Estimation of rice crop evapotranspiration in Perú based on the METRIC algorithm and UAV images

Javier A. Quille-Mamani, Lia Ramos-Fernández, Ronald E. Ontiveros-Capurata

Abstract

Modern remote measurement techniques using cameras mounted on an unmanned aerial vehicle (UAV) have made possible to acquire high-resolution images and estimating evapotranspiration at more detailed spatial and temporal scales. The objective of the present research was to estimate crop evapotranspiration (ETc) of rice crop using the “mapping evapotranspiration with internalized calibration model (METRIC)” using high spatial resolution multispectral and thermal images obtained from a UAV. A total of 18 flights with UAV were performed to get the images; likewise, data were collected from the weather station and thermocouple information installed in the crop canopy under soil water potential conditions of –10 kPa (T1), –15 kPa (T2), –20 kPa (T3) and a control of 0 kPa (T0), from November 13, 2017, to April 30, 2018. The results indicate that the METRIC model compared to ETc measurements recorded by a field drainage lysimeter presents a Pearson correlation coefficient (r) of 0.97, root mean square error (RMSE) of 0.51 mm d–1, Nash-Sutcliffe coefficient (EF) of 0.87 and underestimation of 7 %. Evapotranspiration reached values of 7.48 mm d–1, with differences between treatments of 0.2 %, 6 % and 8 % concerning to T0 and yield reduction of 9 %, 34 % and 35 % for T1, T2 and T3 soil water potential. The high[1]resolution images allowed obtaining detailed information on the spatial variability of ETc that could be used in the more efficient application of plot irrigation.

Keywords

remote sensing; UAV; energy balance; multispectral imaging; thermal imaging; Oryza sativa

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References

Abrishamkar, M., Ahmadi, A. 2017. Evapotranspiration Estimation Using Remote Sensing Technology Based on SEBAL Algorithm. Iranian Journal of Science and Technology Transactions of Civil Engineering, 41, 65-76. https://doi.org/10.1007/s40996-016-0036-x

Alberto, M.C.R., Wassmann, R., Hirano, T., Miyata, A., Hatano, R., Kumar, A., … Amante, M. 2011. Comparisons of energy balance and evapotranspiration between flooded and aerobic rice fields in the Philippines. Agricultural Water Management, 98(9), 1417-1430. https://doi.org/10.1016/j.agwat.2011.04.011

Allen, R., Tasumi, M., Trezza, R., Waters, R. 2002. Bastiaanssen, W. Surface Energy Balance Algorithm for Land (SEBAL)-Advanced Training and Users Manual; Idaho Department of Water Resources, University of Idaho: Moscow, ID, USA.

Allen, R., Tasumi, M., Trezza, R. 2007a. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) model. ASCE, Journal of Irrigation and Drainage Engineering, 133, 380-394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)

Allen, RG., Tasumi, M., Morse, A., Trezza, R., Wright, JL., Bastiaanssen, W., Kramber, W., Lorite, I., Robison, CW. 2007b. Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)-Applications. Journal of Irrigation and Drainage Engineering, 133(4), 395-406. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(395)

Allen, R., Trezza, R., Hendrickx, J., Bastiaanssen, W., Kjaersgaard, J. 2011. Satellite-based ET estimation in agriculture using SEBAL and METRIC. Hydrological Processes, 25(26), 4011-4027. https://doi.org/10.1002/hyp.8408

Allen, RG., Burnett, B., Kramber, W., Huntington, J., Kjaersgaard, J., Kilic, A., Kelly, C., Trezza, R. 2013. Automated calibration of the METRIC-Landsat evapotranspiration process. Journal of the American Water Resources Association, 49(3), 563-576. https://doi.org/10.1111/jawr.12056

Allen, R.G., Wright, J.L. 1997. Translating wind measurements from weather stations to agricultural crops. Journal of Hydrologic Engineering, 2(1), 26-35. https://doi.org/10.1061/(ASCE)1084-0699(1997)2:1(26)

Alou, I.N., Steyn, J.M., Annandale, J.G., van der Laan, M. 2018. Growth, phenological, and yield response of upland rice (Oryza sativa L. cv. Nerica 4®) to water stress during different growth stages. Agricultural Water Management, 198, 39-52. https://doi.org/10.1016/j.agwat.2017.12.005

Bastiaanssen, W.G.M. 1995. Regionalization of Surface Flux Densities and Moisture Indicators in Composite Terrain: A Remote Sensing Approach Under Clear Skies in Mediterranean Climates. PhD. Dissertation, CIP Data Koninklijke Bibliotheek, Den Haag, the Netherlands, 273 pp. https://doi.org/90-5485-465-0

Bastiaanssen, W.G.M.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M. 1998a. A remote sensing surface energy balance algorithm for land (SEBAL) 1. Formulation. Journal of Hydrology 212-213(1- 16), 198-212. https://doi.org/10.1016/S0022-1694(98)00253-4

