10 research outputs found
Monitoring of crop water consumption changing based on remotely sensed data and techniques in North Sinai, Egypt
This paper aims to approximate and verify crop water use based on satellite results. Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were used as the critical parameters derived from NOAA/AVHRR and landsat8 satellite data. Reference evapotranspiration (ETo) was determined using FAO-Penman-Monteith (FPM) agrometeorological data as a standard process. Based on data from remote sensing, the ETo was calculated based on the Hargreaves (Har) process. ETo-FPM has been used to calibrate ETo-Har under the same conditions for five years (2002-2006). Landsat8 data was obtained on 25 June 2013 and 28 June 2014 and used to estimate the crop coefficient (Kc) based on satellite data (Kc-Sat). The LST was used to predict the maximum, minimum, and mean Tair (oC) levels in June 2013 and 2014. ETo was calculated using the expected maximum, minimum, and mean Tair according to the Har method and was used with Kc-Sat to estimate ETc-Har. ETo-FPM is used to measure ETc-FPM with Kc-Sat. LST and NDVI have been used to measure the Water Deficiency Index (WDI). WDI incorporated ETc to measure the actual evapotranspiration of the crop (ETa). ETa-FPM was used for the evaluation of ETa-Har. The relationship between ETa-FPM and ETa-Har was high, where R2 was 0.99 in 2013 and 2014. ETa determined by Hargreaves based on remotely sensed data was overestimated at about 0.8 (mm/day) compared to the FPM process
Estimation of Regional Evapotranspiration Based on Tri-Angle Method Using Thermal and VNIR Data
Evapotranspiration is a critical component in the hydrological cycle, water resources management and climate studies especially in arid and semi-arid regions. This paper aimed at producing a simplified and applicable procedure for estimating spatial distributed daily actual evapotranspiration (ETa) directly at regional scale using thermal and visible-near infra-red (VNIR) data. The findings of this study will be useful for irrigation water management, climate change studies and water resources planning in the study region. Triangle method, which makes a parameterization of priestly-Taylor equation, was used to estimate ETa at daily scale directly by using a simplified approach with realistic hypotheses. This study conducted in Egypt, Salhia, 6th of October Company as an arid region over the winter crops (wheat, potato and sugar beet) cultivated there using multi date Landsat images. The results were compared with ETa values adjusted from crop evapotranspiration ETc “FAO Penmamn-Monteith approach” using the Crop Water Stress Index (CWSI). The results showed high accuracy and good agreement against assessment method. The correlation factor (R2) values for wheat, potato and sugar beet were 0.88, 0.98 and 0.99 and Root Mean Square Error (RMSE) were 0.2, 0.26 and 0.37 respectively over the different dates. In the 16th of April, 2014 there was a significant difference in wheat curves as the RMSE were 0.8 and we explained the reasons of this difference as it is a result of the sprinkler irrigation system effect on the mature wheat. This results show that the proposed procedure is accurate enough at least in most cases of our study for estimating the regional surface ETa but it need to evaluate for wheat under other irrigation systems like surface or drip irrigation systems .
 
Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta
The objective of the current work is to generate statistical empirical rice yield estimation models under the local conditions of the Egyptian Nile delta. The methodology is based on regressing measured yield with satellite derived spectral information or leaf area index (LAI). LAI field measurements and spectral information from SPOT data collected during two crop seasons are examined against measured yield to generate the yield models. Near-infrared and red bands, six vegetation indices and LAI of 100 points are used as the main inputs for the modeling process while 20 points of the same are used for validation process. Nine models are generated and tested against the observed yield. Comparing the generated models show relatively higher superiority of (LAI-yield) and (infrared-yield) models over the rest of the models with (0.061) and (0.090) as a standard error of estimate and (0.945) and (0.883) as coefficient of determinations between modeled and observed yield. The models are applicable a month before harvest for similar regions with same conditions
The Effect of Water and Vegetation Vigor on Citrus Production in Egypt Using Remotely Sensed Data and Techniques
The vigor of vegetation and water availability are major components in agricultural production which are affecting on crop yield quantity and quality. Crop water stress occurs continuously over the total growing period or during any one of the individual growth periods of the crop. This study aims at quantifying the Vegetation and water stress effect on Valencia orange yield through remotely sensed data and techniques to predict the yield. Landsat OLI satellite imageries provide Red (R) and Near-Infra-Red (NIR) measurements which used to calculate the Normalized Difference Vegetation Index (NDVI). Land Surface Temperature (LST) was calculated from the thermal spectral region (band 10) and integrated with air temperature measurements to estimate Crop Water Stress Index (CWSI). Three Valencia orange farms were studied and 27 samples were collected (9 samples/farm). Two cultivation seasons data sets were investigated (2013/2014 and 2014/2015). Many regression models were produced. NDVI and CWSI were modeled with yield through a regression model analysis. The first season multi-regression model was the best model where R2 was high as 0.852 and regression validation was very good. The predicted yield map showed the spatial distribution of Valencia orange yield in the field, which ranged from 6.9 (ton/fed) to 29.2 (ton/fed).
 
