Please wait a minute...
Journal of Arid Land  2019, Vol. 11 Issue (4): 495-512    DOI: 10.1007/s40333-019-0098-2
Comparison of two remote sensing models for estimating evapotranspiration: algorithm evaluation and application in seasonally arid ecosystems in South Africa
DZIKITI Sebinasi1,*(), Z JOVANOVIC Nebo1, DH BUGAN Richard1, RAMOELO Abel2, P MAJOZI Nobuhle2, NICKLESS Alecia2, A CHO Moses2, C LE MAITRE David1, NTSHIDI Zanele1, H PIENAAR Harrison1
1Council for Scientific and Industrial Research, Natural Resources and Environment, Stellenbosch 7599, South Africa
2Council for Scientific and Industrial Research, Natural Resources and Environment, Pretoria 0001, South Africa
Download: HTML     PDF(885KB)
Export: BibTeX | EndNote (RIS)      


Remote sensing tools are becoming increasingly important for providing spatial information on water use by different ecosystems. Despite significant advances in remote sensing based evapotranspiration (ET) models in recent years, important information gaps still exist on the accuracy of the models particularly in arid and semi-arid environments. In this study, we evaluated the Penman-Monteith based MOD16 and the modified Priestley-Taylor (PT-JPL) models at the daily time step against three measured ET datasets. We used data from two summer and one winter rainfall sites in South Africa. One site was dominated by native broad leaf and the other by fine leafed deciduous savanna tree species and C4 grasses. The third site was in the winter rainfall Cape region and had shrubby fynbos vegetation. Actual ET was measured using open-path eddy covariance systems at the summer rainfall sites while a surface energy balance system utilizing the large aperture boundary layer scintillometer was used in the Cape. Model performance varied between sites and between years with the worst estimates (R2<0.50 and RMSE>0.80 mm/d) observed during years with prolonged mid-summer dry spells in the summer rainfall areas. Sensitivity tests on MOD16 showed that the leaf area index, surface conductance and radiation budget parameters had the largest effect on simulated ET. MOD16 ET predictions were improved by: (1) reformulating the emissivity expressions in the net radiation equation; (2) incorporating representative surface conductance values; and (3) including a soil moisture stress function in the transpiration sub-model. Implementing these changes increased the accuracy of MOD16 daily ET predictions at all sites. However, similar adjustments to the PT-JPL model yielded minimal improvements. We conclude that the MOD16 ET model has the potential to accurately predict water use in arid environments provided soil water stress and accurate biome-specific parameters are incorporated.

Key wordsMOD16 ET      drought stress      model validation      Penman-Monteith      Priestley-Taylor      sensitivity analysis     
Received: 28 December 2017      Published: 10 August 2019
Fund:  This work was supported by the South African Parliamentary Grant to the Council for Scientific and Industrial Research Project (ECHS014, EEEO024, ECHS058 and ECHS052)
Corresponding Authors:
About author:

The first and second authors contributed equally to this work.

Cite this article:

DZIKITI Sebinasi, Z JOVANOVIC Nebo, DH BUGAN Richard, RAMOELO Abel, P MAJOZI Nobuhle, NICKLESS Alecia, A CHO Moses, C LE MAITRE David, NTSHIDI Zanele, H PIENAAR Harrison. Comparison of two remote sensing models for estimating evapotranspiration: algorithm evaluation and application in seasonally arid ecosystems in South Africa. Journal of Arid Land, 2019, 11(4): 495-512.

