Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms
Conducting field investigations of farmland irrigation patterns on a large scale is a time-consuming and labor-intensive task. The traditional approach of employing satellite remote sensing for large-scale visual assessments is impractical for identifying irrigation patterns due to interference caus...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425000162 |
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| author | Yuqi Liu Yang Wang Yanling Liao Renkuan Liao Jirka Šimůnek |
| author_facet | Yuqi Liu Yang Wang Yanling Liao Renkuan Liao Jirka Šimůnek |
| author_sort | Yuqi Liu |
| collection | DOAJ |
| description | Conducting field investigations of farmland irrigation patterns on a large scale is a time-consuming and labor-intensive task. The traditional approach of employing satellite remote sensing for large-scale visual assessments is impractical for identifying irrigation patterns due to interference caused by vegetation cover. To address this, we utilized the Google Earth Engine (GEE) platform, integrating environmental covariates and machine learning algorithms, to generate distribution maps of irrigation patterns (micro-irrigation and surface irrigation) at a 30-meter resolution for the Turpan-Hami Basin. Results demonstrate that the Classification and Regression Tree (CART) model achieved a classification accuracy of 0.81, effectively distinguishing between different irrigation patterns. The analysis of feature importance determined NDVI (i.e., Normalized Difference Vegetation Index), EVI (i.e., Enhanced Vegetation Index), MSI (i.e., Moisture Stress Index), Ec (i.e., Transpiration), and NDWI (i.e., Normalized Difference Water Index) as the key indicators linked to irrigation patterns. Regional mapping findings reveal an increase in the proportion of micro-irrigation from 40.2 % in 2015 to 47.0 % in 2023, underscoring the successful implementation of water-saving practices in the Turpan-Hami Basin. Additionally, we developed a GEE-based interactive interface, which enables users to generate corresponding distribution maps of irrigation patterns by selecting a specific year, offering uesful data support for policymakers and farmers to better manage agricultural water resources. |
| format | Article |
| id | doaj-art-9fdb5bebfa9c430aaa7ea2c661fd776a |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-9fdb5bebfa9c430aaa7ea2c661fd776a2025-01-25T04:10:53ZengElsevierAgricultural Water Management1873-22832025-03-01308109302Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithmsYuqi Liu0Yang Wang1Yanling Liao2Renkuan Liao3Jirka Šimůnek4College of Land Science and Technology, China Agricultural University, Beijing 100193, PR ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, PR ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, PR ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, PR China; Corresponding author:.Department of Environmental Sciences, University of California Riverside, CA 92521, USAConducting field investigations of farmland irrigation patterns on a large scale is a time-consuming and labor-intensive task. The traditional approach of employing satellite remote sensing for large-scale visual assessments is impractical for identifying irrigation patterns due to interference caused by vegetation cover. To address this, we utilized the Google Earth Engine (GEE) platform, integrating environmental covariates and machine learning algorithms, to generate distribution maps of irrigation patterns (micro-irrigation and surface irrigation) at a 30-meter resolution for the Turpan-Hami Basin. Results demonstrate that the Classification and Regression Tree (CART) model achieved a classification accuracy of 0.81, effectively distinguishing between different irrigation patterns. The analysis of feature importance determined NDVI (i.e., Normalized Difference Vegetation Index), EVI (i.e., Enhanced Vegetation Index), MSI (i.e., Moisture Stress Index), Ec (i.e., Transpiration), and NDWI (i.e., Normalized Difference Water Index) as the key indicators linked to irrigation patterns. Regional mapping findings reveal an increase in the proportion of micro-irrigation from 40.2 % in 2015 to 47.0 % in 2023, underscoring the successful implementation of water-saving practices in the Turpan-Hami Basin. Additionally, we developed a GEE-based interactive interface, which enables users to generate corresponding distribution maps of irrigation patterns by selecting a specific year, offering uesful data support for policymakers and farmers to better manage agricultural water resources.http://www.sciencedirect.com/science/article/pii/S0378377425000162Agricultural water useRemote sensingIrrigation managementSupervised classificationGoogle Earth Engine |
| spellingShingle | Yuqi Liu Yang Wang Yanling Liao Renkuan Liao Jirka Šimůnek Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms Agricultural Water Management Agricultural water use Remote sensing Irrigation management Supervised classification Google Earth Engine |
| title | Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms |
| title_full | Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms |
| title_fullStr | Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms |
| title_full_unstemmed | Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms |
| title_short | Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms |
| title_sort | generating high precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms |
| topic | Agricultural water use Remote sensing Irrigation management Supervised classification Google Earth Engine |
| url | http://www.sciencedirect.com/science/article/pii/S0378377425000162 |
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