  Current optical vegetation indices (VIs) for monitoring forest ecosystems are
widely used in various applications. However, continuous monitoring based on
optical satellite data can be hampered by atmospheric effects such as clouds.
On the contrary, synthetic aperture radar (SAR) data can offer insightful and
systematic forest monitoring with complete time series due to signal
penetration through clouds and day and night acquisitions. The goal of this
work is to overcome the issues affecting optical data with SAR data and serve
as a substitute for estimating optical VIs for forests using machine learning.
Time series of four VIs (LAI, FAPAR, EVI and NDVI) were estimated using
multitemporal Sentinel-1 SAR and ancillary data. This was enabled by creating a
paired multi-temporal and multi-modal dataset in Google Earth Engine (GEE),
including temporally and spatially aligned Sentinel-1, Sentinel-2, digital
elevation model (DEM), weather and land cover datasets (MMT-GEE). The use of
ancillary features generated from DEM and weather data improved the results.
The open-source Automatic Machine Learning (AutoML) approach, auto-sklearn,
outperformed Random Forest Regression for three out of four VIs, while a 1-hour
optimization length was enough to achieve sufficient results with an R2 of
69-84% low errors (0.05-0.32 of MAE depending on VI). Great agreement was also
found for selected case studies in the time series analysis and in the spatial
comparison between the original and estimated SAR-based VIs. In general,
compared to VIs from currently freely available optical satellite data and
available global VI products, a better temporal resolution (up to 240
measurements/year) and a better spatial resolution (20 m) were achieved using
estimated SAR-based VIs. A great advantage of the SAR-based VI is the ability
to detect abrupt forest changes with a sub-weekly temporal accuracy.