URL:     OR

[1] Allen R G, Pereira L S, Raes D, et al.1998. Crop evapotranspiration-Guidelines for computing crop water requirements - FAO Irrigation and drainage paper No.56. FAO. Rome, Italy.
[2] Brutsaert W.1975. On a derivable formula for long-wave radiation from clear skies. Water Resources Research, 11(5): 742-744.
[3] Burba G G, Verma S B.2005. Seasonal and interannual variability in evapotranspiration of native tallgrass prairie and cultivated wheat ecosystems. Agricultural and Forest Meteorology,135(1-4): 190-201.
[4] Carrasco M, Ortega-Farias S.2007. Evaluation of a model to simulate net radiation over a vineyard cv. Cabernet Sauvignon. Chilean Journal of Agricultural Research, 68: 156-165.
[5] Cleugh H A, Leuning R, Mu Q Z, et al.2007. Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment, 106(3): 285-304.
[6] Cleverly R W, Bistrow J W.1979. Revised volcanic stratigraphy of the Lebombo monocline. South African Journal of Geology, 82(2): 227-230.
[7] Dzikiti S, Jovanovic N Z, Bugan R, et al.2014. Measurement and modelling of evapotranspiration in three fynbos vegetation types. Water SA, 40(2): 189-198.
[8] Dzikiti S, Gush M B, Le Maitre D C, et al.2016. Quantifying potential water savings from clearing invasive alien Eucalyptus camaldulensis using in situ and high resolution remote sensing data in the Berg River Catchment, Western Cape, South Africa. Forest Ecology and Management Journal, 361: 69-80.
[9] Dzikiti S,Volschenk T, Midgley S J E, et al.2018. Estimating the water requirements of high yielding and young apple orchards in the winter rainfall areas of South Africa using a dual source evapotranspiration model. Agricultural Water Management. 208: 152-162.
[10] El Masri B, Rahman A F, Dragoni D.2019. Evaluating a new algorithm for satellite-based evapotranspiration for North American ecosystems: Model development and validation. Agricultural and Forest Meteorology, 268: 234-248.
[11] Ershadi A, McCabe M F, Evans J P, et al.2014. Multi-site evaluation of terrestrial evaporation models using FLUXNET data. Agricultural and Forest Meteorology, 187: 46-61.
[12] Fisher J B, Tu K P, Baldocchi D D.2008. Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment, 112(3): 901-919.
[13] Garcia M, Sandholt I, Ceccato P, et al.2013. Actual evapotranspiration in drylands derived from in-situ and satellite data: Assessing biophysical constraints. Remote Sensing of Environment, 131: 103-118.
[14] Garcia M, Fernández N, Villagarcía L, et al.2014. Accuracy of the temperature-vegetation dryness index using MODIS under water-limited vs. energy-limited evapotranspiration conditions. Remote Sensing of Environment, 149: 100-117.
[15] Green S, McNaughton K, Wünsche J N, et al.2003. Modelling light interception and transpiration of apple tree canopies. Agronomy Journal, 95(6): 1380-1387.
[16] Hwang K Choi M.2013. Seasonal trends of satellite-based evapotranspiration algorithms over a complex ecosystem in East Asia. Remote Sensing of Environment, 137: 244-263.
[17] Impens I, Lemeur R.1969. Extinction of net radiation in different crop canopies. Theoretical and Applied Climatology, 17: 403-412.
[18] Kim H W, Hwang K, Mu Q, et al.2012. Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia. KSCE Journal of Civil Engineering, 16(2): 229-238.
[19] Low A B, Rebelo A G.1996. Vegetation of South Africa, Lesotho and Swaziland. Pretoria: Department of Environmental Affairs and Tourism of South Africa.
[20] Makarau A, Jury M R.1997. Seasonal cycles of convective spells over Southern African during austral summer. International Journal of Climatology, 17(2): 1317-1333.
[21] Marshall M, Tu K, Funk C, et al.2013. Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach. Hydrology and Earth System Sciences, 17: 1079-1091.
[22] Monteith J L, Unsworth M H.1990. Principles of Environmental Physics.Oxford: Butterworth Heinemann Press, 1-291.
[23] Mu Q M, Heinsch F A, Zhao M S, et al.2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111(4): 519-536.
[24] Mu Q M, Zhao M S, Running S W.2011. Improvement to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8): 1781-1800.