The Effect of Water and Vegetation Vigor on Citrus Production in Egypt Using Remotely Sensed Data and Techniques
The vigor of vegetation and water availability are major components in agricultural production which are affecting on crop yield quantity and quality. Crop water stress occurs continuously over the total growing period or during any one of the individual growth periods of the crop. This study aims at quantifying the Vegetation and water stress effect on Valencia orange yield through remotely sensed data and techniques to predict the yield. Landsat OLI satellite imageries provide Red (R) and Near-Infra-Red (NIR) measurements which used to calculate the Normalized Difference Vegetation Index (NDVI). Land Surface Temperature (LST) was calculated from the thermal spectral region (band 10) and integrated with air temperature measurements to estimate Crop Water Stress Index (CWSI). Three Valencia orange farms were studied and 27 samples were collected (9 samples/farm). Two cultivation seasons data sets were investigated (2013/2014 and 2014/2015). Many regression models were produced. NDVI and CWSI were modeled with yield through a regression model analysis. The first season multi-regression model was the best model where R2 was high as 0.852 and regression validation was very good. The predicted yield map showed the spatial distribution of Valencia orange yield in the field, which ranged from 6.9 (ton/fed) to 29.2 (ton/fed).
 
Integrated method for rice cultivation monitoring using Sentinel-2 data and Leaf Area Index
it is necessary to apply a remote sensing-based system for rice cultivation assessment parallel with the field measurements of the crop biophysical parameters. This study aims to map the rice cultivated areas and give an estimate for the expected yield (ton/ha) using Sentinel-2 satellite data. The study was carried out in an experimental site in the Kafr El-Sheikh governorate with a total area of 3240 ha. The multi-temporal Normalized Difference Vegetation Index (NDVI) extracted from nine Sentinel-2 imagery cover the whole summer season. The supervised nearest neighborhood object-based classification method was employed, resulting in a classification map with an overall accuracy of 95% and a kappa coefficient of 0.93. Yield prediction was carried out by using an empirical yield prediction model using the NDVI and the Leaf Area Index (LAI). The LAI was calculated using the Surface Energy Balance Algorithm for Land (SEBAL) model and then validated against the measured LAI. the Mean Absolute Percentage Error (MPAE) was calculated to estimate the error between the measured and predicted LAI and yield. The MPAE was found to be ±6.76% (i.e. ±0.28 m2/m2) with a high correlation between the measured and the calculated LAI with a coefficient of determination (R2 = 0.94). While for the yield, the MPAE was found to be ±6.53% (i.e. ±0.66 ton/ha) and R2 of 0.95. This method is applicable to estimate area and yield of rice in the northern Nile delta in adequate time before harvest. © 2020 National Authority for Remote Sensing and Space Science
Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change
AbstractThis study addressed the increasing challenges of climate change by exploring the use of machine learning (ML) algorithms to predict the reference evapotranspiration (ETo). Accurate ETo prediction is crucial for optimizing irrigation water management. This research aimed to assess the reliability and accuracy of ML algorithms in predicting ETo values. Three ETo calculation methods were employed: Penman-Monteith (PM), Hargreaves (HA), and Blaney-Criddle (BC). The study analyzed ETo and other climate variables using the modified Mann-Kendall test (m-MK) and Theil Sen’s slope estimator methods to identify trends. Multiple ML algorithms, including Support Vector Regression (SVR), Random Forest (RF), XGboost, K-Nearest Neighbor (KNN), Decision Trees (DT), Linear Regression (LR), and Multiple Linear Regression (MLR) were utilized for ETo prediction. The ML algorithms exhibited excellent performance, with coefficients of determination (R2) values ranging from 0.97 to 0.99 for PM, 0.99 for HA, and from 0.91 to 0.92 for BC. The models demonstrated a high value of the Kling-Gupta efficiency (KGE) with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. Strong correlations between the predicted and calculated daily ETo were observed with R2 values of 0.99, 0.99, and 0.92 for PM, HA, and BC methods, respectively. In conclusion, this study affirmed the accuracy and reliability of ML algorithms to match that of standard ETo prediction equations
Assessment of the ability of the radiological incudo-stapedial angle to predict the stapedotomy technique type; a prospective case-series study
Background: Reversal-steps stapedotomy (RSS) has become a standard technique that entails perforation of the footplate when the ossicular chain is still intact, followed by immediate placement and stabilization of the prosthesis and, lastly, removal of the stapes superstructure. It can prevent incus dislocation and footplate luxation, helping to improve surgical outcomes.
Objectives: This study aimed to propose a radiological classification of the incudo-stapedial angle on preoperative high-resolution computed tomography (HRCT) images. We also assessed the proposed radiological classification's ability to predict feasibility of RSS.
Methods: This prospective case-series observational study was conducted at a tertiary referral institute between January 2021 and August 2022, and included 83 candidates for stapedotomy operation because of otosclerosis. Two physicians reviewed the preoperative HRCT images, focusing on the coronal cut on the incudo-stapedial (IS) joint, and measured its radiological angle. According to this measurement, the radiological incudo-stapedial joint could be divided into three types; obtuse, right, and acute angle. This radiological classification was correlated with the intraoperative use of RSS technique.
Results: The RSS technique was used in forty-two (97.7%) patients with an obtuse IS angle and twenty-six (89.7%) patients with a right angle. At the same time, the classical, non-reversal technique was used in all patients with an acute angle. The three groups differed significantly regarding the method used for stapedotomy (P-value<0.001). Moreover, Spearman′s correlation coefficient revealed a significant correlation between the used technique and the radiological type of the IS angle (P-value <0.001).
Conclusions: Our prospective study proposes a preoperative radiological classification of the IS angle at the HRCT. This classification was significantly correlated with stapedotomy technique used. The reversal stapedotomy technique was feasible in most cases with obtuse or right radiological IS angles. Conversely, the non-reversal method was used in all patients with an acute radiological IS angle. This radiological classification could help for predict the feasibility of the RSS technique with an accuracy of 95.18%, a sensitivity of 73.33%, and a specificity of 100%