<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0187-5779</journal-id>
<journal-title><![CDATA[Terra Latinoamericana]]></journal-title>
<abbrev-journal-title><![CDATA[Terra Latinoam]]></abbrev-journal-title>
<issn>0187-5779</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de la Ciencia del Suelo A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-57792023000100130</article-id>
<article-id pub-id-type="doi">10.28940/terra.v41i0.1696</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estimación de Cosecha de Maíz Forrajero (Zea mays L.) Mediante Índices Espectrales Derivados de LANDSAT-8 y SENTINEL-2]]></article-title>
<article-title xml:lang="en"><![CDATA[Harvest Estimation of Forage Corn (Zea mays L.) by Means of Spectral Indices Derived from LANDSAT-8 and SENTINEL-2]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cano-Mejía]]></surname>
<given-names><![CDATA[Bonifacio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Valdez-Cepeda]]></surname>
<given-names><![CDATA[Ricardo D.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Santos]]></surname>
<given-names><![CDATA[Armando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma Chapingo  ]]></institution>
<addr-line><![CDATA[Bermejillo Durango]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma Chapingo Centro Regional Universitario Centro-Norte ]]></institution>
<addr-line><![CDATA[El Orito Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>41</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-57792023000100130&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-57792023000100130&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-57792023000100130&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: La estimación de cosecha basada en índices espectrales conforma un elemento de decisión importante para quienes participan en la actividad agrícola; sin embargo, muchas interrogantes sobre su utilidad aún persisten. Los objetivos de esta investigación fueron: 1) relacionar propiedades radiativas del maíz forrajero (MF) y producción de biomasa mediante imágenes LANDSAT-8 y SENTINEL-2; y 2) seleccionar el índice de vegetación (IV) con mejor desempeño que permita modelar el rendimiento del MF para condiciones similares. El estudio se realizó en el ciclo PV-2019 con mediciones morfológicas en distintas etapas de crecimiento del MF y mediante muestreos aleatorios destructivos a los 72 dds para determinar magnitud de biomasa en laboratorio; los datos de biomasa se relacionaron con valores de reflectancia e IV de LANDAT-8 y SENTINEL-2 para estimar rendimiento mediante regresión lineal múltiple; ocho IV (NDVI, TVI TTVI, RDVI, RVI, RATIO, SAVI, MSAVI2) se evaluaron mediante evaluaciones cruzadas con base en estadísticos clave. Los resultados del análisis de regresión múltiple indicaron que el mejor modelo (R2 = 0.66) se obtuvo con datos de imágenes SENTINEL-2 a partir de las bandas 3 (&#945;3 = 0.54-0.57 µm) y 8 (&#945;8= 0.78-0.90 µm) con estimadores &#946;i muy significativos (P &lt; 0.05); RDVI presentó el mejor desempeño debido a una buena relación espacial entre los valores digitales ráster y la producción de biomasa verde producida con una asociación del 75.41%; en tanto que los indicadores estadísticos fueron R2= 0.75 y CME=17; con ambos recursos (Modelos de Regresión Múltiple e IV) se pronosticó el rendimiento a los 72 dds en un rango de 10.7 - 57.01 Mg ha-1. La conclusión es que SENTINEL-2 superó a LANDSAT-8 como herramienta libre para la evaluación de cultivos y estimación de biomasa debido a una mejor resolución espacial y temporal.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Summary: Crop yield estimation based on spectral indices is crucial for decision-making in agricultural activities; however, questions regarding their usefulness persist. The aims of our research were: 1) to relate the radiative properties of forage corn (FC) and biomass production using LANDSAT-8 and SENTINEL-2 images; and 2) to select the best-performing vegetation index (VI) to model FC yield under similar conditions. The study was conducted along the PV-2019 cycle with morphological measurements at different FC growth stages and with destructive random sampling at 72 dds to determine biomass magnitude in the laboratory. Biomass data were then related to reflectance values and VI&#8217;s from LANDSAT-8 and SENTINEL-2 to estimate yield through multiple linear regression. Eight VI&#8217;s (NDVI, TVI, TTVI, RDVI, RVI, RATIO, SAVI, MSAVI2) were evaluated through cross-evaluations based on key statistics. The results of multiple regression analysis indicated that the best model (R2 = 0.66) was obtained with SENTINEL-2 image data from bands 3 (&#945;3 = 0.54-0.57 µm) and 8 (&#945;8= 0.78-0.90 µm) with highly significant &#946;i estimators (P &lt; 0.05). Moreover, RDVI showed the best performance due to a good spatial relationship between raster digital values and green biomass yield produced with an association of 75.41%, the statistical indicators were R2= 0.75 and CME =17. Yield at 72 dds was predicted both with Multiple Regression Models and VI&#8217;s in a range of 10.7 - 57.01 Mg ha-1. In conclusion, SENTINEL-2 outperformed LANDSAT-8 as a free tool for crop assessment and biomass estimation due to its better spatial and temporal resolution.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[biomasa]]></kwd>