[25] Mu Q M, Zhao M S, Running S W.2013. MOD16 1-km2 terrestrial evapotranspiration (ET) product for the Nile Basin algorithm theoretical basis document. In: Numerical Terradynamic Simulation Group College of Forestry and Conservation University of Montana. Missoula, USA.
[26] Mucina J L, Rutherford M C, Leslie W P, et al.2006. The Vegetation of South Africa, Lesotho and Swaziland. Pretoria: South African National Biodiversity Institute,1-807.
[27] Műnch Z, Conrad J E, Gibson L A, et al.2013. Satellite earth observation as a tool to conceptual hydrological fluxes in the Sandveld, South Africa. Hydrology Journal, 21(5): 1053-1070.
[28] Nishida K, Nemani R R, Glassy J M, et al.2003. Development of an evapotranspiration index from Aqua/MODIS for monitoring surface moisture status. IEEE Transactions on Geoscience and remote sensing, 41(2): 493-501.
[29] Paloscia S, Pettinato S, Santi E, et al.2013. Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation. Remote Sensing of Environment, 134: 234-248.
[30] Polhamus A, Fisher J B, Tu K P.2013. What controls the error structure in evapotranspiration models? Agricultural and Forest Meteorology, 169: 12-24.
[31] Price J C.1977. Thermal inertia mapping: A new view of the earth. Journal of Geophysical Research, 82(18): 2582-2590.
[32] Priestley C H B, Taylor R J.1972. On the assessment of surface heat flux and evaporation using large scale parameters. Monthly Weather Review, 100(2): 81-92.
[33] Ramoelo A, Majozi N, Mathieu R, et al.2014. Validation of global evapotranspiration product (MOD16) using flux tower data in the African Savannah, South Africa. Remote Sensing, 6(8): 7406-7423.
[34] Reinders F B.2013. Irrigation methods for efficient water application: 40 years of South African research excellence. Water SA, 37(5): 765-770.
[35] Ruhoff A L, Paz A R, Aragao L E O C, et al.2013. Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin. Hydrological Sciences Journal, 58(8): 1658-1676.
[36] Savage M J, Everson C S, Odhiambo G O, et al.2004. Theory and practice of evapotranspiration measurement, with special focus on surface layer scintillometer (SLS) as an operational tool for the estimation of spatially-averaged evaporation. In: Water Research Commission Report No 1335/1/04, Implementation of Bichromatic Scintillation as an Operational Tool for the Estimation of Spatially Averaged Evaporation. Pretoria, South Africa.
[37] Scholes R J, Gureja N, Giannecchinni M, et al.2001. The environment and vegetation of the flux measurement site near Skukuza, Kruger National Park. Koedoe, 44(1): 73-83.
[38] Schulze R E, Maharaj M, Warburton M L, et al.2008. South African atlas of climatology and agrohydrology. In: Water Research Commission Report No 1489/1/08. Pretoria, South Africa.
[39] Talsma C J, Good S P, Jimenez C, et al.2018. Partitioning of evapotranspiration in remote sensing based models. Agricultural and Forest Meteorology, 260-261: 131-143.
[40] Tang R, Shao K, Li Z, et al.2015. Multiscale Validation of the 8-day MOD16 Evapotranspiration Product Using Flux Data Collected in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4): 1478-1486.
[41] Velpuri N M, Senay G B, Singh R K, et al.2013. A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET. Remote Sensing of Environment, 139: 35-49.
[42] Verstraeten W W, Veroustraete F, van der Sand C J, et al.2006. Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests. Remote Sensing of Environment, 101(3): 299-314.
[43] Waters R, Allen R, Tasumi M, et al.2002. Surface energy balance algorithms for land: Advanced training and users manual. The Idaho Department of Water Resources. Idaho, USA.
[44] Wever L A, Flanagan L B, Carlson P J.2002. Seasonal and interannual variation in evapotranspiration, energy balance and surface conductance in a northern temperate grassland. Agricultural and Forest Meteorology, 112(1): 31-49.
[45] Yao Y J, Liang S L, Cheng J, et al.2013. MODI-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm. Agricultural and Forest Meteorology, 171-172: 187-202.
