Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China
"> Figure 1
<p>The location of study area. (<b>a</b>) The main winter wheat producing provinces (black line) and counties (orange line), which includes ~76% of the total wheat yield in China; the green shading refers to winter wheat cropping areas. (<b>b</b>) The scope of the China and provincial boundaries (gray line).</p> "> Figure 2
<p>A typical example to illustrate the differences in spatial and temporal patterns of data from Options 1–3. Option 1 uses raw county-level data (the red dashed line shows its linear fit) (<b>a</b>); Option 2 removes the linear trend (the red line in Option 1) from the raw data (<b>b</b>), and Option 3 adds the multi-year average of the raw county-level data into the Option 2 data (<b>c</b>); (<b>e</b>)–(<b>g</b>) express the spatial patterns of the three types of yields corresponding to each Option.</p> "> Figure 3
<p>Fifteen-year-averaged (2001–2015) spatial patterns after normalization of crop yield (<b>a</b>), the satellite-based variables (<b>b</b>–<b>d</b>), climate variables (<b>e</b>–<b>i</b>), socio-economic factors (<b>j</b>–<b>n</b>) and for all counties in the study region. Note: all of the data have a mean of zero and standard deviation of one; b–i are based on March of every year.</p> "> Figure 4
<p>The model performances (predicted R2) of the three methods separated by the three Options with different inputs for the entire growing season. (<b>a</b>) “Raw”; (<b>b</b>) “Detrend”; and (<b>c</b>) “Detrend+Mean”. The blue color is for RR, green for RF and red for LightGBM. The error bars are one standard deviation of predicted R<sup>2</sup> from 100 ensembles by randomly dividing training and testing datasets.</p> "> Figure 5
<p>(<b>a</b>) The model performance (predicted R<sup>2</sup>) using VIs during the whole growing season and climate data during a specific stage, either for Early (Oct. and Feb.), or Peak (Mar. and Apr.), or Late stage (Jun. and Jul.). The dashed line in (<b>a</b>) represents the benchmark model performance by only using VIs. (<b>b</b>) The model performance (predicted R<sup>2</sup>) using the climate data during the whole growing season and VIs during a specific stage (the same stage in (a)). The dashed line in (<b>b</b>) represents the benchmark model performance by only using climate data. The error bars are one standard deviation of predicted R<sup>2</sup> from 100 ensembles by randomly dividing training and testing datasets.</p> "> Figure 6
<p>The temporal progression of the model performance based on the three methods (RR, RF, and LightGBM). The left panel shows the temporal progress of model performance according to each month (i.e., the prediction at any specific month contains input data covering the period from the beginning of the growing season to that specific month, thus the later period contains more inputs and usually has a higher performance). Blue refers to the model performance of using input sources including climate data and VIs; orange for VIs only inputted; and gray for climate data only. The right panel shows the differences of model performance between combined input sources and VIs only (a blue column indicates the benefits from VIs, calculated by subtracting the orange line from the blue line in the corresponding left panel); and the differences between VI + climate and climate only (a gray column indicates the benefits from VIs, calculated by subtracting the gray line from the blue line in the corresponding panel).</p> "> Figure 7
<p>The model performance (predicted R<sup>2</sup>) after including spatial information and soil properties; the green and red columns mean the R<sup>2</sup> after including the soil properties and spatial information in the benchmark model (blue color), respectively. The error bars are one standard deviation of predicted R<sup>2</sup> from 100 ensembles obtained by randomly dividing training and testing datasets.</p> "> Figure 8
<p>The results of the “leave-one-year-out” experiment across different years. One-year data are selected for testing, while data from other years for training. The worst performance in 2002 and 2007 may be due to extreme events (black for RR, red for RF, and green for LightGBM model).</p> "> Figure A1
