{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T02:42:17Z","timestamp":1769049737515,"version":"3.49.0"},"reference-count":86,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T00:00:00Z","timestamp":1571097600000},"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>Assessing crop yield trends over years is a key step in site specific management, in view of improving the economic and environmental profile of agriculture. This study was conducted in a 11.07 ha area under Mediterranean climate in Northern Italy to evaluate the spatial variability and the relationships between six remotely sensed vegetation indices (VIs) and grain yield (GY) in five consecutive years. A total of 25 satellite (Landsat 5, 7, and 8) images were downloaded during crop growth to obtain the following VIs: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI), Green Chlorophyll Index (GCI), and Simple Ratio (SR). The surveyed crops were durum wheat in 2010, sunflower in 2011, bread wheat in 2012 and 2014, and coriander in 2013. Geo-referenced GY and VI data were used to generate spatial trend maps across the experimental field through geostatistical analysis. Crop stages featuring the best correlations between VIs and GY at the same spatial resolution (30 m) were acknowledged as the best periods for GY prediction. Based on this, 2\u20134 VIs were selected each year, totalling 15 VIs in the five years with r values with GY between 0.729** and 0.935**. SR and NDVI were most frequently chosen (six and four times, respectively) across stages from mid vegetative to mid reproductive growth. Conversely, SAVI never had correlations high enough to be selected. Correspondence analysis between remote VIs and GY based on quantile ranking in the 126 (30 m size) pixels exhibited a final agreement between 64% and 86%. Therefore, Landsat imagery with its spatial and temporal resolution proved a good potential for estimating final GY over different crops in a rotation, at a relatively small field scale.<\/jats:p>","DOI":"10.3390\/rs11202384","type":"journal-article","created":{"date-parts":[[2019,10,16]],"date-time":"2019-10-16T03:32:54Z","timestamp":1571196774000},"page":"2384","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Assessing Multiple Years\u2019 Spatial Variability of Crop Yields Using Satellite Vegetation Indices"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9259-1757","authenticated-orcid":false,"given":"Abid","family":"Ali","sequence":"first","affiliation":[{"name":"Department of Agricultural and Food Sciences, University of Bologna, viale Fanin, 50 40127 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6627-0789","authenticated-orcid":false,"given":"Roberta","family":"Martelli","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Food Sciences, University of Bologna, viale Fanin, 50 40127 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8379-9713","authenticated-orcid":false,"given":"Flavio","family":"Lupia","sequence":"additional","affiliation":[{"name":"CREA Research Centre for Agricultural Policies and Bioeconomy, via Po, 14, 00198 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0440-9156","authenticated-orcid":false,"given":"Lorenzo","family":"Barbanti","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Food Sciences, University of Bologna, viale Fanin, 50 40127 Bologna, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,15]]},"reference":[{"key":"ref_1","first-page":"55","article-title":"Crop assessment using remote sensing-part-II: Crop condition and yield assessment","volume":"55","author":"Dadhwal","year":"2000","journal-title":"Indian J. Agric. Econ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3487","DOI":"10.1080\/014311600750037516","article-title":"Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data","volume":"21","author":"Reynolds","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10705-017-9900-8","article-title":"Efficient use of nitrogen in agriculture","volume":"110","author":"Aronsson","year":"2018","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5995","DOI":"10.1073\/pnas.96.11.5995","article-title":"Global environmental impacts of agricultural expansion: The need for sustainable and efficient practices","volume":"96","author":"Tilman","year":"1999","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, J.G., and Huete, A. (2012). Remote Sensing Estimation of Crop Biophysical Characteristics at Various Scales. Hyperspectral Remote Sensing of Vegetation, CRC Press.","DOI":"10.1201\/b11222-41"},{"key":"ref_6","unstructured":"Taylor, J.C., Wood, G.A., and Thomas, G. (1997, January 7\u201310). Mapping yield potential with remote sensing. Proceedings of the First European Conference on Precision Agriculture, London, UK."},{"key":"ref_7","unstructured":"Blackmore, S. (2003). The Role of Yield Maps in Precision Farming. [Ph.D. Thesis, Cranfield University]."},{"key":"ref_8","unstructured":"Griffin, T.W., Lowenberg-DeBoer, J., Lambert, D.M., Peone, J., Payne, T., and Daberkow, S.G. (2004). Adoption, Profitability, and Making Better use of Precision Farming Data, Department of Agricultural Economics, Purdue University."