IntroductionSoil salinization is a global environmental problem with serious economic, social and economic consequences. Measuring soil salinity includes the concentration of all salts dissolved in the soil, generally expressed in units of electrical conductivity (EC). Determining where, when, and how soil salinity occurs is essential to determining the sustainability of land use and development systems. Due to the ability to repeat and capture a wide range of remote sensing images, this technology will be useful for detecting changes even in short periods of time, and as an essential tool in monitoring soil salinity, it provides very valuable information on the size of the captured pixels. Flooding from this rainy season can have a large impact on soil salinity. The purpose of this research is to extract soil surface salinity with high spatial resolution and the effect of heavy rains and spatial analysis of the resulting anomalies in Fars Province using satellite image processing. Materials and Methods Fars Province is located in the south of the central region of Iran with an area of 122.799 square kilometers. The topography of the province consists of mountains and plains. In this province, eight million ha of land are suitable for agriculture and gardens, although only 1.6 million hectares have been used. The agricultural sector in Fars Province, which accounts for a major share of the national gross product, plays one of the most important roles in Iran's production, employment, and food security, so many of the province's agricultural products, such as cereals and citrus fruits, rank first to third in the country. Since our study was carried out in a wide area of the country, it was decided to use Google Earth Engine (GEE) as an open-source platform. Also, the Generalized Difference Vegetation Index (GDVI) prepared by Wu (2014) was used to analyze soil salinity. In order to evaluate the efficiency of the obtained model, R2 and RMSE indices were used. In order to verify the output of the field data collected from the Agricultural and Natural Resources Research Center of Fars Province, which was used as a ground sample for the evaluation By using spatial analysis in the form of geostatistics, spatial structures can be identified and spatial planning can be done. Results and Discussiom For the studied area, soil salinity was between 7.01 and 53.63 decisiemens/meter. The difference between the highest and the lowest soil salinity in the study area is approximately seven times, the highest value being in the east and south of the province in the cities of Niriz, Larestan, Lamard and Zarindasht. A significant point is the sharp increase in soil salinity in the bed of rivers leading to Bakhtegan and Tashk lakes. According to the available ground data, the accuracy of the map was checked, and the square root of the error and the correlation coefficient were calculated as 0.33 and 0.59, respectively. Then, the soil salinity map was extracted using the same algorithm in the period of Farudin 2018 due to the heavy rains that were associated with the arrival of numerous rain systems in Iran. Soil salinity was obtained between 6.35 and 47.9 and was classified into five classes. The results showed that changes have been made in the minimum and maximum values ​​of salinity and soil salinity levels and soil salinity has decreased especially in the south of the province.Then soil salinity anomaly was obtained and spatially analyzed. The term soil salinity anomaly means deviation from the reference value or long-term average. The results showed that the amount of abnormality increased and decreased between 0.8 and -0.9 in the province. Areas with lower salinity have experienced a greater share of positive anomalies. The positive anomaly was mostly around Darab, Zanian and Babamonir in the northeast of Jahrom. The southern and eastern parts, including Lar, Ozer, Rastaq, Ahl and Lamard, which were in the medium salinity class, have suffered less salinity anomalies. In order to understand the cluster or scatter pattern of soil salinity changes, Moran's spatial autocorrelation coefficient was investigated. The results showed that the anomaly of salinity distribution in the rainy year has a cluster pattern. By examining the available maps, it can be said that the clusters of soil salinity anomalies are mostly located in the north of the province, Baba Monir and Zarian at higher altitudes of the province and to some extent in the south of the province around Darab and Jahrom. Also, a little clustering has occurred in terms of anomalies in the plains of the province; That is, the rains could not cause major changes in the soil salinity of the plains. ConclusionIn this research, the soil salinity map using GDVI in two time periods before and after the heavy rains of the water year 1398-1397, using the open source platform Google Earth Engine, extracting and changing the soil salinity classes and converting the salinity classes to each other, as well as the method of spatial clustering. The salinity anomaly was investigated. Soil salinity for the studied area was calculated between 7.01 and 53.63 decisiemens/meter with square error and correlation coefficient of 0.331 and 0.59, respectively. Soil salinity has changed between 6.35 and 47.9 after heavy rains. The most changes due to heavy rains are related to the low salinity layer with 19% and the least changes are related to the very saline layer with 0.3%. The amount of anomaly between 0.8 and 0.9 decisiemens per meter was increasing in the center of the province around Bakhtegan and Tashk lakes and the western highlands of the province and decreasing in the south and east of the province. Areas with lower salinity contribute more positive anomaly. The southern and eastern parts, which have high and very high salinity, undergo less changes. The results of this study showed that the use of remote sensing and satellite data in the Google Earth Engine cloud system and spatial analysis to prepare soil salinity maps in areas that have a large area and are affected by salinity changes, has great financial and time savings, and in Areas where sampling is not done or is associated with issues can be very efficient. Such researches can easily and quickly identify areas that are most exposed to increasing or decreasing soil salinity and can be used in environmental planning to implement preventive measures.