<kwd lng="es"><![CDATA[fenología de cultivo]]></kwd>
<kwd lng="es"><![CDATA[índices de vegetación]]></kwd>
<kwd lng="es"><![CDATA[rendimiento]]></kwd>
<kwd lng="es"><![CDATA[teledetección]]></kwd>
<kwd lng="en"><![CDATA[biomass]]></kwd>
<kwd lng="en"><![CDATA[crop phenology]]></kwd>
<kwd lng="en"><![CDATA[vegetation indices]]></kwd>
<kwd lng="en"><![CDATA[yield]]></kwd>
<kwd lng="en"><![CDATA[remote sensing]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Abd-El Monsef]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Smith]]></surname>
<given-names><![CDATA[S. E.]]></given-names>
</name>
<name>
<surname><![CDATA[Rowland]]></surname>
<given-names><![CDATA[D. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Abd El Rasol]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Using multispectral imagery to extract a pure spectral canopy signature for predicting peanut maturity]]></article-title>
<source><![CDATA[Computers and Electronics in Agriculture]]></source>
<year>2019</year>
<volume>162</volume>
<page-range>561-72</page-range></nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ahmad]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Singh]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Fahad]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Waqas]]></surname>
<given-names><![CDATA[M. M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Remote sensing-based framework to predict and assess the interannual variability of maize yields in Pakistan using Landsat imagery]]></article-title>
<source><![CDATA[Computers and Electronics in Agriculture]]></source>
<year>2020</year>
<volume>178</volume>
</nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ali]]></surname>
<given-names><![CDATA[A. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Abouelghar]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Belal]]></surname>
<given-names><![CDATA[A. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Saleh]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Yones]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Selim]]></surname>
<given-names><![CDATA[A. I.]]></given-names>
</name>
<name>
<surname><![CDATA[Savin]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article)]]></article-title>
<source><![CDATA[The Egyptian Journal of Remote Sensing and Space Science]]></source>
<year>2022</year>
<volume>25</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>711-6</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chavez]]></surname>
<given-names><![CDATA[P. S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Image-based atmospheric corrections - Revisited and improved]]></article-title>
<source><![CDATA[Photogrammetric Engineering and Remote Sensing]]></source>
<year>1996</year>
<volume>62</volume>
<numero>9</numero>
<issue>9</issue>
<page-range>1025-36</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[J. M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications]]></article-title>
<source><![CDATA[Canadian Journal of Remote Sensing]]></source>
<year>1996</year>
<volume>22</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>229-42</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="book">
<collab>CONABIO (Comisión Nacional para el Conocimiento y Uso de la Biodiversidad)</collab>
<source><![CDATA[RTP-52 Mpimí. Comisión Nacional Para El Conocimiento y Uso de La Biodiversidad]]></source>
<year>2019</year>
<publisher-loc><![CDATA[Mexico ]]></publisher-loc>
<publisher-name><![CDATA[CONABIO]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="book">
<collab>CONAGUA (Comisión Nacional del Agua)</collab>
<source><![CDATA[Normales climatológicas de la República Mexicana, por Estado. Estación Meteorológica Tlahualilo, clave 10085 (26° 06&#8217; 23&#8221; N, 103° 26&#8217; 34&#8221; W)]]></source>
<year>2021</year>
<publisher-name><![CDATA[Servicio Meteorológico Nacional]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Congedo]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<source><![CDATA[Semi-Automatic Classification Plugin Documentation Release 7.9.5.1. User Manual]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Deering]]></surname>
<given-names><![CDATA[D. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Rouse]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Measuring forage production of grazing units from Landsat MSS data]]></article-title>
<source><![CDATA[Proceedings of the Tenth International Symposium of Remote Sensing of the Envrionment]]></source>
<year>1975</year>
<page-range>1169-98</page-range><publisher-loc><![CDATA[Ann Arbor, MI, USA ]]></publisher-loc>
<publisher-name><![CDATA[ERIM]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dong]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Qian]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[He]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Shang]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data]]></article-title>