<p>The area of each statistical province from 2001 to 2015.</p> "> Figure A2
<p>The delta value of prediction R2 (Option 3-Option 2)</p> ">
Abstract
:1. Introduction
2. Material and Methods
2.1. Study Area
2.2. Dataset and Preprocessing
2.2.1. Crop Yield and Area
2.2.2. Satellite Data
2.2.3. Climate Data
2.2.4. Socio-Economic Factor
2.2.5. Other Datasets
2.3. Methods
2.3.1. Ridge Regression (RR)
2.3.2. Random Forest (RF)
2.3.3. Light Gradient Boosting Machine (LightGBM)
2.4. Experiment Design
2.4.1. The First Experiment to Separate the Spatial and Temporal Variations of Yields and Combine the Explanatory Ones Differently
2.4.2. The Second Experiment to Quantify the Contributions of Time Series Data to Yield Prediction
2.4.3. The Third Experiment to Investigate the Values of Static Variables on Yield Prediction, and to Validate the Model Performances
3. Results
3.1. Exploratory Data Analysis (EDA)
3.2. The Performances of Multi-Models for Predicting Wheat Yield
3.3. Quantifying Unique and Shared Information from Climate and VIs
3.4. The Effects of Spatial Information and Soil Properties on Improving Yield Estimation
4. Discussion
4.1. The Best Combinations of Explanatory Variables to Explain Spatial or Temporal Variability of Wheat Yield
4.2. The Unique and Shared Contributions of Different Data Sources for Predicting Crop Yield
4.3. Method Comparison
4.4. Some Limitations of this Study
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
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Category | Variables | Spatial Resolution | Temporal Resolution | Available Records | References |
---|---|---|---|---|---|
Crop yield and area | Crop yield | County-level | Yearly | 2001-2015 | http://www.stats.gov.cn |
Crop area | 1 km | Five-year | 2005, 2010, 2015 | [45,46] | |
Satellite data | MOD09A1 | 500 m | 8-day | 2001–2018 | MODIS MOD09A1 |
DEM | 90 m | 2000 | 2000 | SRTM3 V4.1 | |
Climate data | Tmin, Tmax, Pre, Vpd, and Vap | ~4 km | Monthly | 1958–2018 | [51] |
Socio-economic factors | CAP, CCF, ECRA, IA, and TPAM | County-level | Yearly | 2001–2015 | http://www.stats.gov.cn |
Soil properties data | soil depth, soil texture, organic carbon content, pH, cation exchange capacity, and bulk density | 0.00833 (~1 km) | 2012 | 2012 | [52] |
Tmax | Tmin | Pre | Vap | Vpd | CAP | CCF | ECRA | IA | TPAM | NDVI | GCVI | EVI | Yield | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Tmax | 1.00 | 0.78 | 0.33 | 0.58 | 0.35 | −0.09 | −0.16 | −0.14 | −0.19 | −0.19 | 0.48 | 0.41 | 0.45 | 0.03 (**) |
Tmin | - | 1.00 | 0.67 | 0.90 | −0.22 | −0.05 | −0.15 | −0.14 | −0.11 | −0.16 | 0.49 | 0.43 | 0.43 | −0.08 (***) |
Pre | - | - | 1.00 | 0.82 | −0.58 | −0.06 | −0.21 | −0.16 | −0.14 | −0.19 | 0.32 | 0.28 | 0.20 | −0.40 (***) |
Vap | - | - | - | 1.00 | −0.52 | −0.06 | −0.19 | −0.19 | −0.11 | −0.20 | 0.42 | 0.38 | 0.33 | −0.23 (***) |
Vpd | - | - | - | - | 1.00 | −0.01 | 0.03 | 0.03 | −0.09 | 0.01 | 0.01 | 0.00 | 0.09 | 0.29 (***) |
CAP | - | - | - | - | - | 1.00 | 0.42 | 0.08 | 0.37 | 0.42 | 0.05 | 0.04 | 0.05 | 0.10 (***) |
CCF | - | - | - | - | - | - | 1.00 | 0.57 | 0.83 | 0.82 | −0.12 | −0.13 | −0.09 | 0.16 (***) |
ECRA | - | - | - | - | - | - | - | 1.00 | 0.46 | 0.50 | −0.09 | −0.09 | −0.07 | 0.12 (***) |
IA | - | - | - | - | - | - | - | - | 1.00 | 0.74 | −0.16 | −0.16 | −0.13 | 0.09 (***) |
TPAM | - | - | - | - | - | - | - | - | - | 1.00 | −0.10 | −0.10 | −0.07 | 0.16(***) |
NDVI | - | - | - | - | - | - | - | - | - | - | 1.00 | 0.99 | 0.97 | 0.31 (***) |
GCVI | - | - | - | - | - | - | - | - | - | - | - | 1.00 | 0.97 | 0.39 (***) |
EVI | - | - | - | - | - | - | - | - | - | - | - | - | 1.00 | 0.33 (***) |
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Cao, J.; Zhang, Z.; Tao, F.; Zhang, L.; Luo, Y.; Han, J.; Li, Z. Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China. Remote Sens. 2020, 12, 750. https://doi.org/10.3390/rs12050750
Cao J, Zhang Z, Tao F, Zhang L, Luo Y, Han J, Li Z. Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China. Remote Sensing. 2020; 12(5):750. https://doi.org/10.3390/rs12050750
Chicago/Turabian StyleCao, Juan, Zhao Zhang, Fulu Tao, Liangliang Zhang, Yuchuan Luo, Jichong Han, and Ziyue Li. 2020. "Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China" Remote Sensing 12, no. 5: 750. https://doi.org/10.3390/rs12050750