Soil salinity is an important environmental issue that reduces soil productivity. For optimal management of soil resources, quantitative monitoring of soil salinity, temporal changes and spatial analysis of the factors affecting it are necessary. The purpose of this study is to extract the soil salinity distribution map and its spatial analysis after more than normal rainfall in the rainy year of 1997-98 in western Iran. Using Landsat satellite imagery and GDVI index and algorithm written in Google Engine system, soil electrical conductivity map was extracted and classified into five salinity classes. The results showed that in general, soil salinity has decreased in the study area. Areas with high salinity that are in the low altitude class have not changed. If the precipitation factor in this study period is the most important factor in changes in salinity distribution, this factor could not have a great effect on the salinity class, but the medium salinity class had the most changes. Move the soil surface of this class down. For the ellipse, three times the standard spatial deviation of the northwest to the southeast was obtained, which shows that more than 99% of the salinity dispersion follows the spatial arrangement of altitudes, precipitation and dispersion of soil categories in this direction. Statistics of 0. 4566 of Moran index and P_Value showed 0. 00 spatial salinity of soil salinity in the west of the country.

In this research, 23 soil samples with specific geographical characteristics were collected to investigate and monitor salinity changes in the region. Using the Sentinel-2 and Landsat-8 sensors, seven vegetation cover indices and five salinity indices were examined and evaluated in the GEE environment, resulting in a total of 240 outputs from the two sensors. To assess the modeled values, several statistical indices including root mean square error (RMSE), coefficient of determination (R2), normalized root mean square error (NRMSE), and percent bias (PBIAS) were utilized. The results indicated that the SI-2 index exhibited the highest correlation with the measured salinity values in the region, with an R2 value of 0.91, demonstrating its accuracy in estimating salinity levels. In the next step, a multiple regression model was employed to investigate the mean values of measured ECe (electrical conductivity of the saturation extract) and the vegetation indices GDVI (Green Difference Vegetation Index) and CRSI (Crop Salt Stress Index) obtained from the Sentinel-2 sensor, which showed the highest correlation with the salinity data. The results demonstrated that the two-variable regression model achieved a satisfactory accuracy with an R2 value of 0.84 and a PBIAS value of 0.01 in producing a salinity map of the area. Therefore, this model can be utilized as a cost-effective approach for salinity mapping in the region with minimal ground-based data. Furthermore, the investigation of the impact of constructing a barrier drain in the area revealed that the construction of a barrier drain within a distance of 250 meters had a significant effect of approximately 40 percent in controlling salinity. It was able to prevent a substantial increase in salinity levels in the region. Therefore, if a barrier drain is not constructed in the area, salinity progression in the upstream agricultural lands could significantly escalate.