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"999","DOI":"10.2134\/agronj1982.00021962007400060016x","article-title":"Soil Available Water as Influenced by Landscape Position and Aspect 1","volume":"74","author":"Hanna","year":"1982","journal-title":"Agron. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s10584-017-1965-5","article-title":"Vulnerability of Southern Plains agriculture to climate change","volume":"146","author":"Steiner","year":"2018","journal-title":"Clim. Change"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1590\/S0100-204X2007000800006","article-title":"Variabilidade espacial e temporal da produtividade de culturas sob sistema plantio direto","volume":"42","author":"Amado","year":"2007","journal-title":"Pesqui. Agropecu. Bras."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"562","DOI":"10.3390\/rs2020562","article-title":"Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices","volume":"2","author":"Hatfield","year":"2010","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.3233\/JIFS-17075","article-title":"A GIS-based decision making model using fuzzy sets and theory of evidence for seismic vulnerability assessment under uncertainty (case study: Tabriz)","volume":"33","author":"Sadrykia","year":"2017","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"44","DOI":"10.2111\/05-106R2.1","article-title":"Estimating biophysical characteristics of musk thistle (Carduus nutans) with three remote sensing instruments","volume":"59","author":"Mirik","year":"2006","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"S-117","DOI":"10.2134\/agronj2006.0370c","article-title":"Application of Spectral Remote Sensing for Agronomic Decisions","volume":"100","author":"Hatfield","year":"2008","journal-title":"Agron. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/0034-4257(93)90105-7","article-title":"Interpretation of vegetation indices derived from multi-temporal SPOT images","volume":"44","author":"Qi","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_17","unstructured":"Rouse, J., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite Symposium, Washington, DC, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1046\/j.1365-2486.2003.00507.x","article-title":"Northern hemisphere photosynthetic trends 1982\u20131999","volume":"9","author":"Slayback","year":"2003","journal-title":"Glob. Change Biol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1080\/01431160500168686","article-title":"An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data","volume":"26","author":"Tucker","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/0034-4257(84)90008-7","article-title":"Intercepted photosynthetically active radiation estimated by spectral reflectance","volume":"14","author":"Hatfield","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0034-4257(92)90064-Q","article-title":"Multisite analyses of spectral-biophysical data for wheat","volume":"42","author":"Wiegand","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Militino, A., Ugarte, M., and P\u00e9rez-Goya, U. (2017). Stochastic spatio-temporal models for analysing NDVI distribution of GIMMS NDVI3g images. Remote Sens., 9.","DOI":"10.3390\/rs9010076"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/S0034-4257(02)00151-7","article-title":"Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: A comparison of indices based on liquid water and chlorophyll absorption features","volume":"84","author":"Sims","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"641","DOI":"10.2134\/agronj2003.0257","article-title":"Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery","volume":"97","author":"Ustin","year":"2005","journal-title":"Agron. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S0034-4257(01)00342-X","article-title":"Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture","volume":"81","author":"Boegh","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1016\/j.agrformet.2009.03.016","article-title":"Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes","volume":"149","author":"Rocha","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetative index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/rs2030673","article-title":"Application of vegetation indices for agricultural crop yield prediction using neural network techniques","volume":"2","author":"Panda","year":"2010","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"99","DOI":"10.2134\/agronj2005.0099","article-title":"Remote sensing of canopy dynamics and biophysical variables estimation of corn in Michigan","volume":"97","author":"Elwadie","year":"2005","journal-title":"Agron. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1002\/jsfa.1937","article-title":"Spectral variables, growth analysis and yield of sugarcane","volume":"62","author":"Rocha","year":"2005","journal-title":"Sci. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_36","unstructured":"Pearson, R.L., and Miller, L.D. (1972, January 2\u20136). Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Proceedings of the Eighth International Symposium on Remote Sensing of Environment, University of Michigan, Ann Arbor, MI, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gitelson, A.A., Vi\u00f1a, A., Ciganda, V., Rundquist, D.C., and Arkebauer, T.J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL022688"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"535","DOI":"10.13031\/2013.2733","article-title":"Relationships between remotely sensed reflectance data and cotton growth and yield","volume":"43","author":"Plant","year":"2000","journal-title":"Am. Soc. Agric. Eng."