{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T08:15:09Z","timestamp":1774685709444,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T00:00:00Z","timestamp":1665532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"DUPC2 Program\u2014IHE Delft supported by the Ministry of Foreign Affairs, Netherlands","award":["103768"],"award-info":[{"award-number":["103768"]}]},{"name":"DUPC2 Program\u2014IHE Delft supported by the Ministry of Foreign Affairs, Netherlands","award":["103808"],"award-info":[{"award-number":["103808"]}]},{"name":"Google.org","award":["103768"],"award-info":[{"award-number":["103768"]}]},{"name":"Google.org","award":["103808"],"award-info":[{"award-number":["103808"]}]},{"name":"Tides Foundation","award":["103768"],"award-info":[{"award-number":["103768"]}]},{"name":"Tides Foundation","award":["103808"],"award-info":[{"award-number":["103808"]}]},{"name":"Google AI","award":["103768"],"award-info":[{"award-number":["103768"]}]},{"name":"Google AI","award":["103808"],"award-info":[{"award-number":["103808"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the foreseen increase in population and the reliance on water as a key input for agricultural production, greater demand will be placed on freshwater supplies. The objective of this work was to present the newly developed Android smartphone application to calculate crop evapotranspiration in real-time to support field-scale irrigation management. As part of the answer to water shortage, we embraced technology by developing AgSAT, a Google Earth Engine-based application that optimizes water use for food production. AgSAT uses meteorological data to calculate daily water requirements using the ASCE-Penman\u2013Monteith method (ETref) and vegetation indices from satellite imagery to derive the basal crop growth coefficient, Kcb. The performance of AgSAT to estimate ETref was assessed using climatic data from 18 meteorological stations distributed over several climatic zones worldwide. ETref estimation through the app showed acceptable results with values of 1.27, 0.9, 0.79, 0.95, and 0.5 for root mean square error (RMSE), correlation coefficient (r), modeling efficiency (NSE), concordance index (d), and percentage bias (Pbias), respectively. AgSAT guides gross irrigation requirements for crops and rationalizes water quantities used in agricultural production. AgSAT has been released, is currently in use by research scientists, agricultural producers, and irrigation managers, and is freely accessible from the Google Play and IOS Store and also at agsat.app. Our work is geared towards the development of remote sensing-based technologies that transfer significant benefits to farmers and water-saving efforts.<\/jats:p>","DOI":"10.3390\/rs14205090","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T22:45:29Z","timestamp":1665614729000},"page":"5090","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["AgSAT: A Smart Irrigation Application for Field-Scale Daily Crop ET and Water Requirements Using Satellite Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2612-3191","authenticated-orcid":false,"given":"Hadi","family":"Jaafar","sequence":"first","affiliation":[{"name":"Department of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut 2020-1100, Lebanon"}]},{"given":"Roya","family":"Mourad","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut 2020-1100, Lebanon"}]},{"given":"Rim","family":"Hazimeh","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut 2020-1100, Lebanon"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7813-4912","authenticated-orcid":false,"given":"Lara","family":"Sujud","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut 2020-1100, Lebanon"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Vaishali, S., Suraj, S., Vignesh, G., Dhivya, S., and Udhayakumar, S. (2017, January 6\u20138). Mobile integrated smart irrigation management and monitoring system using IOT. Proceedings of the 2017 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2017.8286792"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s00271-013-0421-1","article-title":"Evaluating irrigation applied and nitrogen leached using different smart irrigation technologies on bahiagrass (Paspalumnotatum)","volume":"32","author":"Dobbs","year":"2014","journal-title":"Irrig. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.agwat.2017.09.011","article-title":"Assessing potato transpiration, yield and water productivity under various water regimes and planting dates using the FAO dual Kc approach","volume":"195","author":"Paredes","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.agwat.2015.06.006","article-title":"Modeling malt barley water use and evapotranspiration partitioning in two contrasting rainfall years. Assessing AquaCrop and SIMDualKc models","volume":"159","author":"Pereira","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.agwat.2011.10.018","article-title":"Implementing the dual crop coefficient approach in interactive software: 2. Model testing","volume":"103","author":"Rosa","year":"2012","journal-title":"Agric. Water Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1002\/ird.1942","article-title":"Field evaluation of irrigation scheduling strategies using a mechanistic crop growth model","volume":"65","author":"Seidel","year":"2016","journal-title":"Irrig. Drain."