Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud Computing
Scarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. Thi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/19/4934 |
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| author | Ali Karbalaye Ghorbanpour Isaya Kisekka Abbas Afshar Tim Hessels Mahdi Taraghi Behzad Hessari Mohammad J. Tourian Zheng Duan |
| author_facet | Ali Karbalaye Ghorbanpour Isaya Kisekka Abbas Afshar Tim Hessels Mahdi Taraghi Behzad Hessari Mohammad J. Tourian Zheng Duan |
| author_sort | Ali Karbalaye Ghorbanpour |
| collection | DOAJ |
| description | Scarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49–0.55 (kg/m<sup>3</sup>) for irrigated wheat, 0.27–0.34 for rainfed wheat, 1.7–2.2 for apples, 1.2–1.7 for grapes, 5.5–6.2 for sugar beets, and 0.67–1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices. |
| first_indexed | 2024-03-09T21:12:50Z |
| format | Article |
| id | doaj.art-3b4625da848d46ebb6872546b0db2a53 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:12:50Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3b4625da848d46ebb6872546b0db2a532023-11-23T21:41:07ZengMDPI AGRemote Sensing2072-42922022-10-011419493410.3390/rs14194934Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud ComputingAli Karbalaye Ghorbanpour0Isaya Kisekka1Abbas Afshar2Tim Hessels3Mahdi Taraghi4Behzad Hessari5Mohammad J. Tourian6Zheng Duan7Department of Biological and Agricultural Engineering, University of California Davis, Davis, CA 95616, USADepartment of Biological and Agricultural Engineering, University of California Davis, Davis, CA 95616, USASchool of Civil Engineering, Iran University of Science & Technology, Tehran 16846, IranDepartment of Water Management, Delft University of Technology, 2600 AA Delft, The NetherlandsWater Engineering Department, Urmia Lake Research Institute, Urmia University, Urmia 57179-44514, IranWater Engineering Department, Urmia Lake Research Institute, Urmia University, Urmia 57179-44514, IranInstitute of Geodesy, University of Stuttgart, Stuttgart 70174, GermanyDepartment of Physical Geography and Ecosystem Science, Lund University, S-22362 Lund, SwedenScarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49–0.55 (kg/m<sup>3</sup>) for irrigated wheat, 0.27–0.34 for rainfed wheat, 1.7–2.2 for apples, 1.2–1.7 for grapes, 5.5–6.2 for sugar beets, and 0.67–1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.https://www.mdpi.com/2072-4292/14/19/4934crop water productivityremote sensingGoogle Earth EngineSEBALLandsatLake Urmia |
| spellingShingle | Ali Karbalaye Ghorbanpour Isaya Kisekka Abbas Afshar Tim Hessels Mahdi Taraghi Behzad Hessari Mohammad J. Tourian Zheng Duan Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud Computing Remote Sensing crop water productivity remote sensing Google Earth Engine SEBAL Landsat Lake Urmia |
| title | Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud Computing |
| title_full | Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud Computing |
| title_fullStr | Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud Computing |
| title_full_unstemmed | Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud Computing |
| title_short | Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud Computing |
| title_sort | crop water productivity mapping and benchmarking using remote sensing and google earth engine cloud computing |
| topic | crop water productivity remote sensing Google Earth Engine SEBAL Landsat Lake Urmia |
| url | https://www.mdpi.com/2072-4292/14/19/4934 |
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