2 Aralık 2022 Konferans bildirisi Açık Erişim

Kaya, Fuat; Ferhatoğlu, Caner; Turgut, Yavuz Şahin; Başayiğit, Levent

MARC21 XML

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <controlfield tag="001">262955</controlfield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="a">Creative Commons Attribution Share-Alike</subfield>
    <subfield code="u">http://www.opendefinition.org/licenses/cc-by-sa</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.48623/aperta.262954</subfield>
    <subfield code="n">doi</subfield>
  </datafield>
  <controlfield tag="005">20230813132915.0</controlfield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;Soil electrical conductivity (EC) as a measure of soil salt content is a good indicator of nutrient and water availability or excessiveness in soils, which in return affect the productivity of soils. Therefore, mapping the spatial distribution of EC under intense agricultural management is important for managing soil fertility (e.g., fertilization and soil salinity remediation). However, mapping soil EC with high accuracy and spatial resolution remains to be challenge among digital soil mappers due to being a highly dynamic soil property. In this study, random forest (RF) was applied to map soil EC in an agricultural plain around the lake Manyas in the northwestern T&amp;uuml;rkiye. Fifty soil samples and a unique set of environmental predictors (aka covariates) were used to build a predictive soil EC model. The covariates were produced from Sentinel-2 optical satellite images-based vegetation and salinity indices as well as produced from Sentinel-1 with different polarizations (i.e., VV and VH), and terrain attributes representing the topography at varying scales were produced. Twelve environmental variables were selected to be relevant to predicting soil EC after using a correlation-based feature selection procedure. Resulting model performance was evaluated by root-mean-square-error (RMSE) of 10-fold cross-validation (CV). RF predicted soil EC with an RMSE of 0.07 dS m-1. Per each soil prediction in the final soil EC map, an uncertainty map was created using a sensitivity-based approach. The uncertainty map revealed the areas that were more difficult to accurately predict. Present study successfully mapped soil EC with acceptable error and can provide useful insights for managing soil fertility. In addition, an uncertainty map of soil EC can facilitate future soil sampling campaigns. For nearly a quarter of a century, while satellite-based remote sensing data has become the first choice for generating and updating soil survey information, in the near future, artificial intelligence techniques (e.g., ML) will be able to accompany soil surveyors in drawing map boundaries, especially in updating soil salinity phases.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">State of art approaches, insights, and challenges for digital mapping of electrical conductivity as a dynamic soil property</subfield>
  </datafield>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="o">oai:aperta.ulakbim.gov.tr:262955</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="z">md5:28552aacb8bf2a80ac08a527de8523f5</subfield>
    <subfield code="s">2885093</subfield>
    <subfield code="u">https://aperta.ulakbim.gov.trrecord/262955/files/State_of_1671012304.pdf</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="2">doi</subfield>
    <subfield code="a">10.48623/aperta.262955</subfield>
  </datafield>
  <datafield tag="711" ind1=" " ind2=" ">
    <subfield code="d">2-3 Aralık 2022</subfield>
    <subfield code="a">7th International Scientific Meeting as Soil Science Symposium on "Soil Science &amp; Plant Nutrition"</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Iowa State University</subfield>
    <subfield code="a">Ferhatoğlu, Caner</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Çukurova Üniversitesi</subfield>
    <subfield code="a">Turgut, Yavuz Şahin</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Isparta Uygulamalı Bilimler Üniversitesi</subfield>
    <subfield code="a">Başayiğit, Levent</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">conferencepaper</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2022-12-02</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Digital soil mapping</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Random forest</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Uncertainity</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="0">(orcid)0000-0003-0011-9020</subfield>
    <subfield code="u">Isparta Uygulamalı Bilimler Üniversitesi</subfield>
    <subfield code="a">Kaya, Fuat</subfield>
  </datafield>
</record>