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Vegetation and Evapotranspiration Analyses on Climate Maps

Year 2024, , 616 - 626, 15.07.2024

Nehir Uyar

https://doi.org/10.34248/bsengineering.1426459

Abstract

This study focuses on the investigation of Evapotranspiration (ET) processes under the climatic and geographical characteristics of Türkiye. ET refers to the process by which plants transfer water vapor to the atmosphere and is an important part of the water cycle. This research analyzes ET in Türkiye using imagery data from NASA Global Land Data Assimilation System Version 2 (GLDAS-2), MODIS, TerraClimate, SMAP Level-4, and Penman-Monteith-Leuning ET V2 (PML_V2). Surface Soil Moisture (SSM) data for Türkiye between 2016 and 2022 and Land Surface Temperature (LST) data between 2000 and 2022 were obtained from MODIS images. In the study, regression analyses were performed with ET values and SSM and LST data. The best result was a moderate correlation (R 0.57) between ET produced from SMAP Level-4 data and LST. A high correlation (R 0.59) was observed with SSM. Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) 1981 and 2023 precipitation data and 1981 and 2023 Surface Pressure (PS) data were obtained from MERRA image. Regression analyses were performed between ET data and PS and precipitation values. A moderate relationship (R 0.37) was observed between ET and PS produced from MOD16A2 V105 data. A moderate relationship (R 0.50) was observed between ET and precipitation obtained from TerraClimate data. This study aims to contribute to the development of strategies to effectively manage water resources and improve agricultural sustainability by analyzing ET in various regions of Türkiye.

