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Maryam Mazidi; Mousa Hesam; Khalil Ghorbani; Chooghi Bayram Komaki
Abstract
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 ...
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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.
8
shadman veysi; Milad Nouri; Anahita Jabbari
Abstract
To compensate for the lack, or inadequacy, of weather stations data, one of the most reliable ways is to use the remote sensing and re-analysis dataset, which provides a suitable model for such areas. In this study, the performance of two data sources, namely, WaPOR and ERA5, was evaluated in estimating ...
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To compensate for the lack, or inadequacy, of weather stations data, one of the most reliable ways is to use the remote sensing and re-analysis dataset, which provides a suitable model for such areas. In this study, the performance of two data sources, namely, WaPOR and ERA5, was evaluated in estimating reference evapotranspiration at 64 synoptic stations in the Caspian coastal region in Iran, on a daily and monthly basis. To this end, meteorological data of 64 synoptic stations with a 10-year statistical period (2011-2021) was obtained daily from the Iran Meteorological Organization. The field data used included minimum and maximum temperature, relative humidity, wind speed, and solar radiation intensity. Then, evapotranspiration and reference evapotranspiration were calculated using the Penman-Monteith equation and REF-ET software. Finally, the results were compared with the results from the WaPOR and ERA5 data bases. The results showed that, on average, the nRMSE values of the WaPOR and ERA5 datasets compared to the calculated meteorological station data were 29.6% and 29%, respectively, on a daily basis. Also, on a monthly time scale, in more than 85% of the stations, both datasets provided acceptable results. On a monthly scale, the average nRMSE value for both WaPOR and ERA5 sensors in the catchment area was 19%. The rMBE value showed that the ERA5 dataset underestimated the reference evapotranspiration in most of the stations, while the WaPOR dataset overestimated. Given that the error rate of the two sensors is different in over 30 percent of the stations, a suitable estimate of reference evapotranspiration in the Caspian Sea basin area can be obtained by combining the data from these two datasets. The results showed that in the Caspian Sea coastal areas, 34 stations in the WaPOR dataset and 28 stations in the ERA5 dataset showed the minimum error, with two stations showing the same error. Thus, both WaPOR and ERA5 are suitable databases that can be used for hydrological purposes, including estimation of reference evapotranspiration.
8
Jamal Mohammadi Moalezade; saeid hamzeh; Abdali Naseri
Abstract
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 ...
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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.
8
Hossein Jafari; ali morshedi
Abstract
Estimation of crop water requirement and evapotranspiration by lysimeter is costly and time-consuming and could not be applied to larger field scale. Remote sensing technology can overcome this limitation. The goal of this research was to estimate alfalfa actual evapotranspiration using satellite imagery ...
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Estimation of crop water requirement and evapotranspiration by lysimeter is costly and time-consuming and could not be applied to larger field scale. Remote sensing technology can overcome this limitation. The goal of this research was to estimate alfalfa actual evapotranspiration using satellite imagery and compare it with the in-situ measurement by lysimeter. The study was carried out from 2017 to 2020 in the agricultural lands of Alborz and Charmahal and Bakhtiari provinces employing Surface Energy Balance Algorithm for Land (SEBAL) method. Lysimeter has been implemented under standard conditions. The cold pixels of each satellite image were extracted to estimate net alfalfa crop water requirement. In-situ net crop water requirement for Alborz and Charmahal and Bakhtiari provinces were obtained as 1383 and 1087 mm, respectively. The coefficients of determination (R2) were 73% and 76%, respectively, for the two studied provinces. The statistical analysis showed that there were small deviations from the mean values. The standard evapotranspiration measurements using lysimeter were higher than the satellite estimations. This technique can be useful for the estimation of crop water consumption since it is simple, cheap, fast, and can be used for large areas.
8
hamed poursamsam; elham akbari; ali mohammad akhond ali; saeid boroomand nasab
Abstract
Pressurized irrigation methods can be a suitable solution for optimal use of water resources, provided that the selection, design, implementation and operation of irrigation systems are done with sufficient care and according to the principles. In decision-making and implementation of pressurized irrigation ...
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Pressurized irrigation methods can be a suitable solution for optimal use of water resources, provided that the selection, design, implementation and operation of irrigation systems are done with sufficient care and according to the principles. In decision-making and implementation of pressurized irrigation systems at the sub-regional and regional scale, several factors such as water, soil and climate along with socio-economic factors are of particular importance. Dez Plain (including Lor, Dimcheh, West Dez, East Dez and Sabili plains) is the largest plain and one of the most important agricultural hubs in Khuzestan Province. In this study, using AHP in GIS software, the suitability of various areas for implementing different irrigation systems (localized, solid set, wheel move, centre pivot, linear, Gun, low-pressure, and surface) was investigated in Dez Plain. For this purpose, effective criteria including socio-economic and field physical conditions were considered for implementation of each irrigation method. Socio-economic criteria included four sub-criteria, namely, operation and maintenance, costs, laborers skills, and local culture, and physical conditions included water, climate, soil, and topography. The final location map was prepared in GIS software. According to the results, Dez Plain areas with high suitability for each methods were as follows: 62.77% for all sprinkler systems, 14.6% for localized irrigation, 14.3% for low-pressure irrigation, and 8.3% for surface irrigation. Among all irrigation methods, the solid-set sprinkler obtained the highest score in all parts of the study area and, in total, 15.04% of the whole plain is highly suitable for this system.