ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
<p>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 spat...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-09-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/4047/2023/essd-15-4047-2023.pdf |
| Summary: | <p>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 (<span class="inline-formula"><i>r</i></span>) 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 (<span class="inline-formula"><i>p</i><0.01</span>), indicated by an <span class="inline-formula"><i>r</i></span> (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 <a href="https://doi.org/10.5281/zenodo.7360753">https://doi.org/10.5281/zenodo.7360753</a> (Zhao et al., 2022b).</p> |
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| ISSN: | 1866-3508 1866-3516 |