<source><![CDATA[ISPRS Journal of Photogrammetry and Remote Sensing]]></source>
<year>2020</year>
<volume>168</volume>
<page-range>236-50</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Eastman]]></surname>
<given-names><![CDATA[J. R]]></given-names>
</name>
</person-group>
<source><![CDATA[IDRISI Selva. Guide to GIS and image processing]]></source>
<year>2012</year>
<publisher-loc><![CDATA[Worcester, MA, USA ]]></publisher-loc>
<publisher-name><![CDATA[Clark University]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Elders]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Carroll]]></surname>
<given-names><![CDATA[M. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Neigh]]></surname>
<given-names><![CDATA[C. S. R.]]></given-names>
</name>
<name>
<surname><![CDATA[D&#8217;Agostino]]></surname>
<given-names><![CDATA[A. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Ksoll]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Wooten]]></surname>
<given-names><![CDATA[M. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Brown]]></surname>
<given-names><![CDATA[M. E]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2]]></article-title>
<source><![CDATA[Remote Sensing Applications: Society and Environment]]></source>
<year>2022</year>
<volume>27</volume>
</nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="">
<collab>ESA (European Space Agency)</collab>
<source><![CDATA[Copernicus Sentinels POD Data Hub]]></source>
<year>2022</year>
</nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Espinosa-Espinosa]]></surname>
<given-names><![CDATA[J. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Palacios-Vélez]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Tijerina-Chávez]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Ortiz-Solorio]]></surname>
<given-names><![CDATA[C. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Exebio-García]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Landeros-Sánchez]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Factors that affect agricultural production under irrigation conditions: How to measure and study their effect]]></article-title>
<source><![CDATA[Tecnologia y Ciencias del Agua]]></source>
<year>2018</year>
<volume>9</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>175-91</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="">
<collab>FAO (Organización de las Naciones Unidas para Agriculutura y Alimentación)</collab>
<source><![CDATA[World reference base for soil resources 2014 International soil classification system for naming soils and creating legends for soil maps Update 2015]]></source>
<year>2015</year>
</nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fernandez-Ordonez]]></surname>
<given-names><![CDATA[Y. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Soria-Ruiz]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Maize crop yield estimation with remote sensing and empirical models]]></article-title>
<source><![CDATA[IEEE International Geoscience and Remote Sensing Symposium (IGARSS)]]></source>
<year>2017</year>
<page-range>3035-8</page-range><publisher-loc><![CDATA[Fort Worth, TX, USA ]]></publisher-loc>
<publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Granados-Niño]]></surname>
<given-names><![CDATA[J. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Sánchez-Duarte]]></surname>
<given-names><![CDATA[J. I.]]></given-names>
</name>
<name>
<surname><![CDATA[Ochoa-Martínez]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Rodríguez-Hernández]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Reta-Sánchez]]></surname>
<given-names><![CDATA[D. G.]]></given-names>
</name>
<name>
<surname><![CDATA[López-Calderón]]></surname>
<given-names><![CDATA[M. J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Efecto del ciclo de producción sobre el potencial de rendimiento y calidad nutricional del maíz forrajero en la Comarca Lagunera]]></article-title>
<source><![CDATA[Revista Mexicana de Ciencias Agrícolas]]></source>
<year>2022</year>
<volume>28</volume>
<page-range>207-17</page-range></nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hamlyn]]></surname>
<given-names><![CDATA[G. J]]></given-names>
</name>
</person-group>
<source><![CDATA[Plants and microclimate. A quantitative approach to environmental plant physiology]]></source>
<year>2014</year>
<edition>3rd Ed</edition>
<publisher-loc><![CDATA[Cambridge, United Kingdom ]]></publisher-loc>
<publisher-name><![CDATA[University Press]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Huete]]></surname>
<given-names><![CDATA[A. R]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A Soil-Adjusted Vegetation Index (SAVI)]]></article-title>
<source><![CDATA[Remote Sensing of Environment]]></source>
<year>1988</year>
<volume>25</volume>
<page-range>295-309</page-range></nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="">
<collab>INEGI (Instituto Nacional de Estadística y Geografía)</collab>
<source><![CDATA[Conjunto de datos edafológicos vectoriales, carta G13-09, serie II, escala 1:250,000]]></source>
