"Postdoctoral Researcher in Irrigation Mapping using Remote Sensing"

Job Description:

We are seeking a highly motivated and skilled Postdoctoral Researcher to join our team at the Center for Remote Sensing Applications. The successful candidate will contribute to a research project focused on irrigation mapping, detection of irrigation timing, and quantification of water supply in irrigation systems using remote sensing data.

The primary focus of this research initiative is to gather precise data on irrigation practices, a crucial element for informed decision-making in both land and water resource management. The context of the study revolves around Moroccan smallholder agriculture, characterized by intricate systems conducted on small and irregularly shaped plots. These systems exhibit intra-class variations, such as intercropping and mixed-cropping methodologies, alongside fluctuations in the scheduling of agricultural operations including planting, harvesting, and irrigation.

Moreover, Moroccan smallholder agriculture is often situated in mosaic landscapes, where agricultural activities and natural vegetation interweave over short distances, leading to rapid changes in land cover and land use. In such landscapes, distinguishing between irrigated agriculture, rainfed agriculture, and natural vegetation poses a significant challenge. Particularly, in regions where soil moisture retention is high, for instance, near water bodies or wetlands, the soil moisture patterns may mimic those observed in irrigated croplands.

Key Responsibilities:

• Conduct research to develop algorithms and methodologies for mapping irrigation patterns using satellite imagery.
• Investigate methods for detecting the timing and frequency of irrigation events from time-series remote sensing data.
• Develop models and techniques to quantify water supply and irrigation efficiency in agricultural landscapes.
• Collaborate with multidisciplinary teams to integrate remote sensing data with ground-based observations, physical algorithms, and machine learning models.
• Participate in field data collection and validation to support model accuracy.
• Publish research findings in peer-reviewed journals and present results at conferences and workshops.
• Mentor master students and PhDs

Qualifications:

• Ph.D. in Remote Sensing, Geospatial Science, Environmental Science, Data Science, or related field.
• Strong background in remote sensing data analysis, image processing, and geospatial analysis.
• Experience working with satellite imagery (e.g., Landsat, Sentinel) and other remote sensing data sources.
• Proficiency in programming languages such as Python, R, or MATLAB for data analysis and algorithm development.
• Knowledge of machine learning techniques and statistical modeling for environmental applications.
• Excellent communication skills and ability to work effectively in a collaborative research environment.