Need guidance from remote sensing experts: Sentinel-2 LULC classification across years (2017/2020/2025) with Random Forest
I'm currently working on a research project on LULC classification of Aizawl district (Mizoram, India) using Sentinel-2 Surface Reflectance (10 m), DEM, slope, aspect, spectral indices (NDVI, NDBI, MNDWI, NDRE), and Random Forest/XGBoost. My goal is to publish this work, so I'm trying to build a methodology that is scientifically robust rather than simply achieving high accuracy.
I'm facing several issues and would really appreciate guidance from anyone experienced in remote sensing or Earth Engine.
Current workflow
Sentinel-2 SR (10 m)
Dry season composites (Nov–Feb)
Median composite
Cloud masking
Features: Sentinel bands + DEM + Slope + Aspect + Spectral Indices
Classes: Dense Forest, Open Forest, Agriculture, Barren Land, Water Bodies, Urban
Issues I'm facing
1. Temporal generalization
Should I:
Train separate models for 2017, 2020, and 2025 and compare LULC changes?
Or combine training samples from all three years into a single model and evaluate whether it generalizes across time?
Has anyone successfully built a single Sentinel-2 model that performs well across multiple years?
2. Composite quality / tile seams
Some areas of my Sentinel-2 composites appear faded or radiometrically different, especially near what seem to be Sentinel tile boundaries. These areas often lead to incorrect classifications.
Is this expected when using median composites?
Would you recommend Quality Mosaic, Medoid, or another compositing strategy instead?
Is there a better cloud-masking workflow than the standard QA60/SCL approach?
3. Training sample collection
I'm using:
Sentinel false-color composites in QGIS to digitize Dense Forest and Open Forest.
Google Earth Pro historical imagery to identify Agriculture, Urban, Water, and Barren Land where they're easier to distinguish.
Is this considered an acceptable sampling methodology for a research paper, provided all samples correspond to the same time period?
I'm looking for advice from anyone who has worked on multi-temporal Sentinel-2 classification, Earth Engine, or remote sensing research.
If you've published work in this area or have practical experience, I'd really appreciate your suggestions or even a discussion. I'm still early enough in the project to improve the methodology before writing the paper.