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1061\/(ASCE)IR.1943-4774.0000281","article-title":"Irrigation scheduling for green bell peppers using capacitance soil moisture sensors","volume":"137","author":"Zotarelli","year":"2011","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_8","unstructured":"Smith, M. (1992). CROPWAT: A Computer Program for Irrigation Planning and Management, Food & Agriculture Org."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Orange, M., Matyac, J.S., and Snyder, R.L. (2003, January 1\u20136). Consumptive use program (CUP) model. Proceedings of the IV International Symposium on Irrigation of Horticultural Crops 664, Davis, CA, USA.","DOI":"10.17660\/ActaHortic.2004.664.58"},{"key":"ref_10","unstructured":"Leib, B., and Elliott, T. (2000, January 14\u201316). Washington Irrigation Scheduling Expert (WISE) Software. Proceedings of the National Irrigation Symposium: Proceedings of the 4th Decennial Symposium, Phoenix, AZ, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Adeyemi, O., Grove, I., Peets, S., Domun, Y., and Norton, T. (2018). Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling. Sensors, 18.","DOI":"10.3390\/s18103408"},{"key":"ref_12","first-page":"44","article-title":"Hydro-Tech: An automated smart-tech Decision Support Tool for eco-efficient irrigation management","volume":"25","author":"Todorovic","year":"2016","journal-title":"Int. Agric. Eng. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.compag.2016.06.021","article-title":"Development and assessment of a smartphone application for irrigation scheduling in cotton","volume":"127","author":"Vellidis","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.agwat.2016.11.008","article-title":"Determining water requirements of biblical hyssop using an ET-based drip irrigation system","volume":"180","author":"Jaafar","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.compag.2014.12.021","article-title":"A smartphone app to extend use of a cloud-based irrigation scheduling tool","volume":"111","author":"Bartlett","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.agwat.2014.09.026","article-title":"A mobile application to calculate optimum drip irrigation laterals","volume":"151","author":"Sesma","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.biosystemseng.2007.03.003","article-title":"Linear regressions and neural approaches to water demand forecasting in irrigation districts with telemetry systems","volume":"97","author":"Montesinos","year":"2007","journal-title":"Biosyst. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.agwat.2016.07.017","article-title":"Multiplatform application for precision irrigation scheduling in strawberries","volume":"183","author":"Perea","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_19","unstructured":"Kelley, L., and Miller, S. (2011). Irrigation Scheduling Tools, Michigan State University Extension. Irrigation Fact Sheet 3."},{"key":"ref_20","unstructured":"Malamos, N., Tsirogiannis, I.L., Christofides, A., Anastasiadis, S., and Vanino, S. (2015, January 17\u201320). Main Features and Application of a Web-based Irrigation Management Tool for the Plain of Arta. Proceedings of the HAICTA, Kavala, Greece."},{"key":"ref_21","unstructured":"Scherer, T. (2014). Web-Based Irrigation Scheduler, North Dakota State University."},{"key":"ref_22","unstructured":"Thompson, W. (1998). Irrigation Scheduling Made Simple (r): Wateright Website Does the Hard Work for You, California Grower."},{"key":"ref_23","unstructured":"Cahn, M., Smith, R., Farrara, B., Hartz, T., Johnson, L., Melton, F., and Post, K. (2014, January 4\u20135). Irrigation and nitrogen management decision support tool for vegetables and berries. Proceedings of the US Committee on Irrigation and Drainage Conference: Groundwater Issues and Water Management\u2014Strategies Addressing the Challenges of Sustainability USCID, Sacramento, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1016\/j.proenv.2013.06.091","article-title":"IRRINET: Large scale DSS application for on-farm irrigation scheduling","volume":"19","author":"Mannini","year":"2013","journal-title":"Procedia Environ. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.compag.2012.03.003","article-title":"Using a mobile phone Short Messaging Service (SMS) for irrigation scheduling in Australia\u2013Farmers\u2019 participation and utility evaluation","volume":"84","author":"Car","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","unstructured":"Deumier, J., Leroy, P., and Peyremorte, P. (1996). Tools for Improving Management of Irrigated Agricultural Crop Systems, FAO. Water Reports."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s00271-010-0235-3","article-title":"Improving on-farm water management through an irrigation scheduling service","volume":"29","author":"Montoro","year":"2011","journal-title":"Irrig. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.agwat.2014.07.012","article-title":"Improving water-efficient irrigation: Prospects and difficulties of innovative practices","volume":"146","author":"Levidow","year":"2014","journal-title":"Agric. Water Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.envsci.2018.12.014","article-title":"Influencing factors and incentives on the intention to adopt precision agricultural technologies within arable farming systems","volume":"93","author":"Barnes","year":"2018","journal-title":"Environ. Sci. Policy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.landusepol.2018.10.004","article-title":"Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers","volume":"80","author":"Barnes","year":"2019","journal-title":"Land Use Policy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s11119-013-9338-1","article-title":"Timing of precision agriculture technology adoption in US cotton production","volume":"15","author":"Watcharaanantapong","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.21273\/HORTSCI13183-18","article-title":"Effects of Real-time Location-specific Drip Irrigation Scheduling on Water Use, Plant Growth, Nutrient Accumulation, and Yield of Florida Fresh-market Tomato","volume":"53","author":"Ayankojo","year":"2018","journal-title":"HortScience"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.32473\/edis-ss660-2017","article-title":"Citrus Irrigation Management","volume":"2017","author":"Kadyampakeni","year":"2017","journal-title":"EDIS"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.compag.2018.10.032","article-title":"EVAPO: A smartphone application to estimate potential evapotranspiration using cloud gridded meteorological data from NASA-POWER system","volume":"156","author":"Valeriano","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","unstructured":"Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998). Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Allen, R., Walter, I., Elliott, R., Howell, T., Itenfisu, D., Jensen, M., and Snyder, R. (2005). The ASCE Standardized Reference Evapotranspiration Equation, American Society of Civil Engineers. Microclimate: The Biological Environment, John Wiley & Sons.","DOI":"10.1061\/9780784408056"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"439","DOI":"10.3390\/rs4020439","article-title":"Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in California\u2019s San Joaquin Valley","volume":"4","author":"Johnson","year":"2012","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.3390\/rs5041588","article-title":"Estimating crop coefficients using remote sensing-based vegetation index","volume":"5","author":"Kamble","year":"2013","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1590\/0103-8478cr20150318","article-title":"Temporal variation of normalized difference vegetation index (NDVI) and calculation of the crop coefficient (Kc) from NDVI in areas cultivated with irrigated soybean","volume":"46","author":"Oliveira","year":"2016","journal-title":"Ci\u00eancia Rural"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106266","DOI":"10.1016\/j.agwat.2020.106266","article-title":"Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest","volume":"239","author":"French","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1016\/j.agwat.2018.12.002","article-title":"Estimation of crop evapotranspiration through spatial distributed crop coefficient in a semi-arid environment","volume":"213","author":"Rawat","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_42","first-page":"4525021","article-title":"Estimation of crop evapotranspiration using satellite remote sensing-based vegetation index","volume":"2018","author":"Kjaersgaard","year":"2018","journal-title":"Adv. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Han, W., Niu, X., and Li, G. (2019). Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices. Sensors, 19.","DOI":"10.3390\/s19235250"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/JSTARS.2012.2214474","article-title":"Satellite irrigation management support with the terrestrial observation and prediction system: A framework for integration of satellite and surface observations to support improvements in agricultural water resource management","volume":"5","author":"Melton","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s00271-009-0182-z","article-title":"Estimating crop coefficients from fraction of ground cover and height","volume":"28","author":"Allen","year":"2009","journal-title":"Irrig. Sci."},{"key":"ref_46","unstructured":"Hoffman, G.J., Evans, R.G., Jensen, M.E., Martin, D.L., and Elliott, R.L. (2007). Design and Operation of Farm Irrigation Systems, American Society of Agricultural and Biological Engineers."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111034","DOI":"10.1016\/j.rse.2018.12.033","article-title":"Time series trends of Landsat-based ET using automated calibration in METRIC and SEBAL: The Bekaa Valley, Lebanon","volume":"238","author":"Jaafar","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_48","unstructured":"Walter, I.A., Allen, R.G., Elliott, R., Itenfisu, D., Brown, P., Jensen, M.E., Mecham, B., Howell, T.A., Snyder, R., and Eching, S. (2005). Task Committee on Standardization of Reference Evapotranspiration, ASCE."