Estimation Of Crop Water Requirements Research Articles

Models Comparative Study for Estimating Crop Water Requirement and Irrigation Scheduling of Maize in Metekel Zone, Benishangul Gumuz Regional State, Ethiopia

This study was aimed to compare estimation methods of crop water requirement and irrigation scheduling for major crops using different models and compare the significance of models for adoption at different situations in Metekel zone. Crop water requirement and irrigation scheduling of maize in selected districts of Metekel zone were estimated using CropWat model based on soil, crop and meteorological data and AquaCrop based on soil, crop and meteorological data including Co 2 , groundwater, field management, and fertility status. Model performance was evaluated using Normalized Root mean square errors (NRMSE), model by Nash-Sutcliffe efficiency (NSE), Prediction error (Pe), and Model efficiency (MF). It is observed that the maximum reference evapotranspiration in the study area was found to be 7.1 mm/day in Guba and minimum reference evapotranspiration was 2.9 mm/day in Bullen district. In all cases, the maximum ETo in all districts was fund to in March and the lowest in August. The maximum ETc of maize was found to be 702.4mm in Guba district and minimum ETc was found to be 572.6mm in Bullen district using CropWat but the effective rainfall (Pe) for maize were determined as 185mm respectively in Wembera district. However, using AquaCrop model the maximum ETc of 565 mm was recorded in Guba but 425 mm was recorded as minimum in Wembera district for irrigated maize in the study area. The study revealed that the irrigation scheduling with a fixed interval criterion for maize 10 days with 12 irrigation events has been determined. Moreover, furrow irrigation with 60 % irrigation application efficiency was adjusted during irrigation water applications for all districts. The performance of the irrigation schedule and crop response was evaluated by the analysis results in the simulation using different models. It has been observed that there was a strong relationship and a significant relation between the simulated and observed values for validation. Hence, Normalized Root mean square errors (NRMSE), model by Nash-Sutcliffe efficiency (NSE), Prediction error (Pe), and Model efficiency (MF) showed that AquaCrop model well simulated in all parameters considered. AquaCrop model is the most suitable soil-water-crop-environment management model, so future studies should suggest a focus on addressing deficit irrigation strategy with different field management conditions to improve agricultural water productivity under irrigated agriculture for the study area for major crops. Keywords: Depilation, Irrigation events, AquaCrop, Fixed interval and Deficit Irrigation. DOI: 10.7176/JEES/13-1-01 Publication date: January 31 st 2023

Estimation of Crop Water Requirements and Irrigation Scheduling for Major Crops Grown in India's North-Eastern Region

The water demand of some agricultural crops grown in Assam was assessed in this research. To estimate the crop water requirements of potato, chili, and brinjal, the FAO-CROPWAT model was used. The climatic parameters like rainfall, solar radiation, wind speed, sun hours, relative humidity, minimum and maximum temperature were collected for 30 years from the Indian Meteorological Department (IMD) to estimate the crop water requirement (CWR). The reference evapotranspiration (ETo) was computed by using the Food and Agriculture Organization (FAO)-recommended Penman-Monteith method and was found to vary from 1.84 mm/day to 4.49 mm/day. The calculated ETo was further used to compute the CWR, the net irrigation requirement (NIR), and the gross irrigation requirement (GIR) to plan for irrigation application during the growing stages of the selected crops. The total CWR of potato, chili, and brinjal was estimated to be 260.6 mm, 262.1 mm, and 274.2 mm, respectively, and the total NIR of potato, chili, and brinjal sowed on the 1st of October was found to be 162.8 mm, 149.0 mm, and 167.2 mm, respectively, with an irrigation efficiency of 70%. In December, the CWR and NIR of potato and brinjal were found to be the highest. The month with the least effective rainfall (8.5 mm) was December. The findings of the study showed that efficient irrigation water management and irrigation scheduling can be achieved by using the CROPWAT model to maximize crop yield and minimize crop water stress.

