scholarly journals Spatial Evapotranspiration Modeling Assisted With Landsat 8 Image Using Sebal And Geographically Weighted Regression Methods In Magelang District

2021 ◽  
Vol 884 (1) ◽  
pp. 012026
Author(s):  
AF Nugraha ◽  
BS Hadi
Keyword(s):  
Energy Balance ◽  
Geographically Weighted Regression ◽  
Surface Albedo ◽  
Surface Energy Balance ◽  
Global Scale ◽  
Local Scale ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Regression Methods ◽  
Green Land

Abstract Information about evapotranspiration is very important in relation to vegetation because it can be used for planning both in urban planning and agriculture. Magelang Regency has a lot of vegetated green land, both agricultural and non-agricultural and has no information about evapotranspiration. The calculation of evapotranspiration uses the SEBAL (Surface Energy Balance Algorithm for Land) method and modeling uses the GWR (Geographiccaly Weighted Regression) model. Calculation and modeling assisted by QGIS 2.14, QGIS 3.6, SPSS 20, and GWR 4.09 applications. The results showed that (1) GWR evapotranspiration model with significance (sig.) 5% is divided into 3 sub-district groups according to the significant variables in the sub-district (2) NDVI and Surface Albedo variables have a small effect on a global scale and have a large effect on a local scale.

scholarly journals Surface Albedo and Temperature Models for Surface Energy Balance Fluxes and Evapotranspiration Using SEBAL and Landsat 8 over Cerrado-Pantanal, Brazil

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7196
Author(s):  
Lucas Peres Angelini ◽  
Marcelo Sacardi Biudes ◽  
Nadja Gomes Machado ◽  
Hatim M. E. Geli ◽  
George Louis Vourlitis ◽  
...  
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Temperature ◽  
Transition Region ◽  
Surface Albedo ◽  
Surface Energy Balance ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Mato Grosso ◽  
Better Than

The determination of the surface energy balance fluxes (SEBFs) and evapotranspiration (ET) is fundamental in environmental studies involving the effects of land use change on the water requirement of crops. SEBFs and ET have been estimated by remote sensing techniques, but with the operation of new sensors, some variables need to be parameterized to improve their accuracy. Thus, the objective of this study is to evaluate the performance of algorithms used to calculate surface albedo and surface temperature on the estimation of SEBFs and ET in the Cerrado-Pantanal transition region of Mato Grosso, Brazil. Surface reflectance images of the Operational Land Imager (OLI) and brightness temperature (Tb) of the Thermal Infrared Sensor (TIRS) of the Landsat 8, and surface reflectance images of the MODIS MOD09A1 product from 2013 to 2016 were combined to estimate SEBF and ET by the surface energy balance algorithm for land (SEBAL), which were validated with measurements from two flux towers. The surface temperature (Ts) was recovered by different models from the Tb and by parameters calculated in the atmospheric correction parameter calculator (ATMCORR). A model of surface albedo (asup) with surface reflectance OLI Landsat 8 developed in this study performed better than the conventional model (acon) SEBFs and ET in the Cerrado-Pantanal transition region estimated with asup combined with Ts and Tb performed better than estimates with acon. Among all the evaluated combinations, SEBAL performed better when combining asup with the model developed in this study and the surface temperature recovered by the Barsi model (Tsbarsi). This demonstrates the importance of an asup model based on surface reflectance and atmospheric surface temperature correction in estimating SEBFs and ET by SEBAL.

scholarly journals INFLUENCE OF FOREST COVERAGE IN THE SURFACE ALBEDO

FLORESTA ◽  
2019 ◽  
Vol 50 (1) ◽  
pp. 1011
Author(s):  
Elisiane Alba ◽  
Juliana Marchesan ◽  
Mateus Sabadi Schuh ◽  
José Augusto Spiazzi Favarin ◽  
Emanuel Araújo Silva ◽  
...  
Keyword(s):  
Land Use ◽  
Energy Balance ◽  
Land Cover ◽  
Surface Albedo ◽  
Surface Energy Balance ◽  
Native Forest ◽  
Landsat 8 ◽  
Climatic Variable ◽  
Climatic Variations ◽  
Forest Coverage

The surface albedo controls the energy balance between the surface and the atmosphere, being a primordial variable to identify climatic variations. The objective of this study was to evaluate the changes of the surface albedo in different Land Use and Land Cover in the Atlantic Forest biome from images TM/Landsat 5 and OLI/Landsat 8, verifying its variation in 30 years. The images used were path-row 221-080, which covered the Floresta Nacional de São Francisco de Paula on the dates of 1987 and 2017. The albedo was obtained by the method of the Surface Energy Balance Algorithm for Land, while the mapping of Land Use and Land Cover was performed by the Bhattacharyya algorithm, identifying four thematic classes. Finally, the albedo was crossed with the thematic classes, evidencing their variation in function of the changes in the land cover. The surface albedo ranged from 6 to 22%, but the year 1987 concentrated albedo values higher than in 2017. The native forest presented superior albedo to the Forest Plantations in both dates due to the structure of the canopy of this class. The spatial analysis of the albedo exposes the relation of this climatic variable to the cover of the terrestrial surface. Thus changes in the vegetation cover cause alterations in the albedo, influencing changes in the radiation and atmospheric fluxes.

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.

scholarly journals Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

2021 ◽  
Vol 13 (15) ◽  
pp. 2962
Author(s):  
Jingyi Wang ◽  
Huaqiang Du ◽  
Xuejian Li ◽  
Fangjie Mao ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Geographically Weighted Regression ◽  
Local Variation ◽  
Zhejiang Province ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Bamboo Forest ◽  
Prediction Ability ◽  
Gwr Model ◽  
Forest Aboveground Biomass

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.

