scholarly journals APPLICATION OF SIMPLIFIED SURFACE ENERGY BALANCE INDEX (S-SEBI) FOR CROP EVAPOTRANSPIRATION USING LANDSAT 8

2018 ◽  
Vol XLII-1 ◽  
pp. 33-37 ◽  
Author(s):  
A. Basit ◽  
R. Z. Khalil ◽  
S. Haque
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Irrigation Scheduling ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Landsat 8 ◽  
Crop Evapotranspiration ◽  
Crop Water ◽  
Balance Index

<p><strong>Abstract.</strong> Assessment and monitoring of crop water requirement (CWR) or crop evapotranspiration (ETc) over a large spatial scale is the critical component for irrigation and drought management. Due to growing competition and increasing shortage of water, careful utilization of water in irrigation is essential. The usage of water for irrigation/agriculture is a top priority for countries like Pakistan, where the GDP mostly based on agriculture, and its scarcity may affect the crop production. Remote sensing techniques can be used to estimate crop water requirement or crop evapotranspiration which can help in efficient irrigation. Simplified-surface energy balance index (SSEBI) model is used to estimate evapotranspiration (ET) of wheat during 2015&amp;ndash;16 growing period in Tando Adam, Sindh. Landsat-8 satellite data for the corresponding years were used. With the help of National Agromet Centre report chart of Crop coefficient (Kc) the CWR, ETc of all phonological stages were estimated. Results indicated that maximum ET and maximum CWR were found in the third leaf to tillering stage with a value of 0.75 and 0.89 respectively. This study will help in managing and monitoring of ET spatial distribution over irrigated crops which results in better irrigation scheduling and water consumption.</p>

scholarly journals Estimation of Arecanut Crop Evapotranspiration Rate using Remote Sensing

2020 ◽  
Vol 9 (4) ◽  
pp. 1814-1817
Keyword(s):  
Remote Sensing ◽  
Hydrological Cycle ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Landsat 8 ◽  
Crop Evapotranspiration ◽  
Crop Water ◽  
Evapotranspiration Rate

Arecanut is a plantation crop sustains for decades and its crop water demand varies with the age. For scheduling and management of irrigation water, crop water requirement information is important. To calculate the crop water requirement, estimation of evapotranspiration is crucial. The term Evapotranspiration (ET) refers to transport of water molecules into the atmosphere from soil (soil evaporation) and vegetation (transpiration) surfaces. It is a most important component of hydrological cycle and also the most difficult factor to quantify. Crop water need is the amount of water required for balancing loss due to evapotranspiration. There are different methods proposed by researchers for the estimation of evapotranspiration. The conventional methods of evapotranspiration estimation from ground data are tedious. The advancement in remote sensing data provides estimation of evapotranspiration in a global scale. The invention of thermal remote sensing has benefitted greatly since it reduces the field data requirement for estimation of ET. It also helps to understand spatial distribution of landmass and different estimates also in estimation of evapotranspiration over a larger extent timely and periodically. In this study to estimate Arecanut crop evapotranspiration Hargreaves Samani, Penman Monteith and Priestly Taylor methods were used and compared. Arecanut crop evapotranspiration rate estimated form Landsat 8 and MODIS data are showed similar range of values between 3 to 4.45 mm/day. The study area covers an area of 835.3 hectares of Arecanut crop and the gross crop water need is found to be 23059 m 3 .

scholarly journals Application of an improved surface energy balance model to two large valley glaciers in the St. Elias Mountains, Yukon

2020 ◽  
pp. 1-16
Author(s):  
Tim Hill ◽  
Christine F. Dow ◽  
Eleanor A. Bash ◽  
Luke Copland
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Shortwave Radiation ◽  
Balance Model ◽  
Landsat 8 ◽  
Glacier Surface ◽  
Ice Dynamics ◽  
Surface Melt

