GalaxEye
Website:
galaxeye.space
Job details:
We are looking for an experienced Remote Sensing & GIS Expert with strong expertise in agricultural applications to develop and implement geospatial solutions for crop mapping, acreage estimation, crop health and stress monitoring, yield/productivity assessment, and agricultural decision-support systems. The candidate should have hands-on experience in optical and SAR satellite data processing, GIS-based spatial analysis, and integration of satellite, weather, and field datasets.
Key Responsibilities
- Conduct crop identification, classification, and acreage estimation using multi-temporal satellite imagery.
- Perform crop health, vegetation stress, drought, and soil moisture assessment using spectral and SAR-based indicators.
- Support crop yield/productivity estimation and forecasting by integrating satellite, weather, soil, and ground observations.
- Process and analyze optical and SAR datasets, including Sentinel-1, Sentinel-2, Landsat, and high-resolution satellite imagery.
- Perform crop phenology and time-series analysis using indices such as NDVI, EVI, NDRE, NDWI, NDMI, and SAVI/MSAVI.
- Develop and validate crop classification and prediction models using Random Forest, SVM, XGBoost, and other machine learning approaches.
- Plan and support ground-truth/field data collection, model validation, accuracy assessment, and QA/QC.
- Prepare thematic maps, spatial databases, technical methodologies, reports, presentations, and other project deliverables.
- Coordinate with agronomists, GIS analysts, data scientists, software teams, and project stakeholders.
Requirements
Qualifications & Experience
- Master’s degree in Remote Sensing, GIS, Geo-informatics, Agriculture, Agricultural Engineering, Geography, or a related discipline.
- 5–6 years of relevant professional experience in Remote Sensing and GIS, with significant exposure to agricultural applications.
- Demonstrated experience in crop mapping, acreage estimation, crop monitoring, crop health assessment, or yield estimation.
Technical Skills
- Proficiency in QGIS/ArcGIS and Google Earth Engine (GEE).
- Working knowledge of Python for geospatial processing, automation, and data analysis.
- Strong understanding of optical and SAR remote sensing, including Sentinel-1 and Sentinel-2 time-series analysis.
- Knowledge of GIS analysis, image classification, vegetation indices, crop phenology, and accuracy assessment.
- Experience with machine learning-based classification and regression techniques.
- Familiarity with farm boundary delineation, precision agriculture, agricultural insurance, crop damage assessment, and deep learning applications will be an advantage.
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