DOI: 10.5281/zenodo.14850707
title: BETA-FOR_SP3_ForestStructure_Sentinel1_2014/2023

description: Sentinel-1 (radar) satellite time-series (2014-2023) at patch-level as proxy of canopy structure. The dataset is available for all 234 patches and stored in a single .xlsx file where each sheet name (BETA-FOR patch label) stores information for the respective patch as various metrics (combination of spectral index and spatial statistic to aggregate the pixels per patch, i.e. coefficient of variation). Based on the VH and VV bands as output from Sentinel-1 ARD processing (https://doi.org/10.3390/rs13101954), spatial statistics at patch-level of five different bands/spectral indices (VH, VHVVP, VVVHS, DPDD, VV) were calculated (please find the full name and formula under https://github.com/awesome-spectral-indices/awesome-spectral-indices/blob/main/output/spectral-indices-table.csv and the publication of the spectral indices library under https://doi.org/10.1038/s41597-023-02096-0). The original time-series (column name ending *_original) was decomposed based on the BEAST model (https://doi.org/10.1016/j.rse.2019.04.034) into the seasonal (column name ending *_seasonality) and trend signal (column name ending *_trend). The aggregation of pixels at patch-level was calculated as coefficient of variation (column name *_cv_*). The seasonal and trend signal were modelled as weekly intervals from the original irregular time-series. Please find the data in the attachment since it is structured as nested .xlsx with multiple (234) sheets.
description: Variables Description################

*date: the date when the sample was taken

*VH_cv_original: original time-series of cross-polarization (VH)

*VH_cv_seasonality: seasonal signal of cross-polarization (VH) decomposed from the original time series as weekly intervals

*VH_cv_trend: trend signal of cross-polarization (VH) decomposed from the original time series as weekly intervals

*VHVVP_cv_original: original time-series of the product of VV and VH

*VHVVP_cv_seasonality: seasonal signal of the product of VV and VH decomposed from the original time series as weekly intervals

*VHVVP_cv_trend: trend signal of the product of VV and VH decomposed from the original time series as weekly intervals

*VVVHS_cv_original: original time-series of the sum of VV and VH

*VVVHS_cv_seasonality: seasonal signal of the sum of VV and VH decomposed from the original time series as weekly intervals

*VVVHS_cv_trend: trend signal of the sum of VV and VH decomposed from the original time series as weekly intervals

*DPDD_cv_original: original time-series of Dual-Pol Diagonal Distance

*DPDD_cv_seasonality: seasonal signal of Dual-Pol Diagonal Distance decomposed from the original time series as weekly intervals

*DPDD_cv_trend: trend signal of Dual-Pol Diagonal Distance decomposed from the original time series as weekly intervals

*VV_cv_original: original time-series of co-polarization (VV), the aggregation of pixels at patch-level was calculated as coefficient of variation 

*VV_cv_seasonality: seasonal signal of co-polarization (VV) decomposed from the original time series as weekly intervals

*VV_cv_trend: trend signal of co-polarization (VV) decomposed from the original time series as weekly intervals