Estimating Global Wheat Yields at 4 km Resolution during 1982–2020 by a Spatiotemporal Transferable Method
<p>The spatial distribution of spring and winter wheat covering 54 countries globally.</p> "> Figure 2
<p>Flow chart of spatiotemporal transferable method to estimate global wheat yields.</p> "> Figure 3
<p>Comparisons between mapped area by the spectra–phenology integration method and subnational-level data during 2006–2014. (<b>a</b>) South and East Asia, (<b>b</b>) Central Asia, (<b>c</b>) Europe, (<b>d</b>) spring wheat in the Russian Federation and Kazakhstan, (<b>e</b>) winter wheat in the Russian Federation, (<b>f</b>) Australia, (<b>g</b>) South America, (<b>h</b>) spring wheat in North America, and (<b>i</b>) winter wheat in North America.</p> "> Figure 4
<p>Performance of the RF and LSTM models in yield estimation during 2006–2014 across all regions: (<b>a</b>) R<sup>2</sup>, (<b>b</b>) nRMSE (%).</p> "> Figure 5
<p>Comparisons between the predicted yields of GlobalWheatYield4km and observed yields. (<b>a</b>) South and East Asia, (<b>b</b>) Central Asia, (<b>c</b>) Europe, (<b>d</b>) spring wheat in the Russian Federation and Kazakhstan, (<b>e</b>) winter wheat in the Russian Federation, (<b>f</b>) Australia, (<b>g</b>) South America, (<b>h</b>) spring wheat in North America, (<b>i</b>) winter wheat in North America. The color bar indicates the point density.</p> "> Figure 6
<p>Spatial distribution of the predicted yield aggregated over administrative unit level (<b>a</b>) and the observed yields (<b>b</b>) in 2010.</p> "> Figure 7
<p>Spatial distribution of uncertainty (i.e., nRMSE, %) in GlobalWheatYield4km.</p> "> Figure 8
<p>Subnational-level comparisons between observed yields and estimated yields of SPAM (<b>a1</b>–<b>a3</b>) or GlobalWheatYield4km (<b>b1</b>–<b>b3</b>) for 2000 (<b>a1</b>,<b>b1</b>), 2005 (<b>a2</b>,<b>b2</b>), and 2010 (<b>a3</b>,<b>b3</b>).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Remote Sensing Data
2.2.2. Wheat Harvesting Area and Yield
2.2.3. Environmental Data
2.3. Methods
2.3.1. Identifying the Spatial Distribution of Wheat
2.3.2. Estimating Gridded Yield Using Data-Driven Models
2.3.3. Uncertainty Analysis
2.3.4. Comparison with Other Global Yield Datasets
3. Results
3.1. Assessing Accuracy of Wheat Distribution Maps
3.2. Selecting the Optimal Model of Wheat Yield Estimates
3.3. Comparing GlobalWheatYield4km with SPAM
4. Discussion
4.1. Advantages of GlobalWheatYield4km
4.2. Uncertainties
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Type | Data Product Name | Spatial Resolution | Temporal Resolution | Purposes | Reference |
---|---|---|---|---|---|
Satellite data | NOAA CDR AVHRR NDVI | 0.05° | 1981–2021 | Extracting predictor variable NDVI | [48] |
GLASS LAI | 1 km | 2005–2015 | Identifying phenological characteristics of wheat | http://glass-product.bnu.edu.cn/?pid=3&c=1, accessed on 19 June 2024 | |
GFSAD1KCM | 1 km | 2010 | Deriving cropland mask | [51] | |
Wheat harvesting area and yield | Agricultural census data | - | 1981–2020 | Training and validating yield estimation model | See Table S1 |
ChinaCropArea1km | 1 km | 2000–2015 | Extracting wheat growing areas in China | [52] | |
Environmental data | TerraClimate | 4 km | 1981–2021 | Extracting predictor variables including Tmin, Tmax, Pre, Vap, Vpd, Pet, Soil, Pdsi, Srad | [53] |
HWSD | 0.00833° | - | Extracting predictor variables including bulk density, organic carbon, pH, gravel, clay, sand and silt fraction | [54] |
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Zhang, Z.; Luo, Y.; Han, J.; Xu, J.; Tao, F. Estimating Global Wheat Yields at 4 km Resolution during 1982–2020 by a Spatiotemporal Transferable Method. Remote Sens. 2024, 16, 2342. https://doi.org/10.3390/rs16132342
Zhang Z, Luo Y, Han J, Xu J, Tao F. Estimating Global Wheat Yields at 4 km Resolution during 1982–2020 by a Spatiotemporal Transferable Method. Remote Sensing. 2024; 16(13):2342. https://doi.org/10.3390/rs16132342
Chicago/Turabian StyleZhang, Zhao, Yuchuan Luo, Jichong Han, Jialu Xu, and Fulu Tao. 2024. "Estimating Global Wheat Yields at 4 km Resolution during 1982–2020 by a Spatiotemporal Transferable Method" Remote Sensing 16, no. 13: 2342. https://doi.org/10.3390/rs16132342
APA StyleZhang, Z., Luo, Y., Han, J., Xu, J., & Tao, F. (2024). Estimating Global Wheat Yields at 4 km Resolution during 1982–2020 by a Spatiotemporal Transferable Method. Remote Sensing, 16(13), 2342. https://doi.org/10.3390/rs16132342