, and consequently decrease in the precipitation, along with the population growth in different regions in Iran have led to an increase in demand for water for domestic agricultural, industrial, etc. consumption. This has led to uncontrolled exploitation of groundwater resources, causing severe decrease in the groundwater level. Artificial recharge technique is one of the methods to compensate for the groundwater deficit, especially in arid and semi-arid regions. Selection of suitable sites before artificial recharge can help improve the efficiency of the project and ensure its success. Having in mind the problems related to decrease in groundwater resources in Tehran due to the increasing population and the expansion of industry and agriculture. This study aims to identify and zoning of regions that are suitable for artificial recharge of groundwater in Tehran Province. The GIS can help determine such regions more precisely, faster, and with better results. For this purpose, the present study integrated GIS and Fuzzy AHP to weigh and combine factors that play a positive role in artificial recharge, such as the depth and changes in the groundwater level, precipitation, drainage density, elevation and land slope, distance from fault, distance from river, geological properties, and land use. After investigating the views of experts about the binary comparison of the criteria, and prioritizing them using AHP, it was found that the hydrological properties were the most effective criteria for the subject under study. Results indicated that 6.2% and 15.75% of the entire area of the region under study are very suitable and suitable for artificial recharge of groundwater, respectively. Very suitable regions are mostly located in the east of the province, with suitable geologic formations, short distance from river, and predominant rangeland and agricultural land use. They also, have a very low and decreasing groundwater level.

Soil moisture is one of the key parameters in watershed and water resources studies. Field measurement of this parameter is extremely difficult, time-consuming and costly. Hence, in recent years, numerous satellite-based methods for estimating and modeling soil moisture have been developed and presented. Among proposed methods, surface energy models performed better and have a higher degree of accuracy because of their physical nature. But, due to their particular complexity, they have been used rarely. Therefore, this research was carried out to estimate soil moisture using Landsat 8 Satellite imagery and Surface Energy Balance System (SEBS) near the Shadegan Wetland, located in the south-west of Iran. For this purpose, volumetric soil moisture content was measured at 39 points on 27 June 2016, simultaneous with the overpass of Landsat 8 Satellite over the study area. After necessary image processing, the was calculated using the applying the SEBS on satellite image. Then, the evaporation fraction was used as the main input in an experimental model (saturation ratio model) for estimating the soil moisture. Results showed the good ability of the model for estimating soil moisture with the coefficient of determination of 0.69 and the RMSE error value of 0.03 . It can be concluded that combination of remote sensing data, surface energy balance system and the experimental model of soil moisture can be used for modeling soil moisture in a large scale.

Optical remote sensing of earth surface processes commonly relies on the Red, Green, Blue (RGB), Near Infrared (NIR) and Shortwave Infrared (SWIR) electromagnetic bands. In soil-moisture estimation method using optical remotely-sensed imagery, by assuming a linear relationship between the Red-NIR reflectance, the line of bare soil (soil line) is established as the base and then moisture isoclines are assumed perpendicular to the soil line. This study is intended to show that this assumption is not consistent with the actual Red-NIR space geometry, which in many cases introduces soil moisture estimation errors. Therefore, a new mathematical transformation method was proposed to the original Red-NIR space followed by newly-defined soil moisture isolines. This new Transformed Red-NIR (TRN) model was compared with the conventional Red-NIR (CRN) model using data from Salman Farsi sugarcane field located in Khozestan province in southwestern of Iran. Twelve Landsat-8 satellite images were used during the sugarcane growing season. For validation of the remotely sensed data, ground reference soil moisture was measured at 22 locations at five different depths. Results of the proposed new method significantly improved accuracy of the Red-NIR approach to remote sensing of soil moisture.