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.

scholarly journals GEOSPATIAL ASSESSMENT AND MODELING OF URBAN HEAT ISLANDS IN QUEZON CITY, PHILIPPINES USING OLS AND GEOGRAPHICALLY WEIGHTED REGRESSION

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 85-92
Author(s):  
C. A. Alcantara ◽  
J. D. Escoto ◽  
A. C. Blanco ◽  
A. B. Baloloy ◽  
J. A. Santos ◽  
...  
Keyword(s):  
Geographically Weighted Regression ◽  
Land Surface ◽  
Google Earth ◽  
Urban Heat Islands ◽  
Weighted Regression ◽  
View Factor ◽  
Landsat 8 ◽  
Quezon City ◽  
Heat Islands ◽  
Urban Heat

Abstract. Urbanization has played an important part in the development of the society, yet it is accompanied by environmental concerns including the increase of local temperature compared to its immediate surroundings. The latter is known as Urban Heat Islands (UHI). This research aims to model UHI in Quezon City based on Land Surface Temperature (LST) estimated from Landsat 8 data. Geospatial processing and analyses were performed using Google Earth Engine, ArcGIS, GeoDa, and SAGA GIS. Based on Urban Thermal Field Variance Index (UTFVI) and the normalized mean per barangay (village), areas with strong UHI intensities were mapped and characterized. high intensity UHIs are observed mostly in areas with high Normalized Difference Built-up Index (NDBI) like the residential regions while the weak intensity UHIs are noticed in areas with high Normalized Difference Vegetation Index (NDVI) near the La Mesa Reservoir. In the OLS regression model, around 69% of LST variability is explained by Surface Albedo (SA), Sky View Factor (SVF), Surface Area to Volume Ratio (SVR), Solar Radiation (SR), NDBI and NDVI. OLS yield relatively high residuals (RMSE = 1.67) and the residuals are not normally distributed. Since LST is non-stationary, Geographically Weighted Regression (GWR) regression was conducted, proving normally and randomly distributed residuals (average RMSE = 0.26).

scholarly journals Surface Energy Balance Sensitivity to Meteorological Variability on Haig Glacier, Canadian Rocky Mountains

2016 ◽  
Author(s):  
S. Ebrahimi ◽  
S. J. Marshall
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Rocky Mountains ◽  
Surface Albedo ◽  
Surface Energy Balance ◽  
Specific Humidity ◽  
Shortwave Radiation ◽  
Interannual Variations ◽  
Canadian Rocky Mountains ◽  
Glacier Surface

Abstract. Energy exchanges between the atmosphere and the glacier surface control the net energy available for snow and ice melt. Meteorological and glaciological observations are not always available to measure glacier energy and mass balance directly, so models of energy balance processes are often necessary to understand glacier response to meteorological variability and climate change. This paper explores the theoretical and empirical response of a mid-latitude glacier in the Canadian Rocky Mountains to the daily and interannual variations in the meteorological parameters that govern the surface energy balance. The model's reference conditions are based on 11 years of in situ observations from an automatic weather station at an elevation of 2660 m, in the upper ablation area of Haig Glacier. We use an energy balance model to run sensitivity tests to perturbations in temperature, specific humidity, wind speed, incoming shortwave radiation, and glacier surface albedo. The variables were perturbed one at a time for the duration of the glacier melt season, May to September, for the years 2002–2012. The experiments indicate that summer melt has the strongest sensitivity to interannual variations in incoming shortwave radiation, albedo, and temperature, in that order. To explore more realistic scenarios where meteorological variables and internal feedbacks such as the surface albedo co-evolve, we use the same perturbation approach using meteorological forcing from the North American Regional Reanalysis (NARR) over the period 1979–2014. These experiments provide an estimate of historical variability in Haig Glacier surface energy balance an d melt for years prior to our observational study. The methods introduced in this paper provide a methodology that can be employed in distributed energy balance modelling at regional scales. They also provide the foundation for theoretical framework that can be adapted to compare the climatic sensitivity of glaciers in different climate regimes, e.g., polar, maritime, or tropical environments.

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.

scholarly journals Mass and Energy Balance Estimation of Yala Glacier (2011–2017), Langtang Valley, Nepal

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 6 ◽  
Author(s):  
Anushilan Acharya ◽  
Rijan Kayastha
Keyword(s):  
Energy Balance ◽  
Mass Balance ◽  
Surface Albedo ◽  
Surface Energy Balance ◽  
Primary Energy ◽  
Balance Model ◽  
Net Radiation ◽  
Temperature And Precipitation ◽  
Physically Based ◽  
The Mean

Six-year glaciological mass balance measurements, conducted at the Yala Glacier between November 2011 and November 2017 are presented and analyzed. A physically-based surface energy balance model is used to simulate summer mass and energy balance of the Yala Glacier for the 2012–2014 period. Cumulative mass balance of the Yala Glacier for the 2011–2017 period was negative at −4.88 m w.e. The mean annual glacier-wide mass balance was −0.81 ± 0.27 m w.e. with a standard deviation of ±0.48 m w.e. The modelled mass balance values agreed well with observations. Modelling showed that net radiation was the primary energy source for the melting of the glacier followed by sensible heat and heat conduction fluxes. Sensitivity of mass balance to changes in temperature, precipitation, relative humidity, surface albedo and snow density were examined. Mass balance was found to be most sensitive to changes in temperature and precipitation.

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