Irrigation of protected pepper crops according to growth stage using dynamic evapotranspiration estimates increases the water use efficiency

Current knowledge on crop irrigation in greenhouses is limited, and most irrigation measures are based on evapotranspiration (ET) estimated using external climatic conditions. Moreover, crop water requirement estimation by the Penman-Monteith (PM) model assumes complete coverage of the soil by the plants and usually does not account for partial cover, which changes during the growth of annual crops. The main goals of the present research were: (i) to develop and apply irrigation demand estimates for greenhouse pepper crops during their growth, based on internal meteorological conditions and real-time plant characteristics; and (ii) to examine the effect of that irrigation on yield. Experiments were conducted during two consecutive seasons in greenhouse pepper crops in the Jordan Valley region of eastern Israel. Four irrigation treatments were applied based on four different ET models: (i) PM model for outside reference evapotranspiration based on the FAO56 methodology, also denoted as the commercial treatment; (ii) PMrb model for pepper plants, which is a standard PM model in which a boundary layer resistance replaces the aerodynamic resistance; (iii) Shuttleworth-Wallace Layer (SWL) model which considers the soil and plants as separate interactive layers; (iv) Shuttleworth-Wallace Patch (SWP) model which considers the soil and plants as independent patches. The commercial treatment used external climatic conditions from a nearby meteorological station, whereas the other three treatments used internal meteorological conditions measured inside the greenhouse. Results showed a high correlation (R2 > 0.88) between leaf area index, vegetation cover fraction, and plant height during plant growth. Using the same meteorological conditions inside the greenhouse, the seasonal ET estimated by the PMrb model, which considers full vegetation cover, was larger by 21-28% than the SWL and SWP models, which consider the partial plant cover. Irrigation doses were deduced from ET multiplied by crop coefficients derived in a previous study for the same crop and structures. Using average data of the two seasons, we found that seasonal irrigation based on the two SW models was lower by 5-15% than that based on PMrb, and by 35-42% than the commercial irrigation. The water use efficiency of the commercial treatment was the lowest, while for the other treatments it was about 0.02 ton m-3 and higher by about 40% than the commercial. We conclude that irrigation of annual crops based on partitioning ET into evaporation and transpiration based on crop coverage and using internal climatic conditions allows water-savings without significant yield loss.

Estimation of Crop Water Requirement of Kharif Maize Crop using CROPWAT 8.0 Model in Bilaspur District

Water is one of the most important elements in this world. No life will exist without water. The main consumer of water is agriculture but due to mismanagement, overpopulation, and climatic changes this valuable natural resource is getting scarce position throughout the world. It has become very important to define appropriate strategies for planning, development, and management of water resources. The main objective of this paper is to the estimation of crop water requirement of Kharif maize crop for effective irrigation management. For proper irrigation management, CROPWAT is the best model suggested by FAO. 16-year climatic data were analyzed with the CROPWAT model, which is based on the United Nations Food and Agriculture Organization (FAO) paper number 56 (FAO 56). The Penman-Monteith method was used to estimate reference evapotranspiration (ET0). The model predicted the daily, decadal as well as monthly crop water requirement at different growing stages of maize crop. The average reference crop evapotranspiration (ETc­) during the 16 years was found to be 313.21 mm. It varies from 222.8 mm to 515.9 mm. The average irrigation requirement for maize crops during the period from 25th June to 17th September was 42.01 mm.

An Artificial Intelligence Approach to Manage Crop Water Requirements in South Africa

Estimation of crop water requirements is of paramount importance towards the management of agricultural water resources, which is a major mitigating strategy against the effects of climate change on food security. South Africa water shortage poses a threat on agricultural efficiency. Since irrigation uses about 60% of the fresh water available, it therefore becomes important to optimise the use of irrigation water in order to maximize crop yield at the farm level in order to avoid wastage. In this study, combined application of an artificial neural network (ANN) and a crop – growth simulation model for the estimation of crop irrigation water requirements and the irrigation scheduling of potatoes at Winterton irrigation scheme, South Africa was investigated. The crop-water demand from planting to harvest date, when to irrigate, the optimum stage in the drying cycle when to apply water and the amount of irrigation water to be applied per time, were estimated in this study. Five feed –forward backward propagation artificial neural network predictive models were developed with varied number of neurons and hidden layers and evaluated. The optimal ANN model, which has 5 inputs, 5 neurons, 1 hidden layer and 1 output was used to predict monthly reference evapotranspiration (ETo) in the Winterton area. The optimal ANN model produced a root-mean-square error (RMSE) of 0.67, Pearson correlation coefficient (r) of 0.97 and coefficient of determination (R2) of 0.94. The validation of the model between the measured and predicted ETo shows a r value of 0.9048. The predicted ETo was one of the input variables into a crop growth simulation model, called CROPWAT. The results indicated that the total crop water requirement was 1259.2 mm/decade and net irrigation water requirement was 1276.9 mm/decade, spread over a 5-day irrigation time during the entire 140 days of cropping season for potatoes. A combination of the artificial neural networks and the crop growth simulation models have proved to be a robust technique for estimating crop irrigation water requirements in the face of limited or no daily meteorological datasets.