},{"key":"ref_41","first-page":"36","article-title":"Remotely sensed vegetation indices: Theory and applications for crop management","volume":"1","author":"Basso","year":"2004","journal-title":"Riv. Ital. Agrometeorol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kayad, A.G., Al-Gaadi, K.A., Tola, E., Madugundu, R., Zeyada, A.M., and Kalaitzidis, C. (2016). Assessing the spatial variability of alfalfa yield using satellite imagery and ground-based data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0157166"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/0034-4257(95)00198-0","article-title":"Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices","volume":"55","author":"Huete","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"2","article-title":"Fundamentals of Geostatistics in Five Lessons","volume":"Volume 8","author":"Journel","year":"1989","journal-title":"Short Course in Geology"},{"key":"ref_45","unstructured":"Isaaks, E.H., and Srivastava, R.M. (1989). An Introduction to Applied Geostatistics, Oxford University Press."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/0034-4257(88)90108-3","article-title":"The use of variograms in remote sensing: I. Scene models and simulated images","volume":"25","author":"Woodcock","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0037-0738(01)00261-5","article-title":"Geochemical and mineralogical variations as indicators of provenance changes in Late Quaternary deposits of SE Po Plain","volume":"151","author":"Amorosi","year":"2002","journal-title":"Sediment. Geol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1111\/j.1466-822X.2005.00190.x","article-title":"A climatic stratification of the environment of Europe","volume":"14","author":"Metzger","year":"2005","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.2134\/agronj2006.0326","article-title":"Yield editor: Software for removing errors from crop yield maps","volume":"99","author":"Sudduth","year":"2007","journal-title":"Agron. J."},{"key":"ref_50","first-page":"3511","article-title":"Using Landsat image time series to study a small water body in Northern Spain","volume":"186","author":"Rico","year":"2014","journal-title":"Environ. Monit. Assess."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"501","DOI":"10.17221\/515\/2014-PSE","article-title":"Use of Landsat images for yield evaluation within a small plot","volume":"60","author":"Zemek","year":"2014","journal-title":"Plant Soil Environ."},{"key":"ref_52","first-page":"79","article-title":"Use Landsat image to evaluate vegetation stage in sunflower crops","volume":"4","author":"Herbei","year":"2015","journal-title":"AgroLife Sci. J."},{"key":"ref_53","first-page":"29","article-title":"Semi-Automatic Classification Plugin Documentation","volume":"4","author":"Congedo","year":"2016","journal-title":"Release."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1080\/01431160110109642","article-title":"Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research","volume":"23","author":"Lu","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/0034-4257(88)90019-3","article-title":"An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data","volume":"24","author":"Chavez","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/j.1744-7348.1991.tb04895.x","article-title":"A uniform decimal code for growth stages of crops and weeds","volume":"119","author":"Lancashire","year":"1991","journal-title":"Ann. Appl. Biol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation, Oxford University Press.","DOI":"10.1093\/oso\/9780195115383.001.0001"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/S0100-06832010000100001","article-title":"Spatial and temporal variability of crop yield and some Rhodic Hapludox properties under no-tillage","volume":"34","author":"Vieira","year":"2010","journal-title":"Rev. Bras. Ci\u00eancia Solo"},{"key":"ref_59","unstructured":"Clark, I. (1979). Practical Geostatistics, Applied Science Publisher."},{"key":"ref_60","first-page":"117","article-title":"The application of geostatistics in mapping and assessment of demersal resources, Nephrops norvegicus in the northwestern Mediterranean: A case study","volume":"62","author":"Maynou","year":"1998","journal-title":"Sci. Mar."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.2136\/sssaj1994.03615995005800050033x","article-title":"Field-scale variability of soil properties in central Iowa soils","volume":"58","author":"Cambardella","year":"1994","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.still.2009.12.002","article-title":"Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques","volume":"106","author":"Moral","year":"2010","journal-title":"Soil Tillage Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1186\/s40064-016-2073-0","article-title":"Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China","volume":"5","author":"Xiao","year":"2016","journal-title":"SpringerPlus"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.compag.2009.10.005","article-title":"Spatial analyses to evaluate multi-crop yield stability for a field","volume":"70","author":"McKinion","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"583","DOI":"10.2134\/agronj2001.933583x","article-title":"Use of remote-sensing imagery to estimate corn grain yield","volume":"93","author":"Shanahan","year":"2001","journal-title":"Agron. J."