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description Publicationkeyboard_double_arrow_right Article 2019Publisher:Elsevier BV Funded by:NSF | CIF21 DIBBs: PD: Cyberinf...NSF| CIF21 DIBBs: PD: Cyberinfrastructure Tools for Precision Agriculture in the 21st CenturyAuthors: Daniel L. Warner; Mario Guevara; Shreeram Inamdar; Rodrigo Vargas;Daniel L. Warner; Mario Guevara; Shreeram Inamdar; Rodrigo Vargas;Abstract Upscaling soil-atmosphere greenhouse gas (GHG) fluxes across complex landscapes is a major challenge for environmental scientists and land managers. This study employs a quantile-based digital soil mapping approach for estimating the spatially continuous distributions (2 m spatial resolution) and uncertainties of seasonal mean mid-day soil CO2 and CH4 fluxes. This framework was parameterized using manual chamber measurements collected over two years within a temperate forested headwater watershed. Model accuracy was highest for early (r2 = 0.61) and late summer (r2 = 0.64) for CO2 and CH4 fluxes. Model uncertainty was generally lower for predicted CO2 fluxes than CH4 fluxes. Within the study area, predicted seasonal mean CO2 fluxes ranged from 0.17 to 0.58 μmol m−2 s−1 in winter, and 1.4 to 5.1 μmol m−2 s−1 in early summer. Predicted CH4 fluxes across the study area ranged from −0.52 to 0.02 nmol m−2 s−1 in winter, and −2.1 to 0.61 nmol m−2 s−1 in early and late summer. The models estimated a per hectare net GHG potential ranging from 0.44 to 4.7 kg CO2 eq. hr−1 in winter and early summer, with an estimated 0.4 to 1.5% of emissions offset by CH4 uptake. Flux predictions fell within ranges reported in other temperate forest systems. Soil CO2 fluxes were more sensitive to seasonal temperature changes than CH4 fluxes, with significant temperature relationships for soil CO2 emissions and CH4 uptake in pixels with high slope angles. In contrast, soil CH4 fluxes from flat low-lying areas near the stream network within the watershed were significantly correlated to seasonal precipitation. This study identified key challenges for modeling high spatial resolution soil CO2 and CH4 fluxes, and suggests a larger spatial heterogeneity and complexity of underlying processes that govern CH4 fluxes.
Agricultural and For... arrow_drop_down Agricultural and Forest MeteorologyArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agrformet.2018.09.020&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu17 citations 17 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Agricultural and For... arrow_drop_down Agricultural and Forest MeteorologyArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agrformet.2018.09.020&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2019Publisher:Elsevier BV Funded by:NSF | Sustained-Petascale In Ac..., NSF | Leadership Class Scientif..., NSF | CIF21 DIBBs: Scalable Cap...NSF| Sustained-Petascale In Action: Blue Waters Enabling Transformative Science And Engineering ,NSF| Leadership Class Scientific and Engineering Computing: Breaking Through the Limits ,NSF| CIF21 DIBBs: Scalable Capabilities for Spatial Data SynthesisYaping Cai; Kaiyu Guan; David B. Lobell; Andries Potgieter; Shaowen Wang; Jian Peng; Tianfang Xu; Senthold Asseng; Yongguang Zhang; Liangzhi You; Bin Peng;Abstract Wheat is the most important staple crop grown in Australia, and Australia is one of the top wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is important for regional and global food security. Prior studies use either climate data, or satellite data, or a combination of these two to build empirical models to predict crop yield. However, though the performance of yield prediction using empirical methods is improved by combining the use of climate and satellite data, the contributions from different data sources are still not clear. In addition, how the regression-based methods compare with various machine-learning based methods in their performance in yield prediction is also not well understood and needs in-depth investigation. This work integrated various sources of data to predict wheat yield across Australia from 2000 to 2014 at the statistical division (SD) level. We adopted a well-known regression method (LASSO, as a benchmark) and three mainstream machine learning methods (support vector machine, random forest, and neural network) to build various empirical models for yield prediction. For satellite data, we used the enhanced vegetation index (EVI) from MODIS and solar-induced chlorophyll fluorescence (SIF) from GOME-2 and SCIAMACHY as metrics to approximate crop productivity. The machine-learning based methods outperform the regression method in modeling crop yield. Our results confirm that combining climate and satellite data can achieve high performance of yield prediction at the SD level (R2 ˜ 0.75). The satellite data track crop growth condition and gradually capture the variability of yield evolving with the growing season, and their contributions to yield prediction usually saturate at the peak of the growing season. Climate data provide extra and unique information beyond what the satellite data have offered for yield prediction, and our empirical modeling work shows the added values of climate variables exist across the whole season, not only at some certain stages. We also find that using EVI as an input can achieve better performance in yield prediction than SIF, primarily due to the large noise in the satellite-based SIF data (i.e. coarse resolution in both space and time). In addition, we also explored the potential for timely wheat yield prediction in Australia, and we can achieve the optimal prediction performance with approximately two-month lead time before wheat maturity. The proposed methodology in this paper can be extended to different crops and different regions for crop yield prediction.
