Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products
<p>The location map of the study areas in Shandong and Hebei, China (false color composite GF-1 images acquired on 15 and 20 August 2014, R/G/B vs. band 4/band 3/band 2).</p> "> Figure 2
<p>Flowchart of the aboveground biomass and yield mapping process.</p> "> Figure 3
<p>Time-series Normalized Difference Vegetation Index (NDVI) profiles plotted according to the field surveys: (<b>a</b>) Time-series NDVI profile of winter wheat at Site 1; and (<b>b</b>) Time-series NDVI profile of winter wheat at Site 2.</p> "> Figure 4
<p>Per-pixel comparisons between the observed and predicted PROBA-V 100-m reflectance. The 1:1 line is blue, and the regression line is black.</p> "> Figure 4 Cont.
<p>Per-pixel comparisons between the observed and predicted PROBA-V 100-m reflectance. The 1:1 line is blue, and the regression line is black.</p> "> Figure 5
<p>Comparison between the observed and predicted PROBA-V 100-m NDVI and LSWI. The 1:1 line is blue, and the regression line is black.</p> "> Figure 6
<p>The NDVI profiles of the 10 classes: (<b>a</b>) Profiles of the predicted data at Site 1; and (<b>b</b>) Profiles of the predicted data at Site 2.</p> "> Figure 7
<p>Classification results of the blended time-series 100-m data over the study areas: (<b>a</b>) Site 1; and (<b>b</b>) Site 2.</p> "> Figure 8
<p>Classification results of the time-series 300-m data over the study areas: (<b>a</b>) Site 1; and (<b>b</b>) Site 2.</p> "> Figure 9
<p>Comparisons between the measured and estimated winter wheat biomass: (<b>a</b>) With the 100-m data at Site 1; (<b>b</b>) With the 100-m data at Site 2; (<b>c</b>) With the 300-m data at Site 1; and (<b>d</b>) With the 300-m data at Site 2.</p> "> Figure 10
<p>Comparisons between the measured and estimated winter wheat yields: (<b>a</b>) Site 1; and (<b>b</b>) Site 2.</p> "> Figure 11
<p>The estimated biomass maps of the blended data in mid-May: (<b>a</b>) Site 1; and (<b>b</b>) Site 2.</p> "> Figure 12
<p>The estimated yield maps of the blended data: (<b>a</b>) Site 1; and (<b>b</b>) Site 2.</p> "> Figure 13
<p>Comparison between the Copernicus FPAR and our estimated FPAR: (<b>a</b>) In mid-March; (<b>b</b>) In mid-April; and (<b>c</b>) In mid-May.</p> ">
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data Sources
2.2.1. Field Measured Data
2.2.2. Satellite Data
2.2.3. Meteorological Data
3. Methods
3.1. Daily 100-m Reflectance Dataset Generation
3.2. Crop Identification Based on Time-Series NDVI Clustering
3.3. Algorithms for Biomass and Yield Estimation
3.4. Results Evaluation Strategy
3.4.1. Evaluation of the Data Fusion Result
3.4.2. Assessment of Biomass and Yield Estimation
4. Results
4.1. The ESTARFM Prediction Results
4.2. Generation of Winter Wheat Maps