<year>2007</year>
</nlm-citation>
</ref>
<ref id="B21">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ji]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Pan]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A generalized model to predict large-scale crop yields integrating satellite-based vegetation index time series and phenology metrics]]></article-title>
<source><![CDATA[Ecological Indicators]]></source>
<year>2022</year>
<volume>137</volume>
</nlm-citation>
</ref>
<ref id="B22">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jovanovic]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Sabo]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Govedarica]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Marinkovic]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Crop yield estimation in 2014 for Vojvodina using methods of remote sensing]]></article-title>
<source><![CDATA[Ratarstvo i Povrtarstvo]]></source>
<year>2014</year>
<volume>51</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>145-53</page-range></nlm-citation>
</ref>
<ref id="B23">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kim]]></surname>
<given-names><![CDATA[P. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Jennrich]]></surname>
<given-names><![CDATA[R. I]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Tables of the exact sampling distribution of the two-sample Kolmogorov-Smirnov criterion]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[Harter]]></surname>
<given-names><![CDATA[H. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Owen]]></surname>
<given-names><![CDATA[D. B.]]></given-names>
</name>
</person-group>
<source><![CDATA[Selected Tables in Mathematical Statistics]]></source>
<year>1973</year>
<publisher-loc><![CDATA[Providence, RI, USA ]]></publisher-loc>
<publisher-name><![CDATA[American Mathematical Society]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B24">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kok]]></surname>
<given-names><![CDATA[Z. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Shariff]]></surname>
<given-names><![CDATA[M.A. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Alfatni]]></surname>
<given-names><![CDATA[M. S. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Khairunniza-Bejo]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Support Vector Machine in Precision Agriculture: A review]]></article-title>
<source><![CDATA[Computers and Electronics in Agriculture]]></source>
<year>2021</year>
<volume>191</volume>
</nlm-citation>
</ref>
<ref id="B25">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lai]]></surname>
<given-names><![CDATA[Y. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Pringle]]></surname>
<given-names><![CDATA[M. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Kopittke]]></surname>
<given-names><![CDATA[P. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Menzies]]></surname>
<given-names><![CDATA[N. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Orton]]></surname>
<given-names><![CDATA[T. G.]]></given-names>
</name>
<name>
<surname><![CDATA[Dang]]></surname>
<given-names><![CDATA[Y. P]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An empirical model for prediction of wheat yield, using time-integrated Landsat NDVI]]></article-title>
<source><![CDATA[International Journal of Applied Earth Observation and Geoinformation]]></source>
<year>2018</year>
<volume>72</volume>
<page-range>99-108</page-range></nlm-citation>
</ref>
<ref id="B26">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lobell]]></surname>
<given-names><![CDATA[D. B]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The use of satellite data for crop yield gap analysis]]></article-title>
<source><![CDATA[Field Crops Research]]></source>
<year>2013</year>
<volume>143</volume>
<page-range>56-64</page-range></nlm-citation>
</ref>
<ref id="B27">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lopes da Silva]]></surname>
<given-names><![CDATA[A. V.]]></given-names>
</name>
<name>
<surname><![CDATA[Ferreira]]></surname>
<given-names><![CDATA[C.R. L.]]></given-names>
</name>
<name>
<surname><![CDATA[da Silva]]></surname>
<given-names><![CDATA[A.J.A.]]></given-names>
</name>
<name>
<surname><![CDATA[Cespedes]]></surname>
<given-names><![CDATA[G.G. H]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Linear model alternative to estimate the green biomass of the Bambusa vulgaris schrad. Ex J.C. Wendl.within the appearance of multicollinearity]]></article-title>
<source><![CDATA[Ciencia Florestal]]></source>
<year>2009</year>
<volume>19</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>207-2014</page-range></nlm-citation>
</ref>
<ref id="B28">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[López-García]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Mata-González]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Blanco-Macías]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Méndez-Gallegos]]></surname>
<given-names><![CDATA[S. de J.]]></given-names>
</name>
<name>
<surname><![CDATA[Valdez-Cepeda]]></surname>
