Wheat yield estimation from NDVI and regional climate models in Latvia
Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution a...
Gespeichert in:
Hauptverfasser: | , , , , , , , , , , , , , , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | Vannoppen, Astrid Gobin, Anne Kotova, Lola Top, Sara De Cruz, Lesley Vīksna, Andris Aniskevich, Svetlana Bobylev, Leonid Buntemeyer, Lars Caluwaerts, Steven De Troch, Rozemien Gnatiuk, Natalia Hamdi, Rafiq Reca Remedio, Armelle Sakalli, Abdulla Van De Vyver, Hans Van Schaeybroeck, Bert Termonia, Piet |
description | Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics. |
format | Article |
fullrecord | <record><control><sourceid>ghent</sourceid><recordid>TN_cdi_ghent_librecat_oai_archive_ugent_be_8670277</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>oai_archive_ugent_be_8670277</sourcerecordid><originalsourceid>FETCH-ghent_librecat_oai_archive_ugent_be_86702773</originalsourceid><addsrcrecordid>eNqdi8EKgkAYhJcoSMp3-F9A2FbJPFdSEJ2ijsuv_urGusLuJvT2KXTo3Fxm-GZmxgLBUxElIhPzn7xkoXNPPiqONxlPApY_WkIPb0W6AnJedehVb6C2fQfXw_0MaCqw1IwQNZR6GhB0fUXagTJwQT8oXLNFjdpR-PUVE_nxtj9FTUvGS60KSyV62aOSaMtWDSRfzVQVJHfblIs0jf86fQA0jUlL</addsrcrecordid><sourcetype>Institutional Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Wheat yield estimation from NDVI and regional climate models in Latvia</title><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>Ghent University Academic Bibliography</source><creator>Vannoppen, Astrid ; Gobin, Anne ; Kotova, Lola ; Top, Sara ; De Cruz, Lesley ; Vīksna, Andris ; Aniskevich, Svetlana ; Bobylev, Leonid ; Buntemeyer, Lars ; Caluwaerts, Steven ; De Troch, Rozemien ; Gnatiuk, Natalia ; Hamdi, Rafiq ; Reca Remedio, Armelle ; Sakalli, Abdulla ; Van De Vyver, Hans ; Van Schaeybroeck, Bert ; Termonia, Piet</creator><creatorcontrib>Vannoppen, Astrid ; Gobin, Anne ; Kotova, Lola ; Top, Sara ; De Cruz, Lesley ; Vīksna, Andris ; Aniskevich, Svetlana ; Bobylev, Leonid ; Buntemeyer, Lars ; Caluwaerts, Steven ; De Troch, Rozemien ; Gnatiuk, Natalia ; Hamdi, Rafiq ; Reca Remedio, Armelle ; Sakalli, Abdulla ; Van De Vyver, Hans ; Van Schaeybroeck, Bert ; Termonia, Piet</creatorcontrib><description>Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><language>eng</language><subject>ALARO-0 ; Earth and Environmental Sciences ; EUROPEAN WHEAT ; General Earth and Planetary Sciences ; Latvia ; NDVI ; PREDICTION ; PROBA-V ; regional climate model ; REMO ; spring wheat ; STRESS ; TEMPERATURE ; VALIDATION ; VEGETATION ; weather impact ; winter wheat ; yield estimation</subject><creationdate>2020</creationdate><rights>Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) info:eu-repo/semantics/openAccess</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,315,780,784,4024,27860</link.rule.ids></links><search><creatorcontrib>Vannoppen, Astrid</creatorcontrib><creatorcontrib>Gobin, Anne</creatorcontrib><creatorcontrib>Kotova, Lola</creatorcontrib><creatorcontrib>Top, Sara</creatorcontrib><creatorcontrib>De Cruz, Lesley</creatorcontrib><creatorcontrib>Vīksna, Andris</creatorcontrib><creatorcontrib>Aniskevich, Svetlana</creatorcontrib><creatorcontrib>Bobylev, Leonid</creatorcontrib><creatorcontrib>Buntemeyer, Lars</creatorcontrib><creatorcontrib>Caluwaerts, Steven</creatorcontrib><creatorcontrib>De Troch, Rozemien</creatorcontrib><creatorcontrib>Gnatiuk, Natalia</creatorcontrib><creatorcontrib>Hamdi, Rafiq</creatorcontrib><creatorcontrib>Reca Remedio, Armelle</creatorcontrib><creatorcontrib>Sakalli, Abdulla</creatorcontrib><creatorcontrib>Van De Vyver, Hans</creatorcontrib><creatorcontrib>Van Schaeybroeck, Bert</creatorcontrib><creatorcontrib>Termonia, Piet</creatorcontrib><title>Wheat yield estimation from NDVI and regional climate models in Latvia</title><description>Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.</description><subject>ALARO-0</subject><subject>Earth and Environmental Sciences</subject><subject>EUROPEAN WHEAT</subject><subject>General Earth and Planetary Sciences</subject><subject>Latvia</subject><subject>NDVI</subject><subject>PREDICTION</subject><subject>PROBA-V</subject><subject>regional climate model</subject><subject>REMO</subject><subject>spring wheat</subject><subject>STRESS</subject><subject>TEMPERATURE</subject><subject>VALIDATION</subject><subject>VEGETATION</subject><subject>weather impact</subject><subject>winter wheat</subject><subject>yield estimation</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ADGLB</sourceid><recordid>eNqdi8EKgkAYhJcoSMp3-F9A2FbJPFdSEJ2ijsuv_urGusLuJvT2KXTo3Fxm-GZmxgLBUxElIhPzn7xkoXNPPiqONxlPApY_WkIPb0W6AnJedehVb6C2fQfXw_0MaCqw1IwQNZR6GhB0fUXagTJwQT8oXLNFjdpR-PUVE_nxtj9FTUvGS60KSyV62aOSaMtWDSRfzVQVJHfblIs0jf86fQA0jUlL</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Vannoppen, Astrid</creator><creator>Gobin, Anne</creator><creator>Kotova, Lola</creator><creator>Top, Sara</creator><creator>De