Remote Sensing & GIS Expert (Agriculture)

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.