Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data. Yang, Y., Ren, W., Tao, B., Ji, L., Liang, L., Ruane, A. C., Fisher, J. B., Liu, J., Sama, M., Li, Z., & Tian, Q. ISPRS Journal of Photogrammetry and Remote Sensing, 170:156–173, December, 2020. Publisher: Elsevier B.V.![Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R2, RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p \textless 0.01), and harvesting dates were delayed by 0.78 days/year (p \textless 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p \textless 0. 01) or a 0.66-day harvest delay (r = 0.10, p \textless 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p \textless 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p \textless 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.
@article{yang_characterizing_2020,
title = {Characterizing spatiotemporal patterns of crop phenology across {North} {America} during 2000–2016 using satellite imagery and agricultural survey data},
volume = {170},
issn = {09242716},
url = {https://doi.org/10.1016/j.isprsjprs.2020.10.005 https://linkinghub.elsevier.com/retrieve/pii/S092427162030277X},
doi = {10.1016/j.isprsjprs.2020.10.005},
abstract = {Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R2, RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p {\textless} 0.01), and harvesting dates were delayed by 0.78 days/year (p {\textless} 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p {\textless} 0. 01) or a 0.66-day harvest delay (r = 0.10, p {\textless} 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p {\textless} 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p {\textless} 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
author = {Yang, Yanjun and Ren, Wei and Tao, Bo and Ji, Lei and Liang, Liang and Ruane, Alex C. and Fisher, Joshua B. and Liu, Jiangui and Sama, Michael and Li, Zhe and Tian, Qingjiu},
month = dec,
year = {2020},
note = {Publisher: Elsevier B.V.},
keywords = {NALCMS},
pages = {156--173},
}
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However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R2, RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p \\textless 0.01), and harvesting dates were delayed by 0.78 days/year (p \\textless 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p \\textless 0. 01) or a 0.66-day harvest delay (r = 0.10, p \\textless 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p \\textless 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p \\textless 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.","journal":"ISPRS Journal of Photogrammetry and Remote Sensing","author":[{"propositions":[],"lastnames":["Yang"],"firstnames":["Yanjun"],"suffixes":[]},{"propositions":[],"lastnames":["Ren"],"firstnames":["Wei"],"suffixes":[]},{"propositions":[],"lastnames":["Tao"],"firstnames":["Bo"],"suffixes":[]},{"propositions":[],"lastnames":["Ji"],"firstnames":["Lei"],"suffixes":[]},{"propositions":[],"lastnames":["Liang"],"firstnames":["Liang"],"suffixes":[]},{"propositions":[],"lastnames":["Ruane"],"firstnames":["Alex","C."],"suffixes":[]},{"propositions":[],"lastnames":["Fisher"],"firstnames":["Joshua","B."],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Jiangui"],"suffixes":[]},{"propositions":[],"lastnames":["Sama"],"firstnames":["Michael"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Zhe"],"suffixes":[]},{"propositions":[],"lastnames":["Tian"],"firstnames":["Qingjiu"],"suffixes":[]}],"month":"December","year":"2020","note":"Publisher: Elsevier B.V.","keywords":"NALCMS","pages":"156–173","bibtex":"@article{yang_characterizing_2020,\n\ttitle = {Characterizing spatiotemporal patterns of crop phenology across {North} {America} during 2000–2016 using satellite imagery and agricultural survey data},\n\tvolume = {170},\n\tissn = {09242716},\n\turl = {https://doi.org/10.1016/j.isprsjprs.2020.10.005 https://linkinghub.elsevier.com/retrieve/pii/S092427162030277X},\n\tdoi = {10.1016/j.isprsjprs.2020.10.005},\n\tabstract = {Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R2, RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p {\\textless} 0.01), and harvesting dates were delayed by 0.78 days/year (p {\\textless} 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p {\\textless} 0. 01) or a 0.66-day harvest delay (r = 0.10, p {\\textless} 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p {\\textless} 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p {\\textless} 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. 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