A5054042200- Water resources management and optimization
- Plant Water Relations and Carbon Dynamics
- Irrigation Practices and Water Management
- Hydrology and Watershed Management Studies
- Water-Energy-Food Nexus Studies
- Water Quality Monitoring Technologies
- Hydrology and Drought Analysis
- Remote Sensing in Agriculture
- Sugarcane Cultivation and Processing
- Environmental Impact and Sustainability
- Agricultural Systems and Practices
- Soil Geostatistics and Mapping
- Climate variability and models
- Remote Sensing and Land Use
- Geophysics and Gravity Measurements
- Precipitation Measurement and Analysis
University of Twente
2018-2020
Water scarcity has received global attention in the last decade as it challenges food security arid and semi-arid regions, particularly Middle East North Africa. This research assesses possible alleviation of water by reducing footprint crop production through application soil mulching drip irrigation. The study is first to do so at catchment scale, taking into account various crops, multi-cropping, cropping patterns, spatial differences climate, soil, field management factors, using survey...
The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens kilometer scales. Whilst these data effectively characterize variability regional global scales, they are less suitable for sustainable management local resources, which needs detailed information represent the spatial heterogeneity vegetation. following questions critical exploit from...
Abstract The Food and Agricultural Organization of the United Nations (FAO) portal to monitor water productivity through open‐access remotely sensed derived data (WaPOR) offers continuous actual evapotranspiration interception (ETIa‐WPR) at a 10‐day basis across Africa Middle East from 2009 onwards three spatial resolutions. continental level (250 m) covers (L1). national (100 21 countries 4 river basins (L2). third (30 eight irrigation areas (L3). To quantify uncertainty WaPOR version 2...
Remote sensing techniques have been shown, in several studies, to be an extremely effective tool for assessing the performance of irrigated areas at various scales and diverse climatic regions across world. Open access, ready-made, global ET products were utilized this first-ever-countrywide irrigation assessment study. The study aimed identifying ‘bright spots’, highest performing sugarcane growers, ‘hot or low growers. Four remote sensing-derived indicators applied over 302 growers;...
This paper analyses the effect of spatial assessment scale on irrigation performance indicators in small and medium-scale agriculture. Three indicators—adequacy (i.e., sufficiency water use to meet crop requirement), equity fairness distribution), productivity unit physical production/yield per consumption)—are evaluated five schemes for three resolutions—250 m, 100 30 m. Each scheme has varying plot sizes distributions, with average ranging from 0.2 ha 13 ha. The datasets are derived United...
Crop water productivity (CWP) has become a recognised indicator in assessing the state of Sustainable Development Goals (SDG) 6.4—to substantially increase use efficiency. This indicator, while useful at global scale, is not comprehensive local scale. To fill this gap, research proposes CWP framework, that takes advantage spatio-temporal availability remote sensing, identifies goals and sub-indicators specific to needs targeted domain. Three are considered; (i) score (GWPS), (ii) (LWPS)...
The validation of global remote sensing data comprises multiple methods including comparison to field measurements, cross-comparisons and verification physical consistency. Physical consistency are typically assessed for all pixels the entire product extent, which requires intensive computing. This paper proposes a statistically representative sampling approach reduce time efforts associated with validations having big volume. A progressive approach, as applied in machine learning train...