An Integrated Remote Sensing Drought Monitoring Model Based on Multi-source Information
In order to solve the problem of the traditional remote sensing drought index focuses on the monitoring of a single response factor and lacks a complete analysis of drought.In this paper， we selected TVDI， RVI， PDI， and GVMI daily products estimated from remote sensing data as independent variables，...
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Science Press, PR China
2024-12-01
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| Series: | Gaoyuan qixiang |
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| Online Access: | http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2024.00025 |
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| author | Dejun ZHANG Guan HONG Shiqi YANG Hao ZHU |
| author_facet | Dejun ZHANG Guan HONG Shiqi YANG Hao ZHU |
| author_sort | Dejun ZHANG |
| collection | DOAJ |
| description | In order to solve the problem of the traditional remote sensing drought index focuses on the monitoring of a single response factor and lacks a complete analysis of drought.In this paper， we selected TVDI， RVI， PDI， and GVMI daily products estimated from remote sensing data as independent variables， and MCI calculated from meteorological data at the adjacent moments of satellite transit as dependent variables， and uses the Random Forest Regression （RFR） model to construct a integrated remote sensing drought monitoring model.The results show that the accuracy of RFR model is better than that of the Ordinary Least Squares （OLS） model in bothtraining data and test data.The R value of the RFR training data is 0.97， the RMSE is 0.33， the R value of the RFR test data is 0.90， and the RMSE is 0.53.The R value of the OLS training data is 0.78， the RMSE value is 0.73， the R value of the OLS test data is 0.76， and the RMSE value is 0.79.The comparisons of RFR and OLS model in R and RMSE show that the RFR model is superior than the OLS model in the characterization of regional drought.In the application of drought monitoring in Southwest China in 2022， the RFR results are consistent with the spatiotemporal distribution of the MCI index， which can better characterize the spatial and temporal dynamics of the regional drought， reflecting the practicality of the RFR model in the actual drought monitoring process.However， the accuracy of RFR model is related to the number of regional stations and the spatial distribution of stations， and the accuracy of the RFR model is higher in areas with a large number of stations and uniform distribution of stations. |
| format | Article |
| id | doaj-art-b6a8b16dcdfb4e84907e8cf505e6071e |
| institution | OA Journals |
| issn | 1000-0534 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Science Press, PR China |
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| series | Gaoyuan qixiang |
| spelling | doaj-art-b6a8b16dcdfb4e84907e8cf505e6071e2025-08-20T02:35:18ZzhoScience Press, PR ChinaGaoyuan qixiang1000-05342024-12-014361507151910.7522/j.issn.1000-0534.2024.000251000-0534（2024）06-1507-13An Integrated Remote Sensing Drought Monitoring Model Based on Multi-source InformationDejun ZHANG0Guan HONG1Shiqi YANG2Hao ZHU3China Meteorological Administration Economic Transformation of Climate Resources Key Laboratory， Chongqing Institute of Meteorological Sciences， Chongqing 401147， ChinaMeteorological Observation Center of CMA， Beijing 100081， ChinaChina Meteorological Administration Economic Transformation of Climate Resources Key Laboratory， Chongqing Institute of Meteorological Sciences， Chongqing 401147， ChinaChina Meteorological Administration Economic Transformation of Climate Resources Key Laboratory， Chongqing Institute of Meteorological Sciences， Chongqing 401147， ChinaIn order to solve the problem of the traditional remote sensing drought index focuses on the monitoring of a single response factor and lacks a complete analysis of drought.In this paper， we selected TVDI， RVI， PDI， and GVMI daily products estimated from remote sensing data as independent variables， and MCI calculated from meteorological data at the adjacent moments of satellite transit as dependent variables， and uses the Random Forest Regression （RFR） model to construct a integrated remote sensing drought monitoring model.The results show that the accuracy of RFR model is better than that of the Ordinary Least Squares （OLS） model in bothtraining data and test data.The R value of the RFR training data is 0.97， the RMSE is 0.33， the R value of the RFR test data is 0.90， and the RMSE is 0.53.The R value of the OLS training data is 0.78， the RMSE value is 0.73， the R value of the OLS test data is 0.76， and the RMSE value is 0.79.The comparisons of RFR and OLS model in R and RMSE show that the RFR model is superior than the OLS model in the characterization of regional drought.In the application of drought monitoring in Southwest China in 2022， the RFR results are consistent with the spatiotemporal distribution of the MCI index， which can better characterize the spatial and temporal dynamics of the regional drought， reflecting the practicality of the RFR model in the actual drought monitoring process.However， the accuracy of RFR model is related to the number of regional stations and the spatial distribution of stations， and the accuracy of the RFR model is higher in areas with a large number of stations and uniform distribution of stations.http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2024.00025droughtremote sensingrandom forest regressionordinary least squares |
| spellingShingle | Dejun ZHANG Guan HONG Shiqi YANG Hao ZHU An Integrated Remote Sensing Drought Monitoring Model Based on Multi-source Information Gaoyuan qixiang drought remote sensing random forest regression ordinary least squares |
| title | An Integrated Remote Sensing Drought Monitoring Model Based on Multi-source Information |
| title_full | An Integrated Remote Sensing Drought Monitoring Model Based on Multi-source Information |
| title_fullStr | An Integrated Remote Sensing Drought Monitoring Model Based on Multi-source Information |
| title_full_unstemmed | An Integrated Remote Sensing Drought Monitoring Model Based on Multi-source Information |
| title_short | An Integrated Remote Sensing Drought Monitoring Model Based on Multi-source Information |
| title_sort | integrated remote sensing drought monitoring model based on multi source information |
| topic | drought remote sensing random forest regression ordinary least squares |
| url | http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2024.00025 |
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