Hostname: page-component-f554764f5-246sw Total loading time: 0 Render date: 2025-04-23T00:03:09.991Z Has data issue: false hasContentIssue false

Comparison of four upscaling methods to drive instantaneous evapotranspiration to daily values for maize in two climatic regions in China

Published online by Cambridge University Press:  13 November 2024

Xuanxuan Wang
Affiliation:
Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Biyu Wang
Affiliation:
Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Haofang Yan*
Affiliation:
Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Chuan Zhang
Affiliation:
School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China
Hexiang Zheng
Affiliation:
Institute of Pastoral Hydraulic Research, China Institute of Water Resources and Hydropower Research, Hohhot 010020, China
Guoqing Wang
Affiliation:
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Jianyun Zhang
Affiliation:
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Rongxuan Bao
Affiliation:
Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Run Xue
Affiliation:
School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China
Yudong Zhou
Affiliation:
Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Jun Li
Affiliation:
School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China
Rui Zhou
Affiliation:
Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Bin He
Affiliation:
National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
Beibei Hao
Affiliation:
National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
Yujing Han
Affiliation:
Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
*
Corresponding author: Haofang Yan; Email: [email protected]

Abstract

Accurately converting satellite instantaneous evapotranspiration (λETi) over time to daily evapotranspiration (λETd) is crucial for estimating regional evapotranspiration from remote sensing satellites, which plays an important role in effective water resource management. In this study, four upscaling methods based on the principle of energy balance, including the evaporative fraction method (Eva-f method), revised evaporative fraction method (R-Eva-f method), crop coefficient method (Kc-ET0 method) and direct canopy resistance method (Direct-rc method), were validated based on the measured data of the Bowen ratio energy balance system (BREB) in maize fields in northwestern (NW) and northeastern (NE) China (semi-arid and semi-humid continental climate regions) from 2021 to 2023. Results indicated that Eva-f and R-Eva-f methods were superior to Kc-ET0 and Direct-rc methods in both climatic regions and performed better between 10:00 and 11:00, with mean absolute errors (MAE) and coefficient of efficiency (ɛ) reaching <10 W/m2 and > 0.91, respectively. Comprehensive evaluation of the optimal upscaling time using global performance indicators (GPI) showed that the Eva-f method had the highest GPI of 0.59 at 12:00 for the NW, while the R-Eva-f method had the highest GPI of 1.18 at 11:00 for the NE. As a result, the Eva-f approach is recommended as the best way for upscaling evapotranspiration in NW, with 12:00 being the ideal upscaling time. The R-Eva-f method is the optimum upscaling method for the Northeast area, with an ideal upscaling time of 11:00. The comprehensive results of this study could be useful for converting λETi to λETd.

Type
Climate Change and Agriculture Research Paper
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Allen, RG, Pruitt, WO, Wright, JL, Howell, TA, Ventura, F, Snyder, R, Itenfisu, D, Steduto, P, Berengena, J, Yrisarry, JB, Smith, M, Pereira, LS, Raes, D, Perrier, A, Alves, I, Walter, I and Elliott, R (2006) A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 Penman–Monteith method. Agricultural Water Management 81, 122.CrossRefGoogle Scholar
Allen, RG, Tasumi, M, Morse, A, Trezza, R, Wright, JL, Bastiaanssen, W, Kramber, W, Lorite, I and Robison, CW (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) – applications. Journal of Irrigation and Drainage Engineering 133, 395406.CrossRefGoogle Scholar