Abstract Glacier surficial melt rates are commonly modelled using surface energy balance (SEB) models, with outputs applied to extend point-based mass-balance measurements to regional scales, assess water resource availability, examine supraglacial hydrology and to investigate the relationship between surface melt and ice dynamics. We present an improved SEB model that addresses the primary limitations of existing models by: (1) deriving high-resolution (30 m) surface albedo from Landsat 8 imagery, (2) calculating shadows cast onto the glacier surface by high-relief topography to model incident shortwave radiation, (3) developing an algorithm to map debris sufficiently thick to insulate the glacier surface and (4) presenting a formulation of the SEB model coupled to a subsurface heat conduction model. We drive the model with 6 years of in situ meteorological data from Kaskawulsh Glacier and Nàłùdäy (Lowell) Glacier in the St. Elias Mountains, Yukon, Canada, and validate outputs against in situ measurements. Modelled seasonal melt agrees with observations within 9% across a range of elevations on both glaciers in years with high-quality in situ observations. We recommend applying the model to investigate the impacts of surface melt for individual glaciers when sufficient input data are available.

Estimation of Evapotranspiration using Remote Sensing and Surface Energy Balance Algorithm for Land (SEBAL) in Canal Command

2021 ◽  
Vol 58 (03) ◽  
pp. 274-285
Author(s):  
H. V. Parmar ◽  
N. K. Gontia
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Crop Growth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Surface Temperatures ◽  
Canal Command

Remote sensing based various land surface and bio-physical variables like Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), surface albedo, transmittance and surface emissivity are useful for the estimation of spatio-temporal variations in evapotranspiration (ET) using Surface Energy Balance Algorithm for Land (SEBAL) method. These variables were estimated under the present study for Ozat-II canal command in Junagadh district, Gujarat, India, using Landsat-7 and Landsat-8 images of summer season of years 2014 and 2015. The derived parameters were used in SEBAL to estimate the Actual Evapotranspiration (AET) of groundnut and sesame crops. The lower values NDVI observed during initial (March) and end (May) stages of crop growth indicated low vegetation cover during these periods. With full canopy coverage of the crops, higher value of NDVI (0.90) was observed during the mid-crop growth stage. The remote sensing-based LST was lower for agricultural areas and the area near banks of the canal and Ozat River, while higher surface temperatures were observed for rural settlements, road and areas with exposed dry soil. The maximum surface temperatures in the cropland were observed as 311.0 K during March 25, 2014 and 315.8 K during May 31, 2015. The AET of summer groundnut increased from 3.75 to 7.38 mm.day-1, and then decreased to 3.99 mm.day-1 towards the end stage of crop growth. The daily AET of summer sesame ranged from 1.06 to 7.72 mm.day-1 over different crop growth stages. The seasonal AET of groundnut and sesame worked out to 358.19 mm and 346.31 mm, respectively. The estimated AET would be helpful to schedule irrigation in the large canal command.

Irrigation Scheduling Based on Evaporation and Crop Water Requirement for Summer Peanuts1

1984 ◽  
Vol 11 (1) ◽  
pp. 4-6 ◽  
Author(s):  
D. K. Pahalwan ◽  
R. S. Tripathi
Keyword(s):  
Water Use ◽  
Irrigation Water ◽  
Irrigation Scheduling ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Pan Evaporation ◽  
Crop Water ◽  
Formation Stage ◽  
Evaporation Ratio ◽  
Pod Development

Abstract Field experiment was conducted during dry season of 1981 and 1982 to determine the optimal irrigation schedule for summer peanuts (Arachis hypogaea L.) in relation to evaporative demand and crop water requirement at different growth stages. It was observed that peanut crop requires a higher irrigation frequency schedule during pegging to pod formation stage followed by pod development to maturity and planting to flowering stages. The higher pod yield and water use efficiency was obtained when irrigations were scheduled at an irrigation water to the cumulative pan evaporation ratio of 0.5 during planting to flowering, 0.9 during pegging to pod formation and 0.7 during pod development to maturity stage. The profile water contribution to total crop water use was higher under less frequent irrigation schedules particularly when the irrigations were scheduled at 0.5 irrigation water to the cumulative pan evaporation ratio up to the pod formation stage.

scholarly journals Manajemen Irigasi Pembibitan Sawit (Elaeis guineensis) Presisi dengan Cropwat 8.0

