Articles | Volume 13, issue 10
https://doi.org/10.5194/essd-13-4799-2021
https://doi.org/10.5194/essd-13-4799-2021
Data description paper
 | 
21 Oct 2021
Data description paper |  | 21 Oct 2021

GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery

Miao Zhang, Bingfang Wu, Hongwei Zeng, Guojin He, Chong Liu, Shiqi Tao, Qi Zhang, Mohsen Nabil, Fuyou Tian, José Bofana, Awetahegn Niguse Beyene, Abdelrazek Elnashar, Nana Yan, Zhengdong Wang, and Yiliang Liu

Related authors

Estimates of the spatially complete, observational-data-driven planetary boundary layer height over the contiguous United States
Zolal Ayazpour, Shiqi Tao, Dan Li, Amy Jo Scarino, Ralph E. Kuehn, and Kang Sun
Atmos. Meas. Tech., 16, 563–580, https://doi.org/10.5194/amt-16-563-2023,https://doi.org/10.5194/amt-16-563-2023, 2023
Short summary
CALC-2020: a new baseline land cover map at 10 m resolution for the circumpolar Arctic
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023,https://doi.org/10.5194/essd-15-133-2023, 2023
Short summary
Synthesis of global actual evapotranspiration from 1982 to 2019
Abdelrazek Elnashar, Linjiang Wang, Bingfang Wu, Weiwei Zhu, and Hongwei Zeng
Earth Syst. Sci. Data, 13, 447–480, https://doi.org/10.5194/essd-13-447-2021,https://doi.org/10.5194/essd-13-447-2021, 2021
Short summary
A NEW INDEX FOR IDENTIFYING WATER BODY FROM SENTINEL-2 SATELLITE REMOTE SENSING IMAGERY
W. Jiang, Y. Ni, Z. Pang, G. He, J. Fu, J. Lu, K. Yang, T. Long, and T. Lei
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 33–38, https://doi.org/10.5194/isprs-annals-V-3-2020-33-2020,https://doi.org/10.5194/isprs-annals-V-3-2020-33-2020, 2020
STUDY ON THE WATER BODY EXTRACTION USING GF-1 DATA BASED ON ADABOOST INTEGRATED LEARNING ALGORITHM
J. Y. Sun, G. Z. Wang, G. J. He, D. C. Pu, W. Jiang, T. T. Li, and X. F. Niu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 641–648, https://doi.org/10.5194/isprs-archives-XLII-3-W10-641-2020,https://doi.org/10.5194/isprs-archives-XLII-3-W10-641-2020, 2020

Related subject area

Land Cover and Land Use
A 250 m annual alpine grassland AGB dataset over the Qinghai–Tibet Plateau (2000–2019) in China based on in situ measurements, UAV photos, and MODIS data
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023,https://doi.org/10.5194/essd-15-821-2023, 2023
Short summary
AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015
Huaqing Wu, Jing Zhang, Zhao Zhang, Jichong Han, Juan Cao, Liangliang Zhang, Yuchuan Luo, Qinghang Mei, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 791–808, https://doi.org/10.5194/essd-15-791-2023,https://doi.org/10.5194/essd-15-791-2023, 2023
Short summary
TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023,https://doi.org/10.5194/essd-15-681-2023, 2023
Short summary
UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework
Qian Shi, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu
Earth Syst. Sci. Data, 15, 555–577, https://doi.org/10.5194/essd-15-555-2023,https://doi.org/10.5194/essd-15-555-2023, 2023
Short summary
AI4Boundaries: an open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography
Raphaël d'Andrimont, Martin Claverie, Pieter Kempeneers, Davide Muraro, Momchil Yordanov, Devis Peressutti, Matej Batič, and François Waldner
Earth Syst. Sci. Data, 15, 317–329, https://doi.org/10.5194/essd-15-317-2023,https://doi.org/10.5194/essd-15-317-2023, 2023
Short summary

Cited articles

Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., and Parsian, S.: Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review, IEEE J. Sel. Top. Appl., 13, 5326–5350, https://doi.org/10.1109/JSTARS.2020.3021052, 2020. 
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018. 
Becker, M. and Johnson, D. E.: Cropping intensity effects on upland rice yield and sustainability in West Africa, Nutr. Cycl. Agroecosys., 59, 107–117, https://doi.org/10.1023/A:1017551529813, 2001. 
Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., and Friedl, M. A.: Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery, Remote Sens. Environ., 240, 111685, https://doi.org/10.1016/j.rse.2020.111685, 2020. 
Challinor, A. J., Parkes, B., and Ramirez-Villegas, J.: Crop yield response to climate change varies with cropping intensity, Glob. Change Biol., 21, 1679–1688, https://doi.org/10.1111/gcb.12808, 2015. 
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
Cropping intensity (CI) is essential for agricultural land use management, but fine-resolution global CI is not available. We used multiple satellite data on Google Earth Engine to develop a first 30 m resolution global CI (GCI30). GCI30 performed well, with an overall accuracy of 92 %. GCI30 not only exhibited high agreement with existing CI products but also provided many spatial details. GCI30 can facilitate research on sustained cropland intensification to improve food production.