Bezerra, BG, da Silva, BB, Bezerra, JRC, Sofiatti, V and dos Santos, CAC (2012) Evapotranspiration and crop coefficient for sprinkler-irrigated cotton crop in Apodi Plateau semiarid lands of Brazil. Agricultural Water Management 107, 8693.CrossRefGoogle Scholar
Cammalleri, C, Anderson, MC and Kustas, WP (2014) Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications. Hydrology and Earth System Sciences 18, 18851894.CrossRefGoogle Scholar
Chávez, JL, Neale, CMU, Prueger, JH and Kustas, WP (2008) Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values. Irrigation Science 27, 6781.CrossRefGoogle Scholar
Chemin, Y and Alexandridis, T (2001) Improving spatial resolution of ET seasonal for irrigated rice in Zhanghe. The 22nd Asian Conference on Remote Sensing 5, 9.Google Scholar
Chen, H, Yang, D and , H (2013) Comparison of temporal extrapolation methods for evapotranspiration over variant underlying croplands. Transactions of the Chinese Society of Agricultural Engineering 29, 7381.Google Scholar
Choudhury, BU, Singh, AK and Pradhan, S (2013) Estimation of crop coefficients of dry-seeded irrigated rice-wheat rotation on raised beds by field water balance method in the Indo-Gangetic plains, India. Agricultural Water Management 123, 2031.CrossRefGoogle Scholar
Colaizzi, PD, Evett, SR, Howell, TA and Tolk, JA (2006) Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements. Transactions of the ASABE 49, 14091417.CrossRefGoogle Scholar
Delogu, E, Boulet, G, Olioso, A, Coudert, B, Chirouze, J, Ceschia, E, Le Dantec, V, Marloie, O, Chehbouni, G and Lagouarde, JP (2012) Reconstruction of temporal variations of evapotranspiration using instantaneous estimates at the time of satellite overpass. Hydrology and Earth System Sciences 16, 29953010.CrossRefGoogle Scholar
Despotovic, M, Nedic, V, Despotovic, D and Cvetanovic, S (2015) Review and statistical analysis of different global solar radiation sunshine models. Renewable and Sustainable Energy Reviews 52, 18691880.CrossRefGoogle Scholar
Disasa, KN, Yan, H, Wang, G, Zhang, J, Zhang, C and Zhu, X (2024) Projection of future precipitation, air temperature, and solar radiation changes in southeastern China. Theoretical and Applied Climatology 155, 44814560.CrossRefGoogle Scholar
Evett, SR, Schwartz, RC, Howell, TA, Louis Baumhardt, R and Copeland, KS (2012) Can weighing lysimeter ET represent surrounding field ET well enough to test flux station measurements of daily and sub-daily ET? Advances in Water Resources 50, 7990.CrossRefGoogle Scholar
Farah, HO, Bastiaanssen, WGM and Feddes, RA (2004) Evaluation of the temporal variability of the evaporative fraction in a tropical watershed. International Journal of Applied Earth Observation and Geoinformation 5, 129140.CrossRefGoogle Scholar
Gao, X, Mei, X, Gu, F, Hao, W, Gong, D and Li, H (2018) Evapotranspiration partitioning and energy budget in a rainfed spring maize field on the Loess Plateau, China. Catena 166, 249259.CrossRefGoogle Scholar
Gentine, P, Entekhabi, D, Chehbouni, A, Boulet, G and Duchemin, B (2007) Analysis of evaporative fraction diurnal behaviour. Agricultural and Forest Meteorology 143, 1329.CrossRefGoogle Scholar
Heilman, JL and Brittin, CL (1989) Fetch requirements for Bowen ratio measurements of latent and sensible heat fluxes. Agricultural and Forest Meteorology 44, 261273.CrossRefGoogle Scholar
Hoedjes, JCB, Chehbouni, A, Jacob, F, Ezzahar, J and Boulet, G (2008) Deriving daily evapotranspiration from remotely sensed instantaneous evaporative fraction over olive orchard in semi-arid Morocco. Journal of Hydrology 354, 5364.CrossRefGoogle Scholar
Hossen, MS, Mano, M, Miyata, A, Baten, MA and Hiyama, T (2011) Surface energy partitioning and evapotranspiration over a double-cropping paddy field in Bangladesh. Hydrological Processes 26, 13111320.CrossRefGoogle Scholar
Jiang, X, Kang, S, Tong, L, Li, F, Li, D, Ding, R and Qiu, R (2014) Crop coefficient and evapotranspiration of grain maize modified by planting density in an arid region of northwest China. Agricultural Water Management 142, 135143.CrossRefGoogle Scholar
