National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Software Tools and Processing Platforms
2.3. Methodology
2.3.1. Workflow Description
2.3.2. Definition of Cropland Extent
2.3.3. Data Preparation
Satellite Data
Ancillary Features
Collecting Training Data
2.3.4. Sentinel 10-Day Composite Images Construction
2.3.5. Smoothing and Phenological Metrics Extraction
2.3.6. Comparison to MODIS Collection 6 MCD12Q2 Data
2.3.7. Random Forest Machine Learning Algorithm
2.3.8. Validation of the Cropland Extent Maps
3. Results
3.1. Verification of the Vegetation Phenology Results
3.2. Phenological Feature Importance
3.3. Cropland Extent Map and Accuracy Assessment
3.4. Probability-Based Cropland Maps
3.5. Comparison with National Statistics
3.6. Cropland Dynamics in the Area
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Input Features | Short Description | Spatial Resolution | Reported Unit |
---|---|---|---|
Phenological | |||
SOS10 | Time when the left edge has increased to 10% of the amplitude | 10 m | days since 1-1-1970 |
SOS50 | Time when the left edge has increased to 50% of the amplitude | 10 m | days since 1-1-1970 |
SOS90 | Time when the left edge has increased to 90% of the amplitude | 10 m | days since 1-1-1970 |
EOS10 | Time when the right edge has decreased to 10% of the amplitude | 10 m | days since 1-1-1970 |
EOS50 | Time when the right edge has decreased to 50% of the amplitude | 10 m | days since 1-1-1970 |
EOS90 | Time when the right edge has decreased to 90% of the amplitude | 10 m | days since 1-1-1970 |
SOSV10 | EVI2 value at SOS10 | 10 m | EVI2 unit |
SOSV50 | EVI2 value at SOS50 | 10 m | EVI2 unit |
SOSV90 | EVI2 value at SOS90 | 10 m | EVI2 unit |
EOSV10 | EVI2 value at EOS10 | 10 m | EVI2 unit |
EOSV50 | EVI2 value at EOS50 | 10 m | EVI2 unit |
EOSV90 | EVI2 value at EOS90 | 10 m | EVI2 unit |
MOS | Time of the season on which EVI2 reaches the peak | 10 m | days since 1-1-1970 |
LOS50 | Time from the SOS10 to the EOS10 | 10 m | Number of days |
LOS50 | Time from the SOS50 to the EOS50 | 10 m | Number of days |
LOS90 | Time from the SOS90 to the EOS90 | 10 m | Number of days |
SINTG | The area under the EVI2 curve above the BVAL from the SOS10 to the EOS10 | 10 m | EVI2 unit |
LINTG | The area under the EVI2 curve from the SOS10 to the EOS10 | 10 m | EVI2 unit |
AMPL | Difference between the PEAK and BVAL | 10 m | EVI2 unit |
PEAK | Highest EVI2 value over the season | 10 m | EVI2 unit |
BVAL | Mean of minimum EVI2 values before SOS10 and after EOS10 | 10 m | EVI2 unit |
Environmental | |||
Slope | Terrain slope derived from the Shuttle Radar Topography Mission | 30 m | degrees |
Rainfall | Mean annual rainfall from WorldClim climatic data | 1000 m | Millimeter (mm) |
Temperature | Mean annual temperature from WorldClim climatic data | 1000 m | °C × 10 |
Binary mask | |||
Forest mask | Derived using Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) | 25 m | - |
Water mask | Derived using the Hansen global forest change product | 30 m | - |
Crop | Non-Crop | Total | User’s Accuracy | F1-Score | ||
---|---|---|---|---|---|---|
Crop | 5848 | 128 | 5990 | 97.85% | 98.32% | |
Non-crop | 71 | 3271 | 3328 | 97.85% | 97.04% | |
Total | 5919 | 3399 | 9318 | |||
Producer’s accuracy | 98.80% | 96.23% | ||||
Overall accuracy = 97.86% Kappa coefficient = 0.9537 |
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Htitiou, A.; Boudhar, A.; Chehbouni, A.; Benabdelouahab, T. National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine. Remote Sens. 2021, 13, 4378. https://doi.org/10.3390/rs13214378
Htitiou A, Boudhar A, Chehbouni A, Benabdelouahab T. National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine. Remote Sensing. 2021; 13(21):4378. https://doi.org/10.3390/rs13214378
Chicago/Turabian StyleHtitiou, Abdelaziz, Abdelghani Boudhar, Abdelghani Chehbouni, and Tarik Benabdelouahab. 2021. "National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine" Remote Sensing 13, no. 21: 4378. https://doi.org/10.3390/rs13214378