2019 ◽  
Vol 8 (2) ◽  
pp. 97
Author(s):  
Lisma Safitri
Keyword(s):  
Soil Properties ◽  
Water Use ◽  
Palm Oil ◽  
Irrigation Scheduling ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Climate Data ◽  
Crop Water ◽  
Water Requirements ◽  
Crop Water Requirements

The accurate water use information at each stage of plant growth is important to better understand the efficient and precise crop water requirement for optimal plant productivity. Nurseries of palm oil are a phase where young palm oil requires extra maintenance, particularly in meeting the plant water needs. The palm oil in the nursery phase require the regular irrigation schedule due to the vulnerable root systems. The purpose of this study was to calculate the oil palm water requirement with Cropwat 8.0 toward the precise irrigation management and provide a scenario for irrigation scheduling in palm oil nursery. The study was conducted in palm oil main nurseries at KP2 Instiper Yogyakarta with site-specific climate data and soil properties. The method used is analyzing climate data and soil properties and simulating crop water requirements, actual water use and irrigation scheduling with Cropwat 8.0. Based on the results, the average of crop water requirement (ETP) of palm oil in main nursery is 3.4 mm / day. Based on the water deficit scenario from rainfall and crop water requirements, irrigation is scheduling in April for 1.4 mm, May for 18.3 mm, June for  3.5 mm, July for 44.1 mm and August for 42.8 mm. On a daily scale and taking into account the availability of soil moisture and the water retention of plant roots, the net irrigation scheduling is given at an average of 2.2 mm / day and gross irrigation of 6 mm / day which is given daily depending on rainfall and plant age.

scholarly journals EVAPOTRANSPIRAÇÃO E COEFICIENTE DE CULTURA DO CAFEEIRO IRRIGADO A PARTIR DE IMAGENS DE SENSORES ORBITAIS

Irriga ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 31-37
Author(s):  
THALLES LOIOLA DIAS ◽  
Alex PORTO RODRIGUES ◽  
MILLER RAIK ARCANJO BATISTA ◽  
Marcelo Rossi Vicente ◽  
RONALDO MEDEIROS DOS SANTOS
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Irrigation Management ◽  
Crop Coefficient ◽  
Minas Gerais ◽  
Crop Evapotranspiration ◽  
Landsat 7 ◽  
Sebal Model ◽  
E Mail