Extended AbstractIntroductionVarious Climate factors considerably affect the environment and different vegetation covers show different levels of sensitivity to climate factors in the spatial-temporal scale. Data specifically collected from vegetation cover plays an important role in micro and macro planning and information generation. Methods using air temperature recorded in weather stations to estimate the relative heat in urban areas are considered to be both time-consuming and costly. On the other hands, data with relatively high spatial resolution are capable of measuring ground surface parameters more efficiently and accurately. Thus, remote sensing technology is now considered to be a solution used to improve previously mentioned methods. Remotely sensed data are now widely used to find the quantitative relationship between patterns of vegetation cover and the elements of climate. Predicting the conditions of vegetation cover is considered to be essential for planners seeking an efficient plan for its exploitation and protection.Materials & MethodsThe present study seeks to investigate the effects of climatic factors on the vegetation trend observed in Frame forest in Mazandaran province using Sentinel 2 images and to determine the most suitable index for this area. Climatic Data collected from the nearest weather station in Farim City have been used to model climate factors (temperature and precipitation). Changes in the height above mean sea level were also considered. Following the pre-processing and processing of the Sentinel 2 images, the corresponding digital values were extracted from the spectral bands and applied as independent variables. ENVI software was used for image processing and STATISTICA and R software were used for modeling. 70% of the resulting data were used for training and the rest were used for testing or evaluating the model. Mean square error, correlation, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to evaluate the presented models. Models with the highest correlation and the lowest standard error, the mean square error, the Akaike information evaluation criterion and the Bayesian evaluation criterion were selected as the best models for the studied variables.Results & Discussion       A correlation coefficient of 0.43 and 0.56 was observed between temperature and precipitation and vegetation indices. AIC and BIC values equaled (565 and 3209) and (739 and 3383) respectively. Differential Vegetation Index (DVI) has proved to be the most effective parameter in relation to both temperature and precipitation factors in the region. Results indicated that differential vegetation index, green normalized difference vegetation index (GNDVI) and green difference vegetation index (GDVI) have a positive correlation with temperature, while there is a negative correlation between temperature and normalized vegetation index. Precipitation is considered to be one of the most important factors affecting vegetation. Results indicate that differential vegetation index, green difference vegetation index, green normalized difference vegetation index, non-linear vegetation index and normalized difference vegetation index have the highest impact on precipitation. In forest ecosystems, changes in climatic factors may affect trees differently. ConclusionCollecting information about the state of vegetation cover in forests is considered to be very important. Thus, the present study has endeavored to investigate the relationship between indices of vegetation cover and climatic variables. To reach this aim, satellite data are used as a suitable and efficient tool for investigating forest ecosystems with a relatively low cost. This provides the possibility of continuously monitoring land surface. Results indicated that climatic factors affect vegetation indices in the study area. Vegetation cover protects and stabilizes the environment and thus, many researchers have tried to investigate the growth and spatial patterns of vegetation cover in different regions. It is also suggested to study the effects of climatic factors on the vegetation cover of the study areas in different geographical directions. In addition, using other climatic factors such as relative humidity, wind speed, evaporation, transpiration, and higher resolution images can increase the accuracy of the study.

