Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables
Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highla...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/13/2975 |
| _version_ | 1797408294711590912 |
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| author | Nguyen Thi Thanh Thao Dao Nguyen Khoi Antoine Denis Luong Van Viet Joost Wellens Bernard Tychon |
| author_facet | Nguyen Thi Thanh Thao Dao Nguyen Khoi Antoine Denis Luong Van Viet Joost Wellens Bernard Tychon |
| author_sort | Nguyen Thi Thanh Thao |
| collection | DOAJ |
| description | Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R<sup>2</sup> = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies. |
| first_indexed | 2024-03-09T03:56:22Z |
| format | Article |
| id | doaj.art-375d534da4494e70b4e762e90608a2c4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T03:56:22Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-375d534da4494e70b4e762e90608a2c42023-12-03T14:19:38ZengMDPI AGRemote Sensing2072-42922022-06-011413297510.3390/rs14132975Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical VariablesNguyen Thi Thanh Thao0Dao Nguyen Khoi1Antoine Denis2Luong Van Viet3Joost Wellens4Bernard Tychon5Spheres Research Unit, Water, Environment and Development Laboratory, Environmental Sciences and Management Department, Arlon Campus Environment, University of Liège, 185 Avenue de Longwy, 6700 Arlon, BelgiumFaculty of Environment, University of Science, Ho Chi Minh City 700000, VietnamSpheres Research Unit, Water, Environment and Development Laboratory, Environmental Sciences and Management Department, Arlon Campus Environment, University of Liège, 185 Avenue de Longwy, 6700 Arlon, BelgiumInstitute of Environmental Science, Engineering and Management, Industrial University of Ho Chi Minh City, Ho Chi Minh City 700000, VietnamSpheres Research Unit, Water, Environment and Development Laboratory, Environmental Sciences and Management Department, Arlon Campus Environment, University of Liège, 185 Avenue de Longwy, 6700 Arlon, BelgiumSpheres Research Unit, Water, Environment and Development Laboratory, Environmental Sciences and Management Department, Arlon Campus Environment, University of Liège, 185 Avenue de Longwy, 6700 Arlon, BelgiumGiven the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R<sup>2</sup> = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies.https://www.mdpi.com/2072-4292/14/13/2975coffee yield forecastremote sensing vegetation biophysical variablesearly predictionCGMSstatToolVietnamLAI |
| spellingShingle | Nguyen Thi Thanh Thao Dao Nguyen Khoi Antoine Denis Luong Van Viet Joost Wellens Bernard Tychon Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables Remote Sensing coffee yield forecast remote sensing vegetation biophysical variables early prediction CGMSstatTool Vietnam LAI |
| title | Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables |
| title_full | Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables |
| title_fullStr | Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables |
| title_full_unstemmed | Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables |
| title_short | Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables |
| title_sort | early prediction of coffee yield in the central highlands of vietnam using a statistical approach and satellite remote sensing vegetation biophysical variables |
| topic | coffee yield forecast remote sensing vegetation biophysical variables early prediction CGMSstatTool Vietnam LAI |
| url | https://www.mdpi.com/2072-4292/14/13/2975 |
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