English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Current optical vegetation indices (VIs) for monitoring forest ecosystems arewidely used in various applications. However, continuous monitoring based onoptical satellite data can be hampered by atmospheric effects such as clouds.On the contrary, synthetic aperture radar (SAR) data can offer insightful andsystematic forest monitoring with complete time series due to signalpenetration through clouds and day and night acquisitions. The goal of thiswork is to overcome the issues affecting optical data with SAR data and serveas a substitute for estimating optical VIs for forests using machine learning.Time series of four VIs (LAI, FAPAR, EVI and NDVI) were estimated usingmultitemporal Sentinel-1 SAR and ancillary data. This was enabled by creating apaired multi-temporal and multi-modal dataset in Google Earth Engine (GEE),including temporally and spatially aligned Sentinel-1, Sentinel-2, digitalelevation model (DEM), weather and land cover datasets (MMT-GEE). The use ofancillary 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-houroptimization length was enough to achieve sufficient results with an R2 of69-84% low errors (0.05-0.32 of MAE depending on VI). Great agreement was alsofound for selected case studies in the time series analysis and in the spatialcomparison between the original and estimated SAR-based VIs. In general,compared to VIs from currently freely available optical satellite data andavailable global VI products, a better temporal resolution (up to 240measurements/year) and a better spatial resolution (20 m) were achieved usingestimated SAR-based VIs. A great advantage of the SAR-based VI is the abilityto detect abrupt forest changes with a sub-weekly temporal accuracy.

Estimating optical vegetation indices with Sentinel-1 SAR data and AutoML