جستجوی مقالات مرتبط با کلیدواژه "sentinel-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

Streamflow forecasting models play a crucial role in hydrological issues, such as the determination of reservoir inflows and flood forecasting. In this research, artificial intelligence hybrid models including ANFIS and GA-ANN have been used for short-term (daily) streamflow forecasting. This research aims to predict the outlet of the Latiyan basin, Tehran province, from 2017 to 2018. For this purpose, a snow-covered area (SCA) is obtained from the processing of Sentinel-2 optical satellite images. Then, in order to extract the effective snow, the fusion algorithm is applied for Sentinel-1 and 2 integrations. Finally, the artificial intelligence model with the help of the effective snow parameter along with other daily hydrometric and meteorological data including daily precipitation, temperature, and discharge is applied to forecast the daily outlet of the basin. Also, to improve the model performance, the seasonal index has been used to identify streamflow trends and better model training. The results showed that the prediction model using satellite data has improved its performance by 37%, which shows the direct effect of the snow parameter on the basin runoff. In addition, the trend of changes in the effective snow parameter has a favorable agreement with the flow trend of the basin, especially in the peak flows. Also, using seasonal information as an input parameter can improve the results of the prediction models by approximately 22%. In addition, the AI method based on fuzzy inference (ANFIS) showed better performance than the developed neural network method (GA-ANN) based on statistical indices.

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.

Evapotranspiration (ET) as a major component of the hydrological cycle and an important variable in the calculation of the earth's surface water and energy balance has a key role in agricultural water management. Under limited meteorological data, ET estimations using empirical models are less accurate and require adjustments based on water balance approach or lysimetric measurements. Combining ground observations with remote sensing information (such as satellite images) can improve the accuracy of these estimations. In this study, SENTINEL-2 satellite products were used to estimate actual evapotranspiration values in 8 stations of the Gorganrud-Gharehsoo basin, north of Iran, during the period 2016 to 2018. Considering the number of required images and significate variations of ET during warm season, a four months period of April to June was selected for comparisons. The Penman Monteith equation estimations using observed data of study stations was chosen as an evaluation metric of satellite products.Comparisons were evaluated using statistical indices of R2, RMSE. The coefficient of determination (R2) values between Penman-Monteith equation (PM) estimations and SENTINEL-2 outputs for the study stations of Rezvan, Bandar Turkman, Aliabad-e-katol, Gonbadkavos, Kalaleh, Gorgan, Gorgan Hashem-Abad, and Minoodasht, were 0.95, 0.76, 0.86, 0.87, 0.80, 0.86 and 0.84, respectively. The highest correlation of SENTINEL-2 and PM equation was obtained in Rezvan station (RMSE: 0.21, R2 = 0.95), while the lowest correlation was observed in Ghonbad kavoos stations, (RMSE = 0.37, R2 = 0.87). According to results, the SENTINEL-2 evapotranspiration estimations can be used in regions with in adequate observed data. The finer spatial resoulution of SENTINEL-2 improves the ET estimations accuracy. This satellite products may be recommended for regions with inadequate weather stations especially in semi arid regions

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.

Heavy metal pollution is one of the most important environmental problems worldwide and measuring their concentration in the soil is the first step in providing a solution to reduce their risks. However, conventional methods for estimating the amount of heavy metals in the soil are time-consuming and costly. Imagery and remote sensing techniques have shown good potential to be an alternative to the routine approaches for monitoring heavy metal pollution in soil. The aim of this study was to evaluate the efficiency of multivariate analysis methods and remote sensing technique to quantify soil contamination with zinc and lead under different land uses. Previous studies revealed the high concentrations of zinc and lead in Zanjan’ soils cause a serious concern regarding the health of humans in this province.