Bastiaanssen, W.G.M., Pelgrum, H., Wang, J., Ma, Y., Moreno, J.F., Roerink, G.J., Van Der Wal, T. 1998b. A remote sensing surface energy balance algorithm for land (SEBAL): 2. Validation. Journal of Hydrology, 212-213(1-4), 213-229. https://doi.org/10.1016/S0022-1694(98)00254-6

Bastiaanssen, W.G.M. 2000. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229(1-2), 87-100. https://doi.org/10.1016/S0022-1694(99)00202-4

Bhattarai, N., Quackenbush, L.J., Im, J., Shaw, S.B. 2017. A new optimized algorithm for automating endmember pixel selection in the SEBAL and METRIC models. Remote Sensing of Environment, 196, 178- 192. https://doi.org/10.1016/j.rse.2017.05.009

Brenner, C., Zeeman, M., Bernhardt, M., Schulz, K., 2018. Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems. International Journal of Remote Sensing, 39(15-16), 5141-5174. https://doi.org/10.1080/01431161.2018.1471550

Cha-Um, S., Yooyongwech, S., Supaibulwatana, K. 2010. Water deficit stress in the reproductive stage of four Indica rice (Oryza sativa L.) genotypes. Journal of Botany, 42(5), 3387-3398.

Enciso, J., Jung, J., Chang, A., Chavez, J.C., Yeom, J., Landivar, J., Cavazos, G. 2018. Assessing land leveling needs and performance with unmanned aerial system. Journal of Applied Remote Sensing, 12(1). https://doi.org/10.1117/1.JRS.12.016001

Han, L., Yang, G., Dai, H., Xu, B., Yang, H., Feng, H., Li, Z., Yang. X. 2019. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods, 15, 10(2019). https://doi.org/10.1186/s13007-019-0394-z

Heros, E., Gómez, L., Sosa, G. 2017. Utilización de los índices de selección en la identificación de genotipos de arroz (Oryza sativa L.) tolerantes a sequía. Producción Agropecuaria y Desarrollo Sostenible 2(2), 11-31. https://doi.org/10.5377/payds.v2i0.4326

Hilmi, H., Saad, H. 2005. Estimation of Rice Evapotranspiration in Paddy Fields Using Remote Sensing and Field Measurements. Universiti Putra Malaysia, Malaysia.

Hoffmann, H., Nieto, H., Jensen, R., Guzinski, R., Zarco-Tejada, P., Friborg, T. 2016. Estimating evaporation with thermal UAV data and two-source energy balance models. Hydrology and Earth System Sciences, 20(2), 697-713. https://doi.org/10.5194/hess-20-697-2016

Huete, A.R. 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25, 295- 309. https://doi.org/10.1016/0034-4257(88)90106-X

Kato, Y., Okami, M., Katsura, K. 2009. Yield potential and water use efficiency of aerobic rice (Oryza sativa L.) in Japan. Field Crops Research, 113(3), 328-334. https://doi.org/10.1016/j.fcr.2009.06.010

Kiptala, J.K., Mohamed, Y., Mul, M.L., Van Der Zaag, P. 2013. Mapping evapotranspiration trends using MODIS and SEBAL model in a data scarce and heterogeneous landscape in Eastern Africa. Water Resources Research, 49(12), 8495-8510. https://doi.org/10.1002/2013WR014240

Kukal, S.S., Hira, G.S., Sidlu, A.S. 2005. Soil matric potential-based irrigation scheduling to rice (Oryza sativa). Irrigation Science, 23(4), 153-159. https://doi.org/10.1007/s00271-005-0103-8

Lage, M., Bamouh, A., Karrou, M., El Mourid, M. 2003. Estimation of rice evapotranspiration using a microlysimeter technique and comparison with FAO Penman-Monteith and Pan evaporation methods under Moroccan conditions. Agronomie, EDP Sciences, 23(7), 625-631. https://doi.org/10.1051/agro:2003040

Lee, Y., Kim, S. 2016. The Modified SEBAL for Mapping Daily Spatial Evapotranspiration of South Korea Using Three Flux Towers and Terra MODIS Data. Remote Sensing, 8(12), 983. https://doi.org/10.3390/rs8120983

Li, G., Jing, Y., Wu, Y., Zhang, F. 2018. Improvement of Two Evapotranspiration Estimation Models Using a Linear Spectral Mixture Model over a Small Agricultural Watershed. Water, 10(4), 474. https://doi.org/10.3390/w10040474

Liu, X., Xu, J., Zhou, X., Wang, W., Yang, S. 2019. Evaporative fraction and its application in estimating daily evapotranspiration of water-saving irrigated rice field. Journal of Hydrology, 584, 124317. https://doi.org/10.1016/j.jhydrol.2019.124317

Maruyama, A., Kuwagata, T. 2010. Coupling land surface and crop growth models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies. Agricultural and Forest Meteorology, 150(7-8), 919-930. https://doi.org/10.1016/j.agrformet.2010.02.011

Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L. 2007. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885-900. https://doi.org/10.13031/2013.23153

