{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:21:34Z","timestamp":1779380494242,"version":"3.53.1"},"reference-count":133,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T00:00:00Z","timestamp":1607385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000\u20132017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.<\/jats:p>","DOI":"10.3390\/rs12244018","type":"journal-article","created":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T09:17:04Z","timestamp":1607419024000},"page":"4018","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6973-6644","authenticated-orcid":false,"given":"El houssaine","family":"Bouras","sequence":"first","affiliation":[{"name":"ProcEDE, Department of Applied Physique, Faculty of Sciences and Technologies, Cadi Ayyad University, Marrakech 40000, Morocco"},{"name":"CESBIO, University of Toulouse, IRD\/CNRS\/UPS\/CNES, 31400 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6542-5793","authenticated-orcid":false,"given":"Lionel","family":"Jarlan","sequence":"additional","affiliation":[{"name":"CESBIO, University of Toulouse, IRD\/CNRS\/UPS\/CNES, 31400 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8595-7949","authenticated-orcid":false,"given":"Salah","family":"Er-Raki","sequence":"additional","affiliation":[{"name":"ProcEDE, Department of Applied Physique, Faculty of Sciences and Technologies, Cadi Ayyad University, Marrakech 40000, Morocco"},{"name":"Center for Remote Sensing Applications (CRSA), University Mohammed VI Polytechnic (UM6P), Benguerir 43150, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1095-2702","authenticated-orcid":false,"given":"Cl\u00e9ment","family":"Albergel","sequence":"additional","affiliation":[{"name":"CNRM UMR 3589, M\u00e9t\u00e9o-France\/CNRS, 75016 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bastien","family":"Richard","sequence":"additional","affiliation":[{"name":"G-EAU, University Montpellier, AgroParisTech, CIRAD, IRD, INRAE, Institut Agro, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riad","family":"Balaghi","sequence":"additional","affiliation":[{"name":"National Institute for Agronomic Research (INRA), Rabat 10000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3309-9935","authenticated-orcid":false,"given":"Sa\u00efd","family":"Khabba","sequence":"additional","affiliation":[{"name":"Center for Remote Sensing Applications (CRSA), University Mohammed VI Polytechnic (UM6P), Benguerir 43150, Morocco"},{"name":"LMFE, Department of Physics, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech 40000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kumar, V. (1998). An early warning system for agricultural drought in an arid region using limited data. J. Arid Environ.","DOI":"10.1006\/jare.1998.0437"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"P\u00e1scoa, P., Gouveia, C.M., Russo, A., and Trigo, R.M. (2017). The role of drought on wheat yield interannual variability in the Iberian Peninsula from 1929 to 2012. Int. J. Biometeorol.","DOI":"10.1007\/s00484-016-1224-x"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ribeiro, A.F.S., Russo, A., Gouveia, C.M., and P\u00e1scoa, P. (2019). Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices. Theor. Appl. Climatol.","DOI":"10.1007\/s00704-018-2478-5"},{"key":"ref_4","unstructured":"FAO (2017). The Impact of of Natural Hazards and Disasters on Agriculture, Food Security and Nutrition, FAO."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Schilling, J., Freier, K.P., Hertig, E., and Scheffran, J. (2012). Climate change, vulnerability and adaptation in North Africa with focus on Morocco. Agric. Ecosyst. Environ.","DOI":"10.1016\/j.agee.2012.04.021"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Schilling, J., Hertig, E., Tramblay, Y., and Scheffran, J. (2020). Climate change vulnerability, water resources and social implications in North Africa. Reg. Environ. Chang.","DOI":"10.1007\/s10113-020-01597-7"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tigkas, D., and Tsakiris, G. (2015). Early Estimation of Drought Impacts on Rainfed Wheat Yield in Mediterranean Climate. Environ. Process.","DOI":"10.1007\/s40710-014-0052-4"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Karrou, M., and Oweis, T. (2012). Water and land productivities of wheat and food legumes with deficit supplemental irrigation in a Mediterranean environment. Agric. Water Manag.","DOI":"10.1016\/j.agwat.2012.01.014"},{"key":"ref_9","first-page":"1489","article-title":"Linkages between common wheat yields and climate in Morocco (1982\u20132008)","volume":"58","author":"Jarlan","year":"2014","journal-title":"Int. J. Biometeorol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Balaghi, R., Tychon, B., Eerens, H., and Jlibene, M. (2008). Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. Int. J. Appl. Earth Obs. Geoinf.","DOI":"10.1016\/j.jag.2006.12.001"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.eja.2010.02.004","article-title":"Wheat production in Tunisia: Progress, inter-annual variability and relation to rainfall","volume":"33","author":"Latiri","year":"2010","journal-title":"Eur. J. Agron."},{"key":"ref_12","unstructured":"Agoumi, A. (2003). Vulnerability of North African Countries to Climatic Changes, International Institute for Sustainable Development."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Driouech, F., D\u00e9qu\u00e9, M., and Mokssit, A. (2009). Numerical simulation of the probability distribution function of precipitation over Morocco. Clim. Dyn.","DOI":"10.1007\/s00382-008-0430-6"},{"key":"ref_14","unstructured":"IPCC (2020, November 27). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Available online: https:\/\/www.ipcc.ch\/report\/managing-the-risks-of-extreme-events-and-disasters-to-advance-climate-change-adaptation\/."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hertig, E., and Tramblay, Y. (2017). Regional downscaling of Mediterranean droughts under past and future climatic conditions. Glob. Planet. Chang.","DOI":"10.1016\/j.gloplacha.2016.10.015"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lehner, F., Coats, S., Stocker, T.F., Pendergrass, A.G., Sanderson, B.M., Raible, C.C., and Smerdon, J.E. (2017). Projected drought risk in 1.5 \u00b0C and 2 \u00b0C warmer climates. Geophys. Res. Lett.","DOI":"10.1002\/2017GL074117"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dai, A. (2011). Drought under global warming: A review. Wiley Interdiscip. Rev. Clim. Chang.","DOI":"10.1002\/wcc.81"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Vogel, M.M., Hauser, M., and Seneviratne, S.I. (2020). Projected changes in hot, dry and wet extreme events\u2019 clusters in CMIP6 multi-model ensemble. Environ. Res. Lett.","DOI":"10.1088\/1748-9326\/ab90a7"},{"key":"ref_19","unstructured":"(2020, November 27). Minist\u00e8re de l\u2019Agriculture, de la P\u00eache Maritime, D\u00e9veloppement Rural et des eaux et For\u00eats, Agriculture en chiffre au Maroc, Available online: https:\/\/www.agriculture.gov.ma\/pages\/publications\/agriculture-en-chiffres-2018-edition-2019."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vicente-Serrano, S., Cuadrat-Prats, J.M., and Romo, A. (2006). Early prediction of crop production using drought indices at different time-scales and remote sensing data: Application in the Ebro Valley (north-east Spain). Int. J. Remote Sens.","DOI":"10.1080\/01431160500296032"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/02508068508686328","article-title":"Water International Understanding: The Drought Phenomenon: The Role of Definitions Understanding: The Drought Phenomenon: The Role of Definitions","volume":"10","author":"Wilhite","year":"1985","journal-title":"Water Int."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ciais, P., Reichstein, M., Viovy, N., Granier, A., Og\u00e9e, J., Allard, V., Aubinet, M., Buchmann, N., Bernhofer, C., and Carrara, A. (2005). Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature.","DOI":"10.1038\/nature03972"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mishra, A.K., and Singh, V.P. (2010). A review of drought concepts. J. Hydrol.","DOI":"10.1016\/j.jhydrol.2010.07.012"},{"key":"ref_24","unstructured":"Balaghi, R., Jlibene, M., Tychon, B., and Eerens, H. (2013). Agrometeorological Cereal Yield Forecasting in Morocco, Institut National de la Recherche Agronomique."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0167-8809(01)00352-8","article-title":"Effects of elevated CO2 and drought on wheat: Testing crop simulation models for different experimental and climatic conditions","volume":"93","author":"Ewert","year":"2002","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1071\/A97039","article-title":"\u201cHaying-off\u201d, the negative grain yield response of dryland wheat to nitrogen fertiliser. I. Biomass, grain yield, and water use","volume":"49","author":"Farquhar","year":"1998","journal-title":"Aust. J. Agric. Res."