Introduction One of the emerging environmental hazards arising from the expansion of urbanization is the phenomenon of urban heat island. Urban heat island is a phenomenon in which urban areas experience warmer temperatures than the surrounding countryside. This phenomenon has been studied and recorded in the world over 150 years ago and generally, along with natural vegetation changes, appear to impenetrable surfaces such as pavement streets, cement, asphalt, concrete, etc. The effects of urban heat island on human life include increased energy consumption due to increased demand for building cooling during the warm seasons, increased heat stress and reduced staffing efficiency, increased water consumption and increased urban air pollution. Also, UHI has caused a change in the urban and global climate and given the increase in population, the importance of energy and the issue of global warming will increase over the coming decades. The city of Tehran is the capital and center of economic activity in the country. Each year, a large number of different provinces migrate to the province to work, which will destroy the green space and increase population in the city, which causes a lot of problems, including increasing the surface temperature. Therefore, the aim of this paper is to investigate the effect of demographic and environmental parameters on the spatial distribution of Tehran's metropolis surface temperature. Materials and methods In this study, the Landsat 5 satellite image of TM sensor for the studied area has been used. In order to complete input parameters for mapping surface temperature using satellite imagery of meteorological data, and for providing field samples, Google Earth images and topographic maps prepared by the survey organization, were used to provide surface weather maps. At first, the preprocessing steps were applied to prepare images including atmospheric correction. Then, the images were classified using a Maximum Likelihood and were classified into four landuse, built up, fallow, water and green space. After classification, each of the images was categorized using precision classification controls. In the next step, using the Mono Window algorithm, surface temperature was obtained for each image. Results and discussion Regarding existing landuses, the area was classified by 4 types of built-up, green space, bare land, water, using the supervised classification method. The area built-up, green space, bare land and water are 37061.46, 9512.91, 11470.05 and 44.91 ha, respectively. The most area of landuse is built-up. The surface temperature varies from 294 to 328 degrees Kelvin. The lowest average temperature is for water use and natural areas such as green spaces, forest and urban parks, while the maximum temperatures are in shallow land and impenetrable lands such as asphalt, street paving and other Man-made coatings, as well as industrial and commercial land, residential and transportation applications. Based on the total population of Tehran in 2011, the 21, 22, 12 and 9 regions of the main core of the urban heat island are among the low population areas of Tehran. Due to various landuses (industrial, commercial, transportation, etc.), low population density in these areas appears natural. While the urban heat island in these areas is due to industrial activities, airports, transportation, commercial land use, and bare land degradation. Map of surface temperature and population distribution in different regions of Tehran show that regions with high temperatures in areas 4, 5, 15, 2 and 14, which have a high population density in them, have been created. Although these areas are not among the main heat island in Tehran; however, due to high population density, high traffic volume, transport pollution from these areas is endangered by the emergence and expansion of urban heat islands. Conclusion The purpose of this research is to investigate the effect of demographic and environmental parameters on the spatial distribution of Tehran's surface temperature. The results of the study indicate that heterogeneous spatial distribution factors of surface temperature in Tehran are different. These factors are deliberately due to different land use and vegetation in the region. In the northern regions of Tehran, an uncompromising urban structure along with green space has caused these areas to have a low surface temperature, while the high part of the central part of the city with high building density and poor green space is high. Finally, the results of the relationship between surface temperature and population distribution in different regions of Tehran show that regions with high temperatures in areas 4, 5, 15, 2 and 14, which have a high population density in them, are created.

In recent years, climate change and rising global water demand as a result of population growth has caused water scarcity. In this regard, calculation of agricultural water productivity in order to optimize the management of water resources and reduce the water consumption is essential. One of the promising methods for this purpose is remote sensing. In this research, a functional and fully satellite-based model that called WATPRO was used for direct calculation of agricultural water productivity and its evaluation in the Jiroft plain located in Kerman province. For this aim, Landsat8 satellite imagery were acquired during the growing season of wheat on 2016-2017 years and after necessary image preprocessing, the WATPRO model was implemented. The deployment peak, cultivation and harvesting time for six divided field were determined by using the time series of Normalized Deference Vegetation Index (NDVI) extracted from satellite imagery, then wheat water productivity was calculated and the results were evaluated with ground control points. The results shows that the highest and lowest water productivity for wheat in this area is 0.4 and 0.8 kg m-3, respectively and the average of water productivity in the study area was estimated around 0.5 kg m-3. Also the correlation coefficient of 76.5% was found between average NDVI and water productivity in this area. Assessing the accuracy of the WATPRO model with the measured water productivity at field show that this model perform well for estimation and mapping water productivity with an RMSE and correlation coefficient of 0.16 kgm-3 and 85% respectively.

The land surface temperature (LST) plays a vital role in a wide range of scientific researches including climatology, hydrology, natural resources and etc. There are some determining factors which affect the land surface temperature, such as the kind of surface elements, topography and environmental conditions and also the amount of incoming radiation to the surface. The objective of this study is to investigate the effect of topographic parameters, climatic conditions and downward radiation on land surface temperature using remote sensing data. For this purpose, the Landsat 8 satellite image, ASTER digital elevation model, MODIS water vapor product (MOD07) on 24 July 2018, topography and climate map of Kerman province were used. To calculate the LST and downward shortwave and longwave radiation to surface the single channel and SEBAL energy balance algorithms were used, respectively. Finally, using statistical analysis the relationship between LST and independent variables, including elevation, slope, aspect, vegetation cover index and downward radiation to the surface were studied. The results of the study shown that the correlation coefficient between the LST and each of the independent parameters is more than 0.7. Also, the relationship between LST and topographic, normalized difference vegetation index (NDVI) and downward radiation parameters at the 95% level was significant. The results of the mean of LST values in climatic conditions, including extra-dry, dry, semi-dry, Mediterranean, semi-wet and wet indicate that climates classes with higher LST relative to climates classes with lower LST have means of elevation, NDVI lower and mean longwave downward radiation to surface higher.