Jiang, Y, Tang, R, Jiang, X and Li, Z-L (2018) Impact of clouds on the estimation of daily evapotranspiration from MODIS-derived instantaneous evapotranspiration using the constant global shortwave radiation ratio method. International Journal of Remote Sensing 40, 19301944.CrossRefGoogle Scholar
Jiang, L, Zhang, B, Han, S, Chen, H and Wei, Z (2021) Upscaling evapotranspiration from the instantaneous to the daily time scale: assessing six methods including an optimized coefficient based on worldwide eddy covariance flux network. Journal of Hydrology 596, 126135.CrossRefGoogle Scholar
Jiang, J, Yan, H, Zhang, C, Wang, G, Zhang, J, Liang, S and Deng, S (2024) Simulation of greenhouse cucumber evapotranspiration based on canopy-air temperature difference. Journal of Drainage and Irrigation Machinery Engineering 42, 532540.Google Scholar
Jung, M, Reichstein, M, Ciais, P, Seneviratne, SI, Sheffield, J, Goulden, ML, Bonan, G, Cescatti, A, Chen, J, de Jeu, R, Dolman, AJ, Eugster, W, Gerten, D, Gianelle, D, Gobron, N, Heinke, J, Kimball, J, Law, BE, Montagnani, L, Mu, Q, Mueller, B, Oleson, K, Papale, D, Richardson, AD, Roupsard, O, Running, S, Tomelleri, E, Viovy, N, Weber, U, Williams, C, Wood, E, Zaehle, S and Zhang, K (2010) Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951954.CrossRefGoogle ScholarPubMed
Katimbo, A, Rudnick, DR, Liang, W-Z, DeJonge, KC, Lo, TH, Franz, TE, Ge, Y, Qiao, X, Kabenge, I and Nakabuye, HN (2022) Two source energy balance maize evapotranspiration estimates using close-canopy mobile infrared sensors and upscaling methods under variable water stress conditions. Agricultural Water Management 274, 107972.CrossRefGoogle Scholar
Lakhiar, IA, Yan, H, Zhang, J, Wang, G, Deng, S, Bao, R, Zhang, C, Syed, TN, Wang, B and Zhou, R (2024) Plastic pollution in agriculture as a threat to food security, the ecosystem, and the environment: an overview. Agronomy 14, 548.CrossRefGoogle Scholar
Li, S, Kang, S, Li, F, Zhang, L and Zhang, B (2008) Vineyard evaporative fraction based on eddy covariance in an arid desert region of Northwest China. Agricultural Water Management 95, 937948.CrossRefGoogle Scholar
Li, M, Yan, H, Zhang, C, Zhang, J, Wang, G and Acquah, SJ (2024) Current deficiencies and needed enhancements on greenhouse crop evapotranspiration models. Journal of Drainage and Irrigation Machinery Engineering 42, 5763.Google Scholar
Liu, Z (2021) The accuracy of temporal upscaling of instantaneous evapotranspiration to daily values with seven upscaling methods. Hydrology and Earth System Sciences 25, 44174433.CrossRefGoogle Scholar
Liu, G, Liu, Y and Xu, D (2011) Investigation on performance of evapotranspiration temporal upscaling methods based on eddy covariance measurements. Transactions of the Chinese Society of Agricultural Engineering 27, 712.Google Scholar
Liu, G, Hafeez, M, Liu, Y, Xu, D and Vote, C (2012a) A novel method to convert daytime evapotranspiration into daily evapotranspiration based on variable canopy resistance. Journal of Hydrology 414–415, 278283.CrossRefGoogle Scholar
Liu, G, Liu, Y, Hafeez, M, Xu, D and Vote, C (2012b) Comparison of two methods to derive time series of actual evapotranspiration using eddy covariance measurements in the southeastern Australia. Journal of Hydrology 454–455, 16.CrossRefGoogle Scholar
Liu, X, Yang, S, Xu, J, Zhang, J and Liu, J (2017) Effects of soil heat storage and phase shift correction on energy balance closure of paddy fields. Atmósfera 30, 3952.CrossRefGoogle Scholar
Liu, X, Xu, J, Zhou, X, Wang, W and Yang, S (2020) Evaporative fraction and its application in estimating daily evapotranspiration of water-saving irrigated rice field. Journal of Hydrology 584, 124317.CrossRefGoogle Scholar
Liu, Y, Jiang, Q, Wang, Q, Jin, Y, Yue, Q, Yu, J, Zheng, Y, Jiang, W and Yao, X (2022) The divergence between potential and actual evapotranspiration: an insight from climate, water, and vegetation change. Science of the Total Environment 807, 150648.CrossRefGoogle ScholarPubMed