<given-names><![CDATA[R. D]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Fruit attributes dependence on fruiting cladode dry or fresh matter in Opuntia ficus-indica (L.) Miller variety &#8216;Rojo Pelón]]></article-title>
<source><![CDATA[Scientia Horticulturae]]></source>
<year>2016</year>
<volume>202</volume>
<page-range>57-62</page-range></nlm-citation>
</ref>
<ref id="B29">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Madugundu]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Al-Gaadi]]></surname>
<given-names><![CDATA[K. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Tola]]></surname>
<given-names><![CDATA[E. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Kayad]]></surname>
<given-names><![CDATA[A. G.]]></given-names>
</name>
<name>
<surname><![CDATA[Jha]]></surname>
<given-names><![CDATA[C. S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estimation of gross primary production of irrigated maize using Landsat-8 imagery and Eddy Covariance data]]></article-title>
<source><![CDATA[Saudi Journal of Biological Sciences]]></source>
<year>2017</year>
<volume>24</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>410-20</page-range></nlm-citation>
</ref>
<ref id="B30">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Maponya]]></surname>
<given-names><![CDATA[M. G.]]></given-names>
</name>
<name>
<surname><![CDATA[van Niekerk]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Mashimbye]]></surname>
<given-names><![CDATA[Z. E]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Pre-harvest classification of crop types using a Sentinel-2 time-series and machine learning]]></article-title>
<source><![CDATA[Computers and Electronics in Agriculture]]></source>
<year>2020</year>
<volume>169</volume>
<page-range>105-64</page-range></nlm-citation>
</ref>
<ref id="B31">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Navarrete-Molina]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Meza-Herrera]]></surname>
<given-names><![CDATA[C. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Herrera-Machuca]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Lopez-Villalobos]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Lopez-Santos]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Veliz-Deras]]></surname>
<given-names><![CDATA[F. G]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[To beef or not to beef: Unveiling the economic environmental impact generated by the intensive beef cattle industry in an arid region]]></article-title>
<source><![CDATA[Journal of Cleaner Production]]></source>
<year>2019</year>
<volume>231</volume>
<page-range>1027-35</page-range></nlm-citation>
</ref>
<ref id="B32">
<nlm-citation citation-type="">
<collab>Novasem</collab>
<source><![CDATA[NOVASEM: El poder del maíz]]></source>
<year>2022</year>
</nlm-citation>
</ref>
<ref id="B33">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pearson]]></surname>
<given-names><![CDATA[R. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Miller]]></surname>
<given-names><![CDATA[L. D]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie]]></article-title>
<source><![CDATA[Pawnee National Grasslands]]></source>
<year>1972</year>
<publisher-loc><![CDATA[CO, USA ]]></publisher-loc>
<publisher-name><![CDATA[Department of Watershed Sciences]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B34">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Piedad-Rubio]]></surname>
<given-names><![CDATA[A. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Hernández-López]]></surname>
<given-names><![CDATA[D. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Lárraga-Altamiran]]></surname>
<given-names><![CDATA[H. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Zacarías-González]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Teledeteccion en la agricultra de precisión: estado del arte de los índices de vegetación]]></article-title>
<source><![CDATA[TECTZAPIC: Revista de Divulgación Científica y Tecnológica]]></source>
<year>2020</year>
<volume>6</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>46-58</page-range></nlm-citation>
</ref>
<ref id="B35">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Prasad]]></surname>
<given-names><![CDATA[A. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Chai]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Singh]]></surname>
<given-names><![CDATA[R. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Kafatos]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Crop yield estimation model for Iowa using remote sensing and surface parameters]]></article-title>
<source><![CDATA[International Journal of Applied Earth Observation and Geoinformation]]></source>
<year>2006</year>
<volume>8</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>26-33</page-range></nlm-citation>
</ref>
<ref id="B36">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Qi]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Chehbouni]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Huete]]></surname>
<given-names><![CDATA[A. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Kerr]]></surname>
