Geospatial Analyst
Reston, VA · On-site +1
Apply advanced remote sensing methods and GIS technologies to support crop production mapping and natural resource modeling * Use machine learning approaches for remote sensing and agricultural ...
Geospatial Analyst
Reston, VA · On-site +1
Apply advanced remote sensing methods and GIS technologies to support crop production mapping and natural resource modeling * Use machine learning approaches for remote sensing and agricultural ...
Geospatial Analyst
Reston, VA · On-site +1
Apply advanced remote sensing methods and GIS technologies to support crop production mapping and natural resource modeling * Use machine learning approaches for remote sensing and agricultural ...
Geospatial Analyst
Reston, VA · On-site +1
Apply advanced remote sensing methods and GIS technologies to support crop production mapping and natural resource modeling * Use machine learning approaches for remote sensing and agricultural ...
Remote Crop Modeling information
What are the main challenges faced by professionals working in remote crop modeling, and how can they be overcome?
Professionals in remote crop modeling often face challenges related to integrating large datasets from various sources, such as satellite imagery, weather stations, and soil sensors. Ensuring data quality and model accuracy while working remotely requires strong data management skills and effective collaboration with agronomists, data scientists, and software engineers. Regular communication and use of collaborative platforms are essential to align project goals and share updates. Additionally, staying current with the latest modeling techniques and remote sensing technologies can help overcome technical challenges and improve predictive outcomes.
What are the key skills and qualifications needed to thrive in Remote Crop Modeling, and why are they important?
To thrive in Remote Crop Modeling, you need a strong background in agronomy, data analysis, and environmental science, often supported by a relevant degree such as agricultural engineering or crop science. Familiarity with crop modeling software (e.g., DSSAT, APSIM), remote sensing tools, and programming languages like Python or R is essential. Strong problem-solving abilities, attention to detail, and effective communication skills help you interpret data and collaborate with multidisciplinary teams. These competencies ensure accurate crop predictions, informed decision-making, and the successful application of modeling insights to real-world agricultural challenges.
What is remote crop modeling?
Remote crop modeling is the use of computer simulations and remote sensing technologies, such as satellite imagery and drones, to predict and monitor crop growth, yield, and health from a distance. This approach combines data from various sources, including weather, soil, and plant characteristics, to create accurate models of crop performance. Remote crop modeling helps farmers and agronomists make informed decisions about irrigation, fertilization, and pest control, ultimately improving productivity and sustainability. It is widely used in precision agriculture to optimize resource use and reduce environmental impact.
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Other
Posted 23 days ago
Job description
ASRC Federal Data Networx LLC is seeking a full-time Geospatial Analyst to join our DC-based team and provide agricultural and natural resource analysis support for our government customers. The successful candidate will play a central role in analyzing agricultural and natural resource data, producing actionable insights, and supporting programs that track global crop conditions and food supply. We’re looking for someone who combines strong technical skills with curiosity, adaptability, and a collaborative mindset.
Work Location: Hybrid in DC
Key Role Responsibilities:
- Collect, clean, and analyze datasets such as satellite imagery, weather data, agricultural surveys, and field observations
- Apply advanced remote sensing methods and GIS technologies to support crop production mapping and natural resource modeling
- Use machine learning approaches for remote sensing and agricultural analytics
- Produce maps, visualizations, and technical reports—often on tight, recurring deadlines
- Present findings, support stakeholder engagement, and deliver technical training when needed
- Stay current on geospatial technologies, industry trends, and analytical methods
Basic Qualifications:
- Minimum of 2-4 years’ experience in in geography, GIS, or remote sensing with substantive coursework in geospatial analysis and modeling.
- Bachelor’s degree in geography, GIS, remote sensing, environmental/natural sciences, or related field plus at least 2 years of relevant experience. A degree in another discipline plus at least 3 years of experience in geography, GIS, or remote sensing with substantive coursework in geospatial analysis and modeling.
- 6+ years related experience in geospatial analysis and modeling in the absence of a degree.
- Strong foundation in remote sensing and geospatial workflows (vector and raster analysis, modeling, land cover mapping).
- Proficiency with geospatial tools such as:
- ArcGIS Pro
- Google Earth Engine
- Jupyter Notebooks, Google Colab, or R Studio
- Basic programming experience in Python, JavaScript, or SQL
- Excellent written and verbal communication skills and comfort working in a team-based environment