During my graduate coursework I was asked to compare sensor types for a class project.

I needed to clearly define passive vs. active systems and provide real‑world examples.

I described that passive sensors detect natural radiation reflected or emitted by Earth (e.g., Landsat optical sensors), while active sensors emit their own energy and measure the return (e.g., LiDAR, SAR). I illustrated each with a case study.

My professor highlighted the explanation as concise and accurate, earning me top marks.

While working on a watershed health assessment, the team needed to monitor vegetation stress and surface water extent.

I was responsible for recommending the optimal sensor and platform.

I evaluated spatial resolution, revisit frequency, spectral bands, and cost. For vegetation stress I chose Sentinel‑2 (10 m, red‑edge bands) and for surface water I added Sentinel‑1 SAR (cloud‑penetrating, 5 m). I also considered data accessibility and processing tools.

The combined dataset delivered timely, cloud‑free insights, leading to actionable recommendations for the watershed managers.

For a land‑cover mapping project in a coastal region, raw Sentinel‑2 tiles arrived with atmospheric distortion and cloud cover.

I needed to prepare the imagery for a supervised classification.

I performed radiometric calibration, atmospheric correction using Sen2Cor, cloud masking with the QA band, geometric alignment to a common projection, and mosaicking of adjacent tiles. Finally, I generated a stack of relevant bands and performed histogram equalization.

The cleaned dataset improved classification accuracy from 78 % to 89 %, meeting the client’s quality threshold.

After classifying a mixed urban‑rural area, the client required a formal accuracy assessment.

I had to quantify classification performance and identify error sources.

I collected an independent stratified random sample of 200 reference points using high‑resolution Google Earth imagery. I computed a confusion matrix, overall accuracy, Kappa coefficient, and per‑class user’s and producer’s accuracies. I also performed error analysis to pinpoint misclassifications caused by spectral similarity between built‑up and bare soil.

The assessment yielded 92 % overall accuracy (Kappa 0.89). I presented a concise report with recommendations to refine the training dataset, which the client implemented for the next iteration.

Our agency needed a flood extent map within 48 hours after a severe storm.

I led a 4‑person team to acquire, process, and deliver the map on time.

I assigned roles (data acquisition, preprocessing, classification, QA). We used Sentinel‑1 SAR for rapid cloud‑free data, automated the workflow with Python scripts, and held hourly stand‑ups to track progress. I also communicated status updates to stakeholders.

We delivered the flood map in 36 hours, enabling emergency responders to prioritize rescue operations. The project was praised for its speed and accuracy.

After completing a vegetation health assessment for a regional agriculture board, the audience consisted of policy makers and farm owners.

I needed to translate technical findings into actionable insights.

I created simple maps with clear legends, used color‑coded risk zones, and paired each visual with a one‑sentence takeaway. I prepared a short slide deck focusing on implications (e.g., irrigation needs) rather than algorithms, and held a Q&A session to address concerns.

Stakeholders reported full understanding of the recommendations and approved funding for targeted interventions.

During a time‑series analysis of MODIS data, several scenes returned with missing bands and unexpected pixel values.

I needed to identify the cause and recover usable data for the analysis period.

I inspected the metadata and discovered transmission errors flagged in the quality band. I applied a custom script to replace corrupted pixels using temporal interpolation from adjacent dates and validated the approach against ground truth. When interpolation was insufficient, I sourced alternative data from VIIRS for the same period.

The repaired dataset restored 95 % of the temporal coverage, allowing the study to proceed without significant bias.

A regional flood response required rapid, cloud‑free mapping, but optical imagery was partially obscured by clouds.

I needed to combine Sentinel‑2 optical data with Sentinel‑1 SAR to produce a comprehensive flood extent map.

I preprocessed Sentinel‑1 SAR (radiometric calibration, speckle filtering) and Sentinel‑2 (atmospheric correction). I resampled both datasets to a common 10 m grid, performed water index calculation on optical data, and generated a SAR backscatter threshold. I then fused the layers using a logical OR operation, giving precedence to SAR where clouds existed. Finally, I refined the mask with DEM‑based slope constraints in GIS.

The integrated map achieved 93 % overall accuracy compared to in‑situ measurements, and was delivered within 24 hours, supporting effective emergency response.