Estimation of crop water requirement and irrigation scheduling of rice in Shivamogga district of Karnatakausing FAO CROPWAT

Climate change is expected to have a significant impact on the water needs of rice crop worldwide in the upcoming decades. Proper water management is essential to enhance crop yield as well as maximising the region’s water use efficiency. The purpose of this study was to estimate the crop water requirement (CWR) and irrigation scheduling of rice in Shivamogga district of Karnataka using FAO CROPWAT model for a time span of 20 years (2001 to 2020). It was estimated that the crop water requirement of rice was 565.50 mm (average of 20 years) with the highest and lowest CWR (606.1 and 527.9 mm) in 2011 and 2001, respectively. Crop water requirement value showed a slight increasing trend (R2 = 0.0544) throughout the years from 2001 to 2020. Total gross irrigation (TGI) and total net irrigation (TNI) for rice was 491.61 and 344.12 mm, respectively during the study period. The present study is useful for effective planning and management of irrigation water needs of rice in Shivamogga district of Karnataka.

Combination of Sentinel-2 Satellite Images and Meteorological Data for Crop Water Requirements Estimation in Intensive Agriculture

In arid and semi-arid regions, agriculture is an important element of the national economy, but this sector is a large consumer of water. In a context of high pressure on water resources, appropriate management is required. In semi-arid, intensive agricultural systems, such as the Tadla irrigated perimeter in central Morocco, a large amount of water is lost by evapotranspiration (ET), and farmers need an effective decision support system for good irrigation management. The main objective of this study was to combine a high spatial resolution Sentinel-2 satellite and meteorological data for estimating crop water requirements in the irrigated perimeter of Tadla and qualifying its irrigation strategy. The dual approach of the FAO-56 (Food and Agriculture Organization) model, based on the modulation of evaporative demand, was used for the estimation of crop water requirements. Sentinel-2A temporal images were used for crop type mapping and deriving the basal crop coefficient (Kcb) based on NDVI data. Meteorological data were also used in crop water requirement simulation, using SAMIR (satellite monitoring of irrigation) software. The results allowed for the spatialization of crop water requirements on a large area of irrigated crops during the 2016–2017 agricultural season. In general, the crops’ requirement for water is at its maximum during the months of March and April, and the critical period starts from February for most crops. Maps of water requirements were developed. They showed the variability over time of crop development and their estimated water requirements. The results obtained constitute an important indicator of how water should be distributed over the area in order to improve the efficiency of the irrigation scheduling strategy.

Remote Sensing and GIS Based Crop Acreage Estimation of the Rabi Season Growing Crop of the Middle Gujarat (India)

Accurate and precise information about crop type, crop stage, and crop acreage is essential for sustainable utilization of available water resources. The present study is concerned with the estimation of Rabi season growing crops in the Panchmahal district of Gujarat state, India. In these study generation of spectral profiles and crop acreage estimation, Sentinel-2 satellite images were classified using unsupervised classification with ISODATA clustering classification techniques. The satellite image of the study area was classified into 52 classes and overlaid with a ground truth point shape file on the classified image. Total twelve date NDVI based signature derived from sentinel – 2 data during rabi season 2020 - 2021. NDVI based data set is used to classify the study area's major crops, which are maize, wheat, castor, chilli, cotton, pigeon pea, sorghum, and tobacco. The total research area's growing crops was estimated as 34472.59 ha, 16517.09 ha, 2186.92 ha, 250.59 ha, 8005.56 ha, 1130.93 ha, 1719.38 ha and 1468.52 ha for maize, wheat, castor, chilli, cotton, pigeon pea, sorghum and tobacco, respectively. The overall accuracy and kappa coefficient for crop acreage estimation were calculated to be 90.71% and 0.78%, respectively. The resulted acreage estimation will help to understand the cropping pattern and their interaction with spatial and temporal variability for present and future estimation of crop water requirement and proper resource availability in this selected region.