},{"key":"ref_27","first-page":"179","article-title":"The relationship of drought frequency and duration to time scales","volume":"17","author":"McKee","year":"1993","journal-title":"Prepr. Eighth Conf. Appl. Climatol. Am. Meteor Soc."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kazmi, D.H., and Rasul, G. (2012). Agrometeorological wheat yield prediction in rainfed Potohar region of Pakistan. Agric. Sci.","DOI":"10.4236\/as.2012.32019"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Salman, A.Z., and Al-Karablieh, E.K. (2001). An early warning system for wheat production in low rainfall areas of Jordan. J. Arid Environ.","DOI":"10.1006\/jare.2001.0799"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kumar, V., and Panu, U. (1997). Predictive assessment of severity of agricultural droughts based on agro-climatic factors. J. Am. Water Resour. Assoc.","DOI":"10.1111\/j.1752-1688.1997.tb03550.x"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.eja.2006.09.001","article-title":"Optimizing wheat productivity in two rain-fed environments of the West Asia-North Africa region using a simulation model","volume":"26","author":"Heng","year":"2007","journal-title":"Eur. J. Agron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1017\/S0960258507785628","article-title":"Germination rates of Solanum sisymbriffolium: Temperature response models, effects of temperature fluctuations and soil water potential","volume":"17","author":"Timmermans","year":"2007","journal-title":"Seed Sci. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Vicente-Serrano, S.M., Beguer\u00eda, S., and L\u00f3pez-Moreno, J.I. (2010). A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim.","DOI":"10.1175\/2009JCLI2909.1"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bijaber, N., El Hadani, D., Saidi, M., Svoboda, M.D., Wardlow, B.D., Hain, C.R., Poulsen, C.C., Yessef, M., and Rochdi, A. (2018). Developing a remotely sensed drought monitoring indicator for Morocco. Geosciences, 8.","DOI":"10.3390\/geosciences8020055"},{"key":"ref_35","unstructured":"Hayes, M.J., Svoboda, M.D., Wardlow, B.D., Anderson, M.C., and Kogan, F. (2020, November 27). Drought Monitoring: Historical and Current Perspectives. Available online: https:\/\/digitalcommons.unl.edu\/droughtfacpub\/94\/."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Le Page, M., and Zribi, M. (2019). Analysis and Predictability of Drought In Northwest Africa Using Optical and Microwave Satellite Remote Sensing Products. Sci. Rep.","DOI":"10.1038\/s41598-018-37911-x"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1139\/a11-013","article-title":"A review of drought indices","volume":"19","author":"Zargar","year":"2011","journal-title":"Environ. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hazaymeh, K., and Hassan, Q.K. (2016). Remote sensing of agricultural drought monitoring: A state of art review. AIMS Environ. Sci.","DOI":"10.3934\/environsci.2016.4.604"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Singh, R.P., Roy, S., and Kogan, F. (2003). Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. Int. J. Remote Sens.","DOI":"10.1080\/0143116031000084323"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, W.T., and Kogan, F.N. (1996). Monitoring regional drought using the vegetation condition index. Int. J. Remote Sens.","DOI":"10.1080\/01431169608949106"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Unganai, L.S., and Kogan, F.N. (1998). Drought monitoring and corn yield estimation in southern Africa from AVHRR data. Remote Sens. Environ.","DOI":"10.1016\/S0034-4257(97)00132-6"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Salazar, L., Kogan, F., and Roytman, L. (2007). Use of remote sensing data for estimation of winter wheat yield in the United States. Int. J. Remote Sens.","DOI":"10.1080\/01431160601050395"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Vicente-Serrano, S.M. (2007). Evaluating the impact of drought using remote sensing in a Mediterranean, Semi-arid Region. Nat. Hazards.","DOI":"10.1007\/s11069-006-0009-7"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kogan, F.N. (1997). Global Drought Watch from Space. Bull. Am. Meteorol. Soc.","DOI":"10.1175\/1520-0477(1997)078<0621:GDWFS>2.0.CO;2"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bento, V.A., Trigo, I.F., Gouveia, C.M., and DaCamara, C.C. (2018). Contribution of Land Surface Temperature (TCI) to Vegetation Health Index: A comparative study using clear sky and all-weather climate data records. Remote Sens., 9.","DOI":"10.3390\/rs10091324"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bento, V.A., Gouveia, C.M., DaCamara, C.C., and Trigo, I.F. (2018). A climatological assessment of drought impact on vegetation health index. Agric. For. Meteorol.","DOI":"10.1016\/j.agrformet.2018.05.014"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"103198","DOI":"10.1016\/j.gloplacha.2020.103198","article-title":"The roles of NDVI and Land Surface Temperature when using the Vegetation Health Index over dry regions","volume":"190","author":"Bento","year":"2020","journal-title":"Glob. Planet. Chang."