Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources
Abstract
:1. Introduction
2. Methods and Datasets
2.1. Study Area
2.2. Data Sources
2.2.1. Climate Data
2.2.2. Soil Data
2.2.3. Topography Data
2.2.4. Irrigation Mask Data
2.2.5. Maize Yield Data
2.2.6. Date Processing
2.3. Experiments Design
2.3.1. Description for the Gridded Crop Model
2.3.2. Random Forest
2.3.3. Feature Engineering and Feature Importance
2.3.4. Calculation of Regional Maize Yield Gap
2.4. Metrics for Model Performance Evaluations
3. Results
3.1. Performance of Simulated Maize Yield Downscaling Model
3.1.1. General Performance of Downscaling Model and Spatial Patterns of Maize Yield
3.1.2. Feature Importance from Maize Yield Simulations
3.2. Evaluation of County-Level Maize Yields in China
3.3. Evaluation of County-Level Maize Yield Gap in China
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Variable Descriptions |
---|---|
Climate variables (VARs) | |
TMAX | Maximum temperature (°C) |
TMIN | Minimum temperature (°C) |
TAVG | Average temperature (°C) |
PRATE | Total precipitation (mm) |
SRAD | Solar radiation (MJ/m2) |
PET | Potential evapotranspiration (mm) |
WS | Wind speed (m/s) |
VPD | Vapor pressure deficit (h PA) |
Temporal aggregates of climate variables | |
VAR_X | Monthly value for month X in calendar year (e.g., “TMAX_1”) |
VARsumGS | Sum of climate variables in growing season (e.g., “TMAXsumGS”) |
VARavgGS | Average of climate variables in growing season (e.g., “TMAXavgGS”) |
Soil and topography variables | |
DEPTH | Total soil depth (m) |
OC | Organic carbon content in topsoil (%) |
SAND | Sand content in topsoil (%) |
SB | Sum of bases in topsoil (cmol/kg) |
ROK | Coarse fragment (rock) content in topsoil (%) |
CLAY | Clay content in topsoil (%) |
EC | Electric conductivity in topsoil (mmho/cm) |
BD | Bulk density in topsoil (g/cm3) |
CEC | Cation exchange capacity in topsoil (cmol/kg) |
PH | pH in topsoil |
CARB | Carbonate content in topsoil (%) |
DEM | Digital elevation model |
Target variables | |
YIELD | Simulated maize yield (t/ha) |
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Zou, Y.; Kattel, G.R.; Miao, L. Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources. Remote Sens. 2024, 16, 701. https://doi.org/10.3390/rs16040701
Zou Y, Kattel GR, Miao L. Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources. Remote Sensing. 2024; 16(4):701. https://doi.org/10.3390/rs16040701
Chicago/Turabian StyleZou, Yangfeng, Giri Raj Kattel, and Lijuan Miao. 2024. "Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources" Remote Sensing 16, no. 4: 701. https://doi.org/10.3390/rs16040701