Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing Data
This study investigates the influence of meteorological parameters such as temperature and precipitation on gross primary production (GPP) in the continental United States (CONUS) during boreal summer using satellite-based temperature and precipitation indices and GPP data at various scales (i.e., p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/15/3508 |
| _version_ | 1797412508119597056 |
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| author | Cheolhee Yoo Daehyun Kang Seonyoung Park |
| author_facet | Cheolhee Yoo Daehyun Kang Seonyoung Park |
| author_sort | Cheolhee Yoo |
| collection | DOAJ |
| description | This study investigates the influence of meteorological parameters such as temperature and precipitation on gross primary production (GPP) in the continental United States (CONUS) during boreal summer using satellite-based temperature and precipitation indices and GPP data at various scales (i.e., pixel, county, and state levels). The strong linear relationship between temperature and precipitation indices is presented around the central United States, particularly in the Great Plains, where the year-to-year variation of GPP is very sensitive to meteorological conditions. This sensitive GPP variation is mostly attributable to the semi-arid climate in the Great Plains, where crop productivity and temperature are closely related. The more specific information for the regionality of the relationships across the variables manifests itself at higher resolutions. The impact of the summer meteorological condition on the annual crop yield is particularly significant. Maize and soybean yields show a strong correlation with both Temperature Condition Index (TCI) and Precipitation Condition Index (PCI) in the Great Plains, with a relatively higher relationship with TCI than PCI, which is consistent with the relationship compared with GPP. This study suggests that in-depth investigations into the relationship between maize and soybean yields and the climate are required. The region-dependent relationship between GPP and meteorological conditions in our study would guide agricultural decision making in the future climate. |
| first_indexed | 2024-03-09T05:03:34Z |
| format | Article |
| id | doaj.art-e5bfab23fef54affb6cbaa84ff140651 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:03:34Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e5bfab23fef54affb6cbaa84ff1406512023-12-03T12:57:27ZengMDPI AGRemote Sensing2072-42922022-07-011415350810.3390/rs14153508Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing DataCheolhee Yoo0Daehyun Kang1Seonyoung Park2Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, ChinaCenter for Sustainable Environment Research, Korea Institute of Science and Technology, Seoul 02792, KoreaDepartment of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, KoreaThis study investigates the influence of meteorological parameters such as temperature and precipitation on gross primary production (GPP) in the continental United States (CONUS) during boreal summer using satellite-based temperature and precipitation indices and GPP data at various scales (i.e., pixel, county, and state levels). The strong linear relationship between temperature and precipitation indices is presented around the central United States, particularly in the Great Plains, where the year-to-year variation of GPP is very sensitive to meteorological conditions. This sensitive GPP variation is mostly attributable to the semi-arid climate in the Great Plains, where crop productivity and temperature are closely related. The more specific information for the regionality of the relationships across the variables manifests itself at higher resolutions. The impact of the summer meteorological condition on the annual crop yield is particularly significant. Maize and soybean yields show a strong correlation with both Temperature Condition Index (TCI) and Precipitation Condition Index (PCI) in the Great Plains, with a relatively higher relationship with TCI than PCI, which is consistent with the relationship compared with GPP. This study suggests that in-depth investigations into the relationship between maize and soybean yields and the climate are required. The region-dependent relationship between GPP and meteorological conditions in our study would guide agricultural decision making in the future climate.https://www.mdpi.com/2072-4292/14/15/3508regional climatecrop productivitycrop yieldgross primary productiontemperatureprecipitation |
| spellingShingle | Cheolhee Yoo Daehyun Kang Seonyoung Park Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing Data Remote Sensing regional climate crop productivity crop yield gross primary production temperature precipitation |
| title | Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing Data |
| title_full | Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing Data |
| title_fullStr | Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing Data |
| title_full_unstemmed | Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing Data |
| title_short | Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing Data |
| title_sort | identifying the impact of regional meteorological parameters on us crop yield at various spatial scales using remote sensing data |
| topic | regional climate crop productivity crop yield gross primary production temperature precipitation |
| url | https://www.mdpi.com/2072-4292/14/15/3508 |
| work_keys_str_mv | AT cheolheeyoo identifyingtheimpactofregionalmeteorologicalparametersonuscropyieldatvariousspatialscalesusingremotesensingdata AT daehyunkang identifyingtheimpactofregionalmeteorologicalparametersonuscropyieldatvariousspatialscalesusingremotesensingdata AT seonyoungpark identifyingtheimpactofregionalmeteorologicalparametersonuscropyieldatvariousspatialscalesusingremotesensingdata |