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2013.02.023","article-title":"Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data","volume":"134","author":"Zhang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_49","first-page":"1","article-title":"Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR)","volume":"112","author":"Reichle","year":"2007","journal-title":"J. Geophys. Res."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Albergel, C., Munier, S., Jennifer Leroux, D., Dewaele, H., Fairbairn, D., Lavinia Barbu, A., Gelati, E., Dorigo, W., Faroux, S., and Meurey, C. (2017). Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX-v8.0: LDAS-Monde assessment over the Euro-Mediterranean area. Geosci. Model Dev.","DOI":"10.5194\/gmd-2017-121"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kumar, S.V., Jasinski, M., Mocko, D.M., Rodell, M., Borak, J., Li, B., Beaudoing, H.K., and Peters-Lidard, C.D. (2019). NCA-LDAS Land Analysis: Development and Performance of a Multisensor, Multivariate Land Data Assimilation System for the National Climate Assessment. J. Hydrometeorol.","DOI":"10.1175\/JHM-D-17-0125.1"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sawada, Y., Koike, T., and Walker, J.P. (2015). A land data assimilation system for simultaneous simulation of soil moisture and vegetation dynamics. J. Geophys. Res.","DOI":"10.1002\/2014JD022895"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., Funk, C., Peters-Lidard, C.D., and Verdin, J.P. (2017). A land data assimilation system for sub-Saharan Africa food and water security applications. Sci. Data.","DOI":"10.1038\/sdata.2017.12"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1175\/JHM-D-13-0125.1","article-title":"Benchmarking a soil moisture data assimilation system for agricultural drought monitoring","volume":"15","author":"Han","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Blyverket, J., Hamer, P.D., Schneider, P., Albergel, C., and Lahoz, W.A. (2019). Monitoring soil moisture drought over northern high latitudes from space. Remote Sens., 10.","DOI":"10.20944\/preprints201904.0009.v1"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Bolten, J.D., Crow, W.T., Jackson, T.J., Zhan, X., and Reynolds, C.A. (2010). Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.","DOI":"10.1109\/JSTARS.2009.2037163"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Renzullo, L.J., van Dijk, A.I.J.M., Perraud, J.M., Collins, D., Henderson, B., Jin, H., Smith, A.B., and McJannet, D.L. (2014). Continental satellite soil moisture data assimilation improves root-zone moisture analysis for water resources assessment. J. Hydrol.","DOI":"10.1016\/j.jhydrol.2014.08.008"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Draper, C.S., Mahfouf, J.F., and Walker, J.P. (2009). An EKF assimilation of AMSR-E soil moisture into the ISBA land surface scheme. J. Geophys. Res. Atmos.","DOI":"10.1029\/2008JD011650"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Albergel, C., Calvet, J.C., Mahfouf, J.F., R\u00fcdiger, C., Barbu, A.L., Lafont, S., Roujean, J.L., Walker, J.P., Crapeau, M., and Wigneron, J.P. (2010). Monitoring of water and carbon fluxes using a land data assimilation system: A case study for southwestern France. Hydrol. Earth Syst. Sci.","DOI":"10.5194\/hessd-7-1705-2010"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ragab, R. (1995). Towards a continuous operational system to estimate the root-zone soil moisture from intermittent remotely sensed surface moisture. J. Hydrol.","DOI":"10.1016\/0022-1694(95)02749-F"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Walker, J.P., Willgoose, G.R., and Kalma, J.D. (2001). One-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: A simplified soil moisture model and field application. J. Hydrometeorol.","DOI":"10.1029\/2002WR001545"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bolten, J.D., and Crow, W.T. (2012). Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture. Geophys. Res. Lett.","DOI":"10.1029\/2012GL053470"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Albergel, C., Munier, S., Bocher, A., Bonan, B., Zheng, Y., Draper, C., Leroux, D.J., and Calvet, J.C. (2018). LDAS-Monde sequential assimilation of satellite derived observations applied to the contiguous US: An ERA-5 driven reanalysis of the land surface variables. Remote Sens., 10.","DOI":"10.20944\/preprints201809.0105.v1"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Kogan, F.N. (1995). Application of vegetation index and brightness temperature for drought detection. Adv. Sp. Res.","DOI":"10.1016\/0273-1177(95)00079-T"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Gao, B.C. (1996). NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ.","DOI":"10.1117\/12.210877"},{"key":"ref_66","unstructured":"Wang, P.X., Li, X.W., Gong, J.Y., and Song, C. (2001, January 9\u201313). Vegetation temperature condition index and its application for drought monitoring. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Sydney, Ausralia."},{"key":"ref_67","first-page":"71","article-title":"Drought monitoring with NDVI-based Standardized Vegetation Index","volume":"68","author":"Peters","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Gu, Y., Brown, J.F., Verdin, J.P., and Wardlow, B. (2007). A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophys. Res. Lett.","DOI":"10.1029\/2006GL029127"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wang, L., and Qu, J.J. (2007). NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophys. Res. Lett.","DOI":"10.1029\/2007GL031021"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ghulam, A., Li, Z.L., Qin, Q., Yimit, H., and Wang, J. (2008). Estimating crop water stress with ETM+ NIR and SWIR data. Agric. For. Meteorol.","DOI":"10.1016\/j.agrformet.2008.05.020"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1016\/j.rse.2010.07.005","article-title":"Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data","volume":"114","author":"Rhee","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.rse.2015.11.034","article-title":"The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts","volume":"174","author":"Anderson","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.rse.2016.10.045","article-title":"Multi-sensor integrated framework and index for agricultural drought monitoring","volume":"188","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2019.01.008","article-title":"Agricultural and Forest Meteorology A new multi-sensor integrated index for drought monitoring","volume":"268","author":"Jiao","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Hu, T., Renzullo, L.J., van Dijk, A.I.J.M., He, J., Tian, S., Xu, Z., Zhou, J., Liu, T., and Liu, Q. (2020). Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals. Remote Sens. Environ., 236.","DOI":"10.1016\/j.rse.2019.111419"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.gloplacha.2010.03.004","article-title":"Weather regimes-Moroccan precipitation link in a regional climate change simulation","volume":"72","author":"Driouech","year":"2010","journal-title":"Glob. Planet. Chang."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Knippertz, P., Christoph, M., and Speth, P. (2003). Long-term precipitation variability in Morocco and the link to the large-scale circulation in recent and future climates. Meteorol. Atmos. Phys.","DOI":"10.1007\/s00703-002-0561-y"},{"key":"ref_78","first-page":"169","article-title":"Gestion du risque de s\u00e9cheresse agricole au Maroc","volume":"18","author":"Balaghi","year":"2007","journal-title":"Sci. Chang. Plan\u00e9t. S\u00e9cheresse"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Beguer\u00eda, S., Vicente-Serrano, S.M., Reig, F., and Latorre, B. (2014). Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol.","DOI":"10.1002\/joc.3887"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s00704-019-02825-9","article-title":"Drought monitoring and prediction using SPEI index and gene expression programming model in the west of Urmia Lake","volume":"138","author":"Abbasi","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pce.2018.07.001","article-title":"Drought monitoring and analysis: Standardised Precipitation Evapotranspiration Index (SPEI) and Standardised Precipitation Index (SPI)","volume":"106","author":"Tirivarombo","year":"2018","journal-title":"Phys. Chem. Earth"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Wang, F., Yang, H., Wang, Z., Zhang, Z., and Li, Z. (2019). Drought evaluation with CMORPH satellite precipitation data in the Yellow River basin by using Gridded Standardized Precipitation Evapotranspiration Index. Remote Sens., 11.","DOI":"10.3390\/rs11050485"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Hor\u00e1nyi, A., Mu\u00f1oz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D. (2020). The ERA5 global reanalysis. Q. J. R. Meteorol. Soc.","DOI":"10.1002\/qj.3803"},{"key":"ref_84","unstructured":"Begueria, S., Serrano, V., and Sawasawa, H. (2020, November 27). SPEI: Calculation of Standardised Precipitation-Evapotranspiration Index. R Package Version 1.7. Available online: https:\/\/cran.r-project.org\/web\/packages\/SPEI\/SPEI.pdf."},{"key":"ref_85","unstructured":"Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (2020, November 27). Crop Evapotranspiration\u2014Guidelines for Computing Crop Water Requirements\u2014FAO Irrigation and Drainage Paper 56. Available online: https:\/\/appgeodb.nancy.inra.fr\/biljou\/pdf\/Allen_FAO1998.pdf."},{"key":"ref_86","unstructured":"Jlibene, M. (2020, November 27). Options G\u00e9n\u00e9tiques D\u2019adaptation du Bl\u00e9 Tendre au Changement Climatique. Prix Hassan II pour L\u2019innovation et la Recherche, \u00c9dition 2009. Available online: https:\/\/www.inra.org.ma\/sites\/default\/files\/publications\/ouvrages\/jlibene11.pdf."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Bouras, E., Jarlan, L., Khabba, S., Er-Raki, S., Dezetter, A., Sghir, F., and Tramblay, Y. (2019). Assessing the impact of global climate changes on irrigated wheat yields and water requirements in a semi-arid environment of Morocco. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-55251-2"},{"key":"ref_88","first-page":"227","article-title":"Nitrogen fertilizer response of some barley varieties in semi-arid conditions in Morocco","volume":"11","author":"Ryan","year":"2009","journal-title":"J. Agric. Sci. Technol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"5951","DOI":"10.3390\/rs70505951","article-title":"Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and FORMOSAT-2 Images","volume":"7","author":"Duchemin","year":"2015","journal-title":"Remote Sens."},{"key":"ref_90","unstructured":"Kaufman, L., and Rousseeuw, P.J. (2020, November 27). Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics). Available online: https:\/\/www.wiley.com\/en-us\/Finding+Groups+in+Data%3A+An+Introduction+to+Cluster+Analysis-p-9780470317488."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Du, L., Tian, Q., Yu, T., Meng, Q., Jancso, T., Udvardy, P., and Huang, Y. (2013). A comprehensive drought monitoring method integrating MODIS and TRMM data. Int. J. Appl. Earth Obs. Geoinf.","DOI":"10.1016\/j.jag.2012.09.010"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Kogan, F.N. (1995). Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull. Am. Meteorol. Soc.","DOI":"10.1175\/1520-0477(1995)076<0655:DOTLIT>2.0.CO;2"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Holben, B.N. (1986). Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens.","DOI":"10.1080\/01431168608948945"},{"key":"ref_94","unstructured":"Wan, Z. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Qiu, J., Crow, W.T., Nearing, G.S., Mo, X., and Liu, S. (2014). The impact of vertical measurement depth on the information content of soil moisture times series data. Geophys. Res. Lett.","DOI":"10.1002\/2014GL060017"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Ceballos, A., Scipal, K., Wagner, W., and Mart\u00ednez-Fern\u00e1ndez, J. (2005). Validation of ERS scatterometer-derived soil moisture data in the central part of the Duero Basin, Spain. Hydrol. Process.","DOI":"10.1002\/hyp.5585"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Albergel, C., R\u00fcdiger, C., Pellarin, T., Calvet, J.C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E. (2008). From near-surface to root-zone soil moisture using an exponential filter: An assessment of the method based on in-situ observations and model simulations. Hydrol. Earth Syst. Sci.","DOI":"10.5194\/hessd-5-1603-2008"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Mart\u00ednez-Fern\u00e1ndez, J., and Llorens, P. (2011). Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2011.08.003"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Zribi, M., Paris Anguela, T., Duchemin, B., Lili, Z., Wagner, W., Hasenauer, S., and Chehbouni, A. (2010). Relationship between soil moisture and vegetation in the Kairouan plain region of Tunisia using low spatial resolution satellite data. Water Resour. Res.","DOI":"10.1029\/2009WR008196"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Wagner, W., Lemoine, G., and Rott, H. (1999). A method for estimating soil moisture from ERS Scatterometer and soil data. Remote Sens. Environ.","DOI":"10.1016\/S0034-4257(99)00036-X"},{"key":"ref_101","first-page":"1","article-title":"Copernicus Global Land Operations \u201cVegetation and Energy\u201d","volume":"51","year":"2018","journal-title":"Copernicus Publ. Prod. User Man."},{"key":"ref_102","first-page":"1","article-title":"Validation of the ASCAT soil water index using in situ data from the International Soil moisture network","volume":"30","author":"Paulik","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_103","unstructured":"Bartalis, Z., Naeimi, V., and Wagner, W. (2008). ASCAT Soil Moisture Product Handbook, Institute of Photogrammetry and Remote Sensing, Vienna University of Technology. ASCAT Soil Moisture Report Series, No. 15."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.2134\/agronj2018.09.0558","article-title":"Development and evaluation of soil moisture-based indices for agricultural drought monitoring","volume":"111","author":"Krueger","year":"2019","journal-title":"Agron. J."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2017.07.001","article-title":"ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions","volume":"203","author":"Dorigo","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., and Bouyssel, F. (2013). The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geosci. Model Dev.","DOI":"10.5194\/gmd-6-929-2013"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Fairbairn, D., Lavinia Barbu, A., Napoly, A., Albergel, C., Mahfouf, J.F., and Calvet, J.C. (2017). The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France. Hydrol. Earth Syst. Sci.","DOI":"10.5194\/hess-21-2015-2017"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Mahfouf, J.F., Bergaoui, K., Draper, C., Bouyssel, F., Taillefer, F., and Taseva, L. (2009). A comparison of two off-line soil analysis schemes for assimilation of screen level observations. J. Geophys. Res. Atmos.","DOI":"10.1029\/2008JD011077"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Noilhan, J., and Mahfouf, J.F. (1996). The ISBA land surface parameterisation scheme. Glob. Planet. Chang.","DOI":"10.1016\/0921-8181(95)00043-7"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Calvet, J.C., Noilhan, J., Roujean, J.L., Bessemoulin, P., Cabelguenne, M., Olioso, A., and Wigneron, J.P. (1998). An interactive vegetation SVAT model tested against data from six contrasting sites. Agric. For. Meteorol.","DOI":"10.1016\/S0168-1923(98)00091-4"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Gibelin, A.L., Calvet, J.C., Roujean, J.L., Jarlan, L., and Los, S.O. (2006). Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products. J. Geophys. Res. Atmos.","DOI":"10.1029\/2005JD006691"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Genovese, G., Vignolles, C., N\u00e8gre, T., and Passera, G. (2001). A methodology for a combined use of normalised difference vegetation index and CORINE land cover data for crop yield monitoring and forecasting. A case study on Spain. Agronomie.","DOI":"10.1051\/agro:2001111"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Faroux, S., Kaptu\u00e9 Tchuent\u00e9, A.T., Roujean, J.-L., Masson, V., Martin, E., and Le Moigne, P. (2013). ECOCLIMAP-II\/Europe: A twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models. Geosci. Model Dev.","DOI":"10.5194\/gmdd-5-3573-2012"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Masson, V., Champeaux, J.L., Chauvin, F., Meriguet, C., and Lacaze, R. (2003). A global database of land surface parameters at 1-km resolution in meteorological and climate models. J. Clim.","DOI":"10.1175\/1520-0442-16.9.1261"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Chu, L., Liu, G.H., Huang, C., and Liu, Q.S. (2014, January 11\u201314). Phenology detection of winter wheat in the Yellow River delta using MODIS NDVI time-series data. Proceedings of the 2014 3rd Int. Conf. Agro-Geoinformatics, Agro-Geoinformatics 2014, Beijing, China.","DOI":"10.1109\/Agro-Geoinformatics.2014.6910664"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Bradley, B.A., Jacob, R.W., Hermance, J.F., and Mustard, J.F. (2007). A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2006.08.002"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.2134\/agronj2004.1139","article-title":"Monitoring maize (Zea mays L.) phenology with remote sensing","volume":"96","author":"Gitelson","year":"2004","journal-title":"Agron. J."