Comparison of SWAT and MODIS Evapotranspiration Data for Multiple Timescales

Evapotranspiration (ET) provides important information for hydrological studies, including estimating plant water requirements which can be derived from remote sensing data or simulated using hydrological models. In this study, ET derived from the Moderate Resolution Imaging Spectrometer (MODIS) was compared with ET simulated by the calibrated and validated Soil and Water Assessment Tool (SWAT) model for the Big Sunflower River watershed (BSRW) in Mississippi. The comparisons were made based on 8-day, 1-month, seasonal, and annual timescales. The coefficients of variation (COVs) for the 8-day, 1-month, seasonal, and annual ET simulated by SWAT were 0.42, 0.40, 0.32, and 0.04, respectively, whereas the COVs for the ET derived from MODIS were 0.06, 0.12, 0.08, and 0.01 for the respective time scales. Lower COVs for the ET derived from MODIS indicated lower sensitivity to crop growth in the field. SWAT-simulated ET was the highest during crop growing season and lowest during dormant season, but MODIS-derived ET did not vary considerably according to crop growing or harvesting seasons. As MODIS-derived ET accounts for only climatic conditions and vegetation cover, SWAT-simulated ET is recommended for the short-term estimation of crop water requirements because it accounts for climatic, land use, soil, and slope conditions.

Estimation of crop water requirements within Wainganga sub-basin for Kharif and Rabi season using spatial analysis

In India, largest user of the water is agriculture sector is the, so it is important to do the proper management of available water. Aim of this study to estimate crop water requirements (CWR) for Kharif and Rabi seasons within Wainganga sub-basin by using remote sensing and GIS technique. For this, reference evapotranspiration (ET0) is estimated by using the Food and Agricultural Organization (FAO) Penman-Monteith method. Weather data from 8 weather stations has been collected to estimate ET0. The crop coefficient (KC) is estimated by using linear relationship with Normalized Difference Vegetation Index (NDVI). The MODIS NDVI dataset is used for calculation of crop coefficient. The effective precipitation (Pe) has been calculated to estimate CWR by using FAO recommended empirical method. The spatial variation maps for ET0, KC, actual evapotranspiration (ETa) and CWR are generated using Inverse Distance Weightage (IDW) interpolation technique in ArcGIS software. The results show that ET0 and ETa are higher in Kharif season than the Rabi season. The KC is also found higher in kharif than in rabi. Since the Pe is negligible in Rabi season, CWR in Rabi season is found to be higher than kharif season. The CWR in Rabi varies between 320 mm to 378 mm and in kharif season it varies between 94 mm to 263 mm. It is concluded that the seasonal estimation of CWR helps in understanding the peak water demand in that season in better way. It is required to provide sufficient irrigation to the crops in Rabi season especially as the agricultural production completely depends on the irrigation facilities in the study area because there is no rainfall in this season.

Prediction of daily reference crop evapotranspiration in different Chinese climate zones: Combined application of key meteorological factors and Elman algorithm

Reference crop evapotranspiration (ETO) is a key factor for estimating crop water requirements, which guide agricultural irrigation. To improve the accuracy of predicting ETO in different climate zones in China that lack meteorological data, an ETO hybrid model based on K-nearest neighbour (KNN) machine learning algorithm for extracting factor contribution rates is proposed in this study. Meteorological factors with large contribution rates were selected as input, and a prediction model for ETO was established using the Elman daily ETO prediction model. The ETO prediction model was optimised using three optimisation algorithms [Genetic optimization algorithm (GA), Cuckoo optimization algorithm (CS) and Whale optimization algorithm (WOA)] to improve the accuracy of ETO prediction. The results revealed that surface radiation (Rs) is the most important factor in estimating ETO (contribution rate = 0.392–0.626), followed by temperature factors (T; including maximum, minimum, and average temperatures). And each model has the highest accuracy with the input combination of Rs and T. For the different machine learning models, the CS-Elman model had the highest accuracy (RMSE = 0.468–2.235, R2 = 0.567–0.928, MAE = 0.363–1.343, and NSE = 0.345–0.923), and the machine learning model had higher accuracy than the experience model. The CS-Elman model performed more favourably in tropical monsoon and subtropical monsoon regions than that in other areas, and the model performed best at the junction of two climatic zones. The results can provide a theoretical basis for high-precision prediction of ETO in different climate zones in China.

Climate-driven trends in agricultural water requirement: an ERA5-based assessment at daily scale over 50 years

The impact of climate variability on the water requirements of crops is a key issue in a globalized world with unprecedented population and unevenly distributed water resources. Changes of hydro-climatic forcings may have significant impacts on water resources use, considering the possible effects on irrigation requirements and crop water stress. In this work, a comprehensive estimation of crop water requirements over the 1970–2019 period is presented, considering 26 main agricultural products over a 5 arcmin resolution global grid. The assessment is based on a daily-scale hydrological model considering rainfed and irrigated scenarios, driven by hydro-climatic forcings derived from ERA5, the most recent climate reanalysis product within the Climate Change Service of the Copernicus Programme. Results show the heterogeneous impact of climate variability on harvested areas of the world, quantified by water stressed days and irrigation requirement rates. Increases of irrigation requirement rates were found on more than 60% of irrigated lands, especially in regions like South Europe, North-East China, West US, Brazil and Australia, where the mean rate increased more than 100 mm yr−1 from 1970s to 2010s. The daily analysis of water requirements shows that crops require significantly more days of irrigation per season, especially in Europe, Africa and South-East Asia. Statistically significant trends of water stress duration were found over 38% of rainfed croplands, while only 6% of croplands has been affected by negative trends and shorter stress duration, mainly in India, Malaysia, North Europe and coastal regions of central western Africa.

Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India

Free water evaporation is an imperative parameter for estimation of crop water requirement, and irrigation scheduling. This study aims to evaluate different techniques to estimate evaporation with weather parameters inputs. Multilayer Perception (MLR), Radial Basis Function (RBF) based neural network, traditional statistical Linear Regression (LR) approach and conventional empirical methods of Linacre and Christianson were used to estimate the evaporation at Jabalpur station situated under Kymore Plateau and Satpura Hills Agro-climatic Zone of Madhya Pradesh in the Central India. The weather parameters considered for estimation of evaporation are temperature, humidity, sunshine hours and wind speed. Results indicate that MLP and RBF based models with input of all selected weather parameters is able to estimate evaporation much precisely than LR and empirical approaches. It was found that higher accuracy may be obtained with multiple weather data input and low accuracy with only temperature input. It was observed that with temperature used as input the performance accuracy reduces in estimating evaporation with the selected models. However, neural network approach seems to produce better results as compared to statistical and empirical approach. The neural network based model RBF found more efficient in estimation of evaporation as compared to MLP. This study suggests that evaporation can be estimated by RBF model of a station, where there is no standard instrument available for its observation.

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Proper irrigation scheduling and agricultural water management require a precise estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly estimated, and used further to calculate the evapotranspiration of each crop. In this study, two new coupled models were developed for estimating daily ETo. Two optimization algorithms, the shuffled frog-leaping algorithm (SFLA) and invasive weed optimization (IWO), were coupled on an adaptive neuro-fuzzy inference system (ANFIS) to develop and implement the two novel hybrid models (ANFIS-SFLA and ANFIS-IWO). Additionally, four empirical models with varying complexities, including Hargreaves–Samani, Romanenko, Priestley–Taylor, and Valiantzas, were used and compared with the developed hybrid models. The performance of all investigated models was evaluated using the ETo estimates with the FAO-56 recommended method as a benchmark, as well as multiple statistical indicators including root-mean-square error (RMSE), relative RMSE (RRMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). All models were tested in Tabriz and Shiraz, Iran as the two studied sites. Evaluation results showed that the developed coupled models yielded better results than the classic ANFIS, with the ANFIS-SFLA outperforming the ANFIS-IWO. Among empirical models, generally the Valiantzas model in its original and calibrated versions presented the best performance. In terms of model complexity (the number of predictors), the model performance was obviously enhanced by an increasing number of predictors. The most accurate estimates of the daily ETo for the study sites were achieved via the hybrid ANFIS-SFLA models using full predictors, with RMSE within 0.15 mm day−1, RRMSE within 4%, MAE within 0.11 mm day−1, and both a high R2 and NSE of 0.99 in the test phase at the two studied sites.

An investigation of reference evapotranspiration trends for crop water requirement estimation in Rajasthan

Trends in reference evapotranspiration (ETo) estimated using Penman-Monteith equation were analysed over arid, semi-arid and humid regions of northwest (NW) India during 1985–2018. The MannKendall is used to determine significance of trends. Theil-Sen’s estimator and least square linear fitting methods are adopted to find slopes of the trend lines. The results indicated a significant decrease in ETo on annual basis for most of the locations and NW India as a whole. However, the trend was not statisticallysignificant for seasonal ETo. The significant decrease in solar radiation and wind speed nullified the impact of increased temperature and resulted in slight decrease in ETo over arid and semi-arid regions of NW India which could probably be attributed to the increased dust hazy conditions prevailing. In NW India, water is a limiting resource the decrease in ETo may help researchers in decision makers to develop water assets and utilize the irrigation systems more effectively. There was also an increasing trend in production of major crops in the study region. Further, in near future, if this decreasing ETo trend were to remain, it would help in intensification of cropping system with the existing water resource.