Agricultural and For... arrow_drop_down Agricultural and Forest MeteorologyArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agrformet.2019.03.010&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu304 citations 304 popularity Top 0.1% influence Top 10% impulse Top 0.1% Powered by BIP!more_vert Agricultural and For... arrow_drop_down Agricultural and Forest MeteorologyArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agrformet.2019.03.010&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Article 2019Publisher:Elsevier BV Funded by:NSF | CIF21 DIBBs: PD: Cyberinf...NSF| CIF21 DIBBs: PD: Cyberinfrastructure Tools for Precision Agriculture in the 21st CenturyAuthors: Daniel L. Warner; Mario Guevara; Shreeram Inamdar; Rodrigo Vargas;Daniel L. Warner; Mario Guevara; Shreeram Inamdar; Rodrigo Vargas;Abstract Upscaling soil-atmosphere greenhouse gas (GHG) fluxes across complex landscapes is a major challenge for environmental scientists and land managers. This study employs a quantile-based digital soil mapping approach for estimating the spatially continuous distributions (2 m spatial resolution) and uncertainties of seasonal mean mid-day soil CO2 and CH4 fluxes. This framework was parameterized using manual chamber measurements collected over two years within a temperate forested headwater watershed. Model accuracy was highest for early (r2 = 0.61) and late summer (r2 = 0.64) for CO2 and CH4 fluxes. Model uncertainty was generally lower for predicted CO2 fluxes than CH4 fluxes. Within the study area, predicted seasonal mean CO2 fluxes ranged from 0.17 to 0.58 μmol m−2 s−1 in winter, and 1.4 to 5.1 μmol m−2 s−1 in early summer. Predicted CH4 fluxes across the study area ranged from −0.52 to 0.02 nmol m−2 s−1 in winter, and −2.1 to 0.61 nmol m−2 s−1 in early and late summer. The models estimated a per hectare net GHG potential ranging from 0.44 to 4.7 kg CO2 eq. hr−1 in winter and early summer, with an estimated 0.4 to 1.5% of emissions offset by CH4 uptake. Flux predictions fell within ranges reported in other temperate forest systems. Soil CO2 fluxes were more sensitive to seasonal temperature changes than CH4 fluxes, with significant temperature relationships for soil CO2 emissions and CH4 uptake in pixels with high slope angles. In contrast, soil CH4 fluxes from flat low-lying areas near the stream network within the watershed were significantly correlated to seasonal precipitation. This study identified key challenges for modeling high spatial resolution soil CO2 and CH4 fluxes, and suggests a larger spatial heterogeneity and complexity of underlying processes that govern CH4 fluxes.
Agricultural and For... arrow_drop_down Agricultural and Forest MeteorologyArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agrformet.2018.09.020&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu17 citations 17 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Agricultural and For... arrow_drop_down Agricultural and Forest MeteorologyArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agrformet.2018.09.020&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2019Publisher:Elsevier BV Funded by:NSF | Sustained-Petascale In Ac..., NSF | Leadership Class Scientif..., NSF | CIF21 DIBBs: Scalable Cap...NSF| Sustained-Petascale In Action: Blue Waters Enabling Transformative Science And Engineering ,NSF| Leadership Class Scientific and Engineering Computing: Breaking Through the Limits ,NSF| CIF21 DIBBs: Scalable Capabilities for Spatial Data SynthesisYaping Cai; Kaiyu Guan; David B. Lobell; Andries Potgieter; Shaowen Wang; Jian Peng; Tianfang Xu; Senthold Asseng; Yongguang Zhang; Liangzhi You; Bin Peng;Abstract Wheat is the most important staple crop grown in Australia, and Australia is one of the top wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is important for regional and global food security. Prior studies use either climate data, or satellite data, or a combination of these two to build empirical models to predict crop yield. However, though the performance of yield prediction using empirical methods is improved by combining the use of climate and satellite data, the contributions from different data sources are still not clear. In addition, how the regression-based methods compare with various machine-learning based methods in their performance in yield prediction is also not well understood and needs in-depth investigation. This work integrated various sources of data to predict wheat yield across Australia from 2000 to 2014 at the statistical division (SD) level. We adopted a well-known regression method (LASSO, as a benchmark) and three mainstream machine learning methods (support vector machine, random forest, and neural network) to build various empirical models for yield prediction. For satellite data, we used the enhanced vegetation index (EVI) from MODIS and solar-induced chlorophyll fluorescence (SIF) from GOME-2 and SCIAMACHY as metrics to approximate crop productivity. The machine-learning based methods outperform the regression method in modeling crop yield. Our results confirm that combining climate and satellite data can achieve high performance of yield prediction at the SD level (R2 ˜ 0.75). The satellite data track crop growth condition and gradually capture the variability of yield evolving with the growing season, and their contributions to yield prediction usually saturate at the peak of the growing season. Climate data provide extra and unique information beyond what the satellite data have offered for yield prediction, and our empirical modeling work shows the added values of climate variables exist across the whole season, not only at some certain stages. We also find that using EVI as an input can achieve better performance in yield prediction than SIF, primarily due to the large noise in the satellite-based SIF data (i.e. coarse resolution in both space and time). In addition, we also explored the potential for timely wheat yield prediction in Australia, and we can achieve the optimal prediction performance with approximately two-month lead time before wheat maturity. The proposed methodology in this paper can be extended to different crops and different regions for crop yield prediction.
Agricultural and For... arrow_drop_down Agricultural and Forest MeteorologyArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agrformet.2019.03.010&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu304 citations 304 popularity Top 0.1% influence Top 10% impulse Top 0.1% Powered by BIP!more_vert Agricultural and For... arrow_drop_down Agricultural and Forest MeteorologyArticle . 2019 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agrformet.2019.03.010&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu