Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation District
Quantitative assessment of crop water-use efficiency (WUE) is an important basis for high-efficiency use of agricultural water. Here we assess the WUE of maize in the Hetao Irrigation District, which is a representative irrigation district in the arid region of Northwest China. Specifically, we firs...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/9/2035 |
| _version_ | 1797503055598452736 |
|---|---|
| author | Lei Jiang Yuting Yang Songhao Shang |
| author_facet | Lei Jiang Yuting Yang Songhao Shang |
| author_sort | Lei Jiang |
| collection | DOAJ |
| description | Quantitative assessment of crop water-use efficiency (WUE) is an important basis for high-efficiency use of agricultural water. Here we assess the WUE of maize in the Hetao Irrigation District, which is a representative irrigation district in the arid region of Northwest China. Specifically, we firstly mapped the location of the maize field by using a remote sensing/phenological–based vegetation classifier and then quantified the maize water use and yield by using a dual-source remote-sensing evapotranspiration (ET) model and a crop water production function, respectively. Validation results show that the adopted phenological-based vegetation classifier performed well in mapping the spatial distributions and inter-annual variations of maize planting, with a kappa coefficient of 0.86. In addition, the ET model based on the hybrid dual-source scheme and trapezoid framework also obtained high accuracy in spatiotemporal ET mapping, with an RMSE of 0.52 mm/day at the site scale and 26.21 mm/year during the maize growing season (April–October) at the regional scale. Further, the adopted crop water production function showed high accuracy in estimating the maize yield, with a mean relative error of only 4.3%. Using the estimated ET, transpiration, and yield of maize, the mean maize WUE based on ET and transpiration in the study region were1.94 kg/m<sup>3</sup> and 3.06 kg/m<sup>3</sup>, respectively. Our results demonstrate the usefulness and validity of remote sensing information in mapping regional crop WUE. |
| first_indexed | 2024-03-10T03:45:02Z |
| format | Article |
| id | doaj.art-8af1500aa0c54cb5981fb2f6bac68553 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:45:02Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8af1500aa0c54cb5981fb2f6bac685532023-11-23T09:09:35ZengMDPI AGRemote Sensing2072-42922022-04-01149203510.3390/rs14092035Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation DistrictLei Jiang0Yuting Yang1Songhao Shang2College of Water Conservancy Engineering, Tianjin Agricultural University, Tianjin 300392, ChinaState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaQuantitative assessment of crop water-use efficiency (WUE) is an important basis for high-efficiency use of agricultural water. Here we assess the WUE of maize in the Hetao Irrigation District, which is a representative irrigation district in the arid region of Northwest China. Specifically, we firstly mapped the location of the maize field by using a remote sensing/phenological–based vegetation classifier and then quantified the maize water use and yield by using a dual-source remote-sensing evapotranspiration (ET) model and a crop water production function, respectively. Validation results show that the adopted phenological-based vegetation classifier performed well in mapping the spatial distributions and inter-annual variations of maize planting, with a kappa coefficient of 0.86. In addition, the ET model based on the hybrid dual-source scheme and trapezoid framework also obtained high accuracy in spatiotemporal ET mapping, with an RMSE of 0.52 mm/day at the site scale and 26.21 mm/year during the maize growing season (April–October) at the regional scale. Further, the adopted crop water production function showed high accuracy in estimating the maize yield, with a mean relative error of only 4.3%. Using the estimated ET, transpiration, and yield of maize, the mean maize WUE based on ET and transpiration in the study region were1.94 kg/m<sup>3</sup> and 3.06 kg/m<sup>3</sup>, respectively. Our results demonstrate the usefulness and validity of remote sensing information in mapping regional crop WUE.https://www.mdpi.com/2072-4292/14/9/2035Hetao Irrigation Districtmaizeremote sensingevapotranspirationcrop classificationcrop yield estimation |
| spellingShingle | Lei Jiang Yuting Yang Songhao Shang Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation District Remote Sensing Hetao Irrigation District maize remote sensing evapotranspiration crop classification crop yield estimation |
| title | Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation District |
| title_full | Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation District |
| title_fullStr | Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation District |
| title_full_unstemmed | Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation District |
| title_short | Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation District |
| title_sort | remote sensing based assessment of the water use efficiency of maize over a large arid regional irrigation district |
| topic | Hetao Irrigation District maize remote sensing evapotranspiration crop classification crop yield estimation |
| url | https://www.mdpi.com/2072-4292/14/9/2035 |
| work_keys_str_mv | AT leijiang remotesensingbasedassessmentofthewateruseefficiencyofmaizeoveralargearidregionalirrigationdistrict AT yutingyang remotesensingbasedassessmentofthewateruseefficiencyofmaizeoveralargearidregionalirrigationdistrict AT songhaoshang remotesensingbasedassessmentofthewateruseefficiencyofmaizeoveralargearidregionalirrigationdistrict |