ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
-
Published:2023-09-13
Issue:9
Volume:15
Page:4047-4063
-
ISSN:1866-3516
-
Container-title:Earth System Science Data
-
language:en
-
Short-container-title:Earth Syst. Sci. Data
Author:
Zhao Yu,Han Shaoyu,Zheng Jie,Xue Hanyu,Li Zhenhai,Meng Yang,Li Xuguang,Yang Xiaodong,Li Zhenhong,Cai Shuhong,Yang Guijun
Abstract
Abstract. Generating spatial crop yield information is of great
significance for academic research and guiding agricultural policy. Existing
public yield datasets have a coarse spatial resolution, spanning from 1
to 43 km. Although these datasets are useful for analyzing large-scale
temporal and spatial change in yield, they cannot deal with small-scale
spatial heterogeneity, which happens to be the most significant
characteristic of the Chinese farmers' economy. Hence, we generated a 30 m
Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter-wheat-producing provinces in China for the period 2016–2021 with a
semi-mechanistic model (hierarchical linear model, HLM). The yield
prediction model was built by considering the wheat growth status and
climatic factors. It can estimate wheat yield with excellent accuracy and
low cost using a combination of satellite observations and regional
meteorological information (i.e., Landsat 8, Sentinel 2 and ERA5 data from
the Google Earth Engine (GEE) platform). The results were validated using
in situ measurements and census statistics and indicated a stable
performance of the HLM based on calibration datasets across China,
with a correlation coefficient (r) of 0.81 and a relative root mean square
error (rRMSE) of 12.59 %. With regards to validation, the
ChinaWheatYield30m dataset was highly consistent with in situ measurement
data and statistical data (p<0.01), indicated by an r (rRMSE) of
0.72** (15.34 %) and 0.69** (19.16 %). The ChinaWheatYield30m is a
sophisticated dataset with both high spatial resolution and excellent
accuracy; such a dataset will provide basic knowledge of detailed wheat
yield distribution, which can be applied for many purposes including crop
production modeling and regional climate evaluation. The ChinaWheatYield30m dataset generated from this study can be downloaded from https://doi.org/10.5281/zenodo.7360753 (Zhao et al., 2022b).
Funder
National Key Research and Development Program of China Heilongjiang Provincial Science and Technology Department Beijing Academy of Agricultural and Forestry Sciences
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
Reference59 articles.
1. Arslan, M., Guzel, M., Demirci, M., and Ozdemir, S.: SMOTE and Gaussian Noise
Based Sensor Data Augmentation, in: 2019 4th International Conference on
Computer Science and Engineering (UBMK), 11 September 2019, Samsun, Turkey, 458–462,
https://doi.org/10.1109/UBMK.2019.8907003, 2019. 2. Bailey-Serres, J., Parker, J. E., Ainsworth, E. A., Oldroyd, G. E. D.,
and Schroeder, J. I.: Genetic strategies for improving crop yields, Nature, 575,
109–118, https://doi.org/10.1038/s41586-019-1679-0, 2019. 3. Battude, M., Al Bitar, A., Morin, D., Cros, J., Huc, M., Marais Sicre, C.,
Dantec, V., and Demarez, V.: Estimating maize biomass and yield over large areas
using high spatial and temporal resolution sentinel-2 like remote sensing
data, Remote Sens. Environ., 184, 668–681,
https://doi.org/10.1016/j.rse.2016.07.030, 2016. 4. Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. 5. Cabas, J., Weersink, A., and Olale, E.: Crop yield response to economic, site
and climatic variables, Clim. Change, 101, 599–616, https://doi.org/10.1007/s10584-009-9754-4, 2010.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|