جستجوی مقالات مرتبط با کلیدواژه "سنتینل-2" در نشریات گروه "آب و خاک"

Lack of water, increase in production costs, change in land use, and increase in demand for food have caused the accurate determination of spatial and temporal monitoring of the paddy fields area to be extremely important for planners and decision makers. Actually, the use of field methods to estimate the cultivated area of crops in large areas is costly and time-consuming, and is associated with errors. Hence, the purpose of this study was to use remote sensing images to estimate the paddy fields area in the Guilan Province by the best classification method. Therefore, Sentinel 2 satellite images were analyzed using 6 supervised classification methods including ML, CART, RF, SVM, GME and RF-NDVI methods in GEE environment. The ML method was performed in the ENVI environment and the rest of the methods were performed in the cloud space of the GEE environment. The results of using the classification methods showed that the Random Forest method along with the NDVI (RF-NDVI) with a kappa coefficient of 0.94 and total accuracy of 0.90 had the highest accuracy compared to the other methods, which shows the effect of the vegetation index in distinguishing between paddy fields and other land uses. This estimation of the area under rice cultivation in the province showed that the net area of the total paddy fields in the province was 218,135 ha. This estimate, compared to the available statistics of the Agricultural Jihad Organization (238,012 ha) and the Regional Water Company of Gilan Province (245,000 ha) was, respectively, 8.35%. and 10.96% less

Limited fresh water resources and access to these resources as well as providing food security for the growing world population have led researchers to make extensive efforts in the field of optimal management of water consumption and determining the cultivation pattern in different regions. Therefore, identifying cultivated crops in a region and determining their area can be very effective in land management and water allocation in these regions. With the growth and advancement of technology in the field of satellite and remote sensing in recent decades, the use of satellite images in order to identify types of land use and types of cultivated products has expanded greatly. Sentinel-1 (radar) and Sentinel-2 (multi-spectral) satellites have been very popular in agriculture due to their improved spatial resolution (10 meters) and appropriate time resolution (5 days for Sentinel 2 and 12 days for Sentinel 1).

The studied area is located downstream of the Fariman dam in an area of 22.51 square kilometers (5122 hectares) and the central coordinates are 35 degrees 41 minutes and 59 seconds north latitude and 59 degrees 50 minutes and 49 seconds east longitude. In order to classify satellite images and produce crop maps, ground observation data is needed to train the classification model and also evaluate the accuracy of the results. For this purpose, sample points were taken from different land uses in the region, using GPS. Since it was not possible to take enough samples for all land uses and crops in the determined border, a larger sampling area was selected. Then, all collected data were sorted and for each class, 70% of the data was randomly used to train the classification model and 30% was used to validate the obtained classification results. In the present study, Sentinel 2 satellite images for the first 6 months (crop season) of 2021 and 2022 and digital elevation image (DEM) of the study area were considered. According to the surveys conducted and the reports of the agricultural jihad of Fariman city, the main crops cultivated in the region include maize, tomato, sugar beet, wheat and barley. Therefore, according to the phenological stages of these products in the region, the appropriate time series of images was selected. The accuracy of the classified map was evaluated using the Kappa coefficient and overall accuracy.

In order to identify and separate the land use in the study area according to the major cultivated crops, first the agricultural calendar of the crops was determined. Then, satellite images were selected based on crop cultivation period. Based on the evaluation indexes of commission error, omission error, overall accuracy as well as the Kappa coefficient, it was observed that the identification of classes and land use was done well and with high accuracy, so that the overall accuracy for the classification map of 2022 is equal to 0.97 and the kappa coefficient value was 0.94. In order to compare land use changes during the two years 2022 and 2021, classification was also done for the images of the crop year 2021. Since the training samples of agricultural crops were not available separately and in sufficient numbers in the crop year of 2021, the classification map of this year was produced only based on the type of land use, and all crops in one class entered the classification model training process. The values of overall accuracy and kappa coefficient in 2021 were obtained as 0.97 and 0.95 respectively. According to the obtained results, the area of the orchard class has increased since 2021 compared to 2022. After repeated field visits to the study area and investigation of some land uses that had been changed and turned into orchard use, it was found that in some areas in 2022 there was the growth of villa gardens and in some areas the farmers have converted cropland to orchard (construction of an orchard). Even in some cases, the old orchard in the region was destroyed by the farmers and the land was fallow for 2 to 3 years (2021, fallow). In 2022, the farmer built a new orchard. It is also necessary to mention that fallow lands are included in the soil class depending on whether they are newly plowed or have no vegetation, and if weeds have grown on these lands, they are included in the rangeland class. 

The effective management of water resources from dams for agricultural purposes necessitates the identification of land use downstream of the dams, along with determining the types of crops and their respective areas. In this study, Sentinel 2 satellite images were employed to classify and delineate land use associated with agricultural cultivation downstream of the Fariman dam in Razavi Khorasan Province, spanning the crop years of 2021 and 2022. The results indicate that the Sentinel 2 satellite demonstrates a high capacity to differentiate between various types of land use and crops. The generated map depicting changes in land use and crop cultivation areas can be instrumental in water use planning and the allocation of water resources.

Water stress occurs as a result of the imbalance between soil water in the root zone and plant water use, which necessitates determining the water stress index of the plant. Surface soil moisture is directly related to plant water content. Availability of satellite data has led to temporal and spatial resolution of field data and offers new opportunities for monitoring crop conditions. In this research, accurate and continuous monitoring of soil moisture content, as a representative of soil moisture stress, was done with field measurements of soil moisture, and comparison with multispectral data of Landsat 9 and Sentinel 2 satellite images. The relationship between plant indices, as an independent variable, and soil surface moisture, as a dependent variable, was studied using linear multivariate regression and M5 tree regression methods. Considering the non-linearity of the relationship between soil moisture and spectral reflectance, linear multivariate regression did not show satisfactory results with coefficient of determination (R2) of 0.46 and 0.34 for Landsat 9 and Sentinel 2 satellites, respectively, as well as the root mean square error (RMSE) equal to 0.043 and 0.052. However, M5 tree regression showed more acceptable results, such that by establishing 16 and 20 regression relationships for Landsat 9 and Sentinel 2 satellites, the soil moisture was estimated withR2 of 0.70 and 0.67 and RMSE of 0.033 and 0.038, respectively. The results showed that the estimation of soil moisture with methods based on machine learning, such as the M5 model, increases the accuracy of calculations. In the M5 decision tree regression, a high number of variables does not necessarily lead to an increase in the accuracy of soil moisture estimation, and a relationship with the highest accuracy was found in the low number of variables. Therefore, the relationship obtained at the field level can be used to evaluate soil water stress and determine irrigation time in agricultural lands on a large scale, without measuring soil data.

One way to ensure food security is to produce strategic agricultural products on a large scale using industrial methods. Managing large-scale farms consistently and cohesively is a challenging task that requires the utilization of modern technologies. Crop anomalies refer to uncommon and limited factors during agricultural production, leading to localized differentiation in the crop cultivation process. Factors contributing to crop anomalies in agriculture include imbalances in soil nutrients and fertilizers, grazing during crop growth, pests, variations in soil texture and slope in pastures, weed growth, and drought. Detecting and remediating factors limiting crop growth in vast agricultural lands is difficult and these issues are often noticed at harvest time. This article suggests a solution for continuously monitoring of large agricultural fields by analyzing the time series of Sentinel-2 satellite images. The effectiveness of this solution in detecting various anomalies of farms, in agrarian areas has been demonstrated by the results. The proposed solution offers features such as timely diagnosis, the ability to monitor the continuation of irregularities, and the measurement of compensatory measures' effectiveness. The method has successfully identified over five types of anomalies in the selected farms, achieving a detection accuracy of 95.60%.

Hydraulic conductivity is one of the most important physical properties of soil, and knowing it plays a vital role in investigating the transport of solutes and pollutants in porous environments such as soil. This study aims to obtain saturated hydraulic conductivity pedotransfer functions (PTFs) using soil properties and satellite images. In this regard, the hydraulic conductivity of soil saturation was performed using the Inversed augerhole method in a part of the southwestern lands of Khuzestan province at 50 points. Then, at these points, surface samples of the soil were taken and soil properties such as soil texture, electrical conductivity, soil organic carbon, and saturated moisture were determined in the laboratory. In the next step, the indices of Sentinel-2 satellite images were calculated in three categories of soil, vegetation, and moisture indices and 11 PTFs, (PTF1-PTF11) for saturated hydraulic conductivity were obtained in four stages by combining soil properties and these indices. Finally, the spatial distribution of saturated soil hydraulic conductivity was obtained using the random forest model. The results of the modeling of PTFs of saturated hydraulic conductivity showed that among the 11 models with which PTFs of hydraulic conductivity were performed, the combination of three vegetation indices with soil-found early properties was the most effective for estimating the saturated hydraulic conductivity (PTF7). The values of R2, RMSE and MAE for this case were equal to 0.83, 0.40 and 0.166 respectively. Finally, the spatial distribution of saturated hydraulic conductivity using the Random Forest model showed that this model performs the spatial distribution of saturated soil hydraulic conductivity of soil well. Based on the obtained results, it can be found that the combination of soil properties with the indices obtained from the Sentinel-2 satellite images, creates PTFs of saturated hydraulic conductivity of the soil with very high accuracy.

It is necessary to study the spatial-temporal trend of drought, which affects agriculture and food supply with its slow process and Multiple generating factors; Therefore, the present study was conducted to assess agricultural drought in Kermanshah using satellite indicators (Average images of Sentinel2 and Landsat8 satellites during the growing season) and terrestrial (using rainfall and temperature data from 1990 to 2020). The results of terrestrial indicators the absence of drought in 2002, 2007, 2018 and 2019. The 12-month and 24-month SPEI of 2001 and the 24-month SPI estimated 1999 in the category of very severe drought. Satellite indicators in 2015 and 2017 increased the Land Surface Temperature and in 2019 (increasing soil surface moisture and Temperature Vegetation Dryness) and 2020 estimated vegetation density. The decrease in sugar beet yield in the years 1999 to 2003 and the increase in the years 2006 to 2009, 2012/2011 and 2021/2020 confirm the high results. In the north, northeastern and eastern regions; High temperature, low rainfall and low humidity have dried the soil and negatively affected the growth, health and density of plants, but the southern region has always been humid. The presence of drought during the study period and Sentinel II images were more sensitive to vegetation indicators.

Considering the important role of snow in the groundwater cycle, the study of snow characteristics, especially in mountainous regions, seems necessary. Remote sensing technology can be used to study large areas with high spatial and temporal resolution. Synthetic aperture radar sensors with large frequency bands, small wavelengths, and high permeability are preferred in this type of study. Differential Radar interferometry technique Although the volume of information derived from interferometric analysis is high is a powerful tool in calculating the depth of snow, and the Sentinel data set is preferred due to easy access in interferometric studies. On the other hand, the relationship between LST with snow characteristics is considered to be a lot of researchers. In this study, the radar interferometry technique for estimating the depth of snow, as well as the Google Earth Engine, cloud system, has been used to estimate the snow characteristics, including the depth and surface of the snow cover. Also, the relationship between the component of temperature and snow surface and depth is examined.

The Liqvan watershed with an area of 185 kilometers is located in the northwest of the country and East Azerbaijan province. In this study, for extraction of the depth of snows from 4 radar images of Sentinel 1 related to the time interval of December until March 1398 and a radar image associated with September 1398 in SLC format to implement radar interferometry in SARSCAPE software Used. To increase accuracy part of the work was used from the Google Earth Engine system. For this purpose, to extract the surface of the snow cover and its area of the NDSI daily product of the Modis sensor and the monthly NDSI- DEPTH product was used for extraction of the average depth of snow of each snow month in the Google Earth Engine Cloud System. Also, the Daily Product of Mod11A1 Modis Sensor was used to prepare a temperature map to examine the relationship between temperature and snow characteristics.

Investigating the map of snow surfaces in the area of all months of the study period in the region showed the highest concentration of snow surfaces in high regions. Due to the outputs of the Google Earth Engine system, the highest and lowest snow cover level is calculated by January with 180 kilometers and December with a value of 83 km. The average and the lowest amount of the depth of snow is related to the February and December months, which utilizes the radar interferometry technique of 32 and 9 centimeters and uses the Snow depth- Inst product in the Google Earth Engine system 24 and 4 centimeters Has shown. The values for regression analysis were obtained between the time series of the surface temperature and the surface of the snow cover, respectively, 0/003 and -3/020 for the parameters of Sig and Z. The R2 variable was also obtained 0/47 about the correlation of the depth of snow and lst.

The results of this study indicate the ability of both radar interferometry technique and coding in the Google Earth Engine in calculating the depth of snow. Maps and measures of the depth of snow can be an appropriate tool for managing water resources in the region for various uses. Also, the results of regression coefficients showed a significant relationship between the LST variable and the depth of snow and snow cover. So that the inverse relationship between the two components of LST and the snow cover (SC) and LST, and the depth of snow, as well as the direct relationship between reduced temperature and LST, showed.

Snow cover is one of the important climatic elements based on which climate change may have a special effect. In general, climate change may be reflected in different climatic elements. Therefore, it is very important to study and measure changes in snow level as one of the important sources of water supply. Ardebil and Sarein cities are located at 48° 18׳ east longitude and 38° 15׳ north latitude. In this study, Sentinel-2 optical satellite was used to monitor the snow cover surface in 2018, and NDVI, S3, NWDI, NDSI, Cloud mask indices were applied to detect snow-covered surfaces using ArcGIS and Snap software. Next, to validate the snow maps extracted from the images, it was compared with the snow data in terrestrial stations using linear regression in MATLAB software and to evaluate the accuracy of the model statistical indices including RMSE, MSE, BIAS, CORR were used. The present study showed that according to Ardabil city climatic conditions, maximum-snow covered area in January with an area of 356.52 km2 and minimum snow-covered area in March with an area of 96.10 km2. The highest snow cover is observed in the high slope areas in the western slopes (Sabalan Mountain Heights) and the lowest snow cover is observed in the lower eastern slopes. The results of linear regression with generalization coefficient are 85% and the results of statistical indices of error are equal to MSE: 0.086, BASAS: 0.165, CORR: 0.924, and RMSE: 0.03. Correlation relationships between terrestrial data and estimated snow maps showed a high degree of correlation. This result is statistically significant at the 99% level. The use of optical images in estimating snow levels is very cost-effective due to the size of the areas and the high cost of installing snowmobiles. The results obtained in the present study indicated that traditional radar images with high spatial resolution and good correlation with terrestrial data can be a good alternative to snowmobiling ground stations at high altitudes or in passable areas.

Biomass plays an important role in providing energy and the global carbon cycle and monitoring the biomass is one of the most important issues in the agricultural sector. Using remote sensing techniques is an effective tool for estimating biomass. This reduces field studies and saves time and money. Today, management practices can be improved using remote sensing technology and vegetation indices. The suitability of the weather in Golestan Province for producing most agricultural products has made the province highly diversified in terms of crop and biomass production. For this purpose, in this study, satellite images of Landsat 8 and Sentinel 2 were used to estimate the residue biomass of crops in the watershed of the southwest of Golestan province (Mohammad Abad, Qaresoo, Zaringol, and Gharnabad).

For this, field surveys were carried out to sample the fields at different stages of plant growth, in the winter and spring of 2016. The number of samples for wheat, barley, canola, rice, and soybean was 90, 70, 65, 67, and 80, respectively. The biomass data were also measured from field survey using four quadrates (0. 25m2) and their moisture content was determined by weighting. The Landsat 8 and Sentinel 2 images were downloaded from the USGS site in the early and middle of the growing season for mapping sattelitesatellite-based vegetation indices including, NDVI, SAVI, RVI, DVI, and RDVI. The relation between the indices and obtained biomass was evaluated using regression analysis to produce biomass maps for five crop residues.

The results showed that the NDVI index is the best indicator to estimate the wheat, barley, and rapeseed residue biomass, in April, with a determination coefficient of 0.61, 0.65, and 0.65, respectively, according to the vegetative growth peak, and for the soybean crop in September, according to the peak of vegetative growth with a determination coefficient of 0.65, and the RVI index is the best indicator for estimating rice residue biomass in August in line with peak vegetative growth in the study area. The total amount of wheat, barley, canola, rice, and soybeans residues in the study area was estimated as 751657, 175637, 14979, 42628, and 93712 tons, respectively. The results of this study can be applied to managers and decision-makers of the agricultural sector in the province by identifying the potential for agricultural production and the amount of their residues.

The results of this study showed that the residue biomass of different products can be estimated by using the vegetation indices extracted from satellite images with acceptable accuracy in the study area. In this research, the overlaying for determining the cultivation area of wheat and soybean products was 97.74% and 97.6%, respectively. The precision of this method for peak vegetation periods was higher than the early stages of plant growth. Also, instead of a vegetation index, several vegetation indices were used and the NDVI and RVI index was the best indicator for estimating the residue biomass of different products.