Keywords

ET GLDAS-2 MODIS TerraClimate SSM LST

References

  • Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC. 2018. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci Data, 5 (1): 1-12.
  • Anderson MC, Kustas WP, Norman JM, Hain CR, Mecikalski JR, Schultz L, González-Dugo MP, Cammalleri C, Urso G, Pimstein A, Gao F. 2011. Mapping daily ET at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrol Earth Syst Sci, 15: 223-239.
  • Beaudoing H, Rodell M. 2020. GLDAS Noah land surface model L4 3 hourly 0.25 × 0.25-degree v.2.1. Greenbelt, MD, USA: Goddard Earth Sciences Data and Information Services Center (GES DISC). NASA/GSFC/HSL. https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_3H_2.1/summary (accessed date: September 23, 2023).
  • Bogawski P, Bednorz E. 2014. Comparison and validation of selected evapotranspiration models for conditions in Poland (Central Europe), water resources management. Springer, London, UK, 28(14): 5021-5038.
  • Brooks KN, Ffolliott PF, Gregersen HM, DeBano LF. 2003. Hydrology and the management of watersheds. Iowa State Press, Ames, IA, US, pp: 574.
  • Carlson TN, Dodd JK, Benjamin SG, Cooper JN. 1981. Remote estimation of surface energy balance, moisture availability and thermal. Inertia J Appl Meteor 20: 67-87.
  • Carlson TN, Rose FG, Perry EM. 1984. Regional scale estimates of surface moisture availability from goes satellite. Agron J, 76: 972-979.
  • Cour D, Seguin B, Olioso A. 2005. Review on estimation of ET from remote sensing data: From empirical to numerical modeling approaches. Irrigat Drain Syst, 19(3):223-249
  • Deng C, Zou J, Wang W. 2024. Assimilation of remotely sensed ET products for streamflow simulation based on the CAMELS data sets. J Hydrol, 629: 130574.
  • Du C, Jiang S, Chen C, Guo Q, He Q, Zhan C. 2024. Machine learning-based estimation of daily cropland ET in diverse climate zones. Remote Sens, 16(5): 730.
  • Glenn EP, Huete AR, Nagler PL, Hirschboeck KK, Brown P. 2007. Integrating remote sensing and ground methods to estimate ET. Critical Rev Plant Sci, 26: 3.
  • Granata F. 2019. ET evaluation models based on machine learning algorithms—A comparative study. Agric Water Manag, 217: 303-315.
  • Hao P, Di L, Guo L. 2002. Estimation of crop ET from MODIS data by combining random forest and trapezoidal models Agric. Water Manag, 259: 107249.
  • Huang J, Zhang S, Zhang J, Zheng X, Meng X, Yang S, Bai Y. 2024. Integrating meteorological and remote sensing data to simulate cropland nocturnal ET using machine learning. Sustain, 2024: 16.
  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ, 83: 195-213.
  • Kalma JD, McVicar TR, McCabe MF. 2008. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surveys Geophys, 29: 421-469.
  • Krishna PR. 2019. Evapotranspiration and agriculture—a review. Agric Rev, 40: 1-11.
  • Kharrou MH, Simonneaux V, Raki S, Page LM, Khabba S, Chehbouni A. 2021. Assessingirrigation water use with remote sensing-based soil water balance at an irrigation scheme levelin a semi-arid region of Morocco. Remote Sens, 13(6): 1133.
  • Lucchesi R. 2018. File specification for GEOS-5 FP (forward processing). GMAO Office Note 4 (version 1.2), New York, USA, pp: 62.
  • McCabe MF, Wood EF. 2006. Scale influences on the remote estimation of ET using multiple satellite sensors. Remote Sens Environ, 105: 271-285.
  • Mu QZ, Heinsch FA, Zhao MS, Running SW. 2007. Development of a global ET algorithm based on MODIS and global meteorology data. Remote Sens Environ, 111(4): 519-536.
  • Price JC. 1982. Estimation of Regional Scale ET through Analysis of Satellite thermal-infrared Data. IEEE Transactions on Geoscience and Remote Sensing, GE-20, New York, USA, pp: 286-292.
  • Price JC. 1980. The Potential of Remotely Sensed Thermal Infrared Data to Infer Surface Soil Moisture and Evaporation. Water Resourc Res, 16: 787-795.
  • Reichle RH, De Lannoy GJM, Liu Q, Koster RD, Kimball JS, Crow WT, Ardizzone JV, Chakraborty P, Collins DW, Conaty AL, Girotto M, Jones MA, Kolassa J, Lievens H, Lucchesi RA, Smith EB. 2017. Global assessment of the SMAP level-4 surface and root-zone soil moisture product using assimilation diagnostics. J Hydrometeor, 18: 3217-3237.
  • Reichle RH, Liu Q, Koster RD, Crow WT, De Lannoy GJM, Kimball JS, Ardizzone JV, Bosch D, Colliander A, Cosh M, Kolassa J, Mahanama SP, Prueger J, Starks P, Walker JP. 2019. Version 4 of the SMAP level‐4 soil moisture algorithm and data product. J Adv Model Earth Syst, 11: 3106-3130.
  • Rodell M, Houser PR, Jambor U, Gottschalck J, Mitchell K, Meng CJ, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M. 2004. The global land data assimilation system. Bulletin American Meteorol Soc, 85: 381-394.
  • Soer GJR. 1980. Estimation of regional ET and soil moisture conditions using remotely sensed crop surface temperatures. Remote Sens Environ, 9: 27-45.
  • Wetzel P, Atlas D, Woodward R. 1983. Determining Soil Moisture from Study. J Clim Appl Meteor, 23: 375-391.
  • Zhang K, Kimball JS, Running SW. 2016. A review of remote sensing based actual ET estimation. Wires Water, 3(6): 834-853.

Vegetation and Evapotranspiration Analyses on Climate Maps

Year 2024, , 616 - 626, 15.07.2024

Nehir Uyar

https://doi.org/10.34248/bsengineering.1426459

Abstract

This study focuses on the investigation of Evapotranspiration (ET) processes under the climatic and geographical characteristics of Türkiye. ET refers to the process by which plants transfer water vapor to the atmosphere and is an important part of the water cycle. This research analyzes ET in Türkiye using imagery data from NASA Global Land Data Assimilation System Version 2 (GLDAS-2), MODIS, TerraClimate, SMAP Level-4, and Penman-Monteith-Leuning ET V2 (PML_V2). Surface Soil Moisture (SSM) data for Türkiye between 2016 and 2022 and Land Surface Temperature (LST) data between 2000 and 2022 were obtained from MODIS images. In the study, regression analyses were performed with ET values and SSM and LST data. The best result was a moderate correlation (R 0.57) between ET produced from SMAP Level-4 data and LST. A high correlation (R 0.59) was observed with SSM. Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) 1981 and 2023 precipitation data and 1981 and 2023 Surface Pressure (PS) data were obtained from MERRA image. Regression analyses were performed between ET data and PS and precipitation values. A moderate relationship (R 0.37) was observed between ET and PS produced from MOD16A2 V105 data. A moderate relationship (R 0.50) was observed between ET and precipitation obtained from TerraClimate data. This study aims to contribute to the development of strategies to effectively manage water resources and improve agricultural sustainability by analyzing ET in various regions of Türkiye.