Ma, Z, Yan, N, Wu, B, Stein, A, Zhu, W and Zeng, H (2019) Variation in actual evapotranspiration following changes in climate and vegetation cover during an ecological restoration period (2000–2015) in the Loess Plateau, China. Science of the Total Environment 689, 534545.CrossRefGoogle ScholarPubMed
Ma, Z, Wu, B, Yan, N, Zhu, W and Xu, J (2021) Coupling water and carbon processes to estimate field-scale maize evapotranspiration with Sentinel-2 data. Agricultural and Forest Meteorology 306, 108421.CrossRefGoogle Scholar
Malek, E, Bingham, GE and Mccurdy, GD (1992) Continuous measurement of aerodynamic and alfalfa canopy resistances using the Bowen ratio-energy balance and Penman–Monteith methods. Boundary-Layer Meteorology 59, 187194.CrossRefGoogle Scholar
Miralles, DG, De Jeu, RAM, Gash, JH, Holmes, TRH and Dolman, AJ (2011) Magnitude and variability of land evaporation and its components at the global scale. Hydrology and Earth System Sciences 15, 967981.CrossRefGoogle Scholar
Mu, Q, Zhao, M and Running, SW (2011) Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment 115, 17811800.CrossRefGoogle Scholar
Ohmura, A (1982) Objective criteria for rejecting data for Bowen ratio flux calculations. Journal of Applied Meteorology 21, 595598.2.0.CO;2>CrossRefGoogle Scholar
Perez, PJ, Lecina, S, Castellvi, F, Martínez-Cob, A and Villalobos, FJ (2005) A simple parameterization of bulk canopy resistance from climatic variables for estimating hourly evapotranspiration. Hydrological Processes 20, 515532.CrossRefGoogle Scholar
Pozníková, G, Fischer, M, van Kesteren, B, Orság, M, Hlavinka, P, Žalud, Z and Trnka, M (2018) Quantifying turbulent energy fluxes and evapotranspiration in agricultural field conditions: a comparison of micrometeorological methods. Agricultural Water Management 209, 249263.CrossRefGoogle Scholar
Shuttleworth, W, Gurney, R, Hsu, A and Ormsby, J (1989) FIFE: the variation in energy partition at surface flux sites. IAHS Publication 186, 523534.Google Scholar
Sugita, M and Brutsaert, W (1991) Daily evaporation over a region from lower boundary layer profiles measured with radiosondes. Water Resources Research 27, 747752.CrossRefGoogle Scholar
Suleiman, A and Crago, R (2004) Hourly and daytime evapotranspiration from grassland using radiometric surface temperatures. Agronomy Journal 96, 384390.CrossRefGoogle Scholar
Tang, R and Li, ZL (2017) An improved constant evaporative fraction method for estimating daily evapotranspiration from remotely sensed instantaneous observations. Geophysical Research Letters 44, 23192326.CrossRefGoogle Scholar
Tang, R, Li, Z-L and Sun, X (2013) Temporal upscaling of instantaneous evapotranspiration: an intercomparison of four methods using eddy covariance measurements and MODIS data. Remote Sensing of Environment 138, 102118.CrossRefGoogle Scholar
Tang, R, Li, ZL, Sun, X and Bi, Y (2017) Temporal upscaling of instantaneous evapotranspiration on clear-sky days using the constant reference evaporative fraction method with fixed or variable surface resistances at two cropland sites. Journal of Geophysical Research: Atmospheres 122, 784801.CrossRefGoogle Scholar
Thom, A (1972) Momentum, mass and heat exchange of vegetation. Journal of the Royal Meteorological Society 98, 124134.CrossRefGoogle Scholar
Van Niel, TG, McVicar, TR, Roderick, ML, van Dijk, AIJM, Renzullo, LJ and van Gorsel, E (2011) Correcting for systematic error in satellite-derived latent heat flux due to assumptions in temporal scaling: assessment from flux tower observations. Journal of Hydrology 409, 140148.CrossRefGoogle Scholar
Van Niel, TG, McVicar, TR, Roderick, ML, van Dijk, AIJM, Beringer, J, Hutley, LB and van Gorsel, E (2012) Upscaling latent heat flux for thermal remote sensing studies: comparison of alternative approaches and correction of bias. Journal of Hydrology 468–469, 3546.CrossRefGoogle Scholar
Wandera, L, Mallick, K, Kiely, G, Roupsard, O, Peichl, M and Magliulo, V (2017) Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an artificial neural network approach. Hydrology and Earth System Sciences 21, 197215.CrossRefGoogle Scholar