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"97","DOI":"10.4141\/P03-070","article-title":"Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies","volume":"84","author":"Lafond","year":"2004","journal-title":"Can. J. Plant Sci."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Singla, S.K., Garg, R.D., and Dubey, O.P. (2018). Spatiotemporal analysis of LANDSAT Data for Crop Yield Prediction. J. Eng. Sci. Technol. Rev., 11.","DOI":"10.25103\/jestr.113.02"},{"key":"ref_68","unstructured":"Robertson, G.P. (1998). GS+ Geostatistics for the Environmental Sciences: GS+ User\u2019s Guide, Gamma Design Software."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.geoderma.2005.02.008","article-title":"Accounting for change of support in spatial accuracy assessment of modelled soil mineral phosphorous concentration","volume":"130","author":"Leopold","year":"2006","journal-title":"Geoderma"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.cageo.2004.03.012","article-title":"Multivariable geostatistics in S: The gstat package","volume":"30","author":"Pebesma","year":"2004","journal-title":"Comp. Geosci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1002\/jpln.201500566","article-title":"Validation of topsoil texture derived from agricultural soil maps by current dense soil sampling","volume":"179","author":"Gozdowski","year":"2016","journal-title":"J. Plant. Nutr. Soil Sci."},{"key":"ref_72","unstructured":"Gomes, F.P. (1985). Curso de Estat\u00edstica Experimental, Nobel. [6th ed.]."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"75","DOI":"10.2134\/agronj2000.92175x","article-title":"Correlation of corn and soybean grain yield with topography and soil properties","volume":"92","author":"Kravchenko","year":"2000","journal-title":"Agron. J."},{"key":"ref_74","first-page":"21","article-title":"Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties","volume":"13","author":"Barbanti","year":"2018","journal-title":"Ital. J. Agron."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0034-4257(93)90113-C","article-title":"On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna","volume":"45","author":"Benedetti","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1104\/pp.47.5.656","article-title":"Reflectance and transmittance of light by leaves","volume":"47","author":"Woolley","year":"1971","journal-title":"Plant Physiol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"4169","DOI":"10.1080\/01431160110107653","article-title":"Wheat yield estimates using multi-temporal NDVI satellite imagery","volume":"23","author":"Labus","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","first-page":"26","article-title":"Crop yield estimation model for Iowa using remote sensing and surface parameters","volume":"8","author":"Prasad","year":"2006","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Lisboa, I.P., Damian, J.M., Cherubin, M.R., Barros, P.P.S., Fiorio, P.R., Cerri, C.C., and Cerri, C.E.P. (2018). Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal. Agronomy, 8.","DOI":"10.3390\/agronomy8090196"},{"key":"ref_80","first-page":"055","article-title":"Assessment of the relationship between spectral indices from satellite remote sensing and winter oilseed rape yield","volume":"15","year":"2017","journal-title":"Agron. Res."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1590\/1983-40632016v4743904","article-title":"Vegetation indexes and delineation of management zones for soybean","volume":"47","author":"Kuiawski","year":"2017","journal-title":"Pesqui. Agropecu. Trop."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/0034-4257(90)90066-U","article-title":"Semivariograms of digital imagery for analysis of conifer canopy structure","volume":"34","author":"Cohen","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1515\/intag-2016-0046","article-title":"Yield variability prediction by remote sensing sensors with different spatial resolution","volume":"31","year":"2017","journal-title":"Int. Agrophys."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0034-4257(00)00169-3","article-title":"Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?","volume":"75","author":"Song","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1002\/ecy.1730","article-title":"A survival guide to Landsat preprocessing","volume":"98","author":"Young","year":"2017","journal-title":"Ecology"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2384\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:26:21Z","timestamp":1760189181000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2384"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,15]]},"references-count":86,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11202384"],"URL":"https:\/\/doi.org\/10.3390\/rs11202384","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,15]]}}}