The growing world population needs more food, with less water available for agriculture. This pinching situation can only improve if water is managed more effectively leading to increased crop yield per unit of water consumed. Crop yield is the ultimate indicator for describing agricultural response to water resources management. The need to monitor crop growth and assess the relationships between crop yield and hydrological processes is elementary for improving the productivity of water. Crop yield forecasts a few months before harvest can be of paramount importance for timely initiating food trade secure the national demand and timely organize food transport within countries. Managers and policy markers need information about agricultural products yield at different scales for devise suitable management strategies. These strategies include product prices and market in import or export. But always estimating crop yield due to the lack of sufficient ground information and existing problems was very difficult and costly. The most appropriate strategies are using satellite data and remote sensing. This study was conducted to evaluate the sugarcane crop yield using landsat 8 satellite data in 2013.
In this study a composite model was used to estimate the volume of sugarcane crop. This combined model includes Monteith model, Carnegie Institution Stanford model and the surface energy balance algorithm for land (SEBAL). Monteith’s model is used for the calculation of absorbed photosynthetically active radiation (APAR), the Carnegie Institution Stanford model is used for determining the light use efficiency, and the surface energy balance algorithm for land (SEBAL) is used to describe the spatio _ temporal variability in land wetness conditions. The new model requires a crop identification map and some standard meteorological measurements as inputs. In surface energy balance algorithm for land (SEBAL) method, all fluxes of the energy balance at the earth's surface including net radiation, soil heat flux, and sensible heat flux are calculated from satellite images and finally evaporative fraction is computed based on the energy balance at the earth's surface. The accumulation of biomass is according to the Monteith model proportional to accumulated APAR. Yield mapping was conducted with the implementation of this algorithm in 2013 and with the usage of 10 landsat 8 satellite images. The biomass development and crop yield computations have been executed in a GIS environment. The annual cycle has been split up into 10 discrete intervals to comply with the 10 Landsat 8 images. The time step varies, depending on cloudiness, and is 16 days on average. Each image is representative for a discrete time interval. All calculations are repeated for 10 different Intervals in an independent manner. The total biomass development of a crop lifetime is approximated by integrating the biomass growth over the cropping season using the cropping calendar.
The yield obtained from satellite images was compared and eraluated with the actual measured yield product in the to sugarcane field. Average sugarcane crop yield estimated to 56 tons per hectare. The yield estimation by the composite model revealed correlation and good distribution of farms actual yield. Then the effect of age and varieties on the model accuracy rate for sugarcane yield estimating was examined. It was found among the different varieties, calculated yield in the varieties cultivated fields CP57- 614 have correlation with the yield actual volues that is due to prematurity and better match to the last image. It was also observed with the aging of sugarcane cultivation until the fourth raton, yield dropped, the estimated yield decreased, solidarity and distribution becomes less that the sugarcane actual yield and correlation value is reduced to %51

Distinguishing the type of agricultural production usingfor optimal water resource management the Remote Sensing, despite the low availability of direct access to agricultural land, would significantly reduce costs in the agricultural area. However, agricultural classification is not very accurate due to the high similarity of different products using one image of multi-spectral. One of the ways to dominate this problem is to use the data time series of the satellite. The purpose of this study is to increase the precision of agricultural product separation by using time series data. In this study after the process of optical data, various vegetation indices, as well as albedo and surface temperature of the optical time series data were calculated and using the TIMESAT model, the key phonological parameters of the plant were extracted in part of the Miandoab plain. Given the availability of ground truth data and, vegetation status such as distribution and vegetation characteristics were examined. Then, using all of these features, a map of agricultural products was created using the support vector machine classification algorithm. The support vector machine classification algorithm is due to the high flexibility of this algorithm for different situations and purposes with a total accuracy of 92% and a kappa of 0.91 if the classification process involves the combination of bands, vegetation indices GNDVI and the ALBEDO, LST index, made the most careful distinction between crops.

As a highly productive product, walnut plays an important role in the social and economic condition of farmers in various regions across Iran, and there has been a dramatic increase in the area under cultivation of this product in recent years due to its economic return. Walnut trees are highly sensitive to the climate conditions. Therefore, before Cultivation of this product, which requires a relatively high initial investment, it is necessary to take certain measures to identify and assess suitable lands for planting walnut trees. Therefore, this study aims to identify and assess suitable lands for planting walnut in the entire of Tehran province that would result in a desirable production efficiency. For this purpose, this study uses climatic parameters (including temperature, relative humidity, precipitation, and wind speed) from 12 synoptic stations within Tehran Province and 8 stations from adjacent provinces for an 11-year statistical period (from 2004 to 2014), topography, and land use for site selection and zoning of suitable regions for cultivation of Persian walnut. First, raster data layers were generated using all the parameters under study. Then, through a review of literature and expert views on physiological properties and growth requirements of walnut, the desirable and undesirable thresholds for its cultivation were determined. Next, analytic hierarchy process (AHP) and a researcher-made questionnaire were used to calculate the effectiveness and priority of each parameter. Furthermore, a fuzzy membership function was used to normalize the data layers. Finally, the final zoning map was prepared and extracted using fuzzy overlay tools in ArcGIS. According to this map, an area of 243882.9562 hectares, equal to 17.82% of the entire area of Tehran Province proved very suitable for cultivation of Persian walnut. Moreover, the findings indicate that the three parameters of temperature, relative humidity, and aspect have a special significance and effect on the growth of walnut trees.