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/0168-1923(88)90087-1","article-title":"Applications of solutions to non-linear energy budget equations","volume":"43","author":"Gao","year":"1988","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/0168-1923(92)90125-N","article-title":"A discussion of the Penman form equations and comparisons of some equations to estimate latent energy flux density","volume":"57","author":"Grignon","year":"1992","journal-title":"Agric. For. Meteorol."},{"key":"ref_51","first-page":"8842","article-title":"Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project","volume":"108","author":"Cosgrove","year":"2003","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/S0022-1694(00)00413-3","article-title":"Effect of the sampling frequency of meteorological variables on the estimation of the reference evapotranspiration","volume":"243","author":"Hupet","year":"2001","journal-title":"J. Hydrol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1016\/j.agwat.2010.12.015","article-title":"Evapotranspiration information reporting: I. Factors governing measurement accuracy","volume":"98","author":"Allen","year":"2011","journal-title":"Agric. Water Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"111413","DOI":"10.1016\/j.rse.2019.111413","article-title":"Status of accuracy in remotely sensed and in-situ agricultural water productivity estimates: A review","volume":"234","author":"Blatchford","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1016\/j.agrformet.2008.05.017","article-title":"Evaluation of NASA satellite-and assimilation model-derived long-term daily temperature data over the continental US","volume":"148","author":"White","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_56","unstructured":"Blankenau, P.A. (2017). Bias and Other Error in Gridded Weather Data Sets and Their Impacts on Estimating Reference Evapotranspiration. [Master\u2019s Thesis, University of Nebraska-Lincoln]."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/s00382-015-2597-y","article-title":"Precipitation climatology over India: Validation with observations and reanalysis datasets and spatial trends","volume":"46","author":"Kishore","year":"2016","journal-title":"Clim. Dyn."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Allen, R., Dhungel, R., Dhungara, B., Huntington, J., Kilic, A., and Morton, C. (Agric. Water Manag., 2020). Conditioning point and gridded weather data under aridity conditions during calculation of reference evapotranspiration, Agric. Water Manag., submitted.","DOI":"10.1016\/j.agwat.2020.106531"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"04019018","DOI":"10.1061\/(ASCE)IR.1943-4774.0001404","article-title":"Determining Reference Evapotranspiration in Greenhouses from External Climate","volume":"145","author":"Jaafar","year":"2019","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agwat.2005.02.013","article-title":"Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices","volume":"79","author":"Duchemin","year":"2006","journal-title":"Agric. Water Manag."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.agwat.2010.07.011","article-title":"Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.)","volume":"98","author":"Campos","year":"2010","journal-title":"Agric. Water Manag."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/0034-4257(93)90096-G","article-title":"Soil background effects on reflectance-based crop coefficients for corn","volume":"46","author":"Bausch","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.agwat.2006.10.020","article-title":"Development and validation of canopy reflectance-based crop coefficient for potato","volume":"88","author":"Jayanthi","year":"2007","journal-title":"Agric. Water Manag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.agwat.2007.09.001","article-title":"Spectral vegetation indices for benchmarking water productivity of irrigated cotton and sugarbeet crops","volume":"95","author":"Mateos","year":"2008","journal-title":"Agric. Water Manag."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.agwat.2012.11.005","article-title":"Monitoring evapotranspiration of irrigated crops using crop coefficients derived from time series of satellite images. I. Method validation","volume":"125","author":"Mateos","year":"2013","journal-title":"Agric. Water Manag."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Odi-Lara, M., Campos, I., Neale, C.M., Ortega-Far\u00edas, S., Poblete-Echeverr\u00eda, C., Balbont\u00edn, C., and Calera, A. (2016). Estimating evapotranspiration of an apple orchard using a remote sensing-based soil water balance. Remote Sens., 8.","DOI":"10.3390\/rs8030253"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5090\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:52:40Z","timestamp":1760143960000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5090"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,12]]},"references-count":66,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205090"],"URL":"https:\/\/doi.org\/10.3390\/rs14205090","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,12]]}}}