The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data

Han, Jichong; Zhang, Zhao; Luo, Yuchuan; Cao, Juan; Zhang, Liangliang; Zhang, Jing; Li, Ziyue

doi:10.5194/essd-13-2857-2021

Cited by 38 publications

(23 citation statements)

References 82 publications

(128 reference statements)

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“…Liu et al, 2020). Although there may be some limitations in extracting phenological parameters for zones using random samples, it may be one of the most effective approaches currently available (Clauss et al, 2016;Gumma et al, 2014;Han et al, 2021c;Li et al, 2020;Phan et al, 2019;Phung et al, 2020).…”

Section: Sample Blocks Collected For Extracting Phenological Parametersmentioning

confidence: 99%

NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019

Han

Zhang

Luo

et al. 2021

Earth Syst. Sci. Data

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Abstract. An accurate paddy rice map is crucial for ensuring food security, particularly for Southeast and Northeast Asia. MODIS satellite data are useful for mapping paddy rice at continental scales but have a mixed-pixel problem caused by the coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time series Sentinel-1 and MODIS data. We demonstrated the method by generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (NESEA-Rice10). We compared the resultant paddy rice maps with available agricultural statistics at subnational levels and existing rice maps for some countries. The results demonstrated that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranged from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2=0.93) and Northeast China (R2=0.99). The spatial distribution of the 2017–2019 composite paddy rice map was consistent with that of the rice map from the International Rice Research Institute. The paddy rice planting area may have been underestimated in the region in which the flooding signal was not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.5281/zenodo.5645344 (Han et al., 2021a). Small examples can be found from the following DOI: https://doi.org/10.17632/cnc3tkbwcm.1 (Han et al., 2021b).

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Section: Sample Blocks Collected For Extracting Phenological Parametersmentioning

confidence: 99%

NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019

Han

Zhang

Luo

et al. 2021

Earth Syst. Sci. Data

Self Cite

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“…Finally, we obtained the phenological information for each subzone from the sample blocks (Gumma et al, 2014;Liu et al, 2020). Although there may be some limitations in extracting phenological parameters for zones using random samples, it may be one of the most effective approaches currently available (Clauss et al, 2016;Gumma et al, 2014;Han et al, 2021c;Li et al, 2020;Phan et al, 2019;Phung et al, 2020).…”

Section: Sample Blocks Collected For Extracting Phenological Parametersmentioning

confidence: 99%

AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019

Han

Zhang

Luo

et al. 2021

Preprint

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Abstract. An accurate paddy rice map is crucial for food security, particularly for Southeast and Northeast Asia. The MODIS satellite data is useful for mapping paddy rice at continental scales but has a problem of mixed-pixel due to coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time-series Sentinel-1 and MODIS data. We demonstrated the method in generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (AsiaRiceMap10m). The resultant paddy rice maps were compared with available agricultural statistics at subnational levels and existing rice maps for some countries. The results show that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranges from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2 = 0.93) and Northeast China (R2 = 0.99). The spatial distribution of the 2017–2019 composite paddy rice map is consistent with the rice map from the International Rice Research Institute. The paddy rice planting area may be underestimated in the region where the flooding signal is not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.17632/j34b3jsvr9.1 (Han et al., 2021a). Find small examples here (https://doi.org/10.17632/cnc3tkbwcm.1) (Han et al., 2021b).

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“…For the Atlantic and Continental regions, among the winter crops, rapeseed was considered as an alternative to wheat and barley [72]. As for spring crops in these two areas, maize, potato, sugar beet and soya were considered [71].…”

Section: Cropping Rotations and Sequential Crops Inventorymentioning

confidence: 99%

The Role of Sequential Cropping and Biogasdoneright™ in Enhancing the Sustainability of Agricultural Systems in Europe

Magnolo

Dekker²,

Decorte³

et al. 2021

Agronomy

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Sequential cropping in the Biogasdoneright™ (BDR™) system in Italy has recently gained attention to combine food and renewable energy production in a sustainable way, as well as for carbon sequestration. However, little is known on the potential to expand the practice in other regions of Europe. In this paper, sequential crop calendars were developed for different EU climate regions, and the EU biomethane potential of the anaerobic digestion (AD) of sequential crops was estimated for a Conservative_Scenario and a Maximum_Scenario, assuming different percentages of primary crop land dedicated to the practice and biogas yields. A total EU biomethane potential of 46 bcm/yr and 185 bcm/yr was estimated from the AD of sequential crops in the two scenarios, respectively, and the Continental region registered the highest potential compared to the other regions. The additional benefits of the combination of sequential cropping with other agricultural conservation practices and digestate use included in BDR™ systems were also discussed. In conclusion, the paper shows that with appropriate innovations in crop management, sequential cropping could be applied in different agroclimatic regions of Europe, contributing to climate and renewable energy targets.

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The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data

Cited by 38 publications

References 82 publications

NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019

NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019

AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019

The Role of Sequential Cropping and Biogasdoneright™ in Enhancing the Sustainability of Agricultural Systems in Europe

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