Database for The Article: Leaf Area Index (LAI) Partitioning in Dryland Forests Using Dual-Sensor Sentinel Imagery
Description
Leaf Area Index (LAI) is a key biophysical variable for understanding forest structure and function, particularly in dryland ecosystems where vegetation responses to environmental stressors are highly dynamic. Traditional satellite-derived LAI products are often inaccurate, as they typically represent the effective Plant Area Index (PAIeff), excluding necessary corrections such as consideration of leaf clumping and exclusion of woody elements. Moreover, while most studies report total ecosystem LAI, it is increasingly important to partition LAI into its structural components, such as overstory and understory layers, to better understand forest structure and function. In this study, we developed and validated a novel approach to estimate forest LAI (LAIEcosystem) and partition it into overstory (LAIOverstory) and understory (LAIUnderstory) components using an integrated model based on Sentinel-2 (S2) multispectral and Sentinel-1 SAR data (S1). Field LAI measurements were conducted in two Aleppo pine forests in Israel, HaKedoshim and Yatir, which span an aridity gradient and include multiple thinning treatments. Partial Least Squares Regression (PLS-R) models were calibrated and validated using extensive field data collected between 2018 and 2019. The combined S1+S2 model significantly improved LAIEcosystem predictions (R² = 0.85, RMSE = 0.52) and enabled fine-scale monitoring of vertical forest structure. Notably, S1 data substantially enhanced the prediction of understory LAI (R² = 0.87, RMSE = 0.26), highlighting the value of active sensing in capturing low-canopy structure. Comparisons with commonly used satellite LAI products (i.e., GCOM-C SGLI, Globe PROBA-V, Sentinel-3 OLCI, the MODIS, and the Simplified Level 2 Prototype Processor (SL2P) for S2 at 20 m and 10 m resolutions) revealed strong agreements for PAIeff, underscoring the importance of methodologies for correcting LAI estimation. This study presents an operational approach for LAI partitioning in dryland forests, providing novel insights into the structural responses of forests to climatic aridity and forest management. The methodology holds significant potential for enhancing global forest monitoring and improving the precision of ecosystem models under changing environmental conditions.
Files
Steps to reproduce
The databases were created by combing the field measurements of LAI and mean satellite data along forest strata (overstory & understory) for the small 21 size areas of 40 x 40m with different thinning treatments for two conifer dryland forests. The satellite data included the dual polarized SAR data from Sentinel-1 (VV and VH) and multispectral data including spectral reflectance and vegetation indices from Sentinel 2. The satellite data was extracted from the satellite imagery as the mean value for 40 x 40 m area. The used imagery was atmospherically and geometrically corrected (L2 products), which was download from google earth engine platform (GEE).
Institutions
- Weizmann Institute of Science
- Ben-Gurion University of the Negev French Associates Institute for Agriculture and Biotechnology of Drylands
- Agricultural Research Organization Volcani Center