<given-names><![CDATA[Y. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Sorooshian]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A modified soil adjusted vegetation index]]></article-title>
<source><![CDATA[Remote Sensing of Environment]]></source>
<year>1994</year>
<volume>48</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>119-26</page-range></nlm-citation>
</ref>
<ref id="B37">
<nlm-citation citation-type="book">
<collab>R Core Team</collab>
<source><![CDATA[R: A language and environment for statistical computing]]></source>
<year>2022</year>
<publisher-loc><![CDATA[Vienna, Autria ]]></publisher-loc>
<publisher-name><![CDATA[R Foundation for Statistical Computing]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B38">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Roujean]]></surname>
<given-names><![CDATA[J. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Breon]]></surname>
<given-names><![CDATA[F. M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estimating PAR absorbed by vegetation from bidirectional reflectance measurements]]></article-title>
<source><![CDATA[Remote Sensing of Environment]]></source>
<year>1995</year>
<volume>51</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>375-84</page-range></nlm-citation>
</ref>
<ref id="B39">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rouse]]></surname>
<given-names><![CDATA[J. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Haas]]></surname>
<given-names><![CDATA[R. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Schell]]></surname>
<given-names><![CDATA[J. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Deering]]></surname>
<given-names><![CDATA[D. W]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Monitoring vegetation systems in the Great Plains with ERTS]]></article-title>
<source><![CDATA[Proceedings of the Third Earth Resources Technology Satellite-1. Symposium]]></source>
<year>1974</year>
<page-range>301-4</page-range><publisher-loc><![CDATA[Greenbelt, MD, USA ]]></publisher-loc>
<publisher-name><![CDATA[NASA]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B40">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Roznik]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Boyd]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Porth]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Improving crop yield estimation by applying higher resolution satellite NDVI imagery and high-resolution cropland masks]]></article-title>
<source><![CDATA[Remote Sensing Applications: Society and Environment]]></source>
<year>2022</year>
<volume>25</volume>
</nlm-citation>
</ref>
<ref id="B41">
<nlm-citation citation-type="">
<collab>SADER (Secretaría de Agricultura y Desarrollo Rural)</collab>
<source><![CDATA[Sistema de Información Agroalimentaria de Consulta de la Secretaría de Agricultura y desarrollo Rural]]></source>
<year>2023</year>
</nlm-citation>
</ref>
<ref id="B42">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Salehi]]></surname>
<given-names><![CDATA[S. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Ashourloo]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Moeini-Rad]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Aghighi]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Azadbakht]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Nematollahi]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automatic silage maize detection based on phenological rules using Sentinel-2 time-series dataset]]></article-title>
<source><![CDATA[International Journal of Remote Sensing]]></source>
<year>2020</year>
<volume>41</volume>
<numero>21</numero>
<issue>21</issue>
<page-range>8406-27</page-range></nlm-citation>
</ref>
<ref id="B43">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Savin]]></surname>
<given-names><![CDATA[N. E.]]></given-names>
</name>
<name>
<surname><![CDATA[White]]></surname>
<given-names><![CDATA[K. J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The durbin-watson test for serial correlation with extreme sample sizes or many regressors]]></article-title>
<source><![CDATA[Econometrica]]></source>
<year>1977</year>
<volume>45</volume>
<numero>8</numero>
<issue>8</issue>
<page-range>1989-96</page-range></nlm-citation>
</ref>
<ref id="B44">
<nlm-citation citation-type="journal">
<collab>SEMARNAT (Secretaría de Medio Ambiente y Recursos Naturales)</collab>
<article-title xml:lang=""><![CDATA[Norma Oficial Mexicana NOM-021 SEMARNAT-2000 antes NOM-021-RECNAT-2000. Que establece las especificaciones de fertilidad, salinidad y clasificación de suelos. Estudio, muestreo y análisis]]></article-title>
<source><![CDATA[Diario Oficial de la Federación]]></source>
<year>2002</year>
<publisher-loc><![CDATA[D. F., México ]]></publisher-loc>
<publisher-name><![CDATA[SEGOB]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B45">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shammi]]></surname>
<given-names><![CDATA[S. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Meng]]></surname>
<given-names><![CDATA[Q]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling]]></article-title>