Keywords

ET GLDAS-2 MODIS TerraClimate SSM LST

References

  • Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC. 2018. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci Data, 5 (1): 1-12.
  • Anderson MC, Kustas WP, Norman JM, Hain CR, Mecikalski JR, Schultz L, González-Dugo MP, Cammalleri C, Urso G, Pimstein A, Gao F. 2011. Mapping daily ET at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrol Earth Syst Sci, 15: 223-239.
  • Beaudoing H, Rodell M. 2020. GLDAS Noah land surface model L4 3 hourly 0.25 × 0.25-degree v.2.1. Greenbelt, MD, USA: Goddard Earth Sciences Data and Information Services Center (GES DISC). NASA/GSFC/HSL. https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_3H_2.1/summary (accessed date: September 23, 2023).
  • Bogawski P, Bednorz E. 2014. Comparison and validation of selected evapotranspiration models for conditions in Poland (Central Europe), water resources management. Springer, London, UK, 28(14): 5021-5038.
  • Brooks KN, Ffolliott PF, Gregersen HM, DeBano LF. 2003. Hydrology and the management of watersheds. Iowa State Press, Ames, IA, US, pp: 574.
  • Carlson TN, Dodd JK, Benjamin SG, Cooper JN. 1981. Remote estimation of surface energy balance, moisture availability and thermal. Inertia J Appl Meteor 20: 67-87.
  • Carlson TN, Rose FG, Perry EM. 1984. Regional scale estimates of surface moisture availability from goes satellite. Agron J, 76: 972-979.
  • Cour D, Seguin B, Olioso A. 2005. Review on estimation of ET from remote sensing data: From empirical to numerical modeling approaches. Irrigat Drain Syst, 19(3):223-249
  • Deng C, Zou J, Wang W. 2024. Assimilation of remotely sensed ET products for streamflow simulation based on the CAMELS data sets. J Hydrol, 629: 130574.
  • Du C, Jiang S, Chen C, Guo Q, He Q, Zhan C. 2024. Machine learning-based estimation of daily cropland ET in diverse climate zones. Remote Sens, 16(5): 730.
  • Glenn EP, Huete AR, Nagler PL, Hirschboeck KK, Brown P. 2007. Integrating remote sensing and ground methods to estimate ET. Critical Rev Plant Sci, 26: 3.
  • Granata F. 2019. ET evaluation models based on machine learning algorithms—A comparative study. Agric Water Manag, 217: 303-315.
  • Hao P, Di L, Guo L. 2002. Estimation of crop ET from MODIS data by combining random forest and trapezoidal models Agric. Water Manag, 259: 107249.
  • Huang J, Zhang S, Zhang J, Zheng X, Meng X, Yang S, Bai Y. 2024. Integrating meteorological and remote sensing data to simulate cropland nocturnal ET using machine learning. Sustain, 2024: 16.
  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ, 83: 195-213.
  • Kalma JD, McVicar TR, McCabe MF. 2008. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surveys Geophys, 29: 421-469.
  • Krishna PR. 2019. Evapotranspiration and agriculture—a review. Agric Rev, 40: 1-11.
  • Kharrou MH, Simonneaux V, Raki S, Page LM, Khabba S, Chehbouni A. 2021. Assessingirrigation water use with remote sensing-based soil water balance at an irrigation scheme levelin a semi-arid region of Morocco. Remote Sens, 13(6): 1133.
  • Lucchesi R. 2018. File specification for GEOS-5 FP (forward processing). GMAO Office Note 4 (version 1.2), New York, USA, pp: 62.
  • McCabe MF, Wood EF. 2006. Scale influences on the remote estimation of ET using multiple satellite sensors. Remote Sens Environ, 105: 271-285.
  • Mu QZ, Heinsch FA, Zhao MS, Running SW. 2007. Development of a global ET algorithm based on MODIS and global meteorology data. Remote Sens Environ, 111(4): 519-536.
  • Price JC. 1982. Estimation of Regional Scale ET through Analysis of Satellite thermal-infrared Data. IEEE Transactions on Geoscience and Remote Sensing, GE-20, New York, USA, pp: 286-292.
  • Price JC. 1980. The Potential of Remotely Sensed Thermal Infrared Data to Infer Surface Soil Moisture and Evaporation. Water Resourc Res, 16: 787-795.
  • Reichle RH, De Lannoy GJM, Liu Q, Koster RD, Kimball JS, Crow WT, Ardizzone JV, Chakraborty P, Collins DW, Conaty AL, Girotto M, Jones MA, Kolassa J, Lievens H, Lucchesi RA, Smith EB. 2017. Global assessment of the SMAP level-4 surface and root-zone soil moisture product using assimilation diagnostics. J Hydrometeor, 18: 3217-3237.
  • Reichle RH, Liu Q, Koster RD, Crow WT, De Lannoy GJM, Kimball JS, Ardizzone JV, Bosch D, Colliander A, Cosh M, Kolassa J, Mahanama SP, Prueger J, Starks P, Walker JP. 2019. Version 4 of the SMAP level‐4 soil moisture algorithm and data product. J Adv Model Earth Syst, 11: 3106-3130.
  • Rodell M, Houser PR, Jambor U, Gottschalck J, Mitchell K, Meng CJ, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M. 2004. The global land data assimilation system. Bulletin American Meteorol Soc, 85: 381-394.
  • Soer GJR. 1980. Estimation of regional ET and soil moisture conditions using remotely sensed crop surface temperatures. Remote Sens Environ, 9: 27-45.
  • Wetzel P, Atlas D, Woodward R. 1983. Determining Soil Moisture from Study. J Clim Appl Meteor, 23: 375-391.
  • Zhang K, Kimball JS, Running SW. 2016. A review of remote sensing based actual ET estimation. Wires Water, 3(6): 834-853.
There are 29 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Nehir Uyar 0000-0003-3358-3145