Introduction: Biophysical properties of vegetation, temperature and surface moisture are key parameters to control and evaluate the physical and chemical processes of the land surface. Biophysical properties can be used to monitor various applications such as urban heat island, climate change and drought. Access to timely information and awareness of the changes in land’ s biophysical properties are required in integrated management and sustainable development. Erath’ s surface biophysical properties can be successful being retrieved using different types of remote sensing data. From 1970s, remote sensing data provides unique information in surveying dynamic phenomena. Remote sensing sensors provide data in wide area with a repetitive manner. Remote sensing sensors collect Earth surface data at different part of electromagnetic spectrum, i. e. optic, thermal and microwave. As a result, the same phenomenon may provide different responses depending on the radiation’ s wavelength. These responses are complementary and the joint use of them offers more reliable information. That is the reason, multi-sensor approaches gain more attention. Multi-spectral optical sensors such as Landsat have been widely used in earth surface studies; however, their applications are limited mainly in the presence of smoke, fog and clouds. In contrary, radar sensors (e. g. Synthetic Aperture Radar, SAR) operates well even in cloudy sky. SAR sensors are sensitive to the moisture content and structure (shape, direction, roughness) of the surface. Therefore, the main purpose of this study is to evaluate the efficiency of radar bands for extracting surface biophysical properties. Materials and methods: For the purpose of comprehensive study, three different areas with different types of land cover were considered as the study areas. The first study area, located in the east of Ardebil, encompasses bare land. The second study area is located in the southeast of Ardebil with agricultural land use. The third study area in Mazandaran province is around Noor city. Land cover is dense natural forest. Landsat-8 and Sentinel-1 satellite images dated on 2019 were acquired. The Landsat-8 image has already been geo-referenced with UTM coordinates system, zone 39. The coordinate system for Sentinel images is WGS84 ellipsoid. We used the GRD product which has VV and VH polarizations. In this study, pre-processing steps were done to prepare images including atmospheric correction (Landsat images) and geometric (Sentinel images). FLAASH algorithm has been used for atmospheric correction. Next, spectral indices were computed from Landsat visible and infrared bands to represent surface biophysical properties. Single-channel algorithm is used to calculate surface temperature. Multiple linear regression was applied to model surface biophysical properties by the help of Sentinel polarimetric bands. Finally, based on these model, surface biophysical properties maps were driven. Results and discussion: In this study, 18 spectral indices were extracted from Landsat image. It should be noted that some of these indices are normalized and some are not normalized, so for all indices to be comparable the values of all indices were set to zero and one normal. Case study 1 In the first study area, radar’ s backscattering values showed more significant relationships with LST, NDBI and IPVI indices, while there were weak relationships among radar’ s backscattering values with MNDWI, GDVI and SR indices. High coefficient of determination between radar responses and LST values could be justified by the effect of soil moisture on soil temperature and radar backscattering, as well. That’ s why, radar responses can predict LST values. Case study 2 The investigation of the relationship among spectral indices and radar bands shows that radar bands have high potential to extract biophysical properties in this region. Among 18 spectral indices, EVI, MTVI1 and MTVI2 indices were highly correlated with the radar bands. LST, SGI and SR indices showed the weakest correlations with radar bands. This indicates, LST, SGI and SR could not be predicted by backscattering values in agricultural land. Case study 3 In the third study area, MNDWI showed a high correlation with radar responses. MNDWI index was first developed to study the amount of water available to represent vegetation health. Radar bands are also highly sensitive to moisture content. Therefore, it is not surprising that a high correlation between the radar bands and MNDWI index were reported for the third study area, as it is a high moisture forest area. The lowest correlation was observed between radar bands with NDBI spectral index with correlation coefficient of 0. 418. The reason for this low value is the nature of the study area, because the study area covers with a dense vegetation and the NDBI index has been developed to extract the built-up area. Conclusion: Remote sensing technology provides valuable information in patterns recognition and changes of the biophysical properties of the earth's surface. Optical data have good spatial, spectral and radiometric resolution and have been used in a variety of applications. However, optical data is not available in all seasons because the presence of smoke, fog, clouds limit their availability. In contrast to optical data, SAR sensors has the ability to acquire data in all weather conditions. Therefore, the main objective of this study was to investigate the capability of radar data to extract biophysical properties of the land surface. The results show that radar bands have a high capability to extract surface biophysical properties, so radar data can be considered as a good alternative especially when optical data is not available. Considering the proper relation of spectral indices with the targets’ responses in radar bands, the results of this study can further be used in different environmental applications such as heat island, evapotranspiration and coastline extraction. Based on the findings of the current study, it is recommended that future researches investigate the efficiency of full polarization images in comparison with existing spectral indices to extract biophysical properties of the earth's surface. The full polarized radar images also allow the calculation of radar indices based on the degree of backscattering values in different polarimetric bands. In addition, considering the low saturation level of spectral indices (in comparison with radar, responses) and the loss of sensitivity of these indices to phenomena changes, it is highly suggested to investigate the relationship of biophysical properties with radar bands in such a situation.