The ability of sun-induced chlorophyll fluorescence from OCO-2 and MODIS-EVI to monitor spatial variations of soybean and maize yields in the Midwestern USA
Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite...
| Autores principales: | , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/142881 |
| _version_ | 1855540574026727424 |
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| author | Gao, Yun Wang, Songhan Guan, Kaiyu Wolanin, Aleksandra You, Liangzhi Ju, Weimin Zhang, Yongguang |
| author_browse | Gao, Yun Guan, Kaiyu Ju, Weimin Wang, Songhan Wolanin, Aleksandra You, Liangzhi Zhang, Yongguang |
| author_facet | Gao, Yun Wang, Songhan Guan, Kaiyu Wolanin, Aleksandra You, Liangzhi Ju, Weimin Zhang, Yongguang |
| author_sort | Gao, Yun |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data. |
| format | Journal Article |
| id | CGSpace142881 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1428812024-10-25T08:05:38Z The ability of sun-induced chlorophyll fluorescence from OCO-2 and MODIS-EVI to monitor spatial variations of soybean and maize yields in the Midwestern USA Gao, Yun Wang, Songhan Guan, Kaiyu Wolanin, Aleksandra You, Liangzhi Ju, Weimin Zhang, Yongguang vegetation index remote sensing satellite observation maize capacity development soybeans crop yield spatial analysis fluorescence chlorophylls moderate resolution imaging spectroradiometer Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data. 2020-05-01 2024-05-22T12:11:14Z 2024-05-22T12:11:14Z Journal Article https://hdl.handle.net/10568/142881 en Open Access MDPI Gao, Yun; Wang, Songhan; Guan, Kaiyu; Wolanin, Aleksandra; You, Liangzhi; Ju, Weimin; and Zhang, Yongguang. 2020. The ability of sun-induced chlorophyll fluorescence from OCO-2 and MODIS-EVI to monitor spatial variations of soybean and maize yields in the Midwestern USA. Remote Sensing 12(7): 1111. https://doi.org/10.3390/rs12071111 |
| spellingShingle | vegetation index remote sensing satellite observation maize capacity development soybeans crop yield spatial analysis fluorescence chlorophylls moderate resolution imaging spectroradiometer Gao, Yun Wang, Songhan Guan, Kaiyu Wolanin, Aleksandra You, Liangzhi Ju, Weimin Zhang, Yongguang The ability of sun-induced chlorophyll fluorescence from OCO-2 and MODIS-EVI to monitor spatial variations of soybean and maize yields in the Midwestern USA |
| title | The ability of sun-induced chlorophyll fluorescence from OCO-2 and MODIS-EVI to monitor spatial variations of soybean and maize yields in the Midwestern USA |
| title_full | The ability of sun-induced chlorophyll fluorescence from OCO-2 and MODIS-EVI to monitor spatial variations of soybean and maize yields in the Midwestern USA |
| title_fullStr | The ability of sun-induced chlorophyll fluorescence from OCO-2 and MODIS-EVI to monitor spatial variations of soybean and maize yields in the Midwestern USA |
| title_full_unstemmed | The ability of sun-induced chlorophyll fluorescence from OCO-2 and MODIS-EVI to monitor spatial variations of soybean and maize yields in the Midwestern USA |
| title_short | The ability of sun-induced chlorophyll fluorescence from OCO-2 and MODIS-EVI to monitor spatial variations of soybean and maize yields in the Midwestern USA |
| title_sort | ability of sun induced chlorophyll fluorescence from oco 2 and modis evi to monitor spatial variations of soybean and maize yields in the midwestern usa |
| topic | vegetation index remote sensing satellite observation maize capacity development soybeans crop yield spatial analysis fluorescence chlorophylls moderate resolution imaging spectroradiometer |
| url | https://hdl.handle.net/10568/142881 |
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