4.3. Mapping the Biomass and Yield
5. Discussion
5.1. Data Fusion Methods
5.2. Mixed Pixels
5.3. LUE
5.4. FPAR
5.5. Meteorological Data
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Study Sites | Crop Type | Sample Date | Growing Stage | Number (N) |
---|---|---|---|---|
Site 1 | Winter WHEAT | 16 April 2015 | Booting | 25 |
17 May 2015 | Flowering | 25 | ||
4 June 2015 | Harvest | 25 | ||
Site 2 | Winter Wheat | 15 April 2015 | Booting | 15 |
17 May 2015 | Flowering | 15 | ||
6 June 2015 | Harvest | 15 |
Crop Types | Site 1 | Site 2 | ||
---|---|---|---|---|
Date | Good Pixels Ratio | Date | Good Pixels Ratio | |
Winter wheat | 2 November 2014 | 100% | 2 November 2014 | 100% |
13 December 2014 | 100% | 13 December 2014 | 100% | |
18 December 2014 | 100% | 31 December 2014 | 100% | |
22 December 2014 | 100% | 9 January 2015 | 100% | |
31 December 2014 | 100% | 10 February 2015 | 100% | |
19 January 2015 | 99% | 27 March 2015 | 100% | |
10 February 2015 | 100% | 10 April 2015 | 100% | |
28 March 2015 | 98% | 15 April 2015 | 100% | |
10 April 2015 | 99% | 12 May 2015 | 97% | |
15 April 2015 | 98% | 25 May 2015 | 99% | |
7 May 2015 | 98% | 30 May 2015 | 100% | |
16 May 2015 | 100% | - | - | |
30 May 2015 | 98% | - | - |
Parameter | Description | Value | Unit |
---|---|---|---|
the maximum light use efficiency | 2.54 | g·MJ−1 PAR | |
R | proportion of aboveground productivity | 0.90 | dimensionless |
HI | the harvest index | 0.45 | dimensionless |
Indicators | Formula |
---|---|
Determination coefficient (R2) | |
Root mean square errors (RMSE) | |
Relative RMSE (RRMSE) | |
Average absolute deviation (AAD) | |
Average deviation (AD) |
Sites | Date (100-m) | Date (300-m) | Band | R2 | RMSE | RRMSE | AAD | AD |
---|---|---|---|---|---|---|---|---|
Site 1 | 18 December 2014 | 18 December 2014 | RED | 0.804 | 0.0083 | 0.0775 | 0.0650 | 9.5 × 10−8 |
NIR | 0.899 | 0.0132 | 0.0536 | 0.0099 | 4.8 × 10−7 | |||
SWIR | 0.895 | 0.0062 | 0.0278 | 0.0047 | 3.6 × 10−7 | |||
Site 1 | 22 December 2014 | 22 December 2014 | RED | 0.760 | 0.0089 | 0.0838 | 0.0068 | 1.6 × 10−7 |
NIR | 0.860 | 0.0145 | 0.0624 | 0.0108 | 3.9 × 10−7 | |||
SWIR | 0.880 | 0.0061 | 0.0281 | 0.0046 | 9.6 × 10−8 | |||
Site 1 | 15 April 2015 | 15 April 2015 | RED | 0.893 | 0.0109 | 0.1238 | 0.0082 | 1.8 × 10−7 |
NIR | 0.917 | 0.0172 | 0.0482 | 0.0130 | 4.7 × 10−7 | |||
SWIR | 0.939 | 0.0082 | 0.0412 | 0.0056 | 1.2 × 10−6 | |||
Site 1 | 7 May 2015 | 7 May 2015 | RED | 0.931 | 0.0111 | 0.1432 | 0.0082 | 2.0 × 10−7 |
NIR | 0.915 | 0.0175 | 0.0464 | 0.0130 | 9.4 × 10−7 | |||
SWIR | 0.923 | 0.0086 | 0.0595 | 0.0061 | 3.0 × 10−7 | |||
Site 2 | 31 December 2014 | 31 December 2014 | RED | 0.900 | 0.0055 | 0.0522 | 0.0042 | 8.8 × 10−8 |
NIR | 0.941 | 0.0094 | 0.0308 | 0.0070 | 1.4 × 10−7 | |||