EVAPOTRANSPIRAÇÃO E COEFICIENTE DE CULTURA DO CAFEEIRO IRRIGADO A PARTIR DE IMAGENS DE SENSORES ORBITAIS     THALLES LOIOLA DIAS1; ALEX PORTO RODRIGUES2; MILLER RAIK ARCANJO BATISTA3; Marcelo Rossi Vicente4 e Ronaldo Medeiros dos Santos5   1 Instituto Federal do Norte de Minas Gerais, Campus Salinas, Fazenda Varginha Km 02 Rod. Salinas/Taiobeiras - Salinas/MG -CEP:39560-000, Salinas, MG, Brasil. E-mail: [email protected]. 2 Instituto Federal do Norte de Minas Gerais, Campus Salinas, Fazenda Varginha Km 02 Rod. Salinas/Taiobeiras - Salinas/MG -CEP:39560-000, Salinas, MG, Brasil. E-mail: [email protected]. 3 Instituto Federal do Norte de Minas Gerais, Campus Salinas, Fazenda Varginha Km 02 Rod. Salinas/Taiobeiras - Salinas/MG -CEP:39560-000, Salinas, MG, Brasil. E-mail: [email protected]. 4 Instituto Federal do Norte de Minas Gerais, Campus Salinas, Fazenda Varginha Km 02 Rod. Salinas/Taiobeiras - Salinas/MG -CEP:39560-000, Salinas, MG, Brasil. E-mail: [email protected]. 5 Instituto Federal do Norte de Minas Gerais, Campus Salinas, Fazenda Varginha Km 02 Rod. Salinas/Taiobeiras - Salinas/MG - CEP:39560-000, Salinas, MG, Brasil. E-mail:[email protected].     1 RESUMO   O uso de sensoriamento remoto na agricultura é uma realidade. Dentre os diversos usos, destaca-se a determinação da evapotranspiração dos cultivos para o auxílio do processo de gerenciamento da irrigação. O presente trabalho objetivou determinar a evapotranspiração e o coeficiente da cultura do cafeeiro através do algoritmo SEBAL (Surface Energy Balance Algorithm for Land) na região Oeste da Bahia. Para a realização do estudo foram utilizadas imagens do satélite LANDSAT 7. A evapotranspiração de referência foi estimada pelo método Penman-Monteith FAO e, posteriormente, calculou-se o coeficiente da cultura (Kc) com base na evapotranspiração obtida via SEBAL. Os índices estatísticos para avaliar a eficácia do modelo SEBAL foram: o desvio da raiz quadrada média (RMSE); o erro médio absoluto (MAE); o coeficiente de determinação (R2); e o erro relativo (RE). O modelo SEBAL mostrou-se eficiente na determinação da evapotranspiração da cultura do cafeeiro e no coeficiente de cultura.   Palavras-chave: índice de vegetação; sebal; manejo de irrigação.     DIAS, T.L.; RODRIGUES, A.P.; BATISTA, M.R.A.; VICENTE, M.R.; SANTOS, R. M. EVAPOTRANSPIRATION AND CROP COEFFICIENT OF COFFEE PLANTS FROM ORBITAL SENSORS IMAGES     2 ABSTRACT   The use of remote sensing in agriculture is a reality. Among the various uses, the determination of crop evapotranspiration to aid the irrigation management process is detached. The present work aimed to determine the evapotranspiration and the crop coefficient of coffee through the Surface Energy Balance Algorithm for Land (SEBAL) in western Bahia. LANDSAT 7 satellite images were used to perform the study. The reference evapotranspiration was estimated by the FAO Penman-Monteith method and subsequently the crop coefficient (Kc) was calculated based on the evapotranspiration obtained by SEBAL. The statistical indexes for evaluating the effectiveness of the SEBAL model were the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of determination (R²) and the relative error (RE). The SEBAL model proved to be efficient in determining coffee crop evapotranspiration and crop coefficient.   Keywords: vegetation index; sebal; water management.

scholarly journals Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 188 ◽  
Author(s):  
Huaiwei Sun ◽  
Yong Yang ◽  
Ruiying Wu ◽  
Dongwei Gui ◽  
Jie Xue ◽  
...  
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Bayesian Model ◽  
Surface Energy Balance ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Semiarid Region ◽  
Accurate Estimation ◽  
Landsat 8 ◽  
Efficiency Coefficient

Evapotranspiration (ET) is one of the key components of the global hydrological cycle. Many models have been established to obtain an accurate estimation of ET, but the uncertainty of each model has not been satisfactorily addressed, and the weight determination in multi-model simulation methods remains unclear. In this study, the Bayesian model averaging (BMA) method was adopted to tackle this issue. We explored the combination of four surface energy balance (SEB) models (SEBAL, SSEB, S-SEBI and SEBS) with the BMA method by using Landsat 8 images over two study areas in China, the Huailai flux station (semiarid region) and the Sidaoqiao flux station (arid/semiarid region), and the data from two stations were used as validation for this method. The performances of SEB models and different BMA methods is revealed by three statistical parameters (i.e., the coefficient of determination (R2), root mean squared error (RMSE), and the Nash-Sutcliffe efficiency coefficient (NSE)). We found the best performing SEB model was SEBAL, with an R2 of 0.609 (0.672), RMSE of 1.345 (0.876) mm/day, and NSE of 0.407 (0.563) at Huailai (Sidaoqiao) station. Compared with the four individual SEB models, each of the BMA methods (fixed, posterior inclusion probability, or random) can provide a more accurate and reliable simulation result. Similarly, in Huailai (Sidaoqiao) station, the best performing BMA random model provided an R2 of 0.750 (0.796), RMSE of 0.902 (0.602) mm/day, and NSE of 0.746 (0.793). We conclude that the BMA method outperformed the four SEB models alone and obtained a more accurate prediction of ET in two cropland areas, which provides important guidance for water resource allocation and management in arid and semiarid regions.

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