<source><![CDATA[Ecological Indicators]]></source>
<year>2021</year>
<volume>121</volume>
</nlm-citation>
</ref>
<ref id="B46">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shuai]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Basso]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models]]></article-title>
<source><![CDATA[Remote Sensing of Environment]]></source>
<year>2022</year>
<volume>272</volume>
</nlm-citation>
</ref>
<ref id="B47">
<nlm-citation citation-type="">
<collab>SIAP (Servicio de Información Agroalimentaria y Pesquera)</collab>
<source><![CDATA[Datos Abiertos: Estadística de Producción Agrícola]]></source>
<year>2021</year>
</nlm-citation>
</ref>
<ref id="B48">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Silleos]]></surname>
<given-names><![CDATA[N. G.]]></given-names>
</name>
<name>
<surname><![CDATA[Alexandridis]]></surname>
<given-names><![CDATA[T. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Gitas]]></surname>
<given-names><![CDATA[I. Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Perakis]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Vegetation indices: Advances made in biomass estimation and vegetation monitoring in the last 30 years]]></article-title>
<source><![CDATA[Geocarto International]]></source>
<year>2006</year>
<volume>21</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>21-8</page-range></nlm-citation>
</ref>
<ref id="B49">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Son]]></surname>
<given-names><![CDATA[N. T.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[C. F.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[C. C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Remote Sensing Time Series Analysis for Early Rice Yield Forecasting Using Random Forest Algorithm]]></article-title>
<source><![CDATA[Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries]]></source>
<year>2022</year>
<page-range>353-66</page-range><publisher-loc><![CDATA[Cham ]]></publisher-loc>
<publisher-name><![CDATA[Springer International Publishing]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B50">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Thiam]]></surname>
<given-names><![CDATA[A. K]]></given-names>
</name>
</person-group>
<source><![CDATA[Geografic information systems and Remote sensing methods for assessing and monitoring land degradation in the sahel: The Case of Southern Mauritania]]></source>
<year>1997</year>
<publisher-loc><![CDATA[Ann Arbor, MI, USA ]]></publisher-loc>
<publisher-name><![CDATA[Clark University]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B51">
<nlm-citation citation-type="">
<collab>USGS (United States Geological Survey)</collab>
<source><![CDATA[EarthExplorer]]></source>
<year>2022</year>
</nlm-citation>
</ref>
<ref id="B52">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Guan]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Ainsworth]]></surname>
<given-names><![CDATA[E. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Zheng]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Townsend]]></surname>
<given-names><![CDATA[P. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling]]></article-title>
<source><![CDATA[International Journal of Applied Earth Observation and Geoinformation]]></source>
<year>2021</year>
<volume>105</volume>
</nlm-citation>
</ref>
<ref id="B53">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Xin]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Gong]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Broich]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Suyker]]></surname>
<given-names><![CDATA[A. E.]]></given-names>
</name>
<name>
<surname><![CDATA[Myneni]]></surname>
<given-names><![CDATA[R. B]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A production efficiency model-based method for satellite estimates of corn and soybean yields in the midwestern US]]></article-title>
<source><![CDATA[Remote Sensing]]></source>
<year>2013</year>
<volume>5</volume>
<numero>11</numero>
<issue>11</issue>
<page-range>5926-43</page-range></nlm-citation>
</ref>
<ref id="B54">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[H. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Roy]]></surname>
<given-names><![CDATA[D. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Yan]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Vermote]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Roger]]></surname>
<given-names><![CDATA[J. C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences]]></article-title>
<source><![CDATA[Remote Sensing of Environment]]></source>
<year>2018</year>
<volume>215</volume>
<page-range>482-94</page-range></nlm-citation>
</ref>
<ref id="B55">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhong]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Hu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Wei]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions]]></article-title>
<source><![CDATA[Remote Sensing of Environment]]></source>
<year>2020</year>
<volume>239</volume>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