SWIR | 0.948 | 0.0097 | 0.0224 | 0.0067 | 3.5 × 10−7 | |||
Site 2 | 15 April 2015 | 15 April 2015 | RED | 0.930 | 0.0112 | 0.1365 | 0.0081 | 5.3 × 10−8 |
NIR | 0.932 | 0.0181 | 0.0443 | 0.0134 | 3.9 × 10−8 | |||
SWIR | 0.964 | 0.0074 | 0.0356 | 0.0051 | 9.4 × 10−8 | |||
Site 2 | 25 May 2015 | 25 May 2015 | RED | 0.712 | 0.0175 | 0.1813 | 0.0122 | 3.1 × 10−7 |
NIR | 0.503 | 0.0190 | 0.0592 | 0.0137 | 4.3 × 10−6 | |||
SWIR | 0.910 | 0.0115 | 0.0629 | 0.0082 | 3.8 × 10−7 |
Sites | Date (100-m) | Date (300-m) | Indices | R2 | RMSE | AAD | AD |
---|---|---|---|---|---|---|---|
Site 1 | 18 December 2014 | 18 December 2014 | NDVI | 0.878 | 0.0447 | 0.0344 | 5.9 × 10−7 |
LSWI | 0.892 | 0.0285 | 0.0203 | 4.3 × 10−8 | |||
Site 1 | 22 December 2014 | 22 December 2014 | NDVI | 0.846 | 0.0379 | 0.0496 | 2.4 × 10−7 |
LSWI | 0.840 | 0.0329 | 0.0238 | 5.1 × 10−8 | |||
Site 1 | 15 April 2015 | 15 April 2015 | NDVI | 0.924 | 0.0473 | 0.0361 | 6.1 × 10−7 |
LSWI | 0.957 | 0.0293 | 0.0215 | 1.7 × 10−7 | |||
Site 1 | 7 May 2015 | 7 May 2015 | NDVI | 0.937 | 0.0423 | 0.0316 | 1.6 × 10−6 |
LSWI | 0.955 | 0.0281 | 0.0204 | 5.7 × 10−7 | |||
Site 2 | 31 December 2014 | 31 December 2014 | NDVI | 0.950 | 0.0274 | 0.0270 | 3.5 × 10−7 |
LSWI | 0.957 | 0.0213 | 0.0156 | 2.9 × 10−8 | |||
Site 2 | 15 April 2015 | 15 April 2015 | NDVI | 0.944 | 0.0453 | 0.0335 | 1.1 × 10−6 |
LSWI | 0.973 | 0.0256 | 0.0181 | 2.5 × 10−7 | |||
Site 2 | 25 May 2015 | 25 May 2015 | NDVI | 0.699 | 0.0703 | 0.0509 | 3.7 × 10−7 |
LSWI | 0.860 | 0.0407 | 0.0298 | 2.0 × 10−8 |
Class | 100-m | 300-m | ||
---|---|---|---|---|
Producer’s Accuracy | User’s Accuracy | Producer’s Accuracy | User’s Accuracy | |
Wheat | 86.96% | 81.63% | 72.83% | 65.05% |
Others | 82.69% | 87.76% | 65.38% | 73.91% |
Overall Accuracy: 84.69%; Kappa: 0.7198 | Overall Accuracy: 68.88%; Kappa: 0.3795 |
Class | 100-m | 300-m | ||
---|---|---|---|---|
Producer’s Accuracy | User’s Accuracy | Producer’s Accuracy | User’s Accuracy | |
Wheat | 80.95% | 73.91% | 69.05% | 63.04% |
Others | 76.62% | 83.1% | 66.88% | 72.54% |
Overall Accuracy: 78.57%; Kappa: 0.5708 | Overall Accuracy: 67.86%; Kappa: 0.3562 |
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zheng, Y.; Zhang, M.; Zhang, X.; Zeng, H.; Wu, B. Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products. Remote Sens. 2016, 8, 824. https://doi.org/10.3390/rs8100824
Zheng Y, Zhang M, Zhang X, Zeng H, Wu B. Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products. Remote Sensing. 2016; 8(10):824. https://doi.org/10.3390/rs8100824
Chicago/Turabian StyleZheng, Yang, Miao Zhang, Xin Zhang, Hongwei Zeng, and Bingfang Wu. 2016. "Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products" Remote Sensing 8, no. 10: 824. https://doi.org/10.3390/rs8100824