Wang, B, Bao, R, Yan, H, Zheng, H, Wu, J, Zhang, C and Wang, G (2024a) Study of evapotranspiration and crop coefficients for eggplant in a Venlo-type greenhouse in South China. Irrigation and Drainage.CrossRefGoogle Scholar
Wang, B, Yan, H, Zheng, H, Wu, J, Tian, D, Zhang, C, Zhu, X, Wang, G, Lakhiar, IA and Liu, Y (2024b) Estimation of latent heat flux of pasture and maize in arid region of Northwest China based on canopy resistance modeling. Frontiers in Environmental Science 12, 1397704.CrossRefGoogle Scholar
Xu, T, Liu, S, Xu, L, Chen, Y, Jia, Z, Xu, Z and Nielson, J (2015) Temporal upscaling and reconstruction of thermal remotely sensed instantaneous evapotranspiration. Remote Sensing 7, 34003425.CrossRefGoogle Scholar
Xu, X, Li, X, Wang, X, He, C, Tian, W, Tian, J and Yang, L (2020) Estimating daily evapotranspiration in the agricultural-pastoral ecotone in Northwest China: a comparative analysis of the complementary relationship, WRF-CLM4.0, and WRF-Noah methods. Science of the Total Environment 729, 138635.CrossRefGoogle Scholar
Yan, H, Zhang, C, Coenders Gerrits, M, Acquah, SJ, Zhang, H, Wu, H, Zhao, B, Huang, S and Fu, H (2018) Parametrization of aerodynamic and canopy resistances for modeling evapotranspiration of greenhouse cucumber. Agricultural and Forest Meteorology 262, 370378.CrossRefGoogle Scholar
Yan, H, Acquah, SJ, Zhang, C, Wang, G, Huang, S, Zhang, H, Zhao, B and Wu, H (2019) Energy partitioning of greenhouse cucumber based on the application of Penman-Monteith and bulk transfer models. Agricultural Water Management 217, 201211.CrossRefGoogle Scholar
Yan, H, Yu, J, Zhang, C, Wang, G, Huang, S and Ma, J (2021) Comparison of two canopy resistance models to estimate evapotranspiration for tea and wheat in southeast China. Agricultural Water Management 245, 106581.CrossRefGoogle Scholar
Yan, H, Huang, S, Zhang, J, Zhang, C, Wang, G, Li, L, Zhao, S, Li, M and Zhao, B (2022a) Comparison of Shuttleworth–Wallace and dual crop coefficient method for estimating evapotranspiration of a tea field in Southeast China. Agriculture 12, 1392.CrossRefGoogle Scholar
Yan, H, Li, M, Zhang, C, Zhang, J, Wang, G, Yu, J, Ma, J and Zhao, S (2022b) Comparison of evapotranspiration upscaling methods from instantaneous to daytime scale for tea and wheat in southeast China. Agricultural Water Management 264, 107464.CrossRefGoogle Scholar
Yan, H, Deng, S, Zhang, C, Wang, G, Zhao, S, Li, M, Liang, S, Jiang, J and Zhou, Y (2023) Determination of energy partition of a cucumber grown venlo-type greenhouse in southeast China. Agricultural Water Management 276, 108047.CrossRefGoogle Scholar
Yang, D-W, Chen, H and Lei, H-M (2013) Analysis of the diurnal pattern of evaporative fraction and its controlling factors over croplands in the Northern China. Journal of Integrative Agriculture 12, 13161329.CrossRefGoogle Scholar
Yang, P, Hu, H, Tian, F, Zhang, Z and Dai, C (2016) Crop coefficient for cotton under plastic mulch and drip irrigation based on eddy covariance observation in an arid area of northwestern China. Agricultural Water Management 171, 2130.CrossRefGoogle Scholar
Zhang, BZ, Kang, SZ, Zhang, L, Du, TS, Li, SE and Yang, XY (2010) Estimation of seasonal crop water consumption in a vineyard using Bowen ratio-energy balance method. Hydrological Processes 21, 36353641.CrossRefGoogle Scholar
Zhang, B, Chen, H, Xu, D and Li, F (2017) Methods to estimate daily evapotranspiration from hourly evapotranspiration. Biosystems Engineering 153, 129139.CrossRefGoogle Scholar
Zhang, Y, Kong, D, Gan, R, Chiew, FHS, McVicar, TR, Zhang, Q and Yang, Y (2019) Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017. Remote Sensing of Environment 222, 165182.CrossRefGoogle Scholar
Zhao, S, Yan, H, Zhang, C, Li, M, Deng, S, Liang, S and Jiang, J (2023) Estimation of cucumber evapotranspiration in greenhouse based on improved dual crop coefficient model and Priestley-Taylor model. Journal of Drainage and Irrigation Machinery Engineering 41, 849857.Google Scholar