Remote Sensing Drought Monitoring and Assessment in Southwestern China based on Machine Learning
Due to the complexity of drought and the diversity of influencing factors， the accurate monitoring of drought still faces many problems， especially the increasing frequency and aggravation of drought in Southwestern China， and the formation and disaster causing process have certain particularity.How...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Science Press, PR China
2022-12-01
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| Series: | Gaoyuan qixiang |
| Subjects: | |
| Online Access: | http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2022.00006 |
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| Summary: | Due to the complexity of drought and the diversity of influencing factors， the accurate monitoring of drought still faces many problems， especially the increasing frequency and aggravation of drought in Southwestern China， and the formation and disaster causing process have certain particularity.However， the traditional drought monitoring methods cannot meet the requirements of regional drought monitoring， so more scientific monitoring methods and means are needed.Since machine learning can comprehensively consider a variety of disaster causing factors to establish a comprehensive drought monitoring model， it undoubtedly provides a new technical means for drought monitoring.Therefore， this paper used multi-source remote sensing data from 2010 -2019 and meteorological station data from 1980-2019 to first construct a random forest monitoring model to reconstruct and supplement the surface temperature in Southwestern China， and then constructed XGBoost monitoring model to monitor， evaluate and validate the drought in Southwestern China.The results showed that： （1） The correlation coefficients between the training set and test set of the random forest model and the actual surface temperature of the stations exceeded 0.9， which reached a significant correlation.The spatial distribution of LST reconstruction values was similar to that of remote sensing monitoring values， and the values were close to the observed values of meteorological stations.（2） The correlation coefficients between the monitoring values of XGBoost model training set and test set and the SPEI calculated values at the stations were more than 0.86， with significant correlation.The overall consistency rate of drought grade between the monitored value and the calculated SPEI values exceeded 85%.（3） The overall consistent rate between the monitored values of the XGBoost model and the MCI values was above 67.88%， which was more consistent.The consistent rate of all months exceeded 58%， with the highest consistent rate of 75.07% in September and the lowest consistent rate of 58.26% in February.（4） The drought in each season monitored by the model was basically consistent with the actual drought， which could better reflect the spatial distribution and drought in Southwestern China. |
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| ISSN: | 1000-0534 |