Large-Scale Maize Condition Mapping to Support Agricultural Risk Management
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Remote Sensing-Based Vegetation Indices
2.3. Maize Area Classification
2.4. Crop Condition Mapping Workflow
2.5. Crop Yield Data
2.6. Damage Claim Data from the Hungarian Agricultural Risk Management System (ARMS)
2.7. Post-Processing of Remote Sensing Data and Statistical Analysis
2.7.1. Phenology Reconstruction
2.7.2. Parcel-Level Analysis
2.7.3. Country-Level Analysis
3. Results
3.1. Characteristic Maize Profiles Based on the Different Vis
3.2. Parcel-Level Crop Condition Mapping and Yield Prediction
3.3. Country-Level Crop Condition Mapping
3.4. Relationship Between the Claim Data and the Crop Condition Mapping
4. Discussion
4.1. Analysis of Maize Profiles/Remote Sensing of Crop Production/Growth
4.2. Fine-Scale Crop Condition Mapping and Yield Prediction
4.3. Country-Level Crop Condition Mapping and Yield Prediction
4.4. Assessment of Damage Claims
4.5. Limitations of the Study
5. Conclusions and Outlook
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parcel | Area (ha) | Number of Valid Yield Data Points | Date of Sowing | Date of Harvesting | Maximum Yield (t/ha) |
---|---|---|---|---|---|
AS1 | 2.51 | 1574 | 24 April 2023 | 24 October 2023 | 15 |
AF1 | 6.58 | 4157 | 24 April 2023 | 24 October 2023 | 16 |
D5 | 21.50 | 12,832 | 12 April 2023 | 14 October 2023 | 13 |
R1 | 51.71 | 31,564 | 26 April 2023 | 16 October 2023 | 15 |
AN1 | 60.34 | 37,613 | 24 April 2023 | 19 October 2023 | 13 |
H3 | 70.97 | 40,602 | 13 April 2023 | 9 October 2023 | 15 |
Breakpoints Based on Harvester-Derived Yield | |||||||
Name of Categories | y1 | y2 | y3 | y4 | y5 | y6 | |
Number of Categories | 2 | 0 | 8.5 | ||||
3 | 0 | 7.7 | 9 | ||||
4 | 0 | 7.1 | 8.5 | 9.2 | |||
5 | 0 | 6.6 | 8 | 8.8 | 9.4 | ||
6 | 0 | 6.1 | 7.7 | 8.5 | 9 | 9.4 | |
Breakpoints Based on County (NUTS3-Level) Mean Yields (2000–2024) [t/ha] | |||||||
Name of Categories | y1 | y2 | y3 | y4 | y5 | y6 | |
Number of Categories | 2 | 0 | 7.3 | ||||
3 | 0 | 7.1 | 8.4 | ||||
4 | 0 | 5.7 | 7.9 | 9.0 | |||
5 | 0 | 5.0 | 7.3 | 8.1 | 9.2 | ||
6 | 0 | 4.9 | 7.1 | 7.9 | 8.4 | 9.2 |
Actual Yield Category | |||||||
---|---|---|---|---|---|---|---|
y1 | y2 | y3 | y4 | y5 | y6 | ||
Predicted Yield Category | y1 | 708 | 84 | 30 | 11 | 12 | 8 |
y2 | 145 | 559 | 154 | 57 | 40 | 29 | |
y3 | 30 | 221 | 546 | 307 | 135 | 75 | |
y4 | 16 | 99 | 215 | 416 | 270 | 184 | |
y5 | 13 | 16 | 46 | 109 | 213 | 152 | |
y6 | 16 | 62 | 77 | 183 | 417 | 680 |
(a) | Number of Categories | Cross Validation Accuracy | Cross Validation F1 Score | Order of Importance to 90% (Date) | ||||||
2 | 80.03 | 0.80 | 11 July | 23 July | 22 August | 22 May | 27 August | 31 July | ||
3 | 68.52 | 0.69 | 11 July | 23 July | 27 August | 22 August | 31 July | 22 May | ||
5 | 50.75 | 0.50 | 23 July | 11 July | 27 August | 31 July | 22 August | 22 May | 9 May | |
6 | 44.53 | 0.43 | 23 July | 11 July | 31 July | 27 August | 22 August | 22 May | 9 May | |
(b) | Number of Categories | Cross Validation Accuracy | Cross Validation F1 Score | Order of Importance to 90% (Date) | ||||||
2 | 84.56 | 0.74 | 23 July | 11 July | 22 May | 22 August | 31 July | 9 May | ||
3 | 72.99 | 0.68 | 23 July | 11 July | 31 July | 22 August | 22 May | 27 August | 9 May | |
5 | 55.83 | 0.51 | 23 July | 11 July | 31 July | 27 August | 22 August | 22 May | 26 June | |
6 | 48.57 | 0.42 | 23 July | 11 July | 27 August | 31 July | 22 August | 22 May | 9 May |
Used Data | 0 | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|---|
2017 | 0.239 | 0.005 | 0 | 0.322 | 0.048 | 0.229 |
2022 | 0.306 | 0.664 | 0.513 | 0.567 | 0.208 | 0.612 |
2023 | 0.048 | 0.232 | 0.003 | 0 | 0 | 0.021 |
2017, 2022 | 0.695 | 0.648 | 0.458 | 0.227 | 0.016 | 0.814 |
2017, 2023 | 0.077 | 0.002 | 0.005 | 0.089 | 0.001 | 0.134 |
2022, 2023 | 0.717 | 0.473 | 0.267 | 0.151 | 0.026 | 0.888 |
2017, 2022, 2023 | 0.640 | 0.419 | 0.255 | 0.093 | 0.011 | 0.797 |
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Birinyi, E.; Kristóf, D.; Hollós, R.; Barcza, Z.; Kern, A. Large-Scale Maize Condition Mapping to Support Agricultural Risk Management. Remote Sens. 2024, 16, 4672. https://doi.org/10.3390/rs16244672
Birinyi E, Kristóf D, Hollós R, Barcza Z, Kern A. Large-Scale Maize Condition Mapping to Support Agricultural Risk Management. Remote Sensing. 2024; 16(24):4672. https://doi.org/10.3390/rs16244672
Chicago/Turabian StyleBirinyi, Edina, Dániel Kristóf, Roland Hollós, Zoltán Barcza, and Anikó Kern. 2024. "Large-Scale Maize Condition Mapping to Support Agricultural Risk Management" Remote Sensing 16, no. 24: 4672. https://doi.org/10.3390/rs16244672
APA StyleBirinyi, E., Kristóf, D., Hollós, R., Barcza, Z., & Kern, A. (2024). Large-Scale Maize Condition Mapping to Support Agricultural Risk Management. Remote Sensing, 16(24), 4672. https://doi.org/10.3390/rs16244672