Morton, C.G., Huntington, J.L., Pohll, G.M., Allen, R.G., Mcgwire, K.C., Bassett, S.D. 2013. Assessing Calibration Uncertainty and Automation for Estimating Evapotranspiration from Agricultural Areas Using METRIC. Journal of the American Water Resources Association, 49(3), 549-562. https://doi.org/10.1111/jawr.12054

Nahar, S., Vemireddy, L.R., Sahoo, L., Tanti, B. 2018. Antioxidant Protection Mechanisms Reveal Significant Response in Drought-Induced Oxidative Stress in Some Traditional Rice of Assam, India. Rice Science, 25(4), 185-196. https://doi.org/10.1016/j.rsci.2018.06.002

Nassar, A., Torres-Rua, A., Kustas, W., Nieto, H., McKee, M., Hipps, L., Stevens, D., Alfieri, J., Prueger, J., Mar Alsina, M., McKee, L., Coopmans, C., Sanchez, L., Dokoozlian, N. 2020. Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards, Remote Sensing, 12(3), 342. https://doi.org/10.3390/rs12030342

Norasma, C.Y.N., Abu Sari, M.Y., Fadzilah, M.A., Ismail, M.R., Omar, M.H., Zulkarami, B., Hassim Y.M.M., Tarmidi, Z. 2018. Rice crop monitoring using multirotor UAV and RGB digital camera at early stage of growth. IOP Conference Series: Earth and Environmental Science, 169, 012095. https://doi.org/10.1088/1755-1315/169/1/012095

Ortega-Farías, S., Ortega-Salazar, S., Poblete, T., Kilic, A., Allen, R., Poblete-Echeverría, C., Ahumada-Orellana, L., Zuñiga, M., Sepúlveda, D. 2016. Estimation of energy balance components over a drip-irrigated olive orchard using thermal and multispectral cameras placed on a helicopter-based unmanned aerial vehicle (UAV). Remote Sensing, 8(8), 1-18. https://doi.org/10.3390/rs8080638

Ramírez-Cuesta, J.M., Allen, R.G., Zarco-Tejada, P.J., Kilic, A., Santos, C., Lorite, I.J. 2019. Impact of the spatial resolution on the energy balance components on an open-canopy olive orchard. International Journal of Applied Earth Observation and Geoinformation, 74, 88-102. https://doi.org/10.1016/j.jag.2018.09.001

Rauneker, P., Lischeid, G. 2012. Spatial distribution of water stress and evapotranspiration estimates using an unmanned aerial vehicle (UAV). EGU General Assembly Conference Abstracts, 14, 10477.

Sagan, V., Maimaitijiang, M., Sidike, P., Eblimit, K., Peterson, KT., Hartling, S., Esposito, F., Khanal, K., Newcomb, M., Pauli, D., Ward, R., Fritschi, F., Shakoor, N., Mockler, T. 2019. UAV-based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640, and thermomap cameras. Remote Sensing, 11(3). https://doi.org/10.3390/rs11030330

Sandhu, N., Singh, V., Sihag, M.K. 2019. Genomic Footprints Uncovering Abiotic Stress Tolerance in Rice. Advances in Rice Research for Aboitic Stress Tolerance, 737-753. https://doi.org/10.1016/B978-0-12-814332-2.00036-8

Tasumi, M. 2003. Progress in operational estimation of regional evapotranspiration using satellite imagery. Ph.D. dissertation, Univ. of Idaho, Moscow, Id.

Tucker, C.J., Sellers, P.J. 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing, 7(11), 1395-1416. https://doi.org/10.1080/01431168608948944

Tsouni, A., Kontoes, C., Koutsoyiannis, D., Elias, P., Mamassis, N. 2008. Estimation of actual evapotranspiration by remote sensing: Application in Thessaly plain, Greece. Sensors, 8(6), 3586-3600. https://doi.org/10.3390/s8063586

Vogt, J. 1990. Cloud Masking for AVHRR. Commission of the European Communities, Joint Research Centre, ISPRA. Special Publication, I.90.33.

Wagle, P., Bhattarai, N., Gowda, PH., Kakani, VG. 2017. Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 192- 203. https://doi.org/10.1016/j.isprsjprs.2017.03.022

Weiss, M., Jacob, F., Duveiller, G. 2020. Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402. https://doi.org/10.1016/j.rse.2019.111402

Xu, J., Liu, X., Yang, S., Qi, Z., Wang, Y. 2017. Modeling rice evapotranspiration under water saving irrigation by calibrating canopy resistance model parameters in the Penman-Monteith equation. Agricultural Water Management, 182, 55-66. https://doi.org/10.1016/j.agwat.2016.12.010

Zaman, N.K., Abdullah, M.Y., Othman, S., Zaman, N.K. 2018. Growth and Physiological Performance of Aerobic and Lowland Rice as Affected by Water Stress at Selected Growth Stages. Rice Science, 25(2), 82-93. https://doi.org/10.1016/j.rsci.2018.02.001

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