Crop water requirement estimation using pan evaporimeter for high density apple plantation system in Kashmir region of India

Crop water requirement estimation using pan evaporimeter for high density apple plantation system in Kashmir region of India

Estimating crop water requirements for irrigation scheduling in different crops in humid subtropical agro-climate of Western Himalayas

Estimating crop water requirements for irrigation scheduling in different crops in humid subtropical agro-climate of Western Himalayas

Integrating Drone Technology into an Innovative Agrometeorological Methodology for the Precise and Real-Time Estimation of Crop Water Requirements

Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The proposed methodology was based on the crop water stress index (CWSI) and was applied in Greece within the ongoing research project GreenWaterDrone. The innovative approach combines real spatial data, such as infrared canopy temperature, air temperature, air relative humidity, and thermal infrared image data, taken above the crop field using an aerial micrometeorological station (AMMS) and a thermal (IR) camera installed on an unmanned aerial vehicle (UAV). Following an initial calibration phase, where the ground micrometeorological station (GMMS) was installed in the crop, no equipment needed to be maintained in the field. Aerial and ground measurements were transferred in real time to sophisticated databases and applications over existing mobile networks for further processing and estimation of the actual water requirements of a specific crop at the field level, dynamically alerting/informing local farmers/agronomists of the irrigation necessity and additionally for potential risks concerning their fields. The supported services address farmers’, agricultural scientists’, and local stakeholders’ needs to conform to regional water management and sustainable agriculture policies. As preliminary results of this study, we present indicative original illustrations and data from applying the methodology to assess UAV functionality while aiming to evaluate and standardize all system processes.

Estimation of crop water requirement for rice- wheat and rice- maize cropping system using CROPWAT model for Pusa, Samastipur district, Bihar

This study is about estimation of crop water requirement for rice-wheat and rice-rabi maize cropping system for Pusa Region of Samastipur district of Bihar using CROPWAT model for year 2017-18.The effective rainfall was calculated using USDA S.C. Method. Reference crop evaporation was calculated using meteorological data viz temperature, relative humidity, wind speed and Sunshine using Penman Monteith equation. The meteorological data were collected from university observatory of R.P.C.A.U Pusa. Crop coefficient (Kc) value was taken according to crop growth stages. Effective rainfall and crop water requirement was used for determining net irrigation requirement. The annual crop water requirement of Rice- Wheat cropping system was found to be 904.1 mm whereas the crop-water requirement of Rice- Rabi Maize cropping system was 991.7 mm.

Estimation of Actual Evapotranspiration in Panam Canal Command Using Remote Sensing and Geographical Information System (GIS)

Estimation of reference evapotranspiration (ET0) and actual evapotranspiration (ETc) is a key factor for estimation of crop water requirement, water balance and irrigation scheduling. The FAO-56 Penman–Monteith equation has been accepted universally for estimating of reference evapotranspiration (ET0). Considering the high spatial variation of meteorological phenomenon and limited availability of such dense network for data collection, application of remote sensing and GIS has gained momentum for estimation of ET0 and ETc over the larger area with accurately and efficiently. For estimation of ET0 and ETc, the most widely applied MOD16 remote sensing images as well as Landsat 8 remote sensing images are applied in this study of Panam canal command area which is located in the semi-arid middle Gujarat region. Initially, FAO-56 PM method is used to estimate ET0 and MOD16 data is used to estimate ET0, whereas Landsat 8 is used to estimate land surface temperature and then by using the regression equation to estimate maximum and minimum temperature to find out ET0 for the study area. Based on result obtained, it was found that Landsat 8 remote sensing-based data have better capacity to estimate actual evapotranspiration compared to the MOD16 remote sensing data. The better performance of Landsat 8 data compared to MOD16 data is due to the reason that it has better spatial resolution(30m) compared to MOD16 (1 km) remote sensing image and can represent the actual field conditions of farm fields which are generally smaller.