Comparison of Two Synergy Approaches for Hybrid Cropland Mapping
Abstract
:1. Introduction
2. Principles of the two synergy methods
2.1. Geographical Weighted Regression
2.2. Modified Fuzzy Agreement Scoring
3. Data and Experiment Design
3.1. Data and processing
3.2. Experiment Description
3.2.1. Synergy Cropland Mapping with Various Training Sample Sizes
3.2.2. Synergy Cropland Mapping with Different Satellite-Based Maps
3.2.3. Synergy Cropland Mapping with Various Landscapes
3.3. Performance Assessment
4. Results
4.1. Influence of Training Samples
4.2. Influence of Satellite-Based Maps
4.3. Influence of Various Landscapes
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Cropland Maps | Overall Accuracy (%) | R2 between Maps and the Cropland Percentage | R2 between Maps and the Cropland Area Statistics |
---|---|---|---|
Unified Cropland | 81.18 | 0.68 | 0.79 |
GlobeLand30 | 77.76 | 0.60 | 0.80 |
NLUD-C | 76.76 | 0.55 | 0.83 |
MODIS Collection5 | 76.58 | 0.38 | 0.74 |
CCI-LC | 75.69 | 0.36 | 0.58 |
MODIS Cropland | 71.86 | 0.27 | 0.44 |
GlobCover 2009 | 69.50 | 0.23 | 0.38 |
Samples 1 | Samples 2 | Samples 3 | Samples 4 | Samples 5 | Samples 6 | Samples 7 | |
---|---|---|---|---|---|---|---|
Proportion of total training sample | 90% | 70% | 50% | 30% | 10% | 5% | 1% |
Cropland training samples | 1777 | 1383 | 969 | 574 | 176 | 92 | 15 |
Noncropland training samples | 1783 | 1386 | 1009 | 613 | 220 | 106 | 25 |
Validation samples | Cropland: 847 Noncropland: 848 | ||||||
Input datasets combination | #1, #2, #3 |
Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | Group 6 | Group 7 | |
---|---|---|---|---|---|---|---|
Input map combination | #1, #2, #3 | #1, #4, #5 | #1, #4, #6 | #2, #5, #6 | #2, #3, #7 | #3, #5, #7 | #5, #6, #7 |
Average accuracy (%) | 78.57 | 77.82 | 76.54 | 75.10 | 74.67 | 73.98 | 72.35 |
Training samples | Cropland: 1953 Noncropland: 2003 Total: 3956 | ||||||
Validation samples | Cropland: 847 Noncropland: 848 |
Test 1 | Test 2 | Test 3 | Test 4 | Test 5 | ||
---|---|---|---|---|---|---|
Province | Jiangsu | Anhui | Henan | Shanxi | Yunnan | |
Landscape | Plain | Hill | Low mountain | Medium mountain | High mountain | |
Average DEM (m) | 13.26 | 119.01 | 247.59 | 1160.68 | 1889.64 | |
Validation samples | Cropland | 74 | 70 | 70 | 60 | 35 |
Noncropland | 26 | 30 | 30 | 40 | 65 |
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Chen, D.; Lu, M.; Zhou, Q.; Xiao, J.; Ru, Y.; Wei, Y.; Wu, W. Comparison of Two Synergy Approaches for Hybrid Cropland Mapping. Remote Sens. 2019, 11, 213. https://doi.org/10.3390/rs11030213
Chen D, Lu M, Zhou Q, Xiao J, Ru Y, Wei Y, Wu W. Comparison of Two Synergy Approaches for Hybrid Cropland Mapping. Remote Sensing. 2019; 11(3):213. https://doi.org/10.3390/rs11030213
Chicago/Turabian StyleChen, Di, Miao Lu, Qingbo Zhou, Jingfeng Xiao, Yating Ru, Yanbing Wei, and Wenbin Wu. 2019. "Comparison of Two Synergy Approaches for Hybrid Cropland Mapping" Remote Sensing 11, no. 3: 213. https://doi.org/10.3390/rs11030213
APA StyleChen, D., Lu, M., Zhou, Q., Xiao, J., Ru, Y., Wei, Y., & Wu, W. (2019). Comparison of Two Synergy Approaches for Hybrid Cropland Mapping. Remote Sensing, 11(3), 213. https://doi.org/10.3390/rs11030213