Publication Date July 15, 2024
Submission Date January 26, 2024
Acceptance Date May 15, 2024
Published in Issue Year 2024

Cite

APA Uyar, N. (2024). Vegetation and Evapotranspiration Analyses on Climate Maps. Black Sea Journal of Engineering and Science, 7(4), 616-626. https://doi.org/10.34248/bsengineering.1426459
AMA Uyar N. Vegetation and Evapotranspiration Analyses on Climate Maps. BSJ Eng. Sci. July 2024;7(4):616-626. doi:10.34248/bsengineering.1426459
Chicago Uyar, Nehir. “Vegetation and Evapotranspiration Analyses on Climate Maps”. Black Sea Journal of Engineering and Science 7, no. 4 (July 2024): 616-26. https://doi.org/10.34248/bsengineering.1426459.
EndNote Uyar N (July 1, 2024) Vegetation and Evapotranspiration Analyses on Climate Maps. Black Sea Journal of Engineering and Science 7 4 616–626.
IEEE N. Uyar, “Vegetation and Evapotranspiration Analyses on Climate Maps”, BSJ Eng. Sci., vol. 7, no. 4, pp. 616–626, 2024, doi: 10.34248/bsengineering.1426459.
ISNAD Uyar, Nehir. “Vegetation and Evapotranspiration Analyses on Climate Maps”. Black Sea Journal of Engineering and Science 7/4 (July 2024), 616-626. https://doi.org/10.34248/bsengineering.1426459.
JAMA Uyar N. Vegetation and Evapotranspiration Analyses on Climate Maps. BSJ Eng. Sci. 2024;7:616–626.
MLA Uyar, Nehir. “Vegetation and Evapotranspiration Analyses on Climate Maps”. Black Sea Journal of Engineering and Science, vol. 7, no. 4, 2024, pp. 616-2, doi:10.34248/bsengineering.1426459.
Vancouver Uyar N. Vegetation and Evapotranspiration Analyses on Climate Maps. BSJ Eng. Sci. 2024;7(4):616-2.

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