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.3390\/rs6032473","article-title":"Mapping crop cycles in China using MODIS-EVI time series","volume":"11","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Zhang, X., Obringer, R., Wei, C., Chen, N., and Niyogi, D. (2017). Droughts in India from 1981 to 2013 and Implications to Wheat Production. Sci. Rep.","DOI":"10.1038\/srep44552"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Modanesi, S., Massari, C., Camici, S., Brocca, L., and Amarnath, G. (2020). Do Satellite Surface Soil Moisture Observations Better Retain Information About Crop-Yield Variability in Drought Conditions?. Water Resour. Res.","DOI":"10.1029\/2019WR025855"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Kogan, F., Yang, B., Guo, W., Pei, Z., and Jiao, X. (2005). Modelling corn production in China using AVHRR-based vegetation health indices. Int. J. Remote Sens.","DOI":"10.1080\/01431160500034235"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Jung, T., Vitart, F., Ferranti, L., and Morcrette, J.J. (2011). Origin and predictability of the extreme negative NAO winter of 2009\/10. Geophys. Res. Lett., 38.","DOI":"10.1029\/2011GL046786"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Savin, R., and Slafer, G.A. (1991). Shading effects on the yield of an Argentinian wheat cultivar. J. Agric. Sci.","DOI":"10.1017\/S0021859600076085"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Warrington, I.J., Dunstone, R.L., and Green, L.M. (1977). Temperature effects at three development stages on the yield of the wheat ear. Aust. J. Agric. Res.","DOI":"10.1071\/AR9770011"},{"key":"ref_125","unstructured":"Ritchie, J.T., Singh, U., Godwin, D.C., and Bowen, W.T. (2020, November 27). Cereal Growth, Development and Yield. Available online: https:\/\/link.springer.com\/chapter\/10.1007\/978-94-017-3624-4_5."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Tuvdendorj, B., Wu, B., Zeng, H., Batdelger, G., and Nanzad, L. (2019). Determination of appropriate remote sensing indices for spring wheat yield estimation in Mongolia. Remote Sens., 11.","DOI":"10.3390\/rs11212568"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Li, X., and Troy, T.J. (2018). Changes in rainfed and irrigated crop yield response to climate in the western US. Environ. Res. Lett.","DOI":"10.1088\/1748-9326\/aac4b1"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Bachmair, S., Tanguy, M., Hannaford, J., and Stahl, K. (2018). How well do meteorological indicators represent agricultural and forest drought across Europe?. Environ. Res. Lett., 13.","DOI":"10.1088\/1748-9326\/aaafda"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Amri, R., Zribi, M., Lili-Chabaane, Z., Wagner, W., and Hasenauer, S. (2012). Analysis of C-band scatterometer moisture estimations derived over a semiarid region. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2012.2186458"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Sawada, Y., Koike, T., Ikoma, E., and Kitsuregawa, M. (2019). Monitoring and Predicting Agricultural Droughts for a Water-Limited Subcontinental Region by Integrating a Land Surface Model and Microwave Remote Sensing. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2019.2927342"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Sarto, M.V.M., Sarto, J.R.W., Rampim, L., Bassegio, D., da Costa, P.F., and Inagaki, A.M. (2017). Wheat phenology and yield under drought: A review. Aust. J. Crop Sci.","DOI":"10.21475\/ajcs.17.11.08.pne351"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1002\/joc.1444","article-title":"Drought index evaluation for assessing future wheat production in Greece","volume":"27","author":"Mavromatis","year":"2007","journal-title":"Int. J. Climatol."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1002\/joc.1028","article-title":"An agricultural drought risk-assessment model for corn and soybeans","volume":"24","author":"Wu","year":"2004","journal-title":"Int. J. Climatol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4018\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:42:25Z","timestamp":1760179345000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,8]]},"references-count":133,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12244018"],"URL":"https:\/\/doi.org\/10.3390\/rs12244018","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,8]]}}}