Cruz, Lesley</creator><creator>Vīksna, Andris</creator><creator>Aniskevich, Svetlana</creator><creator>Bobylev, Leonid</creator><creator>Buntemeyer, Lars</creator><creator>Caluwaerts, Steven</creator><creator>De Troch, Rozemien</creator><creator>Gnatiuk, Natalia</creator><creator>Hamdi, Rafiq</creator><creator>Reca Remedio, Armelle</creator><creator>Sakalli, Abdulla</creator><creator>Van De Vyver, Hans</creator><creator>Van Schaeybroeck, Bert</creator><creator>Termonia, Piet</creator><scope>ADGLB</scope></search><sort><creationdate>2020</creationdate><title>Wheat yield estimation from NDVI and regional climate models in Latvia</title><author>Vannoppen, Astrid ; Gobin, Anne ; Kotova, Lola ; Top, Sara ; De Cruz, Lesley ; Vīksna, Andris ; Aniskevich, Svetlana ; Bobylev, Leonid ; Buntemeyer, Lars ; Caluwaerts, Steven ; De Troch, Rozemien ; Gnatiuk, Natalia ; Hamdi, Rafiq ; Reca Remedio, Armelle ; Sakalli, Abdulla ; Van De Vyver, Hans ; Van Schaeybroeck, Bert ; Termonia, Piet</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ghent_librecat_oai_archive_ugent_be_86702773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>ALARO-0</topic><topic>Earth and Environmental Sciences</topic><topic>EUROPEAN WHEAT</topic><topic>General Earth and Planetary Sciences</topic><topic>Latvia</topic><topic>NDVI</topic><topic>PREDICTION</topic><topic>PROBA-V</topic><topic>regional climate model</topic><topic>REMO</topic><topic>spring wheat</topic><topic>STRESS</topic><topic>TEMPERATURE</topic><topic>VALIDATION</topic><topic>VEGETATION</topic><topic>weather impact</topic><topic>winter wheat</topic><topic>yield estimation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vannoppen, Astrid</creatorcontrib><creatorcontrib>Gobin, Anne</creatorcontrib><creatorcontrib>Kotova, Lola</creatorcontrib><creatorcontrib>Top, Sara</creatorcontrib><creatorcontrib>De Cruz, Lesley</creatorcontrib><creatorcontrib>Vīksna, Andris</creatorcontrib><creatorcontrib>Aniskevich, Svetlana</creatorcontrib><creatorcontrib>Bobylev, Leonid</creatorcontrib><creatorcontrib>Buntemeyer, Lars</creatorcontrib><creatorcontrib>Caluwaerts, Steven</creatorcontrib><creatorcontrib>De Troch, Rozemien</creatorcontrib><creatorcontrib>Gnatiuk, Natalia</creatorcontrib><creatorcontrib>Hamdi, Rafiq</creatorcontrib><creatorcontrib>Reca Remedio, Armelle</creatorcontrib><creatorcontrib>Sakalli, Abdulla</creatorcontrib><creatorcontrib>Van De Vyver, Hans</creatorcontrib><creatorcontrib>Van Schaeybroeck, Bert</creatorcontrib><creatorcontrib>Termonia, Piet</creatorcontrib><collection>Ghent University Academic Bibliography</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vannoppen, Astrid</au><au>Gobin, Anne</au><au>Kotova, Lola</au><au>Top, Sara</au><au>De Cruz, Lesley</au><au>Vīksna, Andris</au><au>Aniskevich, Svetlana</au><au>Bobylev, Leonid</au><au>Buntemeyer, Lars</au><au>Caluwaerts, Steven</au><au>De Troch, Rozemien</au><au>Gnatiuk, Natalia</au><au>Hamdi, Rafiq</au><au>Reca Remedio, Armelle</au><au>Sakalli, Abdulla</au><au>Van De Vyver, Hans</au><au>Van Schaeybroeck, Bert</au><au>Termonia, Piet</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Wheat yield estimation from NDVI and regional climate models in Latvia</atitle><date>2020</date><risdate>2020</risdate><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2072-4292 |
ispartof | |
issn | 2072-4292 2072-4292 |
language | eng |
recordid | cdi_ghent_librecat_oai_archive_ugent_be_8670277 |
source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; MDPI - Multidisciplinary Digital Publishing Institute; Ghent University Academic Bibliography |
subjects | ALARO-0 Earth and Environmental Sciences EUROPEAN WHEAT General Earth and Planetary Sciences Latvia NDVI PREDICTION PROBA-V regional climate model REMO spring wheat STRESS TEMPERATURE VALIDATION VEGETATION weather impact winter wheat yield estimation |
title | Wheat yield estimation from NDVI and regional climate models in Latvia |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T21%3A58%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ghent&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Wheat%20yield%20estimation%20from%20NDVI%20and%20regional%20climate%20models%20in%20Latvia&rft.au=Vannoppen,%20Astrid&rft.date=2020&rft.issn=2072-4292&rft.eissn=2072-4292&rft_id=info:doi/&rft_dat=%3Cghent%3Eoai_archive_ugent_be_8670277%3C/ghent%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |