Articles | Volume 16, issue 8
https://doi.org/10.5194/essd-16-3893-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3893-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Monsoon Asia Rice Calendar (MARC): a gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images
Xin Zhao
CORRESPONDING AUTHOR
Biogeochemical Cycle Modeling and Analysis Section, Earth System Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
Biogeochemical Cycle Modeling and Analysis Section, Earth System Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
Haruka Izumisawa
Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, Japan
Yuji Masutomi
Asia-Pacific Climate Change Adaptation Research Section, Center for Climate Change Adaption, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
Seima Osako
DATAFLUCT, Inc., 1-19-9 Dogenzaka, Shibuya, Tokyo, 150-0043, Japan
Shuhei Yamamoto
DATAFLUCT, Inc., 1-19-9 Dogenzaka, Shibuya, Tokyo, 150-0043, Japan
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We developed maize version of process-based crop model coupled with a land surface model (MATCRO). It extends the original MATCRO-Rice by incorporating C4 photosynthesis and maize-specific parameters. The model was validated using field data from four sites and global yield data from FAOSTAT. MATCRO-Maize captured the interannual yield variability in global and county-level yield data, demonstrating its potential for climate impact assessments on maize production.
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Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
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Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
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Revised manuscript not accepted
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The accuracy of seasonal climate forecasts for monthly precipitation of JMA/MRI-CPS2, a dynamical seasonal climate forecast (SCF) system, is higher than that of statistical SCF (St-SCF) system using climate indices around the equator (10° S–10° N) even for six-month lead forecasts. On a global scale, the forecast accuracy of JMA/MRI-CPS2 is higher for one-month lead forecasts; however, St-SCFs were more accurate for forecasts more than two months in advance.
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Short summary
Mapping a rice calendar in a spatially explicit manner with a consistent framework remains challenging at a global or continental scale. We successfully developed a new gridded rice calendar for monsoon Asia based on Sentinel-1 and Sentinel-2 images, which characterize transplanting and harvesting dates and the number of rice croppings in a comprehensive framework. Our rice calendar will be beneficial for rice management, production prediction, and the estimation of greenhouse gas emissions.
Mapping a rice calendar in a spatially explicit manner with a consistent framework remains...
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