The study was conducted between the latitude 36° 20' 48" N to 36° 47'45" N, and longitude 48° 15' 51" E to 49° 00' 55" E in Zanjan province. The average daily temperature in the study area is 15.7°C and the mean annual precipitation is 335 mm. Rangeland, agriculture, industrial and residential areas are the main land uses in Zanjan. A total of 230 soil samples were collected at depths of 0–10 cm under different land uses from an area of ​​3424 Km2 at two grids intervals of 1.5 kilometers in industrial and residential areas, and 3 kilometers in rangeland and agriculture. Before chemical analysis, soil samples were air-dried and sieved. Soil lead and zinc contents were measured using an atomic absorption spectrometry (Perkin- Elmer: AA 200). Radiometric and geomatic corrections were performed on satellite images and the “special resampling” procedure was used to resample the 20 m images to 10 m. Thirty spectral indices were determined using Sentinel 2 satellite images. These indices were reported as practical indices for remote sensing assessment of land condition. The indices included; NDVI, NDRE, MTVI, MCAR, MNLI, GNDVI, SAVI, LCI, MTCI, PSRI, CI-RedEdge, CI-Green, NLI, TVI, EVI, STAVI, GRI, LSWI, MSAVI, BI, BI2, RI, CI and seven spectral ratios. Spectral estimation models were developed and evaluated by three methods of principal component regression (PCR), partial least squares regression (PLSR) and support vector machine regression (SVMR). The samples were randomly divided into the validation set (30%) and the calibration set (70 %). Therefore, 161 samples were used for calibrating and 69 samples for validating models. Levene’s test was performed to test the variance homogeneity between calibration and validation sets. The accuracy of models was evaluated using the coefficient of determination (R2), root mean square error of prediction (RMSEP) and the ratio of predicted deviation (RPD). Martens’ uncertainty test was used to identify important wavelengths for zinc and lead estimations.

The range of lead in the studied soils was 40 to 364 mg kg-1 and the range of zinc was 96 to 824 mg kg-1. The accuracy of spectral models was categorised into excellent (RPD ≥ 2.5 and R2 ≥ 0.8), good (2 ≤ RPD < 2.5 and R2 ≥ 0.7), moderate (1.5 ≤ RPD < 2 and R2 ≥ 0.6) and poor accuracy (RPD < 1.5 and R2 < 0.6) based on the soil spectral accuracy classification, that is presented by Askari et al., (2015 and 2019). The highest concentration of metals was observed in industrial and residential land uses. The SVMR model (RPD≥2.6 and R2≥0.84, RMSE≤ 40), had a better spectral estimation for both lead and zinc than the PLSR model (RPD≥ 1.9 and R2≥  0.7, RMSE≤ 53) and the PCR model  (RPD≥1.3 and R≥ 0.45, RMSE≤ 75). Red-edge and infrared range were identified as the most effective wavelength ranges for monitoring the contamination of lead and zinc in soil. Brightness and modified triangular vegetation index were the most effective indicators for spectral estimation of lead and zinc in the studied soils. The SVMR model showed high accuracy and the PLSR model showed acceptable accuracy for evaluating and monitoring lead and zinc contamination using Sentinel 2 images. Comparing the predicted and measured values of heavy metals with a 1:1 line showed an overestimation for low values of lead and zinc, and an underestimation for high values of lead and zinc.

This study revealed that the method of multivariate analysis and remote sensing data could provide a practical approach for rapid and quantitative assessment of soil heavy metal pollution in Zanjan province and areas with similar soil conditions. An accurate prediction of heavy metal pollution can be acquired using freely available Sentinel-2 multispectral imagery system.

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.

Mapping and spatial analysis of wheat fields are very important in studying macroeconomic and social issues, including agricultural management. Highly variable crop pattern maps and its preparation using terrestrial data are associated with many problems. The purpose of this study is to implement a practical method for extracting wheat fields by using changes in vegetation index and spatial analysis of wheat fields in western of Iran. Investigation of the changes curve of vegetation index of wheat typic farms showed that the highest amount of reflection index of wheat farms is in June and early July and after harvest the reflectance index decreases extremely. In this regard, Sentile sensor data was processed in the Earth Engine system and the 12-month vegetation index of 1398 was extracted as a data set. By introducing training data to the data set created by the support vector machine classification method, the land use of the study area was obtained in five classes. By applying altitude filter and removing the extracted fields above 3,000 meters, the distribution map of wheat fields was verified with the remaining 48 ground data. The total accuracy and the kappa coefficient were obtained 0.86 and 0.79, respectively. Since in the proposed method, more training data are given to the algorithm, the overall accuracy of the classification is increased. The spatial pattern of wheat fields with the mean function of the nearest neighbor and P_value <0.05 indicating the cluster dispersion of the fields and the Caripley function indicating the non-random scattering of wheat fields up to distances of 21,000 meters. The results of this research and its output maps can be used to obtain information for agricultural planning as well as the allocation and spatial distribution of resources and facilities.

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.