Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud Computing
(2022) In Remote Sensing 14(19).- Abstract
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... (More)
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/m3) 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.
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- author
- Ghorbanpour, Ali Karbalaye ; Kisekka, Isaya ; Afshar, Abbas ; Hessels, Tim ; Taraghi, Mahdi ; Hessari, Behzad ; Tourian, Mohammad J. and Duan, Zheng LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- crop water productivity, Google Earth Engine, Lake Urmia, Landsat, remote sensing, SEBAL
- in
- Remote Sensing
- volume
- 14
- issue
- 19
- article number
- 4934
- publisher
- MDPI AG
- external identifiers
-
- scopus:85140009437
- ISSN
- 2072-4292
- DOI
- 10.3390/rs14194934
- language
- English
- LU publication?
- yes
- id
- 24b95f5c-3a98-4a6a-801d-44af36e8ceeb
- date added to LUP
- 2022-12-14 13:35:21
- date last changed
- 2022-12-14 13:35:21
@article{24b95f5c-3a98-4a6a-801d-44af36e8ceeb,
abstract = {{<p>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.</p>}},
author = {{Ghorbanpour, Ali Karbalaye and Kisekka, Isaya and Afshar, Abbas and Hessels, Tim and Taraghi, Mahdi and Hessari, Behzad and Tourian, Mohammad J. and Duan, Zheng}},
issn = {{2072-4292}},
keywords = {{crop water productivity; Google Earth Engine; Lake Urmia; Landsat; remote sensing; SEBAL}},
language = {{eng}},
number = {{19}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud Computing}},
url = {{http://dx.doi.org/10.3390/rs14194934}},
doi = {{10.3390/rs14194934}},
volume = {{14}},
year = {{2022}},
}