[46] Yao Y J, Liang S L, Li X L, et al.2015. A satellite-based hybrid algorithm to determine the Priestley-Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes. Remote Sensing of Environment, 165: 216-233
[47] Zhang D, Zhang Q, Werner A D, et al.2016. Assessment of the reliability of popular satellite products in characterizing the water balance of the Yangtze River Basin, China. Hydrology Research 47 (S1): 8-23.
[48] Zhang H P, Simmonds L P, Morison J I L, et al.1997. Estimation of transpiration by single trees: comparison of sap flow measurements with a combination equation. Agricultural and Forest Meteorology, 87(2-3): 155-169.
[49] Zhang Y Q, Chiew F H S, Zhang L, et al.2008. Estimating catchment evaporation and runoff using MODIS leaf area index and the Penman-Monteith equation. Water Resources Research, 44(10): W10420.
[1] Fateme RIGI, Morteza SABERI, Mahdieh EBRAHIMI. Improved drought tolerance in Festuca ovina L. using plant growth promoting bacteria[J]. Journal of Arid Land, 2023, 15(6): 740-755.
[2] Mohammad Hossein TAGHIZADEH, Mohammad FARZAM, Jafar NABATI. Rhizobacteria facilitate physiological and biochemical drought tolerance of Halimodendron halodendron (Pall.) Voss[J]. Journal of Arid Land, 2023, 15(2): 205-217.
[3] Lobna MNIF FAKHFAKH, Mohamed CHAIEB. Effects of water stress on growth phenology photosynthesis and leaf water potential in Stipagrostis ciliata (Desf.) De Winter in North Africa[J]. Journal of Arid Land, 2023, 15(1): 77-90.
[4] ZHANG Yin, GULIMIRE Hanati, SULITAN Danierhan, HU Keke. Monitoring and analysis of snow cover change in an alpine mountainous area in the Tianshan Mountains, China[J]. Journal of Arid Land, 2022, 14(9): 962-977.
[5] JIN Junfang, YIN Shuyan, YIN Hanmin. Impact of land use/land cover types on surface humidity in northern China in the early 21st century[J]. Journal of Arid Land, 2022, 14(7): 705-718.
[6] WU Changxue, Xu Ruirui, QIU Dexun, DING Yingying, GAO Peng, MU Xingmin, ZHAO Guangju. Runoff characteristics and its sensitivity to climate factors in the Weihe River Basin from 2006 to 2018[J]. Journal of Arid Land, 2022, 14(12): 1344-1360.
[7] WANG Chunyuan, YU Minghan, DING Guodong, GAO Guanglei, ZHANG Linlin, HE Yingying, LIU Wei. Size- and leaf age-dependent effects on the photosynthetic and physiological responses of Artemisia ordosica to drought stress[J]. Journal of Arid Land, 2021, 13(7): 744-758.
[8] HUANG Laiming, ZHAO Wen, SHAO Ming'an. Response of plant physiological parameters to soil water availability during prolonged drought is affected by soil texture[J]. Journal of Arid Land, 2021, 13(7): 688-698.
[9] Sanim BISSENBAYEVA, Jilili ABUDUWAILI, Assel SAPAROVA, Toqeer AHMED. Long-term variations in runoff of the Syr Darya River Basin under climate change and human activities[J]. Journal of Arid Land, 2021, 13(1): 56-70.
[10] HUA Ding, HAO Xingming, ZHANG Ying, QIN Jingxiu. Uncertainty assessment of potential evapotranspiration in arid areas, as estimated by the Penman-Monteith method[J]. Journal of Arid Land, 2020, 12(1): 166-180.
[11] MAMUT Jannathan, Dunyan TAN, C BASKIN Carol, M BASKIN Jerry. Effects of water stress and NaCl stress on different life cycle stages of the cold desert annual Lachnoloma lehmannii in China[J]. Journal of Arid Land, 2019, 11(5): 774-784.
[12] GAO Guanlong, ZHANG Xiaoyou, YU Tengfei, LIU Bing. Comparison of three evapotranspiration models with eddy covariance measurements for a Populus euphratica Oliv. forest in an arid region of northwestern China[J]. Journal of Arid Land, 2016, 8(1): 146-156.
[13] QiQiang GUO, WenHui ZHANG, HuiE LI. Comparison of photosynthesis and antioxidative protection in Sophora moorcroftiana and Caragana maximovicziana under water stress[J]. Journal of Arid Land, 2014, 6(5): 637-645.
[14] YuBi YAO, RunYuan WANG, JinHu YANG, Ping YUE, DengRong LU, GuoJu XIAO, Yang WANG, LinChun LIU. Changes in terrestrial surface dry and wet conditions on the Loess Plateau (China) during the last half century[J]. Journal of Arid Land, 2013, 5(1): 15-24.