Leaf area index (LAI) is an important biophysical parameter describing vegetation. LAI is typically retrieved from optical remote sensing by empirical models relating LAI to vegetation indices, such as the Normalized Difference Vegetation Index (NDVI). As the relationship between LAI and NDVI is non-linear and crop type dependant, several specific empirical equations relating LAI to NDVI have been developed using field data. This study presented LAIr, an R package to derive LAI from NDVI data from the most comprehensive library of conversion equations. In the package, the range of functions differs on environmental factors, sensors, and vegetation types, allowing flexibility in choosing appropriate options based on specific application, scale of investigation and data availability. We illustrated the use of the package with a case study to compare a generic LAI product with specific NDVI-based LAI estimations. By leveraging empirical knowledge, LAIr enables accurate and context-specific estimation of LAI. The deployment of an open-source R package serves as a valuable tool for aiding researchers in selecting the most appropriate equations for conducting NDVI-to-LAI conversion. © 2024

Forest phenology plays a key role in the global terrestrial ecosystem influencing a range of ecosystem processes such as the annual carbon uptake period, and many food webs and changes in their timing and progression. The timing of the start of the phenology season has been successfully determined at a range of scales, from the individual tree by in situ observations to landscape and continental scales by using remotely sensed vegetation indices (VIs). The spatial resolution of satellites is much coarser than traditional methods, creating a gap between space-borne and actual field observations, which brings limitations to phenological research at the ecosystem level. Several unconsidered methodological and observational-related limitations may lead to misinterpretation of the timing of the satellite-derived signals. The aim of this study is therefore to clarify the meaning of a set of spring phenology metrics derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series in beech forests distributed across Europe with respect to PEP725 in situ observations, from 2003 to 2020. To this aim, we (i) tested the differences between remotely sensed and in situ start-of-season (SOS) metrics and (ii) quantified the influence of latitude, elevation, temperature, and precipitation on such differences. Results demonstrated that there is a clear temporal gradient among the different SOS metrics, all of them occurring prior to the in situ observations. Furthermore, latitude and temperatures proved to be the main factors guiding the differences between remotely sensed and in situ SOS metrics. Evidence from this study may help in recognizing the actual meaning of what we see by means of remotely sensed phenology metrics. In this perspective, field observations are crucial in understanding phenology events and provide a reference base. Satellite data, on the other hand, complement field observations by filling in gaps in spatial and temporal coverage, thus enhancing the overall understanding. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Monitoring tree functional traits is essential for understanding forest ecosystems' capability to respond to climate change. Advancements in continuous proximal sensors and IoT technologies hold great potential for monitoring forest and tree ecosystem processes at the finest spatial and temporal scale. An example is the TreeTalker (TT) technology, which features sensors for measurements of the radial growth, sap flow, multispectral light transmission, air temperature, and humidity at tree level with an hourly frequency rate. Such information can be linked with remote sensing data acquired by the Sentinel-2 (S2) mission, allowing for scaling results over more spatially extensive areas. Firstly, we compared six TT with four S2 spectral bands with similar wavelengths. No correlation was found for blue, green and red channels (R² ranged between 0.04 and 0.09) while higher values were found for the near-infrared channel (R² = 0.9). To obtain an accurate prediction of TTs bands, also for those TTs bands which wavelengths are not similar to that of S2 bands, we implemented a Sentinel-2 to TreeTalker model (S2TT) by using an 8-layers fully connected deep neural network. The model was tested by using 23 Sentinel-2 imagery and data acquired by 40 TreeTalkers located in two different sites in Tuscany (a beech and a silver fir forest stand) in the period between 2020-07-15 and 2020-11-15. The R² ranged between 0.61 (B7, blue) and 0.96 (B6, near-infrared band). The S2TT model represents the first link between remote sensing and TreeTalkers, which might allow predicting tree functional traits using Sentinel-2 imagery. © 2021, Italian Society of Remote Sensing. All rights reserved.